Nonlinear dynamics in health care service delivery

Material Information

Nonlinear dynamics in health care service delivery an exploratory inquiry and simulation
Almendarez, Michele R
Publication Date:
Physical Description:
xvii, 255 leaves : illustrations ; 28 cm


Subjects / Keywords:
Health services administration -- Simulation methods ( lcsh )
Medicaid -- Cost control -- Simulation methods ( lcsh )
Medicaid beneficiaries ( lcsh )
Nonlinear theories ( lcsh )
Dynamics ( lcsh )
bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )


Includes bibliographical references (leaves 240-255).
General Note:
School of Public Affairs
Statement of Responsibility:
by Michele R. Almenderez.

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Source Institution:
|University of Colorado Denver
Holding Location:
Auraria Library
Rights Management:
All applicable rights reserved by the source institution and holding location.
Resource Identifier:
57497377 ( OCLC )
LD1190.P86 2004d A54 ( lcc )

Full Text
Michele R. Almendarez
B.A., University of California, Los Angees, 1986
M.P.A., University of Colorado, Denver, 1991
A thesis submitted to the
University of Colorado, Denver
in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy
Public Administration

2004 by Michele R. Almendarez
All rights reserved.

This thesis for the Doctor of Philosophy
degree by
Michele R. Almendarez
has been approved

Almendarez, Michele R. (Ph. D., Public Administration)
Nonlinear Dynamics in Health Care Delivery: An Exploratory Inquiry and Simulation
Thesis directed by Professor Peter deLeon
Traditional bureaucratic administration faced with rapid and changing public
demands has been challenged as outdated. The new public management movement
argues that government institutions, challenged by fiscal burdens, has exerted strong
budgetary pressures on administrators to engage more cost-effective procedures to
achieve their mandated purposes. Many scholars suspect that traditional organizational
theory and approaches can no longer accommodate the growing complexity of this
institutional environment.
Governmental organizations are increasingly seen as complex systems. Public
management literature has proposed new models to deal with these complexity issues.
Recognizing these challenges, public organizations must examine their management
orientations and practices and consider the addition of new techniques.
Especially relevant to these changes is found in the health care environment, a
condition exacerbated by the continued aging of the U.S. population. The greater usage
of medical services forces Medicare policymakers to implement cost strategy programs
such as Medicare-!- Choice. Administrators currently serving this population must continue
to plan and provide efficient clinic service systems. To assist health care administrators in
this endeavor, planning tools and techniques must advance to improve the delivery of
health services to its government-funded populations.
This thesis combines insights from dynamics, organizational, and management
literatures to propose that system dynamics modeling can assist health care administrators
in delivering medical services more efficiently to the Medicare population. Modeling

Medicare primary care services by simulation and subsystem analysis indicates that health
care administrators can: (1) achieve superior appointment process efficiencies and
significant cost savings for clinics; (2) develop the ability to design initiatives, manage
resources and communicate results to decision-makers; and (3) benefit from incorporating
nonlinear tools and approaches in their analytical tasks in an effort to improve
organizational performance.
A change in the understanding and performance of internal organizational work
activities not only directly benefits medical organizations but indirectly enhances
government-funded Medicare cost strategy programs. Public managers and their
successful administration ultimately make the difference between the triumph or failure in
the delivery of public policy services.
This abstract accurately represents the content of the candidates thesis. I recommend its
Peter deLeon

Tim, your thoughtfulness and guidance ensured achievement,
Bill, your attention to detail and unique perspective was welcomed;
Laurie and Carol, your counsel was invaluable;
My family Ray, Marc and John for your advice, assistance and support;
Peter, your patience and encouragement is truly appreciated.

Figures------------------------------------- xii
Tables-------------------------------------- xvi
1. INTRODUCTION---------------------------------- 1
Medical Costs------------------------------- 2
Access to Health Care----------------------- 7
Managed Competition------------------------- 9
Impact on Primary Care Services------------ 12
Research Issue----------------------------- 17
Theoretical and Practical Implications----- 19
Government-Funded Health Care
Programs----------------------------------- 21
Organization of Thesis--------------------- 24
DELIVERY------------------------------------- 26
Growth and Development of Managed Care 26
Health Maintenance Organizations (HMOs) 29
HMOs and the Medicare Program-------------- 32
Managed Care Medicare------------------- 32
Balanced Budget Act of 1997------------- 33
Future of Managed Care Medicare--------- 35
Use of Medical Services---------------- 36
Medical Demand Improvement-

3. LITERATURE REVIEW-------------------------------- 42
Organizational Theory and Practice------------- 42
Scientific Management---------------------- 43
Bureaucracy-------------------------------- 45
Mechanistic Organizations------------------ 45
Human Complexities------------------------- 47
Science of Dynamics---------------------------- 50
Overview----------------------------------- 51
Classification of Dynamics----------------- 53
Complex Systems---------------------------- 57
Open Systems Theory---------------------------- 62
General Systems Theory------------------------- 64
Mental Models Guide How We Manage--------- 68
New Mental Models Require New
Analytical Methods----------------------------- 69
Approaches and Tools--------------------------- 71
Summary------------------------------------ 74
CARE DELIVERY SYSTEMS---------------------------- 79
Boundary Selection----------------------------- 79
Research Objective----------------------------- 81
Research Questions and Hypotheses-------------- 83
Causal Diagram--------------------------------- 86
Validity, Reliability and Limitations---------- 91
Source Data: Reliability and Validity- 91
Simulation Model: Validity and Verification 94

SYSTEM MODEL------------------------------------ 97
I. Structure: Examination of Three Subsystems- 101
Subsystem 1: Patient Demand for Primary
Care Services-------------------------------- 103
Subsystem 2: Clinic Resources (Workforce)- 105
Physician Productivity-------------------- 106
Practice Delivery------------------------- 109
Patient Scheduling--------------------- 110
Administrative Practices--------------- 110
Physical Space---------------------------- 111
Leveraging Technology--------------------- 111
Subsystem 3: The Appointing Process------- 112
II. Behavior Subsystems Patterns------------- 117
Variables: Modes of Behavior----------------- 117
Time Series---------------------------------- 121
Rate of Change------------------------------- 123
Phase Portraits------------------------------ 125
III. The Appointment-Workforce Model------ 130
Diagramming of Subsystems-------------------- 131
Loop 1: Patient Demand for Appointments--- 133
Loop 2: Labor for Appointments--------------- 135
Loop 3: The Appointing Process--------------- 138
Formulation---------------------------------- 139
Patient Demand: Stock and Flow Map----------- 141
Labor: Stock and Flow Map-------------------- 144
Appointing Process: Stock and Flow Map---- 147

The Appointment-Workforce Interaction
Model----------------------------------------- 149
IV. Testing---------------------------------- 150
Model Baseline-------------------------------- 151
Summary--------------------------------------- 154
6. FINDINGS AND OBSERVATIONS----------------------- 157
General Approach------------------------------ 157
Research Questions and Hypotheses------------- 158
Question One---------------------------------- 158
Practice Delivery: Nurse Visits----------- 158
Administration/Leave Policies------------- 161
Experience/Productivity------------------- 162
Hypothesis One (Hlo)-------------------------- 163
Findings---------------------------------- 163
Question Two---------------------------------- 166
Hypothesis Two (H2o)-------------------------- 169
Findings---------------------------------- 169
Question Three-------------------------------- 171
Actual Behavior Linear-------------------- 171
Actual Behavior. Nonlinear---------------- 174
Simulated Behavior Linear----------------- 177
Simulated Behavior: Nonlinear------------- 180
Hypothesis Three (H3o)------------------------ 182
Findings---------------------------------- 183
Summary--------------------------------------- 184
Reliability and Validity------------------ 185

Implications for Health Care--------------
Words of Caution--------------------------
Medicare Implications---------------------
Significance to Public Management Theory--
Significance to Public Management Practice
Future Research---------------------------
Other Future Research------
AND VENSIM CODE----------------
D. RATE OF CHANGE----------------

1.1 U.S. Medical Care Cost Inflation vs Overall
Cost Inflation------------------------------ 4
1.2 Estimated Percentage of GDP Spent on
Health Care for Elderly--------------------- 7
3.1a Convergence to an Equilibrium-------------- 55
3.1b Stable Oscillation------------------------- 55
3.1c Unstable and Explosive--------------------- 56
3.Id Chaos-------------------------------------- 56
3.2 The General Systems Model------------------ 64
4.1 Appointment-Health Care Provider
Workforce Interaction---------------------- 87
5.1 Kaiser Permanente Appointment Demand
vs Clinic Capacity------------------------- 97
5.2 Stock & Flow Map Appointment
Workforce Model---------------------------- 98
5.3 Kaiser Permanente vs Simulation:
Appointment Demand vs Qinic Capacity 99
5.4 Appointment-Workforce System:
Subsystems and Key Variables-------------- 102
5.5 Total Facility Membership----------------- 103
5.6 Percentage of Medicare and Non-Medicare
Mbrs of Total Population------------------ 104
5.7 Medicare: Member Visit Rate (inc Urgent
Care-------------------------------------- 105
5.8 Medicare: Percentage of Budgeted MD
Clinical FTEs----------------------------- 108
5.9 Medicare: Patient Visit Demand vs Clinic
Appointment Capacity vs Patients Seen in
Qinic-------------------------------- 114

5.10 Percentage of Medicare Urgent Care Visits
of Total Visits----------------------------- 115
5.11 Medicare-Net Clinic Appt Capacity vs
Urgent Care Overflow------------------------ 116
5.12 Common Modes of Behavior in Dynamic
Systems------------------------------------- 118
5.13 Exponential Growth-Causal Loop Diagram 119
5.14 Goal Seeking-Causal Loop Diagram--------- 119
5.15 Oscillations-Causal Loop Diagram------------ 120
5.16 Medicare-Available Appointments and
Patient Clinic Visits vs Time--------------- 121
5.16A Mcdicarc-Paticnt Clinic Visits vs
Available Appointments---------------------- 122
5.17 Medicare-Available Appointments and
Urgent Care Visits vs Time------------------ 122
5.17A Medicare-Urgent Care Visits vs
Available Appointments---------------------- 122
5.18 Medicare-Capacity, Clinic Visits and Urgent
Care Overflow Monthly Rate of Change(%) 124
5.19 Phase Portrait: Medicare Monthly Overflow
vs Capacity--------------------------------- 125
5.19a Phase Portrait Monthly Medicare Overflow
vs Capacity Rate of Change------------------ 126
5.19b Phase Portrait: Monthly Medicare Capacity
Rate of Change------------------------------ 127
5.19c Phase Portrait: Monthly Medicare Overflow
Rate of Change------------------------------ 128
5.20 Feedback Interaction------------------------ 131
5.21 Patient Demand for Primary Care Services- 133
5.22 Labor for Primary Care Services------------- 136

5.23 Appointing Process--------------------------- 138
5.24 Stock and Flow Map- Patient Demand------- 142
5.25 Stock and How Map- Labor--------------------- 145
5.26 Stock and How Map- Appointing Process 147
5.27 Appointment Fulfillment Ratio---------------- 148
5.2 Stock & How Map Appointment
Workforce Model------------------------------ 150
6.1 Neighborhood View-Appointing Process 172
6.2 Actual Medicare Data: Appt Slots vs Appts
Seen in Clinic------------------------------- 173
6.3 Actual Medicare Data: Appt Slots vs Appts
Seen in Clinic (Scatter plot)---------------- 174
6.4 Phase Portrait Appts Slots vs Appts Seen
in Clinic------------------------------------ 174
6.5 Phase Portrait-Actual Monthly Rate of
Change in Appt Slots vs Appts Seen in
Clinic--------------------------------------- 176
6.6 Phase Portrait-Monthly Rate of Change
for Appt Slots vs Appt Seen in Clinic (Case
A: Most Overflow)---------------------------- 176
6.7 Phase Portrait-Monthly Rate of Change
for Appt Slots vs Appt Seen in Clinic (Case
C: Least Overflow)--------------------------- 176
6.8 Max Appt Seen (Clinic Appt Capacity) vs
Appt Slots (Case A)-------------------------- 178
6.8A Max Appt Seen (Clinic Appt Capacity) vs
Appt Slots (Case A)-with Actual Appts Seen 178
6.9 Max Appt Seen (Clinic Appt Capacity) vs
Appt Slots (Case Q--------------------------- 178

6.9A Max Appt Seen (Clinic Appt Capacity) vs
Appt Slots (Case Q-with Actual Appts Seen 178
6.10 Phase Portrait-Max Appt Seen (Clinic Appt
Capacity) vs Appt Slots (Case A)-------- 180
6.11 Phase Portrait-Max Appt Seen (Clinic Appt
Capacity) vs Appt Slots (Case Q--------- 180
6.12 Phase Portrait Monthly Rate of Change:
Max Appt Seen (Clinic Appt Capacity) vs
Appt Slots (Case A: Most Overflow)------ 181
6.13 Phase Portrait Monthly Rate of Change:
Max Appt Seen (Clinic Appt Capacity) vs
Appt Slots (Case C: Least Overflow)----- 181
6.14 Phase Portrait: Max Appt Seen (Clinic Appt
Capacity) vs Appt Slots (Case A/
200 months)--------------------------------- 181
6.14A Rate of Change Phase Portrait Max Appt
Seen vs Appt Slots (Case A/ 200 months) 181
6.15 Phase Portrait Max Appt Seen (Clinic Appt
Capacity) vs Appt Slots (Case C/
200 months)--------------------------------- 182
6.15A Rate of Change Phase Portrait Max Appt
Seen vs Appt Slots (Case C/200 months) 182
6.16 Medicare: Actual vs Baseline Simulated
Overflow Appts-------------------------- 186
6.17 Medicare: Actual vs Baseline Simulated
Overflow" Appts (Trend)--------------------- 186
6.18 Medicare Actual vs Avg Simulated
Overflow------------------------------------ 187

1.1 Differences in Management Theories------ 20
2.1 Lakewood Facility: Oct 96-Sept 97---------- 39
3.1 Summary of the Hierarchy of a Dynamic
System--------------------------------------- 54
3.2 Nonlinear Dynamic Social Systems-Lessons
Learned-------------------------------------- 61
3.3 Analysis Summary----------------------------- 70
4.1 Hypotheses and Observations------------------ 85
4.2 Key Components------------------------------- 88
4.3 Medicare Beneficiaries Demographics:
National vs Colorado------------------------- 93
4.4 System Detail-------------------------------- 94
4.5 Health Care Simulation Issues---------------- 95
5.1 Approach to Problem Solving----------------- 100
5.2 Kaiser Permanente Primary Care Services 106
5.3 Selected Technology Initiatives------------- 111
5.4 CLDs and Stock and Flow Diagram
Figures and Purpose------------------------- 130
5.5 Stock and Flow Notation--------------------- 140
5.6 Crosswalk for Patient Demand---------------- 141
5.7 Stock and Flow Map Selected Variables
for Patient Demand-------------------------- 143
5.8 Crosswalk for Labor------------------------- 144
5.9 Stock and Flow Map: Labor Variable
Description--------------------------------- 146
5.10 Baseline Model Input Variables-------------- 151
5.11 Model Inputs: Degree of Control of Model
Variables Categorized by Personnel Type 152

5.12 Performance Outcomes---------------------- 154
6.1 Hypotheses Hlo-HICo----------------------- 158
6.2 Nurse Visit Scenarios- Urgent Care
Overflow---------------------------------- 159
6.3 Admin/Leave Scenarios- Urgent Care
Overflow---------------------------------- 161
6.4 Productivity (physician visits/day) and
Urgent Care Overflow---------------------- 163
6.5 Selected Organizational Processes--------- 165
6.6 Hypotheses H2o-H2Co----------------------- 166
6.7 Multiple Scenarios------------------------ 168
6.8 Hypotheses H3o-H3Bo----------------------- 171
6.9 Regression Analyses Results--------------- 179
7.1 Differences Between System Dynamics
vs Agent-Based Modeling-

American society invests a great and growing amount of its social and economic
resources in medical care. Our society unquestionably has some of the wodds finest
physicians, hospitals, and medical schools, and is in the forefront in developing medical
and technological advances for the treatment of disease and illness. This on-going
investment in American health care is consuming over 15 percent of the gross domestic
product (GDP), yet we continue to lag behind other developed countries in health status
(WHO, [2000]; Levit, et al., [2003]). In addition, our health care outlay is creating pivotal
trade-offs between investments in health care and other services and technologies (Rivlin,
[1971]; Enthoven, [1993]).
Important elements contributing to this predicament include the sharp rise in
medical costs, difficulty in accessing medical care, aging demographics, the problematic
market solution of managed competition, the increase in die cost of medical malpractice
insurance among physicians and the ever-increasing specialization of medicine. In
combination, these elements cause a disruption in the availability and delivery of primary
care services (Conrad and Kem, [1988]; Grumbach and Bodenheimer, [2002]).
For most of the twentieth century, the traditional U.S. health economy had three
defining features:
patients relied on autonomous physicians to act as their agents,
patients received complex care from independent, nonprofit hospitals, and
in terms of health insurance, insurers did not intervene in medical decision
making and reimbursed physicians, hospitals, and other providers on a fee-for-
service basis (Dranove, 2000).
These features helped patients resolve the problems of determining what medical
services to buy, where to buy them, and how to assure coordination of care given the
inherent asymmetries of die physician-patient relationship. Even with the advent of
managed competition in the 1980s and its promise to lower health care costs and improve

access, the resulting health economy has continued to experience an escalation in medical
costs, with 43.6 million people presently uninsured (Lundy, et al., [2002]; Bureau of Labor
Statistics, [2003]).
In addition, this environment is creating heightened performance expectations for
physicians, especially those in the primary care, making the current practice of medical
care delivery more challenging. These circumstances are also causing calls for government
involvement into the health care arena. In 2003, Congress passed die Medicare
Prescription Drug and Modernization Act. This Act scheduled for implementation on
January 1,2006, contains a variety of program changes including an annual drug benefit,
prescription drug coverage choices, and low income and dual eligible subsidies.
Beneficiaries annual deductibles will increase. Recipients with incomes over $80,000 will
pay an even higher annual deductible based on a sliding scale.
The following sections highlight the rise in medical costs, the growing difficulty in
accessing health care and the influence of managed care upon primary care physicians and
Medical Costs
In research that emerged during the 1960s and 1970s, health economists
identified two sources of inefficiency in the traditional health economy. First, insured
patients demand any and all medical services regardless of cost, even those offering the
slightest possible health benefit because their out-of-pocket expenses continue to be
relatively low. This excessive demand phenomenon is labeled moral hazard. Second,
autonomous providers receiving fee-for-service payments have a financial incentive to
recommend the costliest treatments, even those that are of little value. This incentive is
called demand inducement. Moral hazard and demand inducement result from
incentives facing both patients and providers. Together, they drive up health care costs,
without commensurate increases in the quality of care, thus creating enormous waste in
the American health care system (Enthoven, [1993]; Dranove, [2000]).

In the last four decades, medical costs have risen dramatically, even more so in
the last few years. From 1960 to 2000, annual health care spending increased from 4
percent to 14.1 percent of the nations GDP. From 1992 to 2000, while the United States
experienced the longest economic expansion in its history, health care accounted for a
stable share of the GDP, 13.1 percent to 13.4 percent. However, in 2001, health spending
rose 8.7 percent, to $1.4 trillion, and accounted for 14.1 percent of the total economy, die
largest share on record and the highest among industrialized countries such as
Switzerland, Germany, France and Canada (Levit, 2001).
In 2001, about one-third of the U.S. spending on health care was for hospital care
(32%), over one-fifth for physician services (22%), and almost ten percent for prescription
drugs (9%). Although not the largest category of health services spending, prescription
drug spending is the fastest rising (Lundy, et al., 2002).
Medical care cost inflation between 1992 and 2002 has outpaced overall cost
inflation. As illustrated in Figure 1.1, medical care cost inflation averaged almost 5 percent
during this period while overall cost inflation was approximately 3 percent.
The American governmental twin health programs of Medicare and Medicaid
claimed an increasing proportion of the federal budget. Driven by increases in Medicaid
expenditures due in large part to rising drug costs as well as increases in coverage the
share of personal health spending contributed by public sources increased from 39
percent to 43 percent from 1990 to 2000 (Lundy, et al. 2002).

Figure 1.1. U. S. Medical Care Cost Inflation vs. Overall Cost Inflation
* i
£ 8.0% -
a- 7.0% -
s 6.0% -
5.0% -
* 4.0% -
3.0% -
g 2*% -
^ 1.0% -
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
Simn*; Bttmm of Labor Slarisiu*, Cantataor Prkv Index for AU Uri'iiti CV>m;twr( January 1QQ2-January 2002
Medicare is the federal health insurance program that covers 41 million
Americans. Medicare covers more than 35 million Americans ages 65 and over, as well as
6 million younger adults with permanent disabilities. Medicare serves all eligible
beneficiaries without regard to income or medical history. Currently, Medicare benefit
payments account for 19 percent of total national spending for personal health services.
In 2001, Medicare financed 30 percent of the nations hospital services and 21 percent of
physician and clinical services, but only 2 percent of outpatient prescription drugs (Kaiser
Family Foundation [(KFFj, 2003a).
The Congressional Budget Office (CBO) projects Medicare benefit spending to
be $269 billion in 2003, accounting for 13 percent of die federal budget Medicare
spending increased by 7.8 percent in 2002, less than the 10.5 percent rise in private health-

care spending (Altman and Levitt, 2002). While spending in Medicare is growing more
slowly than in private plans, it is increasing more rapidly than it did between 1997 and
2000, when spending grew at an annual average rate of 1.2 percent. The CBO projects
that Medicare spending will grow by 6 percent in 2003 and by an average of 6.7 percent
between 2003 and 2012. Government spending for Medicare is estimated at $3.2 trillion
over the next ten years. CBO estimates that government outlays for Medicare as a share of
the nations GDP will remain constant at 2.2 percent through 2007; it will then begin to
rise, reaching 2.5 percent by 2012. This rise is driven both by the large increase in
enrollment as the baby-boom generation turns 65 and by the ever expanding demand for
health care (CBO, [2002]; KFF, [2003a]). The CBO estimates does not include the
Medicare legislation (i.e., prescription benefit) passed by Congress in December, 2003.
Medicaid is the primary source of health and long-term care assistance for one in
seven Americans, accounting for 16 percent of the nations spending on health care.
Jointly financed by the federal and state governments, Medicaid reaches 44 million people
accounting for an estimated seven percent of all federal outlays in FY 2002. The CBO
estimated that die federal government spent $129.8 billion on Medicaid in FY 2001 and a
projected $295.4 billion in FY 2011, an average annual rate of growth of 8.6 percent.
State and local governments may spend an additional $100 billion per year on this
program (KFF, 2002). There are no national eligibility levels for Medicaid and inclusion
into die program is set by die states based on financial, health status or disability criteria.
The federal share for Medicaid expenditures averages 57 percent with a range
between 50 percent and 83 percent. Although nearly 75 percent of Medicaid beneficiaries
are financially qualified children and adults (under the age of 65), these groups account for
less than 30 percent of spending on benefits. The remaining 70 percent of expenditures
are for services provided to the eldedy and disabled, who make up only 25 percent of the
Medicaid beneficianes (KFF, 2002).
Currendy, due to the sluggish economy, virtually every state (49 states and
Washington, D.C.) has already taken Medicaid cost-containment actions for FY 2003.
These actions come in the face of a worsening fiscal situation and widening budget
deficits. More than half of the states (27) report that their Medicaid budget shortfall is

even greater than they had projected at the beginning of the fiscal year. One study reveals
that the seven states studied (California, Colorado, Florida, Michigan, Mississippi, New
Jersey, and Washington) have already expended their one-time financial sources such as
rainy day funds and tobacco settlement money to shore up state budgets and avoid
making larger cuts in Medicaid and other large state responsibilities like education. In
previous times of fiscal crisis and the past two years, states have also tried to maximize the
federal dollars being drawn down to their Medicaid programs to obtain fiscal relief
However, these options have been curtailed by the federal government and now provide
limited relief (KFF, 2003b).
Besides the inherent inefficient incentives in the traditional health economy,
another reason for the acceleration of health care costs is the success of biomedical
research, which led to the introduction of new, highly effective but very expensive
diagnostic and therapeutic procedures (e.g., organ transplantations). Furthermore, as
Levit, et al., (2001) suggest, the current rise in health spending is an increase in the amount
of medical goods and services purchased to care for an increasingly aging population. The
demographic growth in the quantity of senior citizens assumes many forms: more days
spent in hospitals; more outpatient services; more diagnostic tests; more prescriptions; and
greater use of new technology, which has the potential to extend life and improve its
Other industrialized nations use the same expensive diagnostic and therapeutic
procedures and their citizens are likewise aging but their health care costs are not as high
as the U.S (see Figure 1.2). The Centers for Disease Control and Prevention estimate that
between 2000 and 2030, the population of U.S. residents 65 and older is expected to rise
from 35 million to 71 million, increasing the overall senior population from 12.4 percent
to 20 percent of the total population and placing greater demands on the health care
system. A large, aging baby boom population and major medical advances that have
lengthened life expectancy both contribute to these projections (AP/USA Today.
2/13/03). Health care programs and their government sponsors need to brace themselves
for increased demands on the health care system.

Figure 1.2: Estimated Percentage of GDP Spent on Health Care
for Elderly
m -its -16;- .....Iw

1 3 1 1 12.; * 1 11.1 7
5.C 21 3.5 34 34 3 f ^ e
JJ \jt _1| J _ Ja Tt .. H rt -JM
United Canada Germany Japan France Australia New United
States__________________________________________________Zealand Kingdom
B Estimated % of GDP* spent on health of people age 65 & older
% of population age 65 & older
* Gross Domestic Product
Source: Heath and Population Agng: A Multinational Perspective, The Commonwealth Fund, 1997.
Access to Health Care
Dalen (2000) suggests that the number of Americans with optimal health care
has diminished in the 1990s, indicating that access to medical care is becoming an
increasing problem. Optimal health care is defined by Dalen as:
those who have well-trained primary care physicians of their choosing
who provide continuity of primary and preventive care. When the need
arises, they are referred to specialists and/or a hospital chosen by them
and their primary care physician. They have ready access to all available
diagnostic and dierapeutic procedures, including newer medications that
may be very expensive (Dalen, 2000. p. 2573).
In the 1970s and 1980s, many employed Americans (especially those in large
companies) were provided indemnity insurance and Medicare patients had access to
optimal health as defined above. The essence of indemnity health insurance was fee-for-

service, that is, physicians and hospitals were paid their charges. There was no incentive
for either physicians or their patients to be concerned with the acceleration of health care
However, as a result of the rapid acceleration of health care costs, many
employers and employees found they could no longer afford health insurance even with
sizable deductions and co-payment requirements. Not surprisingly, the number of
medically uninsured began to increase. The U.S. Census Bureau reports 43.6 million
people or 15.3 percent of the U.S. population, have no health insurance (Reuters Health
Information. 2003) and perhaps another 40 million are underinsured, so that they do not
have adequate financial access to health care when they are sick (Altman and Levit, 2002).
An analysis prepared by Gruber and Levitt (2000) finds that the number of
uninsured grows by 1.2 million for every 1 percentage point increase in the unemployment
rate, which rose from 4.0 percent in October 2000 to 6.0 percent in October 2003. Recent
data from the Census Bureau reflects an increase in the uninsured population from 14.6
percent (41.2 million) in 2001 to 15.3 percent (43.6 million) in 2002 (Census Bureau,
Gilmer and Kronenke (2001) suggest that if health expenditures increase at
approximately twice the rate of growth of personal income over the next decade,
Americans can expect the percentage of the under-sixty-five population that is uninsured
to rise from 16 percent to 21 percent. If the current geographic disparities in insurance
rates persist, more than one-quarter of the under-sixty-five population in California,
Texas, and Florida will be uninsured.
The uninsured are much more likely than the insured to have problems accessing
health care services and likely to use expensive emergency department facilities. In 2000,
39 percent of the uninsured reported postponing care, 36 percent had no regular source of
care, and 20 percent reported not getting medical care for a serious condition. In addition,
between 1997 and 2001, the ranks of physicians providing charity care sunk nearly 5
percentage points from 76.3 percent to 71.5 percent (Cunningham, 2002).

Managed Competition
As health care costs skyrocketed in the 1980s and early 1990s, American
employers found themselves at a severe disadvantage when competing in the global
economy. By the early 1990s, health care costs were accelerating and an increasing number
of Americans (the uninsured) basically lacked access to health care. The proposed Clinton
Health Plan to control health care costs and to increase access to health care, was soundly
rejected (Begley, et al., 2002). Rather, employers and the government turned to the
marketplace, in which they embraced managed competition. This proposition was that
for-profit managed care organizations would lower health care costs by competing in the
marketplace. The defeat in 1994 of President Clintons health care reform proposal
sparked the ongoing experiment with private health care markets. Many if not most
employers directed their employees to managed care plans. The increase in managed care
plans has been striking, rising from 27 percent in 1988 to 93 percent in 2001 (Lundy, et
al, 2002).
The federal government followed the lead of businesses and encouraged
Medicare beneficiaries to join health maintenance organizations (HMOs). Many Medicare
patients switched to HMOs to have access to their necessary medications at reduced costs
since Medicare does not presently cover prescriptions. Of the nations 41 million
Medicare enrollees in 2002,13 percent were in HMOs (KFF, 2002).
During this same period, the amount of Medicaid-supported programs grew
dramatically. State governments (who are mandated to provide Medicaid services to their
medically indigent population) directed their Medicaid patients to HMOs. All states
(except AK and WY) have enrolled a portion of their Medicaid beneficiaries in managed
care plans. Medicaid managed care grew rapidly in the 1990s, with the proportion of
enrollees in managed care increasing from 9 percent in 1991 to 56 percent in 2000. Nearly
19 million Medicaid beneficiaries are in managed care plans including fourteen states
having 75 percent or more of their Medicaid population enrolled (KFF, 2001).
Managed care did, at least initially dampen the escalating health care costs
especially hospital care. At first, managed care organizations administratively reduced the

length of their members hospital stays. Indeed, Congress had to pass federal legislation
to ensure that new mothers were not prematurely released from the hospital as a function
of cost controls. Furthermore, in some cases, expensive hospitalizations could be avoided
by performing surgery and other procedures as outpatient procedures in ambulatory
centers; intravenous therapy and other procedures normally performed in hospitals could
be done in the patients home by home health care providers. Rates for hospitalizations
could be decreased by requiring pre-approval from the managed care company. The
length of hospital stays could be reduced appropriately by the use of nurse reviewers.
Hospital capacity has continued to decline as lengths of stay decrease and use of
outpatient procedures grows. In 2000, the nations hospitals housed 349 beds for every
100,000 residents, just over half the capacity that existed in 1975 (Lundy, et al., 2002).
These managed care strategies severely affected a hospitals cost structure. Until
the early 1980s, market conditions enabled even badly managed hospitals to survive.
Private insurers either paid whatever price the hospital charged or paid the hospital for its
costs plus a predetermined profit margin. Government insurers -Medicare and
Medicaidalso paid on a cost-plus basis. Dranove (2000) suggests that this resulted in
those hospitals that provided unprofitable services or cared for the uninsured covered
these expenses by charging higher prices to everyone else. The idea that hospitals could
raise prices to their privately insured patients to generate die revenues necessary to pursue
their social mission became known as cost-shifting.
In the early 1970s, some state Medicaid programs reduced their payments to
hospitals. Hospitals responded by raising prices to privately insured patients. Concern
about cost shifting intensified in the early 1980s as more states cut their Medicaid
payments. But recent research suggests that managed care has put an end to cost shifting
(Dranove, 2000).
By the 1990s, it became difficult to further reduce hospital days. Other strategies
that managed care organizations and the government found to minimize the rise in
medical premiums and health care costs included:
payment reductions to physicians, physician groups, hospitals and other
providers of health care, and

reducing patient services by impeding access to specialists and limiting access to
expensive tests, treatments and medications. Also compelling physicians to
obtain prior approval for expensive procedures and medications.
These strategies also have had an effect on the way all physicians practice
medicine and on the medical care services available to their patients. The majority of
physicians say that managed care has increased the amount of paperwork required,
decreased the amount of time they can spend with their patients, decreased the ability of
patients to see specialists, increased overhead costs for physicians practices, decreased the
ability of patients to get needed prescription drugs, and, in general, seemed to decrease
health care quality (Lundy, et al., 2002).
The momentum behind a market solution to health care problems is again
sputtering. As a result, health care costs have been rising dramatically since 1998 (Levit et
al., 2001). Health insurance premiums have charted a similar course, increasing sharply
from 1996 to 2001. Increased hospital charges accounted for most of 2002 premiums
cost increase. Medical costs are also increasing because hospitals and other providers have
negotiated higher payments from health plans. This, in turn, has health plans increasing
their premiums to improve their finances (Los Angeles Times. 12/2/02). At the same
time, the U.S. population is aging, resulting in a higher utilization of health benefits. In
addition, consumers have flocked to looser forms of managed care, which impose fewer
restrictions than health maintenance organizations (Levit et al., 2001).
There have been no comprehensive efforts to address health care problems since
President Clinton's effort to overhaul the system collapsed in 1994. Congress has nibbled
at the edges of the problem, most notably by developing a program aimed at providing
coverage to children without health insurance and the passage of the new Medicare
reform legislation. There was also a brief respite from climbing costs caused by managed-
care programs. Still, health care remains a daunting and serious problem, central to the
social welfare mission of the U.S.
In recent public surveys, nearly half of Americans are very or somewhat worried
about being able to afford health care services and prescription drugs, and over half of
those with insurance coverage are worried about not being able to afford insurance or

having their benefits cut back in the coming year. As one would expect, these worries are
greatest among those with lower incomes and those who do not have health insurance
(NPR, 2002).
Growing public frustration and fear with health care may escalate, especially if
employers continue to drop health benefits, especially in light of continued political
promises and failures to address die problem. While a momentum may build for an
overhaul of the health care system, Congressional ideological differences and other
significant new spending such as die ongoing war on terrorism, creation of the
Department of Homeland Security, and the occupation of Iraq may likely squeeze out
ideas and programs considered by Congress to deal with any long term solutions to the
health care crisis.
In summary, neither regulation, voluntary action by the health care industry, nor
managed care and market competition have had a lasting impact on the nations health
care costs, exacerbating the problems of health care access and challenging the delivery of
health care services (Altman and Levitt, 2002).
Impact on Primary Care Services
The cost solutions to the health economy resulted in changes in the health care
environment over the last two decades, which have affected primary care physicians and
services. Primary care, through which a physician addresses a majority of patients health
care needs throughout a long time span, was developed to serve as a medical home or
base for prevention and treatment (Institute of Medicine, 1996). This primary care base
has several functions including typically first contact care (accessibility), providing
comprehensive, encompassing a spectrum of preventive, acute and chronic health care
(comprehensiveness) and a center from which other accommodations, specialists and
other providers are arranged (coordination). The primary care base provides longitudinal
care with sustained relationships (Starfield, 1998). It is estimated that 75-85 percent of
individuals needing medical attention require only primary care services annually. In other
words, referrals to secondary care settings or short-term consultations account for only

10-15 percent of services, and referral to tertiary care setting only 5-10 percent annually
(Institute of Medicine, 1996).
In a survey of patients enrolled in California physician groups, 94 percent valued
having a primary care physician (PCP) who knew about all their medical problems. Most
preferred to seek initial care for common problems from their PCP rather than a
specialist. (Institute of Medicine, 1996). Abundant evidence indicates the benefits to
patients and health systems of having a primary care base with these essential attributes
(Grumbach and Bodenheimer, 2002). Yet the delivery of primary care health care in the
U.S. is showing increasing signs of disruption because advances in medical care, changing
disease patterns, greater demand for clinical accountability and evolving professional
norms are creating heightened expectations for performance in primary care (Grumbach
and Bodenheimer, 2002).
The number of US medical school graduates entering family practice and primary
care internal medicine residency training programs is decreasing, erasing substantial gains
made in die 1990s. Increasing specialization of physicians has made PCPs relatively scarce.
Fewer than one out of four physicians can be identified as a PCP (general and family
practitioners, pediatricians, internists and obstetrician-gynecologists). In many rural and
urban areas, the only primary care available is in hospital emergency rooms (Brotherton, et
al., 2000).
Contributing to the lack of PCPs is the traditional medical culture in the US that
financially rewards specialization over generalists. For example, in 2002, the median yearly
compensation for PCPs (internal medicine, pediatrics and family practice) completing
their residency program and entering the market is $130,000. Physicians in extended
residency programs such as medicine sub-specialties and surgery, entering the job market
receive about $170,000 and $200,000 (respectfully) in yearly compensation (Gans, 2002).
Thus, the market reinforces the diminution of the physicians most likely to be seen by the
This change in monetary compensation neglects evidence that the scope of
primary care has expanded. In clinical practice, PCPs are expected to propedy manage a
wide range of chronic and preventive care. For example, changes in the last decade such

as the availability of new vaccines for children and adults and cancer screening for breast,
colon and prostate cancer constitutes routine primary care with considerable patient
education required. Primary care physicians provide 80 percent of visits for common
chronic conditions such as diabetes and hypertension. An aging, more sedentary,
increasingly obese U.S. population has resulted in a greater prevalence of type 2 diabetes
mellitus that is complicated to manage. Depression and other forms of mental illnesses are
increasingly recognized and treated at the primary care level (Grumbach and
Bodenheimer, 2002).
This higher standard for comprehensiveness of primary care creates tension for
patient accessibility. The traditional primary care practice was organized to respond to
acute and urgent medical care problems. PCPs attempting to schedule time for health
maintenance and chronic care visits reduce physician availability for acute and urgent
problems, throwing patients into the much more expensive emergency room regimen.
This condition grows more fragile as patients become less tolerant of long waits,
inconvenient office hours, and a system designed around provider than rather than patient
Moreover, the Institute of Medicines (1996) definition of primary care
emphasizes that primary care must be accountable. Health plans expect primary care
plans to measure and document their standard of care, meet explicit standards for clinical
performance and participate in quality improvement processes. Health Employer and
Information Set (HEDIS) performance measures are heavily weighted toward primary
care items such as preventive services and chronic disease management (Begley, et al.,
2002). Many PCPs receive report cards indicating where their practice falls in relation to
clinical indicator and patient satisfaction benchmarks. Managed care organizations may
also profile the cost patterns of primary care physicians, including accountability for
Adding to physicians clinical practice worries is the rise in medical malpractice
insurance premium rates beginning in the late 1990s after several years of relative stability.
Previous cycles of rising premiums have also occurred during the 1970s and 1980s.
Between 2001 and 2002, premium rates for the specialties of general surgery, internal

medicine, and obstetrics/gynecology (OB/GYN) increased by about 15 percent on
average nationally, and over 100 percent for certain of these specialists in some states
(GAO, 2003).
Laws governing medical malpractice vary from state to state, but among the
goals of tort law are compensation for the victim and deterrence of malpractice. For
example, die average reported claims payment made on behalf of physicians and other
licensed health care practitioners in 2001 was about $300,000 for all settlements, and
about $500,000 for trial verdicts.11 The most common policies sold by insurers provide
$1 million of coverage per incident and $3 million of total coverage per year. Medical
malpractice premium rates differ widely by medical specialty and geography. Premium
rates also vary across and within states.
In response to these rising premiums, representatives of health care providers
including physicians, hospitals, and nursing homesand the media have reported that
physicians have moved out of states experiencing the highest increases, retired, or reduced
or eliminated certain high-risk services. Also the fear of malpractice litigation may
encourage physicians to practice defensive medicine, for example, ordering additional
tests or procedures, thus increasing total health care costs. In an effort to mitigate rising
malpractice costs, states have passed various tort reform laws, some of which include caps
to restrict the size of damage award payments and other measures to limit costs associated
with malpractice litigation. Congress is considering similar federal legislation.12
The GAO (2003) examined how health care provider responses to rising
malpractice premiums have affected access to health care. The GAO reviewed whether
uSee Physician Insurers Association of America (PLAA), PIAA Claim Trend Analysis, 2001 Edition
(Rockville, MD: 2002). About % percent of closed claims were out-of-court settlements and about
4 percent through a trial verdict
12 Medical malpractice lawsuits are generally based on principles of tort law To reduce malpractice
claims payments and insurance premiums and for other reasons, some have advocated changes to
tort laws, such as placing caps on the amount of damages or limits on the amount of attorney fees
that may be paid under a malpractice lawsuit These changes are collectively referred to as tort
reforms (GAO, 2003).

physicians practice defensive medicine, and how growth in malpractice premiums and
claims payments compares across states with varying tort reform laws.
GAO studied five states with reported malpractice related problems (Florida,
Nevada, Pennsylvania, Mississippi, and West Virginia), and four control states without
reported problems (California, Colorado, Minnesota, and Montana), and growth in
malpractice premiums and claims payments across all states and the District of Columbia.
Its findings suggest that rising malpractice premiums have contributed to localized health
care access problems in the five problem states. These problems reduced access to
hospital-based services affecting emergency surgery and newborn deliveries in scattered,
often rural, areas where providers identified other long-standing factors that also affect
the availability of services. GAO also determined that many of the reported provider
actions (e.g., relocated and reduced high risk services) were not substantiated or did not
affect access to health care on a widespread basis.
Upon reviewing the literature, the GAO concludes that studies designed to
measure physicians defensive medicine practices have been limited in scope and cannot
be generalized to estimate the extent and cost of defensive medicine practices across the
health care system. Based on limited data, the growth in malpractice premiums and claims
payments has been slower in states that enacted tort reform laws that include certain caps
on noneconomic damages. For example, between 2001 and 2002, average premiums for
three physician specialties general surgery, internal medicine, and OBGYN grew about
10 percent in states with caps on noneconomic damages of $250,000 compared to 29
percent in states with limited reforms. However, the GAO study could not determine
which differences in premiums and claims payment across states were caused by tort
reform laws or other factors.
At the same time, managed care is considered another major culprit in troubles
experienced by physicians. Administrative reviews, challenges to clinical autonomy, and
income reductions are souring some physicians on the practice of medicine as filtered
through managed care. In addition, the managed care gatekeeper role has caused many
patients to be apprehensive about restriction of specialty care and financial conflicts of
interest for primary care physicians.

Morrison and Smith (1995) have termed the current predicament in the delivery
of health care "hamster health care, (Le., physicians are made to feel like hamsters on a
treadmill). As the wheel is going faster but remaining in place, there is a reduction in
professional satisfaction and increase burnout among physicians. Many claim this system
is not sustainable and health care must be re-designed.
If primary care practices are to step off the hamster treadmill, major innovations
and practice of primary care management will be required. Managed care has failed to
catalyze fundamental changes in how health care is delivered in the physician practice,
leaving intact glaring inefficiencies (Grumbach and Bodenheimer, 2002). New ideas and
practices are needed. Primary care physician practices need a new environment
intertwined with systems of care that improve access and quality while they relieve
physicians workload and stress. Most difficult, these changes must take place without
major increases in total health care costs.
Research Issue
As described above, the present health care system, a hybrid between die public
and private sectors, is challenged by a combination of rising medical costs, increased
number of uninsured, and a delivery strain put upon primary care physicians and services.
Improvements in the access and delivery of primary care services must offer relief to
primary care physicians as they struggle with heightened expectations, changing
demographics and disease patterns, fiscal constraints and the changing external
Primary care service redesign that incorporates efficient and effective practices
may relieve physician workload stress, while improving health care access and quality.
Efficient medical practice increases productivity. Critical operational areas that impact
productivity are the appropriate number and type of support staff, patient flow processes,
and the physician-patient encounter (Walker and Gans, 2001).
Woodcock (2000) estimates that a typical practicing primary care physician is only
60 percent productive in the office setting (i.e., patient contact time). Physician time is

seemingly wasted in non-value or delegatable activities (e.g. insurance paperwork and
injections). Low physician productivity increases practice cost (regardless of who die
payer is), decreases satisfaction, and further contributes to the hamster care
New managerial techniques are available to assist physician practice
administrators in raising productivity. In todays current health environment, medical
practice redesign encourages clinicians and staff to explore innovative ways to provide
patient care, streamline charting, scheduling patients and other processes. In addition,
redesign holds the promise of giving staff more control and delegating more power
and decision making back in the hands of physicians and patients.
The traditional (e.g., linear or heuristic) performance techniques employed to
assist clinic administrators in understanding patient visit activity at their public or private
facility are incomplete. The inability to plan a medical practices resources efficiently can
lead to increase medical costs, revenue loss, poor patient satisfaction and low staff morale,
physician burnout and an erosion in patient health status (Walker and Gans, [2001];
Grumbach and Bodenheimer, [2002]).
To improve understanding and the performance of health care organizations, this
dissertation explores the use of dynamic theory, in particular, nonlinear dynamics to
enhance the analytic effort. Traditional systems analysis techniques such as time series,
queuing theory, simulation, linear programming and linear regression are based on linear
mathematics and may not always offer the health care administrator a hill understanding
of the complexities of his/her environment. But developments in the science of
dynamics, particularly system dynamics, may provide managers an understanding of how
processes and changes within their health care system relate to the production of needed
solution alternatives.
The current climate of the health care industry provides health care
administrators with a complex environment that is forcing managers to improve service
performance. Variables in predicting and understanding a systems behavior that have
been posed as linear relationships may now possibly be analyzed by applying nonlinear
dynamic analysis. Variable relationships that had been ignored due to their nonlinear

awkwardness will now be subject to analysis and synthesis. Administrators will no longer
look for simple correlation to continue their linear component thinking but instead can
reach out to understand the rugged predictions and the systematic complexity of the
organizational environment. Hence, administrators need to turn to nonlinear dynamics.
This dissertation explores how nonlinear dynamics and feedback can be utilized in
the primary care health care setting for the Medicare population. Specifically, it asks: can
system dynamics modeling assist health care administrators in delivering medical
services more efficiently to their client population? To understand the interaction
between Medicare patient access to primary care services (via the appointment system) and
the health care provider workforce, this research delves into the structure and behavior of
these health care subsystems and their primary components. Research questions we are
interested in exploring are:
1) Does system dynamics modeling, specifically a causal-feedback
simulation model, improve a health care administrators understanding of
patient care services for future improvement?
2) Can a system dynamics modeled appointment process provide more
efficiencies (i.e., reduce cost) than are currently available from a linear
3) How does a system dynamics model offer administrators new insights
about how health care organizations work?
We are here to test the applicability of the system dynamics approach to the
medical practice setting. It is expected the results of this dissertation will reveal some
implications of nonlinear dynamics system theory and suggest its uses in the practical wodd
of contemporary health care and its public sector management.
Theoretical andPtaflical Implications
This thesis draws upon two theories relevant to the management of public
systems: the classical theory of management and general systems theory. The classical

theory originated early in the twentieth century in the works of Frederick Taylor (Cayer
and Weschler, 1988), Max Weber (Gerth and Mills, 1946), and others. It was dominant in
the 1930s and still forms one basis for management practice to this day. The classic
principles of management theory encourage the systematic study of management, the
measurement of management, the measurement of organizational performance and the
development of management as a role and a profession in public and private life.
General systems theory became prominent in the 1960s and imposes a common
conceptual order on a fairly wide range of events. Systems theory has built upon classical
management theory and added more sophisticated concepts and technologies to solve
problems of organization and coordination, to understand complex phenomena, and to
plan and design for the future. It encourages die recognition of analogies and structural
similarities between phenomena previously thought to share little in common. Its
concepts primarily come from the physical and biological sciences. Table 1.1 displays
distinctive features between classical and general system theories of management (White et
al, 1980).
Table 1.1 Differences in Management Theories
Classical Theory General Systems Theory
Fixed principles of organization Principles depend on the nature of the work and the work environment
Reduction in variation and ambiguity in organizations through the isolation of work from external forces Embraces environmental disturbances and ambiguity
One best way Contingency theories
Tendency for principles to be answers looking for questions Emphasis and outcome of investigation are questions rather than answers
Associated with closed systems Associated with open systems
Source: White, et al., 1980. Maturing Public Systems

Large health care organizations traditionally have followed a bureaucratic (classic)
approach, as reflected in their primary analytic paradigm, health economics. Authority is
based upon formal rules and work is carefully structured according to a rational hierarchy.
Organization charts, written rules and regulations, and specialization of tasks are relied on
for organizing work. Health care administrators, facing the rapidly growing demands of
their environment, tend to operate in an organization that was primarily designed from a
mechanistic approach. Thereby, in some circumstances, health care administrators are
impeded in responding to their internal and external stakeholders.
Conflicting and complex demands are difficult to control, requiring greater
management skills from administrators and other personnel throughout the organization.
Whereas managers in the past dealt with the unusual in simple increments, problems today
accumulate in rapid progressions. Rather than merely accepting problems and the course
of events, health care organizations must take a pro-active stance to remain competitive.
Inefficient delivery of health care services has a negative governmental fiscal implication.
Health care organizations ability to react will depend upon their institutional health,
viability and their administrators skill in managing change and resources in turbulent
times (Bass, 1989).
The theory of nonlinear dynamics and feedback may offer managers increased
understanding of the changing environment affecting organizational behavior and
functions in an administrative setting. This knowledge can be added to an administrators
managerial toolbox and used in analyzing, diagnosing and measuring performance. It may
not only provide a greater quantity of information to aid in resource manipulation but it
can illuminate questions on the basic logic of management philosophy resulting in a
turning point in our understanding of the organization and its environment.
Government-Funded Health-CamTrograms
Health care managers serve beneficiaries in government funded programs such as
Medicare, Medicaid, Veterans Administration and TRICARE (military) work in
challenging external and internal environments. For example, the Medicare Physician Fee

Schedule decreased by 5.4 percent in 2002 and increased a modest 1.6 percent in 2003 by
Congressional mandate but is now facing cuts in the range of 4 percent for each of the
next several years. If the Centers of Medicare and Medicaids projections become reality,
the Medicare payment rates in 2005 will be lower than they were in 1991.
The cut in 2002 was the fourth time in 11 years that Medicare physician payment
rates were reduced. During that time, physicians have been inundated with expensive new
requirements to comply with numerous federal regulations and an increase in Medicare
beneficiaries due to an aging population. Overall, during the same 11 years, Medicare
payments to physicians have risen by an annual average of just 1.1 percent, or 13 percent
less than the governments own estimate of medical practice cost inflation (American
Academy of Family Physicians, 2003).
Medicaid program spending growth slowed in 2003, the first time in seven years.
For 2003, it was 9.3 percent, down from 12.8 percent in 2002, as states continue to cope
with the fiscal crisis. Over the past three years, 50 states have taken action to control drug
costs, 50 have reduced or frozen health care provider payments, 34 have reduced or
restricted eligibility, 35 have reduced benefits, and 32 have increased co-payments. The
primary cause of the fiscal crisis is the reduction in state tax revenue, with the decline in
revenue collection being $62 billion, while spending increased about $7 billion in FY 2002.
Medicaid spending growth between 2000 and 2002 has been driven in part by enrollment
growth due to the economic downturn, as well as continued increases in hospital and
prescription drug costs (KFF, 2003b).
Perhaps the most visible of all Veteran Administration (VA) benefits and services
is health care, which had a total spending of approximately $28 billion in 2003. VA health
care facilities provide a broad spectrum of medical, surgical and rehabilitative care. Access
to VA medical care is based on a discretionary basis rather than a mandatory one. That is,
there is a priority system in place for medical treatment. Demand for treatment has grown.
Veterans medical care funding has increased by 49 percent over the past 5 years but the
number of veterans enrolled in the VA medical program has increased by 70 percent over
the same time period. According to the VA (2003), the number of veterans using its
health care system has drastically risen over the years, from 2.7 million in 1995 to 4.5

million in 2002. More than 6.5 million veterans are enrolled in the VA health care system,
but because of insufficient funding for health care, nearly 236,000 veterans currently are
waiting six months or longer for care. Additionally, the VA has closed enrollments to new
patients at many hospitals and clinics.
The Department of Defense (DoD) is responsible for providing medical care and
coverage for over 8 million beneficiaries. Through the TRICARE program, which
combines hundreds of military medical treatment facilities with several regional networks
of civilian health care providers. Although die primary purpose of the system is to ensure
a healthy active duty force capable of performing critical national security missions,
TRICARE provides coverage for a wide variety of eligible beneficiaries, not only active-
duty service members, but also military retirees and mobilized reservists, along with their
families and survivors.
From fiscal year 1988 to 2003, DoD's spending on medical care almost doubled
in real terms when providing peacetime health care for eligible beneficiaries. That growth
occurred despite large reductions in the size of the active-duty military force and a
substantial reduction in die size of the military's own hospital system. Adjusted for the
overall rate of inflation in the U.S. economy, the Department's annual spending on
medical care almost doubled from 1988 to 2003, rising from $14.6 billion to $27.2 billion.
Furthermore, because DoD cut the size of the active-duty force by 38 percent
over that same period, medical spending per active-duty service member nearly tripled,
rising from $6,600 to $19,600 (CBO, 2003). Assuming no change in DoD policies, the
CBO estimates that projected medical care will rise from $27 billion in 2003 to almost $46
billion in 2020. This figure has not yet factored in the recent hostilities in Iraq and
As cost pressures mount on these and other government funded health care
programs (in part due to the increasing federal budget deficit exceeding $370 billion in FY
2003 and projected to increase in future years), administrators responsible for the delivery
of medical care services to government funded beneficiaries are challenged to continue to
deliver medical care more efficiently.

The focus of this dissertation is limited to the Medicare population at Kaiser
Permanente in an effort to better understand the relationships between several variables in
this organization. This thesis examines Medicare beneficiaries access to primary care
services at the clinic level through the modeling and exploration of the structure and
behavior of the appointment-workforce system. Kaiser Permanente was chosen due to its
pioneering efforts in health care delivery as described in Chapter Two. The articulated
results are to specifically understand, process and improve appointment access, and, more
generally, to enhance the performance of the general health care system. While the nexus
to government programs is indirect, certain analysis will be explored.
Organization of Thesis
The first chapter has documented the problems with the present health care
system. Rising medical costs, increasing numbers of uninsured and die increasing
demands on primary care health care delivery suggest some theoretical and practical
implications for researching organization and management theories in conjunction with
the theory of nonlinear dynamics and system dynamics.
Due to the complexity and changes of the health care industry in the last two
decades. Chapter Two provides an overview of the growth of managed health care,
specifically health maintenance organizations (HMOs), and their delivery of medical services
to patients through the Medicare program. Chapter Three examines the literature of
organizational theory and practice, the dynamic sciences, with emphasis on nonlinear
dynamics, and its usefulness to management practices. Chapter Four describes the
methodological framework for examining nonlinear behavior in health care delivery systems.
The studys research approach follows Stermans (2000) modeling process. This process is
comprised of five stages: boundary selection, diagramming of selected systems and
subsystems, the formulation of a simulation model, testing, and scenario building for future
Boundary selection or the conceptual model is discussed in detail in Chapter Four.
Boundary selection orients the reader to the general problem area, establishes the objective

of the study, provides research questions to be addressed, and describes the selected basic
system in causal diagram form. In Chapter Five, the conceptual model is modified further by
detailed diagrams. Next, the qualitative-verbal representation of the system flow is replaced
by mathematical equations defining decision rules, estimation of parameters, behavioral
relationships, and initial conditions to formulate the model. Testing the model concludes the
In Chapter Six alternative model scenarios are simulated to answer the proposed
research questions and hypotheses. Concluding the study, Chapter Seven reviews this
thesis implications to health care, the field of Public Management and comments on
future research directions.

This chapter familiarizes the reader with the growth and development of
managed health care, specifically health maintenance organizations (HMOs) and their
delivery of medical services to the public sector through the Medicare program. A brief
description of Medicare managed care and its influence upon participating HMO plans is
also discussed. In conclusion, the use of medical services by the senior population and the
challenges faced by HMOs as they deliver medical care are highlighted.
Growth .and Development of Managed Gag
From the start of U.S. social policy after the Civil War (Skocpol, 1992), American
health policy was basically a matter of private welfare institutions and friendly societies. The
U.S. heavily relied on private initiative for health care. In contrast in Europe (e.g.,
Germany), die goal of social and health policy development was the containment of social
upheaval through government (Labisch, 1997). Health care was considered to be a public
good, and therefore addressed as a public policy concern.
In the U.S., industrial medicine initiated employment-based health benefit
programs. In the 1870s, for example, railroad, mining, and other industries began to provide
the services of company doctors to workers. Pnor to Wodd War II, few Americans had
health insurance, and most policies covered only hospital room, board, and ancillary
services. During Wodd War II, the number of persons with employment-based health
insurance coverage started to increase as companies used this option to recruit and retain
scarce workers. For example, Kaiser Shipyards provided on-site health care for 30,000
workers and their families in the San Francisco area.
Paul Starr (1982) has traced the origins of managed care back to the 1890s, when
individual physicians agreed to provide prepaid medical care to lodges e.g., fraternal

orders, unions, and other association of workers. Prior to this time, physicians in the
private practice of medicine almost universally billed the patient directly on a fee-for-
service (FFS) basis even though, in some instances the fee was not necessarily monetary
in payment. Patients reimbursed the physician directly at the time of service or upon
mutually agreed payment arrangements. With prepaid medical care, physicians provided
lodge members with unlimited free access to health care service in exchange for an annual
fee of one to two dollars per member. Although lodges reached into nearly one-third of
American families, prepaid medicine was largely concentrated among immigrant
communities and most physicians refused to participate (Dranove, 2000).
Prepaid physician group practice began, in 1910, when groups such as Western
Clinic in Tacoma, Washington, offered to provide industrial medicine (medical care for
work-related injuries and illnesses) to workers at a lumber mill for a prepaid monthly fee
(Fuchs, 1974). By 1920, two dozen clinics in Oregon and Washington offered similar
prepayment plans. A rural farmers cooperative health plan was formed in Elk City,
Okalahoma, in 1929. An annual dues schedule was devised that covered the cost of medical
care, surgery and house calls. By 1934, in Los Angeles, California, Donald Ross, MD and H.
Clifford Loos, MD, entered into a prepaid contract to provide comprehensive health
services to about 2,000 water company employees (MacLeod, 1993).
Prepaid medical care primarily initiated and expanded through industrial medicine
began to branch out to employers and unions after World War II. For example, Kaiser
Foundation Medical Care Program opened to community groups in 1945 in California and
Oregon (i.e., non-Kaiser industrial groups). Prepaid medical care was popular among trade
unions and spurred several other prepaid group practice plans to begin after the war. These
plans enjoyed many years of successful growth even in die face of strong opposition from
organized medicine. Examples included, the Group Health Association, Group Health
Cooperative of Puget Sound, Health Insurance Plan of Greater New York and Group
Health Plan of Minneapolis.
A variant of the prepaid group practice plan appeared in 1954, when a prototype
IP A (Individual Practice Association) was established by the San Joaquin County Medical
Society in Stockton, California Fearing competition from Kaiser Permanente, which had

grown since its wartime genesis, the Medical Society set up a prepaid foundation for
medical care in the same community.
As the medical community was experimenting with these non-traditional payment
mechanisms when delivering health care, state and federal governments were grappling with
the issue of medical care for the aged and poor. Health insurance was excluded from the
original Social Security Act of 1935 because of the opposition of the medical profession
and private insurance interests (MacLeod, 1993). Similar pressure kept the program from
being enacted during the 1940s and 1950s. During this time period, federal funding was
implemented in 1946 to assist states towards hospital construction through the Hospital
Survey and Construction Act (Hill-Burton program) and its subsequent amendment in
1954. Public support for the concept of medical care for the aged and poor grew during
these years. This led to the Kerr-Mills Act of 1960, which provided federal support for
state medical programs serving the aged poor and the Migrant Health Act in 1962 funding
health services for migrants.
By 1965, there was a growing consensus that a broader program was needed, with
a stronger federal role and wider eligibility. The elderly population was continuing to
increase at a rapid rate. Between 1950 and 1963, their number grew from about 12 million
to 17.5 million, or from 8.1 to 9.4 percent of the total population. Meanwhile, the cost of
hospital care continued to rise at about 6.7 percent a year, several times the annual
increase in the cost of living. From 1960 to 1964, average hospital costs increased from
about $29 to $40 a day, with no sign of any letup in the rate of increase. As a result,
private health insurance carriers were repeatedly forced to increase premium rates (or else
"bleed the coverage of their policies), making private insurance ever more prohibitive (or
less adequate) for the many old people who were living on fixed incomes. By 1964, the
proportion of the aged who had adequate hospital insurance protection was only about 1
in 4 (Coming, 1969).
Two new programs "Medicare, and Medicaid were established by the 1965
amendments to the Social Security program. The Medicare legislation created health
insurance for the elderiy and the disabled administered by the federal government (Marmor,
1970). Medicaid was designed to serve both those eligible for public assistance and those

whose incomes fell just above that level but were judged to be "medically indigent." Like
Medicare, Medicaid permits those who are eligible to purchase health care from the same
hospitals and physicians as the general public, with the fees paid by the program. Unlike
Medicare, however, Medicaid is administered by the individual states, with a combination
of state and federal funding.
The new Medicare program reimbursed physicians on a fee-for-service system,
based on "customary, prevailing, and reasonable (CPR)" fees. This reimbursement method
was the standard practice used in the program. But Medicare also recognized that for
prepaid plans and group practices, a different kind of payment method was necessary. These
plans were accommodated by permitting them to be paid on a reasonable cost basis for
physician services that the program would otherwise be paying on a reasonable charge (fee-
for-service ) basis.
As new knowledge regimes in health care (e.g., health economics, health services
research, and outcomes research) started to evolve in the U.S., Fuchs (1993) suggested
evidence for the increasing significance of economic analysis in health policy from the 1970s
onwards. For example, Medicares escalating medical expenditures spurred the federal
governments interest in prepaid medical care as a cost containment strategy. The Medicare
program introduced HMO enrollment and contracting in 1972. This provided beneficiaries
the option of enrolling in a variety of private health care plans to access their entitled
benefits. Other governmental efforts included HMO legislation to encourage its
development in the private sector.
Health Maintenance Organizations ..(HMQs)
In 1973, Congress passed the Health Maintenance Organization (HMO) Act that
legitimized the concept of prepaid health care, making it easier for employers to embrace.
The political desire to foster HMOs stemmed from the issue of cost containment. Managed
care was politically interpreted as a suitable means for retarding inflation in the health care
sector (Buchanan et al., 1992). The rising health care inflation was interpreted by some as a
threat to international competitiveness of U.S. industry. Cost pressures on the employers

were the major force behind the fostering of managed care mechanisms and organizations.
The Act encouraged die development of HMOs in the short term, but laid the foundation
for preferred provider organizations, point-of-service plans, and other forms of managed
care in the long term (Mitka, 1998).
After the passage of the HMO Act, strong support for the HMO concept came
from business, the federal government and several states, including California and
Minnesota, where managed care proliferated. Bipartisan support for managed care was
based on the understanding that HMOs could decrease cost and encourage ffee-market
competition in the medical care arena with only limited government intervention (Enthoven,
1996). Perhaps one measure of success of this policy was in the disappearance of 17
national health insurance bills introduced into Congress in the eady 1970s (MacLeod, 1993)!
HMOs are oiganized health care systems responsible for both the financing and the
delivery of a broad range of comprehensive health services to an enrolled population.
HMOs can thus be viewed as a combination of a health insurer and a health care delivery
system (Kongstevedt, 1993). Whereas traditional health care insurance companies are
responsible for reimbursing individuals for covered services, HMOs are responsible for
providing health care services to their members through affiliated providers who are
reimbursed under various financial methods (eg direct salary, discounted fee-for-service,
capitation, etc.).
HMOs arrange for this care either directly in their own group practice and/or
through doctors and other health care professionals under contract. Typically members
choose a primary care physician to provide them with medical services and can refer them
to other providers as deemed necessary. Usually, the choices of doctors and hospitals are
limited to those that have agreements with the HMO to provide care except in emergencies
or when medically necessary. Because HMOs receive a fixed fee for covered medical care, it
is in their interest to make sure their members receive basic health care for problems (lower
costs) before they become serious (higher costs).
Both prepaid group and individual practice associations were the foundation for a
new kind of health delivery in the U.S., whereby physicians shared the risk of financing
health care for an enrolled population. These models led to a widespread dissemination

of managed care plans. HMOs assumed responsibility for providing a comprehensive
range of health services to enrolled populations at a fixed annual premium. Total medical
costs will likely be lower and more predictable in an HMO than with other types of
managed care or fee-for-service insurance plans (Enthoven, 1996).
Proponents of this new delivery system expected lower costs and high quality of
care as part of an effort to contain the ever-growing health care share of the GDP. Not
stated is the implicit trade-off -that something in terms of quality of care, satisfaction, or
access to care might have to be given up in exchange for cost containment. Recent
evidence indicates that, overall, die trade-off lias not been as severe as some had feared.
HMOs have led to lower costs, and while access and satisfaction seem to have suffered,
quality of care has been comparable for all types of enollees combined. Miller and Luft
(2001) analyzed peer reviewed literature from 1997 to mid-2001 and suggest that HMO
plans and non-HMO plans provide roughly comparable quality of care.
The evolution of these prepaid group and individual practice plans in the private
sector was one of the most extraordinary developments in the history of medical care
organizations in the world (MacLeod, 1993). Prepaid plans went on to serve as a template
for financing and organizing health care services and at the very least ushered in a new era
of corporate influence into the practice of medicine (Dranove, 2000).
HMOs deliver medical services to both the privately and publicly insured.
Enrollment in managed care grew dramatically over the last decade. By 2000,92 percent
of the population with employer-sponsored insurance was enrolled in some form of
managed care up from 73 percent in 1996 (Rogal and Gauthier, 2001). HMO penetration
(the percent of a states population enrolled in an HMO) varied strikingly across states in
2000. Penetration rates ranged from a low of less than 2 percent in Alaska, Mississippi,
and Wyoming to a high of 54 percent in California. Massachusetts, Connecticut,
Maryland Oregon, Rhode Island and Colorado populations enrolled in HMOs exceeded
35 percent (Lundy, 2002).
The physician marketplace has also been changed by the advent of managed care
plans. In die early 1980s, 41 percent of all physicians were self-employed in solo practice.
By 1999, only 26 percent of all physicians worked on their own, while 41 percent of

physicians were employees and 33 percent worked in group practices. An important driver
for this trend away from solo practice is physicians pursuit of market leverage in
negotiating payments from managed care plans (Lundy, 2002).
HMOs and the Medicare Progam
In 2002, the Medicare program provided health benefits to 41 million elderly and
disabled Americans. Most (89 percent) have their health bills paid directly by Medicares
traditional fee-for- service program. The remaining 11 percent are covered by Medicare +
Choice (M+Q plans. These private plans include HMOs (by far the most widely available
alternative to beneficiaries), preferred provider organizations, private fee for service plans
and medical savings accounts coupled with high deductible insurance plans (KFF, 2003a).
For our research purposes, this sub-section describes an overview of the M+C plans, its role
in die Medicare program and its effect on participating HMOs.
Managed Care Medicare
Medicare provisions in the 1972 Social Security Act introduced Medicare HMO
enrollment and contracting, as opposed to the traditional Medicare payments based on a
fee-for-service system. HMOs had to meet certain standards, provide a full range of
available Medicare services and conduct open enrollment (i.e., insurers cannot deny
coverage or charge more for current or past health problems) for all Medicare beneficiaries.
Medicare HMOs were paid either on a risk bearing or on a continuation of the non-risk
cost reimbursement contract basis. Very few HMOs took advantage of the risk-bearing
option (Langwell and Hadley, 1989).
Plans operating on a full risk basis began in mid-1985 after changes to the Tax
Equity and Fiscal Responsibility Act of 1982 (TEFRA). Under full risk contracting, HMOs
or a competitive medical plan would be paid 95 percent of the adjusted average per capita
cost (AAPCQ on a full risk basis for each Medicare enrollee. The 5 percent differential
recognized and presumed greater efficiency of HMOs and their ability to reduce program

expenditures. Any additional savings had to be 1) returned to the beneficiaries in the form of
extra benefits or reduced cost sharing; 2) used to fund future additional benefits; or 3)
returned to the federal government HMOs were allowed the profit or retained earnings that
they customarily received in the private sector. By the end of 1987, the number of risk
contractors rose from 87 to 161 (Group Health Association, 1993). Risk enrollment rose
from nearly 1.1 million in 1985 to 2 million by the end of 1990 (KFF, 2003a).
With the managed care revolution of the mid-1990s, HMOs burgeoned in the
private sector as well as the public sector. Medicare risk HMO enrollment rose to 6.3
million beneficiaries or over 16 percent of the Medicare population in 2000 (KFF, 2003).
Within areas where Medicare HMOs were available, one in five beneficiaries elected to
enroll in a plan, in contrast, to the private employer market, where about one-third of
individuals were covered by an HMO (Buckley and DAmaro, 1998).
Balanced Budget Act (BBAXaf-lSi?
Private health plans* participation in Medicare was envisioned as a way to save
taxpayers money and offer Medicare beneficiaries more choices and benefits. As enrollment
grew, there were concerns about overpayments to some private health plans and wide
geographic variation in plan payments. The Balanced Budget Act of 1997 (BBA) introduced
significant payment changes and regulatory requirements for plans participating in the
Medicare risk contracting program (which the legislation renamed M+Q. These changes
included budget savings and the expansion of M+C plans, especially to rural areas. These
modifications also included incentives to increase enrollment overall with an emphasis to
subpopulations such as the physically disabled.
Before the passage of the BBA, the risk contracting methodology was evaluated
primarily (although not solely) on its ability to generate savings for the Medicare program.
Yet potential for these savings (assuming perfect risk adjustment) was always limited by the
payment system, which established plan payments at 95 percent of FFS spending.
In practice, HMO plans experienced favorable selection of Medicare enrollees.
That is, healthier Medicare beneficianes tended to participate with HMOs. The most

striking differences are found in the comparison of average risk factors, indicating a clear
bias in the managed care populations toward beneficianes predicted to be less costly
(Greenwald, et al., 2000). This resulted in higher Medicare spending. In 1998, M+C paid
plans an average of 13.2 percent more than the Medicare program would have paid under
traditional FFS due to favorable selection by HMOs (GAO, 2000). The volume of literature
examining this issue concluded that prior to the BBA, the risk contracting program
increased Medicare spending five to seven percent (Thorpe and Atherly, 2002).
Recent analyses have concluded that many of the BBA objectives have yet been
met and that the program continues to increase overall Medicare spending. Contrary to the
BBAs policy intent, the M+C program has experienced:
1. Lower enrollment: since 2000, enrollment (from 6.3 million) has declined
by 27 percent to 4.6 million or 11 percent of Medicare population (KFF,
2. Plan terminations: participation in M+C by plans has declined from 346 in
1998 to 152 in 2002. Plans cite insufficient funding, especially inadequate
adjustment for health status of enrollees, medial service increases and
negative profit margins (Schmitz and Komfield, 2001).While the BBA
often is blamed for this turnabout, research indicates private market forces
also played a key role in M+Cs decline (Grossman et al., 2002).
3. Decreases in benefits available to enrollees: all M+C plans provide
benefits covered under traditional Medicare without imposing additional
out-of-pocket costs. Plans with costs below Medicare payment levels must
distribute savings to beneficiaries in the form of lower plan premiums or
additional benefits. Most M+C plans offer supplemental benefits (e.g.,
prescription drugs, preventive care and routine eye exams). However there
has been decline in availability and breadth of key benefits, particularly
prescription drugs (Achman and Gold, 2002).

Future of Managed Care Medicare
Bodenheimer (2002) suggests that overall the Medicare program has slowed its rate
of expenditure growth through government efforts, -prospective payment to hospitals,
volume performance standards for physicians, and the unpopular but effective BBA of
1997. Adjusting for inflation using CPI, annual increases in Medicare spending per capita
dropped from 112 percent for 1975-80 to 1.8 percent for 1995-99. Furthermore, a number
of changes have been adopted since 1997 to encourage M+C plans to stay in the market,
such as increased payments to plans and the easing of administrative requirements. In
addition, congressional leaders have proposed various legislative remedies to improve the
program, including the creation of an entirely new pricing structure for the program based
on a competitive bidding process (Layne, 2002).
But in light of Medicares current approach for paying M+C plans, it does not
appear reasonable to simultaneously expect the program to deliver savings to Medicare,
provide a stable volume of supplemental benefits, and expand into currently underserved
areas. By keeping the growth in M+C payments below the growth in FFS Medicare, the
M+C plans will actually produce savings over the next decade. At die same time, keeping
the growth in M+C payments below the growth in the cost of providing Medicare
covered services will continue to erode the value of supplemental benefits provided and
result in fewer M+C participating plans.
An analysis by Thorpe and Atherly (2002) suggests that these trends may reduce
M+C enrollment by 1 million in next 3 years. Health plans cite the combination of low
payments and excessive regulations as the primary reasons for withdrawing from the
marketplace. In fact, the Congressional Budget Office projects that by 2012, only 3.9
million beneficiaries (8 percent of all Medicare beneficiaries) will be enrolled in M+C
plans. Moreover, Thorpe and Atherly suggest that in the absence of M+C program, 18
percent of current M+C are eligible for and would enroll in Medicaid (or other low
income beneficiary program). This would result in higher federal and state Medicaid
spending for such beneficiaries.

Managed care in Medicare is likely to have a future but the role of M+C is
uncertain. Withdrawals and service area reductions by health care plans, decreases in
prescription services and other benefits make M+C a less attractive option to Medicare
beneficiaries. As policymakers struggle to design the right balance in controlling spending
growth, setting payments to plans fairly, providing greater market stability and expansion
into rural areas, participating M+C plans, such as Kaiser Permanente, operating in this
unstable market environment are confronted with providing medical services effectively and
efficiently to this population.
Use of Medical Services
As previously stated, the U.S. population of residents age 65 and older is expected
to rise from 35 million to 71 million, increasing the overall senior population from 12.4
percent to 20 percent by 2030. These older adults (whether in traditional Medicare or
M+Q experience more health problems than younger adults. In a recent study, 26 percent
of adults age 65+ reported poor to fair health in 1999 compared with 11.5 percent-18.5
percent of adults aged 45-64. People age 75+ reported an average of three chronic health
problems at any time and use 4.5 prescriptions. Over half of the respondents replied that
among chronic conditions, arthritis and hypertension were primary; one-third reported
heart disease. The incidence of many illnesses -- such as cancers, osteoarthritis, and hip
fractures also rise with age (Kovner et al., 2002).
These medical conditions experienced by older adults translate into a higher use
of medical services than with the population in general. Kovner et al., (2002) further
elaborate that in 1999, the senior population had more then 192 million visits to physician
offices or 25 percent of all office visits. The average older person had 10.5 ambulatory
visits that year compared to 6.4 visits for the under 65 population. For inpatient hospital
care, older adults used 48 percent of hospital days, that is, 2,257 days per 1,000 population
while the next closest cohort persons 55-64 used 795 per 1,000. In M+C plans (e.g.,
Kaiser Permanente), HMOs significantly reduce both the probability of hospitalization

and the number of inpatient days used by those who are hospitalized. Medicare HMOs do
not, however, appear to reduce the use of physician services (Mello, et al., 2002).
Although the over 65 population utilizes significant medical services, studies
indicate that Medicare beneficiaries have lapses in care resulting in negative outcomes
even though treatments were covered by Medicare. Jenks et al., (2000) found widespread
geographical disparities in care while the Asch et al., (2000) study suggest that fewer than
two-thirds of the patients received care for 14 out of 37 generally recommended
procedures, including preventive care, diagnostic tests, and hospitalizations.
The current and projected high usage of services for preventive, chronic and acute
medical care and gaps in treating these conditions raises concerns on die availability, access
and training of primary care physicians. For example, Yamell et al., (2003) suggest that time
constraints limit the ability of physicians to comply with services recommendations. This
forces physicians to make decisions about preventive services on a case-by-case basis,
implicidy weighing the clinical efficacy of the service and the other medical needs of the
patient In this dynamic process, a patients current medical problem usually takes
precedence over screening and counseling. Discussions of health care workforce issues also
take into account the projected increase in the number of services required, workforce
capacity, training in geriatrics and reorganization of staff in die clinic setting (Kovner, 2002).
Health plans operating in this relatively unstable Medicare market environment --
confronted by an aging population requiring more health care services, while primary care
physician capacity is diminishing -challenges managers when planning medical care
demand. Improved techniques and tools may assist administrators when managing in these
turbulent surroundings.
Medical Demand Improvement
An enrolled population in a competitive managed care environment with high
levels of potential beneficiary health care service utilization (moral hazard) and high
expectations ascribes the HMOs with tenuous goals for each of the their private and
public enrollees. HMOs are expected to provide high quality care, meet organization

access standards, exceed beneficiary expectations, develop systems that reduce cost but
increase service, and provide easy beneficiary access to health services (user friendly) while
managing the patient flow system. These goals are basically incongruent yet
commonplace for any contemporary HMO (Ledlow, 2000). Organizations strive to meet
their private and public population needs more exactly. Organizations want to attain
higher levels of provider/climc productivity without increasing medical costs, such as
employing additional personnel.
However, most physicians have been trained to think and deliver health care in
terms of treating individual clients, not populations (Bruns and Johnson, 1999). Even
though managed care has focused on population health, physician practices continue to
deliver health care services in die traditional mode provided through a fee-for-service
model, usually in a private practice setting.
Demand planning for health care services continues to be based on actual patient
utilization as opposed to population requirements despite managed cares shift: to population
management. Actual utilization is generally accepted as equivalent to demand. Demand
represents reality and planning seems safer, practical and simple to employ. Such an
approach accepts all utilization (actual services) as appropriate and recognizes no possibility
of unmet need (MacStravic, 1978).
Currendy, patient demographics (i.e., age, gender, race and ethnicity) are the most
widely used in terms of predicting health services utilization (demand). When used alone,
these variables represent proxies for health status and are generally considered inadequate
markers of health status by themselves (Rosen, et al., 2003). But, the sheer number of people
serviced by an organization is the element (variable) that best predicts its utilization. A
change in the number of people served tends to have the greatest impact in terms of
changes in utilization expected in the future. Other health status predictive approaches that
are used to a lesser extent include: diagnosis-based, pharmacy-based, and patient self-
For illustrative purposes, we assess the relationship between membership and
patient visits at the Lakewood facility of Kaiser Permanente. This facility has approximately
12 percent of their members (about 5,800) age 65 years and older. Simple linear regression is

applied to examine the variables relationship. This test is used to determine if this
relationship between membership and patient visits (two variables) are linear and
Typically, this relationship, i.e., membership demographics, the independent
variable may account for upwards to 40 percent of the variation in the number of patient
visits, the dependent variable for the clinic. This is true especially for younger children and
older adults. (MacStravic, 1984). Results are shown in Table 2.1. The test resulted in a
coefficient of determination or R2 = 24.6 (see Appendix E, Figure El) with a p-value of
0.022 that is significant at the p < .05 level. Patient age explains about 25 percent of visits
to the clinic. Resources (budgets) based on membership levels and age provide meaningful
predictors for service activity.
Table 2.1: Lakewood Facility: Oct 96 Sept *97
Equation R2 F P*
Y=-678+0.471 (X) Y = Patient Visits X = Membership 24.6 6.19 0.022
* Significant at the .05 level
Gender mix is another important population factor but less subject to significant
change over time. Other useful prediction approaches involves the present and future
population into diagnostic segments. This requires an extensive database in which virtually
all health services utilization by die population of interest has been recorded. Typically, this
information is not easily available. But with the onset of electronic medical records and
information technology, future prediction of the utilization of services may be improved.
In helping physicians serve their patient populations more efficiently and
effectively, a systems approach to planning for medical demand is required. The group
practice (e.g., Kasier Permanente) with a large, relatively stable panel of captured patients
and integrated service delivery organization (and aligned financial incentives) is ideal for the

systems approach to medical planning (Rivo, 1998). The typical health care delivery
situation is where services such as primary care, specialty care, behavioral health, hospital,
and pharmacy are each contracted separately. Therefore services (and financial incentives)
are not coordinated. Consequently, in these more fragmented systems, change needs to be
incremental and does not offer an opportunity to use a more holistic approach (Bruns and
Johnson, 1999).
In summary, as policymakers continue to debate the role of managed care in
Medicare, administrators currently serving this population must continue to plan and
provide efficient service systems within medical facilities, especially in primary care
departments. To assist health care administrators in this endeavor, planning tools and
techniques must also advance to provide organizations an improved way (i.e., cost effective)
of delivering health services to its government funded populations.
Kaiser Permanente, a Colorado group practice health plan with a substantial M+C
population, provides a setting to study the use of system dynamics to enlighten our
understanding of how the eldedy population accesses primary care. Once we recognize
organizational structure and behaviors, managers are able to design service delivery
functions to meet stakeholders expectations.
Kaiser Petmanentes role at providing health care at reduced costs has also been
recognized by the National Committee for Quality Assurance (NCQA), an independent not-
for-profit organization based in Washington, D.C. NCQA works with consumers, health
care purchasers, state regulators, and the managed-care industry in developing standards that
evaluate the structure and function of medical and quality management systems in managed-
care organizations. NCQAs standards for accreditation of managed-care organizations
evaluate a managed-care plans performance in the areas of quality management and
improvement, utilization management, credentialing providers, members rights and
responsibilities, preventive health services, and medical record-keeping The Colorado health
plan has received NCQAs highest accreditation status (i.e., excellent) over the last several
years. Additionally, Kaiser Permanente in Colorado was ranked among the top 15 health
plans in a National Quality Report by the NCQA, the only Colorado health plan listed.

We next provide a literature review of organizational theory and practice, the
sciences of dynamics, open and general systems theories and their effects upon management
to serve as our knowledge foundation for this dissertation.

The theoretical basis for this research lies in the areas of organizational theory,
dynamics and management practice. In traditional organizational theories and management
practices, structure and processes are designed along Weberian lines, or what we will call
linear mechanistic relationships. As we shall argue, these traditions and processes can no
longer accommodate an increasing complex environment. Faced with uncertainties, public
sector managers dealing with these interconnected systems and environmental turbulence
require an expansion of their management paradigms and analytic toolbox. The science of
dynamics, specifically nonlinear dynamics, may offer administrators an improved capability
for understanding and managing in their complex environment.
Organizational Theory and Practice
Scientists from the natural science disciplines have studied cause and effect
relationships to predict the future behavior of a system. Newtons Laws of Motion resulted
from this causal methodology intertwined with the philosophy of determinism.
Determinism,31 defined by Pierre Simons Laplace in the late Eighteenth Century, posits that
with sufficient knowledge of the current state of the universe, one can accurately predict its
future. That is, as the ability to describe the universe increases, our knowledge of its future
would be similady increased (Gleick, 1988). These ideas and applications from Newton and
Laplace as well as sociologists, like August Comte and die philosophers of the French
Enlightenment (e.g., Descartes), have provided the foundation of logical empiricism, or
positivism, that affects how we think and inquire in other disciplines such as the social,
political, and administrative sciences.
31 Kad Popper (1956,1982), defines scientific determinism as "the doctrine that die structure of
the world is such that any event can be rationally predicted, with any desired degree ofprecision, if we are given a
sufficiently precise description ofpast events, together with all the laws of nature" (italics in die original). The
Open Universe, London: Hutchinson & Co. pp. 1-2.

Knowledge acquisition in the social sciences was based on the foundation of
natural sciences that resulted in a positivist approach to problem solving. Positivists took
physics as the paradigmatic science and the thesis that the methodology approach is the
same for all sciences was one of their guiding assumptions (Brown* 1996). In most general
terms* positivism is an epistemologyi.e., theory of knowledgewhich hold that reality exists
and is driven by laws of cause and effect that can be discovered through empirical testing of
hypotheses (Fischer* 1998). The positivist frame mandated a rigorous separation of facts and
values, the principle of the "fact-value dichotomy" (Bernstein* 1976). The positivist
approach to science is characterized by repeatability* reductionism and refutability (Galliers*
1992). It assumes the observations of the phenomenon under investigation can be made
objectively and rigorously.
In positivist science* "science is a structure built upon facts" (Davies* 1968* p. 8).
The advantage of positivist research is that it can identify the precise relationships between
chosen variables. Using analytical techniques, the aim is to make generalizable statements
applicable to real-life situations (Chalmers, 1978). Through controlling the number of
variables* complexity is reduced. Reduced complexity generates less distraction, i.e.* more
focus on the analysis* thus allowing for a more discerning study of the variables (Galliers*
The strength of this approach is that it promotes quantitative methods such as cost-
benefit analysis and linear modeling based on past behavior (e.g.* Bayesian analysis and
multiple regression analysis) and stresses optimization of microeconomic assumptions (e.g.*
economic efficiency and Pareto optimality). Limits of this approach include that it is time
bound, fails to deal with multiple dependency among variables* fails to consider dimensions
of organizational behavior, and lacks the measurement ability to include value judgments.
Scientific Management
Logical empiricism had an effect on organizational theory and practice such as the
design and implementation of scientific management methods by Fredenck Taylor (Cayer
and Weschler* 1988). Taylor held that science could produce principles* guides to action and

explanations to aid administrators in improving organizational efficiencies for example,
through time and motion studies. He was influenced by and adhered to the classic
conceptions of mechanically balanced systems. Taylor tended to understand social
organizations in terms of the dosed, mechanistic systems of the Newtonian wodd-view. His
prindples of sdentific management provided the cornerstone for work design. Taylor relied
on fixed time and space, simple causal relationships, and the primacy of physical reality
(Schachter, [1989]; Overman, [1996]).
Sdentific management was based on four main principles: (1) the devdopment of
an ideal or best method; (2) the sdection and devdopment of workers; (3) an incentive
system whereby each worker was paid according to die number of units produced; and (4)
division of labor where, for example, the managers were assigned responsibility for the
planning and preparation of work (Filey, et al., 1976). Other than Taylor, major
contributors to sdentific prindple and management theorists induded Leonard White
(1926) and W.F. Willougjiby (1927), who focused on building organizational structures that
would operate with high effidency with a strong chief executive vested with the power and
authority to carry out the assigned work. The chief executive would be most successful if he
or she operated through an organizational structure characterized by unity of command,
hierarchical authority and a strict division of labor. Or following Gulicks (1937)
administrative modd, the work of the executive was planning, organizing, staffing, directing,
coordinating, reporting and budgeting (POSDCORB). But again, organization effidency
was the key value accepted by most early writers and practitioners (Denhardt and Denhardt,
2002). The workplace effidendes mirrored the ideas of logical empiridsm in the
administrative sdentific community. The philosophy of sdentific management could guide
the actions of managers in improving organizational effidency. The assumption was that
general principles of organizational design could be applied to all organizations, public,
private or not-for-profit The effect of this influence in public organizations was that their
problems were seen as die same as those of private organizations, with the same solutions.
In the public sector, efficiency was set as a goal. This led to the overwhelming adaptation of
an empirical orientation such as cause and effect. (Cayer and Weschler, 1988).

The roots of classical organization theory deeming die goal of efficiency in
accomplishing tasks are evident in Woodrow Wilsons articulation of management principles
and techniques (1987/1887), Gulicks focus on discovering universal principles of
administration (1937), and Max Webers bureaucratic concepts (Gerth and Mills, 1946). In
Fileys et al., view, Max Weber viewed bureaucracy as the most efficient form, that could be
used most effectively for complex organizations business, government, military, for
example arising out of the needs of modem society (Filey, et al., 1976, p. 87). Among the
characteristics of organizational design that Weber saw as typifying the bureaucratic
organization were: (1) a continuous organization of official functions bounded by rules; (2)
specific areas of competence, (3) utilization of the principle of hierarchy; (4) promotion into
the administrative ranks those individuals who demonstrate technical competence; and (5)
administrative acts, rules and decision are recorded in writing (Gerth and Mills, 1946).
Weber wished to show to what extent the bureaucratic organization was a rational
solution to the complexities of modem problems. March and Simon (1958/1993) suggest
Weber went beyond the machine model, perceiving bureaucracy as an adaptive device for
using specialized skills.
Bureaucracy meant there was a division of labor, clear responsibility for decisions
and job roles. Bureaucracy produces a hierarchical structure. Policies are set by people in
other job roles; this meant there was a disconnection between the set of actions denoted by
die role and the human being who was incumbent in that role. Bureaucracy and its system
of roles were based on a value of rationalitya basis of efficiency that in turn is based on an
assumption of a mechanistic system (Daneke, 1999).
Mechanistic Organizations
Gareth Morgan (1986, p. 36) noted that die use of machines has radically
transformed the nature of productive activity and has left its mark on the imagination,
thoughts, and feelings of humans throughout the ages. This is no more evident than in the
modem organization. Organizations that are designed and operated as if they were

machines -i.e., with replicable units called staff members are now usually called
bureaucracies. Stading (1975, p. 156) stated that in his formalization of the bureaucratic
social form, Max Weber deady implies that bureaucracy is a mechanical equilibrium
system. Bureaucracy, the most highly devdoped organizational form, tends toward rigidity
as an explicit means to reduce product uncertainty. It is likely to be resistant to change
because its members tend to see change as destructive or at least counter productive.
The major strength of a mechanistic or bureaucratic approach to organization work
is where: (1) there are straightforward tasks to perform; (2) the environment is stable; (3)
one wishes to produce (reproduce) the same product time and again; (4) precision is at a
premium; and (5) humans are compliant. However, despite these strengths, mechanistic
approaches to organizations often have severe limitations. Specifically, they can: (1) create
organizational forms that have difficulty in adapting to changing circumstances; (2) result in
mindless and unquestioning bureaucracy; (3) have unanticipated and undesirable
consequences as the interests of those working in the organization take precedence over the
goals the organization was designed to achieve; and (4) have a dehumanizing effect upoh
employees, especially those at the lower levels of the organizational hierarchy (Morgan,
Mechanistic approaches to organizations have proven, however, incredibly popular,
partly because of their efficiency in the performance of certain tasks, but also because of
their ability to reinforce and sustain particular patterns of power and control. For example,
insurance companies began to assume the responsibilities of organizing health care using
organization-bv-design by top management that replaced the self-organizational model used
by Kaiser Permanente. Utilization management and preventive practices originating from
physicians were a central organizing principle for Kaiser Permanente. These switched to a
top management design with implementation delegated to nurses and other health care
workers in die manner of Taylors scientific management (and the new public
management movement) whose primary goal was to reduce cost (Pierce, [2000]; Gaebler
and Osborne, [1992]).
Bureaucratic organizations may well prove to be one kind of specialized
organization, generated by and suited for the requirements of the mechanical age. Now that

the U.S. is entering an age with different technological bases and demands, new
organizational principles are likely to become increasingly important (Morgan, 1986).
Human Complexities
Viewing the organization as a rational, technical process, mechanical imagery tends
to underplay the human aspects of organization and to overiook the evidence that the tasks
facing organizations are often much more complex, uncertain and difficult than those that
can be performed by a machine. The recognition that organizations are complex changes
their imagery. Organizations, as we see them, are bundles of activity with common
elements that allow activities and people to be grouped and treated as an entity (Kanter, et
al., 1992, p.13). Viewing an organization as a coalition of interests (Cyert and March,
1963/1992) and a network of activities within a momentum-bearing structure has two
implications. The first is that change of one sort or another is always occurring, though it
may not always be guided by organizational leaders nor be consistent with stakeholder
purposes. Second, managers concerned with controlling events or guiding change must be
aware of both the nature of the networks within and around the organization and the
sources and effects of the organizations momentum.
In many contemporary organizations, whether they be private, public or not-for-
profit, concerns regarding the utilization of resources effectively and efficiently are often an
administrators priority. The pressure for improved performance in service delivery has
brought demands for fundamental and qualitative changes in organizations and work
methods. Many management techniques such as total quality management (Deming, 1986),
continuous quality improvement (James, 1992), reinventing (Osborne and Gaebler, 1992),
and breaking through bureaucracy (Barzlay, 1992) have become familiar jargon in the
work place. It is important to note that they were conceived for the most part by linear
techniques and mechanistic approaches.
Management, as a profession and field of inquiry, has long searched for specific
methods to assist managers in analyzing, designing and implementing change as
organizations strive to enhance their efficiency and effectiveness. Oiganizations that

produced the best for least were considered superior. Managements job was assembling the
right pieces, pointing them toward the optimum and making sure the system never wavered
(Stacey, 1991).
This law-like consistency blinded individuals in the organization to any other
purpose than to optimize the organization itself (March and Simon, 1958/1993). But while
the old order persists, the new order is rising quickly and poised to overtake it. For example,
Petizenger (1999) argues that the Newtonian model is giving away to a natural one for two
reasons. First, the marketplace is leaving organizations with no choice. In an era where
change comes quickly and with no warning, organizations can survive only by engaging the
collective eyes, ears and minds of its members and encouraging them to act on their current
knowledge and beliefs. Secondly, this more-encompassing way of business will persist
because it closely resembles a less rigid or formal understanding to what organizations being
inherently extensions of humans.
Traditional bureaucratic administration, grounded in the mechanical age and faced
with rapid and changing public demands, has been challenged as inept, inefficient and
unaccountable (Gaebler and Osborne, 1992). The rise of the new public management
(NPM) movement argues that fundamental change to government institutions is required.
The theoretical background of the NPM movement or reinventing government as coined
by Gaebler and Osborne (1992) is based on institutional economics, public choice theory
and transaction costs economics (Scott, [1995]; Weick, [1995]).
Voter dissatisfaction has been linked to an increasingly unfavorable gap between
voters experience of public as opposed to private services. This has challenged the public
sector and its overseers to find more cost-effective and customer-sensitive ways of doing
business. These new demands on government with fiscal burdens and erosion of the tax
base have exerted strong budgetary pressures on government administrations to perform
more efficiently (Gaebler and Osborne, 1992).
Thompson and Riccucci (1998) identified four dominant principles in their analysis
to distill the core ideas of die reinvention movement. Government must
1) substantially reduce the procedural rules (red tape) that constrain the discretion of

2) become more mission driven by emphasizing performance measurement and
management, especially that focused on outcomes or results;
3) support decentralization of critical staff functions to line departments through the
reduction of hierarchical levels, greater investment in training, and;
4) endorse the creation of market dynamics by fostering competition (e.g.,
government and pnvate agents bidding to deliver a public service) and by
emphasizing customer service.
Many scholars including those advocating reinventing government reform, suspect
that the traditions of organizational theory can no longer accommodate the growing internal
and external complexity of their environment. According to Kiel (1994) and others (e.g.,
Overman, 1996), a new paradigm is required to help managers deal with the challenges of
change in the performance and delivery of services. Administrative sciences have not
adequately evolved but the realization of the incompleteness or inconsistencies of
administrative theory have encouraged social scientists (as suggested by the post-positivist
view) to continue their search for further explanations. Modeling social sciences on die
natural sciences is incomplete. The general dissatisfaction with the positivistic paradigm
(deLeon, 1988) fueled a search for approaches to accommodate and offer resolution to
social processes. Technical considerations did not take into account qualitative (normative)
ideals. The purpose of this knowledge search is to pragmatically aid managers in their quest
for improvements in service and organizational performance. Of specific interest, health
care organizations may be required to change their organizational design and administrative
practices to meet current challenges.
Large health care organizations, like Kaiser Permanente that provide medical
services to private and public insured beneficiaries, traditionally have followed a
bureaucratic approach as reflected in their primary analytic paradigm, health economics.
Authority is based upon formal rules, and work is carefully structured and monitored
according to a hierarchy. Organization charts, written rules and regulations, and
specialization of tasks are relied on for organizing work. Kaiser Permanente is primarily
designed from a mechanistic approach that previously was thought to provide the most

effective pathway to deliver medical services. Yet the environment and the organizations
tasks performed by this organization and similar organizations do not necessarily support a
mechanistic design. Their tasks are not 1) straightforward all the time; 2) one product that
is produced time and again; 3) in an environment that is stable; and 4) performed by
personnel that are always compliant The tasks facing managed care organizations are
complex, uncertain and more difficult than those that can be performed effectively in a
standard bureaucratic organization.
In summary, organizations are formally structured and social collectives that acquire
resources from the environment and direct those resources to activities perceived as relevant
to their goals and values (Bozeman, 1987). The traditional organizational theories and
practices, measures of performance, and service delivery based on bureaucratic design and
linear relationships do not appear to be in unison in the current environment. The
recognition and challenges of this complex environment has led scholars and practitioners
to investigate and propose new paradigms for public, private and non-profit managers.
Organizations need to explore new approaches to understand their complex environment.
Organizations should examine die practices they currently use that support a mechanistic
environment and consider new techniques and measures that avail themselves to a complex
environment. The science of dynamics, specifically nonlinear dynamics may offer
administrators an improved capability for understanding and managing in a complex
Science of Dynamics
The subject of dynamics deals with change and with systems that evolve in time.
Whether the system in question setdes down to equilibrium, keeps repeating in cycles or acts
in a somewhat more complicated manner, its dynamics are used to analyze its behavior. Due
to this temporal interaction, the field of dynamics pertains to management practices in
organizations. For one, health care organizations that include multiple systems for the

delivery of patient care, exhibit complex dynamical behavior. The following section
presents a brief historical view of dynamics.
Although dynamics is an interdisciplinary subject today, it was originally a unique
branch of physics. The orientation began in the mid-1600s, when Issac Newton and
Gottfried von Leibniz (independently) invented differential calculus and discovered the laws
of motion and universal gravitation (Gleick, 1987). The combination of these two
discoveries explained Johan Kepler's laws of planetary motion. Specifically, Newton solved
the two-body problem of calculating the motion around the sun. Subsequent generations of
scientists tried to extend Newton's analytical methods to the three-body problem (e.g., Sun,
Earth and Moon). After decades of effort, it appeared to be impossible to obtain explicit
formulae for die simultaneous motion of three bodies.
The physical sciences were challenged proceeding from two to three bodies (six to
nine variables), while die social sciences (such as public administration) must deal with vast
quantities of variables. Social scientists developed mathematical tools for spotting patterns in
random data. Starting with error analysis (i.e., normal distribution) in astronomy, statistics
flowed between die physical and social sciences. For example, in the 19th century,
descriptive statistics were used on social issues such as urban and emigration problems
(deLeon, 1988). Physicists repeatedly cited the success of statistical methods in the social
sciences as justification for probabilistic procedures (Stewart, 2002).
The breakthrough on die three-body problem came in the work of Henri Poincare
in the late 1800s, who emphasized qualitative rather than quantitative questions. For
example, instead of asking what are the exact positions of planets at all times, he asked Is
the solar system stable forever or will some planets eventually fly off to infinity?"
Qualitative questions refer, in the broadest sense, to descriptive data and the development of
concepts from patterns in the data. The researcher looks at the setting holistically. Variables
are not reduced, but are viewed as a whole (Taylor and Bogdan, 1984). Poincare had a

geometric imagination in analyzing the laws of motion in the physical wodd (deck, 1987).
This approach flowered into the modem study of complexity in dynamics.
By the end of 19th century, science acquired two very different paradigms for
mathematical modeling. The first and older was high precision analysis through differential
equations applied to relatively simple and well-structured problems. The second was
statistical analysis of averaged quantities, representing coarse features of the motion of
highly complex systems (Stewart, 2002).
Statistical methodology took its place along side deterministic modeling as an equal
partner. The mathematics of stochastic processes -sequence of events determined by die
influence of chance -flourished alongside the mathematics of deterministic processes.
Scientists knew they were differentsimple systems behave in simple ways and complicated
systems behave in complicated ways. The whole progress of science was based on the belief
that the way to seek simplicity in nature is by finding simple equations to describe it
(Stewart, 2002).
The invention of the high-speed computer was a watershed in the history of
dynamics. The computer allowed one to experiment with equations in a way that was
previously impossible. Such experiments led Edward Lorenz (1961) to gain insight into the
unpredictability of the weather. Lorenz found that solutions to his equations continued to
oscillate in an irregular, aperiodic fashion rather than the periodic pattern. If Lorenz started
his simulations from two slightly different initial conditions, the resulting behaviors would
soon become totally different. The implication was that the system was inherently
unpredictable. But Lorenz showed there was structure in the apparent chaos if it were
plotted in three dimensions. This simple looking deterministic system could have extremely
erratic dynamics over a wide range of parameters. The solutions oscillated irregularly, never
exactly repeating but always remaining in a bounded region of phase space. The solutions to
his equations fell onto a butterfly-shaped set of points, or what is now called a strange
attractor. He argued that this set was unlike stable fixed points and limit cycles; the

strange attractor was not a point or a curve, it had to be an infinite complex of surfaces
an example of a fractal. 32 (Gleick, 1987)
Nonlinear dynamics is the study of systems governed by equations in which a small
change in one variable can induce a large systematic change; the discipline is more popularly
known as chaos. Unlike a linear system, in which a small change in one variable produces a
small and easily quantifiable systematic change, a nonlinear system may exhibit a sensitive
dependence on initial conditions: small or virtually unmeasurable differences in initial
conditions can lead to wildly differing outcomes. May provided the first written use of the
word chaos to describe deterministic nonlinear behavior, but he credits James Yorke with
coining the term (Devaney, 1990). He also stressed the importance of studying simple
nonlinear systems to counterbalance the often-misled linear intuition fostered by traditional
education. Next, Mitchell Feigenbaum (Strogatz, 1994), a physicist, discovered that certain
universal laws govern the transition from regular to chaotic behavior.
The study of dynamics was originally undertaken to model natural phenomena,
such as the motion of objects in physics and engineering. In the last half of the 20th century,
theorists in various scientific disciplines began to believe that the type of linear analysis used
in classical applied mathematics presumes an orderly periodicity that rarely occurs in nature.
In the quest to discover regularities, disorder had been ignored. The evolution of nonlinear
dynamics was made possible by the application of high-speed computers, particularly in the
area of computer graphics, to facilitate the use of mathematical theories developed during
the first half of the 20th century.
Classification of Dyn^mi^
A classification system developed by Foster Morrison (1991) illustrates the
hierarchy of dynamic systems by their mathematical solutions. These systems are numbered
32 A fractal is a geometric shape that has symmetry of scale. This means that it is a shape that when
magnified an infinite number of times it still looks the same. This is also called self-similarity.
Computer-generated fractals are produced mathematically, and can create detailed pictures of
mountains, plants, waves, and planets.

zero, I, II, III and IV. Type zero has no dynamics. Its state remains unchanged (i.e.,
equilibrium). Type I is comprised of solvable systems. Problems that can be solved by
perturbation (power series) techniques are placed in Type II. In practice, this means that
useful approximate solutions can be constructed by limit processes involving small
parameters. Type III systems have chaotic solutions; that is, a typical trajectory will seem to
behave normally and then execute a sudden change of course, while other systems have
irregular cycles that may disappear for a while and then return. This is the most difficult
type of system to model since there are no apparent regularities. Type IV systems are
random and best treated by statistical techniques; a roulette wheel is a good one-
dimensional example. The descriptions are summarized in Table 3.1.
Table 3.1. Summary of the Hierarchy of a Dynamic System
(Morrison, 1991; p.168)
Constraints Description Examples
Zero Absolute Constant State Images, gravity models
I Analytic integrals Solvable dynamic system 2-body problem, physical pendulum
II Approximate analytic integrals Amenable to perturbation theory Satellite orbits, lunar and planetary theories
III Quasi-deterministic; smooth but erratic trajectory Chaotic dynamic system Climatology, Lorenz equations
IV Rigorously defined only by averages over time or state space Turbulent/stochas tic Quantum mechanics
The above classifications may also be viewed from an organizational perspective.
Meday (1992) suggests that as organizations increase in size and scope individual
subsystems of an organization become more complex, larger and more differentiated.
Subsequendy, these subsystems become more independent (p. 107). Therefore, a complex
organization such as Kaiser Permanentes clinical facilities and the internal and external

relationships between functions, roles and departments, become interrelated yet
independent to each other as a nonlinear relationship exists between the subsystems. This
results in a need for information required by managers that is not necessarily defined or
bounded by traditional linear techniques.
As organizations vertically integrate, satisfy patient requirements, diversify services,
and try to become more cost- effective, modeling patient demand becomes more complex.
Organizations have systems and subsystems that continuously cycle through and transition
from types II, ID, to IV. These transitions should show smooth and virtually continuous
behavior. An effective model emulates die behavior of these transitions over a range from
deterministic and perhaps periodic, to almost periodic and then irregular as some of the
parameters change.
L. Douglas Kiel (1993) helps us to understand die nonlinear dynamics of complex
administrative systems by graphically quantifying work processes in the communications
division of an Oklahoma state agency. Kiel says that nonlinear dynamical systems "exhibit
four distinct types of temporal behavior. These behaviors can be labeled as (1) convergence
to a stable equilibrium, (2) stable oscillation, (3) unstable and explosive, and (4) chaotic (pp.
144-45). See Figures 3.1a-3.1d for illustrations of this behavior.
Figure 3.1 a Figure 3.1b
Figure 3.1a Convergence to an Equilibrium
Figure 3.1b. Stable Oscillation

Figure 3.1a suggests this system experiences litde or no change over time. Systems
behavior exhibited in Figure 3.1b is typified by stable oscillation. Figure 3.1c illustrates
unstable and explosive behavior. An example is fire rescue personnel. At some times,
activity level is stable, while periods of extreme activity levels interrupt stable periods.
Chaotic behavior (Figure 3. Id) occurs within definable parameters but is devoid of pattern.
Systems with this chaotic behavior reveal an undedying order or structure in their behavior
(Kiel, 1993). A typical system can exist in a variety of states, some ordered, some chaotic.
Instead of two opposed polarities, there is a continuous spectrum. (Stewart, 2002, p.17).
Figure 3.1 c Figure 3.1 d
Figure3.1c Unstable and Explosive
Figure3.Id Chaos
Feedback is the process by which systems outputs return as inputs. It implies that
information about the results of a given process is used to change that process. Feedback,
with its loops of causality, is the essence of nonlinearity. Negative or self-balancing
feedback can produce relatively predictable decreases in the direction of change. For
example, the valve on Thomas Watt's steam engine created a negative feedback loop
because it opened when the engine was running fast in order to release steam so the
machinery would not explode, but closed to keep the pressure up when the engine started to
slow down. Positive or self-reinforcing feedback is relatively indeterminate as it can quickly

amplify variables in the direction of change. Positive feedback, which despite its name is
not always a good thing, actually pushes systems to explode or spiral out of control.
Pointing a TV camera at its own monitor gives the visual equivalent of the positive feedback
loop screech that comes from a microphone placed too near its speaker.
Richard Dawkins (1990) argues that evolution itself is a grand feedback
performance. He notes, for example, that a mutation that improves die design of a predator
changes the pressures on its prey so that the prey, in turn, evolves better defenses in order to
avoid these better-adapted predators. As the prey get more wily, the predators once again
selectively undergo a design change. Here the positive feedback kicks evolution forward.
Meanwhile, negative feedback in evolution keeps mutation changes from spiraling out of
control, that is, the checking power of many negative feedback loops simply wipes out most
mutations and keeps the designed species stable for long periods of time. In other words,
feedback is the central mechanism of nonlinear dynamics.
To summarize, the subject of dynamics deals with change and with systems that
evolve over time. This brief history and framework offer a guide to the placement of
nonlinear dynamics within the body of knowledge of dynamic systems and suggests that as
systems become increasingly complex, distinct types of modeling and analysis may be
In the preface to The Systems Approach (1968), C. West Churchman voiced a
common sentiment in the social scientific literature, The systems in which we live are far
too complicated as yet for our intellectual powers and technology to understand (p. xi).
The changes most commonly associated with this new problem are those included in the
improvement of modem science and the creation of a large-scale technological environment
(Morgan, 1986). These twin developments have encouraged the natural and social sciences
to focus on the study of complex systems.
Cavaleri and Obloi (1993, p. 29) define systems as a grouping of component parts
that individually establish relationships with each other and that interact with their

environment both as individuals and as a collective/ Complex systems have many
interacting parts where the relationship between cause and effect shifts, stabilizes, and
reforms over time.
What leads us to determine that one system is more complex than another?
Prigogine and Stengers (1984) explain that the complexity of the system is defined by the
complexity of the model necessary to predict effectively the behavior of the system. The
more the model must look like the actual system to predict its results, the more complex the
system is considered to be. Weather is an example of a complex system, as demonstrated by
Lorenz (1961), that can only be effectively modeled with an exact duplicate of itself. An
example of a fairly simple transportation model is an express train. The model objective is
to predict the time a train takes to travel from City X to City Y. If it travels at a given speed
(assuming that die train does not start at City X or stop at City Y and has no stops in
between), we would only need to know the speed of the train (mph) and the distance
(miles). The formula for travel time is mph/miles a mathematical model of a simple
system. Yet if we add that the train stops at several cities and varies its speed, we then create
a more complex system. Complexity, then, refers to systems that are essentially
unpredictable in specific cases but, over a long period, appear to generate patterns of
behavior with surprising regularity (Kaye, 1993).
The essential difference between linear and nonlinear systems is that linear systems
can nominally be broken down into parts. The machine age paradigm believed in the
possibility of complete understanding derived from understanding indivisible parts or
elements. The doctrine based on this belief is called reductionism. Once an understanding
of the parts is acquired, an understanding of the whole is achieved. According to Ackoff
(1979), it is not surprising that the machine-age conviction that one elementary relationship
was sufficient for the purpose: cause and effect.
This reduction or disaggregation allows for a simplification of complex problems
and underlies such methods as normal modes, superposition arguments, and basic systems
analyses. A linear system is precisely equal to the sum of its parts. The relationship between
relevant variables in a linear system remains stable over time. The dynamics show smooth,
regular, and well-behaved motions. The parameters in these systems respond to change in a

proportionate and consistent manner. Thus, linear systems appear much simpler than
nonlinear systems. Until the growth of interest in nonlinear systems, most models were
analyzed as though they were linear systems for there was little computational alternative
(Devaney, 1990). When the mathematical formulae representing the behavior of the
systems were put into graph form, the results looked like (or were forced to resemble) a
straight line (i.e., regression).
Devaney (1990) suggests that one does not concentrate on finding a formula that
will make possible the exact prediction of a future state from a present one. Instead,
mathematical techniques are used to provide some idea about the long-term behavior of
the solutions Qualitative analysis investigates a system by asking general character
questions of its long-term behavior, rather than seeking to arrive at numerical predictions
about its exact future state. For instance, it would make holistic studies impossible.
To review: complex systems have many interacting variables while simple systems
have relatively few. The study of these complex interactions including positive and negative
feedback can reveal patterns of order out of seemingly chaotic behavior throughout the life
of a nonlinear system. Complex systems can be random. The use of linear models to
analyze complex systems is limited. Complex systems do not respond to change in a
proportionate and consistent manner and are thought to predict different things than linear
systems might otherwise suggest. Therefore, as a general rule for complex systems, we
cannot create a model that accurately predicts outcomes. However, one can create models
that simulate a system's processes of patterns, development and changes.
A few authors have posed that the significance of nonlinear dynamics and related
models of quantum theory and "fuzzy" logic will become vital to the social sciences and
heralded them as "the new sciences of administration" (Overman, [1996]; Morcol, [1996]).
Specifically, nonlinear dynamics can aid in the process of organizational change and
adaptation (Daneke 1999). Organizations can relate their structure and development to the
above dynamic frameworks. By understanding organizations as complex systems, managers
can improve decision-making and search for innovative solutions (Levy, 1994).

However, nonlinear dynamics in the social sciences is not a new phenomena. In
the social sciences, published literature on this topic has been on the rise since the late
1970s and continues to increase. Henrickson (2000) performed a keyword citation study33
researching trends in the use of chaos and complexity theories and computer simulation in
the social sciences between 1971 and 1999. For each of the theory keyword searches,
there were few citations in 1971, but by 1999 a total of about 1,000 citations occurred for
the combined disciplines of business, economics, psychology and sociology. Business had
the most with 350. The results documented below showed general trends and suggest die
growing use of simulation research coupled with the nonlinear, chaos and complexity
topics. Specifically, the findings include:
citation trends increased noticeably about 1988,
business leads and dominates citation output from 1988 to the present,
there is a noticeable drop-off in citations 1996-1998 in business and sociology, a
constant rate in economics and slight increase in psychology,
theory and method citations have mixed qualitative and quantitative approaches
psychology is more quantitative than business.
To get a sense of the trend in Public Administration literature, a (nonscientific)
selected scan of Public Administration journals3 4 on the keywords of nonlinear, chaos and
complexity theories was completed. The task resulted in relatively few journal articles
published between 1987 through 2002. The articles can be categorized into three areas:
budget (Kiel, 1992), organizations (Svyantek, and DeShon, [1993]; Kiel, [1993]; Overman
3 3 Keywords searches performed were: Theory Nonlinear theory, chaos theory, complexity
theory and Method-computer simulation and types (complex adaptive systems, neural networks,
genetic algorithm). The following databases were searched for citations: ABI/Inform, PsychINFO,
EconLit, and Sociological Abstracts covering selected periods between 1971-1999.
3 4 Journal sample selected from die Public Administration Societys journal section list. Journals
reviewed: Public Administration Review Journal of Health and Human Services Administration,
Public Administration Quarteriy. Public Budgeting and Finance. Public Performance and
Management Review. Review of Public Personnel Administration. State and I .or al Government
Review. Criminal Justice Review. Journal of Policy Analysis and Management, and journal of
Public Administration Research and Theory.

and Lorraine, [1994]), and theory and research (Overman, [1996]; Morcol, [1996]). All of
these journal articles were published between 1992-1996.
The trend of no articles found within die Public Administration scan after 1996
may indicate that published literature on this topic may be in books rather than journals,
the lack of article submission or acceptance, or pertinent work is published in other
journals not in our sample.
Daneke (1999) summarizes lessons learned (Table 3.2) derived from the science
of nonlinear dynamics and applies them to social systems (e.g., institutions/organizations).
The first suggests simple social systems exhibit complexity despite being deterministic.
That is, many seemingly random events are actually the result of deterministic processes
that we can describe using relatively simple equations. For example, health care
organizations can be described by equations and mapped qualitatively and expressed
quantitatively by mathematical equations.
The second implies that
complex social systems are driven by
feedback (both positive and negative)
and small changes often engender
disproportional effects. Small
differences in the initial conditions of a
situation can evolve into radically
different system states later on in time
(Gleick, 1987). Health care
organizations also experience feedback
and, when changes occur in the initial
conditions, their systems will transform
over time.
Third, complex social systems are, at root, the result of simple rules and
institutions interacting with turbulent external environments. Health care organizations
are also the result of simple rules and organizational entities interacting within its external
Table 3.2: Nonlinear Dynamic Social
________Systems Lessons Learned_______
1. Even simple social systems exhibit
complexity (behaviors with convoluted
and indeterminate causal paths) despite
being deterministic;____________________
2. Complex social systems are driven by
feedback (both positive and negative), and
small changes often engender
disproportional effects;________________
3. Complex social systems are, at root, the
result of simple rules and institutions
interacting with turbulent external
4. Complex social systems exhibit self-
organization (spontaneous reordering) and
chaos (e.g., nearly random behavior).

environment. These entities include the market, competing organizations and
governmental bodies.
Lastly, complex social systems exhibit self-organization (spontaneous
reordering) and chaos (e.g., nearly random behavior). Health care organizations also may
exhibit patterns of behavior such as self-organization and chaos.
Health care organizations that illustrate these forms of behaviors derived from
nonlinear dynamics are complex systems that require improved modeling and analysis
techniques as current linear modeling and analysis may not be complete. Systems thinking
offers administrators a path to begin to explore nonlinear modeling in their search for
information to increase managerial effectiveness to answer rapidly changing market
conditions. The next section will provide an overview of open systems and general systems
theories and their context in management.
Open Systems Theory
Ludwig von Bertalanffys research (1950) challenged the relevance of 19th century
physics to the appreciation of living things. His central concept was the distinction between
open and closed systems. Open systems evolve toward higher levels of complexity at the
expense of energy yielding processes in die environment (von Bertalannfy, 1950). He
developed a theory of open systems that employs functional and relational criteria rather
than reductionism analysis of fundamental parts. That is, holism is simply the position that
systems cannot be understood merely by looking at the parts individually. Holism may be
delimiting (Kline 1994). Reductionism, by contrast, maintains that one can understand the
whole by studying the parts. Reductionism breaks things into parts and the studies forces
acting on them seeking to establish laws and principles of their behavior. It does this by
treating parts as closed systems that are separate independent units of analysis. This process
is called analysis and was the dominant mode of thinking since the Machine Age (Ackoff,
Open systems theory suggests that structure and function in bounded organizations
are physical entities just like organisms. An organism co-exists in relation to its environment.

Its function and structure diversify or are maintained by continuous flow of energy and
information between organism and environment
An organization is open to its environment Action is taken to hold the
organization in a steady state. To ensure survival, the organization transforms inputs and
adapts to changes when they occur. Open systems theory observes organizations as
complex systems made up of parts most usefully studied as a whole. Since parts comprise
people, management is concerned about the nature of people at work. Parts or subsystems
have lists of needs that must be met. For example, an individuals job can be enriched
leading to greater satisfaction and productivity.
The study of complex, deterministic, nonlinear, dynamic systems "also has its roots
in simple [open] systems theory" (Overman, 1996, p. 487). Both Dennard's (1996) and
Overmans essays (1996) point out that nonlinear dynamic systems theory is based
comfortably on the classic theories of public administration and decision science. In 1967,
James D. Thompson wrote about "open-system strategy" versus a rational model (or
"closed-system") as the two fundamental approaches in the literature to understanding
complex organizations (in Shafritz and Ott, 1992). To Thompson, open-system strategy is
based on "the expectation of uncertainty." Further, Thompson cites Chester Barnard as a
major developer of this concept who led to the conclusion that:
...Organizations are not autonomous entities; instead, the best laid plans of
managers have unintended consequences and are conditioned or upset by
other social units other complex organizations or publics on whom the
organization is dependent (Thompson in Shafritz and Ott, 1992, p. 283).
Simon (1957), Barnard (1966), Thompson (1967) and Gawthrop (1971) all
emphasize process over structure as the best way to visualize the complex organization. The
impact on open systems theory on management and organization pervades administrative
practice today. For instance, planning and decision making today is often couched in terms
such as differentiation, environment, functions, growth, interrelatedness and teleology.
Open systems theory is the forerunner of general systems theory.

General Systems Theory
General systems theory aims to formulate and derive principles applicable to
systems in general. Von Bertalanffy developed general system theory throughout the 1940s
and into the 1950s. Rosen (1979) described Von Bertalanffys general systems idea simply.
If two systems S and S are physically different but nevertheless behave similarly, then there
is a sense in which we can learn about S through S. Learning is through isomorphism (i.e.,
they draw direct correspondence to reality), not metaphors or analogies.
General systems theory thinking thus identifies common themes regarding the
behavior of living systems. The theory also generates explanations for phenomena that had
not been forthcoming through the conventional analysis of localized problems and whose
relationships can be mathematically defined.
A system is any given entity that exists through the complex interaction of its parts.
That is, a system maintains its existence through mutual causal interplay of various elements
(Rosen, 1979). A system is a whole that is greater than die sum of its parts. This increase in
complexity or intncacy is a function of the organizing processes that the system provides.
These organizing relations and their emergent properties cannot be discerned from the
The general systems model (Figure 3.2) provides the framework for an examination
of specific quantitative methods. This model focuses attention on inputs, conversion
processes, outputs, feedback loops, and modifiers. To understand the complexity of activity
within any organization, the general systems model provides the ability to assign activities or
features of the organization to one of four categories: inputs (or resources), conversion
processes, outcomes (what is desired), and feedback.
Figure 3.2 The General Systems Model
f t i
----------------FEEDBACK --------------------

Today public managers in particular are more likely to rely on general systems
theory for concepts and direction. Health care organizations are one example of goal-
directed systems with identifiable inputs, work processes that convert inputs into outputs
(outcomes), identifiable outputs and feedback loops that serve to direct and control the
system. Efficiency and effectiveness are die two primary performance measures used in
health care organizations (Seidel, et al., 1995, p.14). To be a competent health care manager
requires the ability to view the health care organization as a system.
Systems thought rejects the mechanistic wodd view inherited from Newton and
Descartes. Social systems theorists maintained that the interaction of individuals and their
artifacts (e.g., institutions, organizations, etc.) cannot be described with the concepts of
mechanistic science regarding linearity and reductionism (Boulding, [1985]; Tinstone,
[1989]). Ackoff (1979) suggests systems are wholes that lose their essential properties when
taken apart. Therefore systems are wholes that cannot be understood by the principle of
reductionism. This realization gave rise to systems thinking.
Systems thinking is comprised of three steps. First, the event we study is part of
one or more larger wholes not as a whole to be taken apart Second, the understanding of
the larger containing the system is sought Finally the system to be understood is explained
in terms of its role or function. The analysis of a system reveals its structure and how it
works (know how knowledge) not why it works the way it does.
According to Ackoff (1979), this approach to systems thinking is the doctrine of
expansion. Compared with reductionism, it asserts that ultimate understanding is an ideal
that can be continuously approached but never attained. This understanding (in contrast to
know how knowledge) flows from larger to smaller systems, not as reductionism assumes,
from smaller to larger.
The conceptual potential of systems theory has been substantially enhanced by
recent computational advances. These new tools and methods not only enliven traditional
system pursuits, they expand their conceptual reach. Moreover, these devices arise at the
point when social and organizational turbulence are laying waste to conventional
mechanistic policy and management approaches. A number of managerial best sellers

have begun to extol the virtues of a systems approach (Senge, [1990]; Wheatley, [1992]; De
Geus, [1997]).
As a paradigm of social inquiry generally, the general systems approach has been
in decline since the late 1960s and had for the most part been abandoned as a basic
research paradigm and displaced by reductionism and positivistic approaches (Daneke,
1999). Backoff and Mitnick (1986) analyzed various factors including definition integrity,
holism, isomorphism, and problem solving and concluded that its untimely demise was
ideological rather than methodological. Simon (1973) asserted that general systems theory
attempted to describe all systems. But as there is very little that is true for all known
systems, or even all known large systems, therefore the theory found little content coupled
with the lack of tangible scientific results.
Many themes developed in the literature of nonlinear dynamics have direct
antecedents in the systems theory movement. Daneke (1999) suggests the systems theory
movement is in the midst of a resurgence due to the advances in nonlinear methods. This
may also have implications for social inquiry.
Over die past 40 years or so, a number of philosophers, historians, and others
who are interested in the overall development of science have argued that the positivist
framework is seriously defective. Postpositivism is fundamentally grounded in the idea
that reality exists, but can never be fully understood or explained, due to the intricacies of
multiple causes and effects and their contextual meaning (Fischer, 1998). Objectivity can
serve as an ideal, but requires discourse through a critical community of interpreters.
In contrast, positivism contends that the only reliable approach to knowledge
accumulation is objective hypothesis-testing of rigorously formulated causal
generalizations that is capable of explaining behavior across social and historical contexts
or circumstances. Agreement under positivism is tethered to the reproduction of empirical
tests and statistical confirmation while postposifivisms concurrence (and therefore
knowledge) is gained upon a discourse of competing perspectives (Danziger, 1995).
Postpositivists suggest that new findings in nonlinear dynamics (i.e., chaos theory)
demonstrate science cannot predict and control all aspects of the real world (Phelan,
1999). For example, small changes fin the initial conditions) in a system can transform the

system over time. The traditional understanding of the physical world as a stable or fixed
entity is no longer adequate. This loss of the ability to predict destroys the clockwork
universe that Newton established.
But caution is needed regarding the postpositivist assertion of the soon to be
demise of the positivist approach. One cannot assume that almost any system that exhibits
complexity (i.e., health care organizations) must be a chaotic system. Testing for chaos
systems require large amounts of data (thousands of observations) that are typically not
available in the social sciences. This results in many more systems are assumed to be
chaotic than shown to be chaotic.
Second, complexity theory assumes that most complex systems do not stay in a
state of chaos for long, rather gravitating towards the edge of chaos where prediction is
possible to some degree. Third, the nonlinear dynamic equations used in chaos theory are
only accepted as proxy for reality because their output mimics some aspect of the real
wodd. Finally, many chaotic systems follow a predictable path in state space allows chaos
to be controlled (Phelan, 1999).
Systems theorists first recognized the importance of patterns and processes now
being embellished in nonlinear dynamics, chaos and complexity (Overman, 1996). In many
ways, these eady systems advocates fully anticipated the development of tools designed to
unravel these complex patterns of interaction. The development of these tools, such as the
recent advances in machine intelligence and computational skills, provides for systems
theory as an extremely useful conduit for conveying the importance of nonlinear processes
and revitalizing a central paradigm of social inquiry (Danke, [1999]; Richardson, [1996]).
Decision- making in complex systems is difficult due to uncertainty, disagreement
and complexity (LaPorte, [1975]; Brewer and deLeon, [1983]). Managers and policymakers
may wish to design organizational processes and build systems that make allowances for
nonlinear patterns and systemic influences. To accomplish this, managers must update their
mental models to accommodate their new complex environment. These new mental models
based on complexity require new analytic methods.

Mental Models Guide How We Manage
To make decisions, managers must have an understanding about any given situation
with sufficient confidence to follow the course of corrective action it may suggest Under
uncertainty, individuals seek ideas that enable them to deal with a given situation,
terminating this search only when such an understanding has been obtained (Choi, 1993).
These ideas are predicated upon an associated paradigm a model, an example, of
something that is believed to work. If an individual lack examples of viable practice, they will
find them through experimentation or the more likely techniques such as trial and error.
The acceptance of these paradigms is the crux of decision-making and a powerful
explication of the potential role of metaphor (Choi, [1993]; Lissack, [1997]). Metaphors can
allow one to re-conceptualize a problem.
Mental models have power in how we shape our culture and institutions. Business
people and public administrators saw their worlds through the Industrial Age metaphor of
the machine and built their organizations accordingly. Public organizations have
traditionally mirrored the mechanistic influence towards efficiency and predictability. Public
managers then make decisions based on values and causal relationships. AD this activity in
an effort to maintain certainty, stability, and order in an environment presenting us with
messy data that are random, incomplete, unrepresentative, ambiguous, inconsistent, or
secondhand. Our flawed attempts to cope with these difficulties may result in producing
erroneous facts and subsequent decisions that are misguided (Gilovich, 1991).
Senge suggested in the Fifth Discipline (1990) that peoples own limited mental model
drives cognitive processes of understanding that restrains learning and leads to inertia for
individuals and for organizations. For example, faced with overwhelming complexity of the
real wodd, time pressure and limited cognitive capabilities (i.e., bounded rationality),
managers are forced to fafl back on standard operating procedures, habits, rules of thumb
and simple mental models to make decisions (Simon, 1957). Administrators trying to make
the best decisions often faU short limiting their ability to improve from experience. The
mental models people use to guide their decisions are dynamicaHy deficient (Sterman, 2000).

Ideas such as open systems theory are useful to stimulate learning and
understanding. As suggested eadier by Choi (1993) and Lissack (1997), Wheatley (1992,
p.150) urges the use of management complexity ideas to make sense of our own experience.
So long as we give that experience powers of veto, management complexity can serve as a
constructive provocation. However, metaphors are not sufficient as an explanatory device or
Gareth Morgans approach in Images in Organisations (1986) employed open systems
metaphor as one possible image of organizational dynamics. Overman (1996, p.490) agrees,
suggesting that .. .the image of organizations as complex, dynamic, self-organizing systems
will improve our ability to become administrative in the times of apparent instability and
newly emergent orders. Stacey (1991) comments that this different way of thinking makes
it more likely that we will use the same implicit models to explain and to act in open-ended
Lissack (1997) argues that metaphors can aid managers in their quest for value
added knowledge by allowing managers new ways of thinking and the use of a different
language. For example, if we define an organization as systems of coordinated actions
among individuals and groups whose preference, information, interests and knowledge
differ (March and Simon 1958/1993, p. 2) then the central task of an organizational
manger is the delicate conversation of conflict into cooperation. Such a conversation can
be gready assisted when the participants speak a common language and share access to
somewhat similar views if the wodd they inhabit. The greater the cooperation, the better
the results.
NewJdental Models Requite New Analytical Methods
The descriptive metaphor that everything is changing and thus the organization
must be poised to adapt to change says nothing about what to do next. What to change,
how, when and why is not connected to the management experts observation regarding loss
of control. Something more is needed.

Any influence over organizational decision-making depends on the vividness of
the metaphor and on practice inferences the metaphor may imply. The metaphor may be
of value (see Morgan 1986) in stimulating thought. It works by transferring a whole set of
ideas and associations from one domain (nonlinear dynamics) to the other (management).
But without rigorous analysis in the management domain, it can carry no authority and
actually have a detrimental affect if misapplied (Rosenhead, 2001).
The role for rigorous or elaborate analysis in many popular management writings
such as Stacey (1991) is a marginal role. For example, Table 3.3 summarizes Stacey's
assertion of downplaying analysis. Because the organizational wodd is one of nonlinear
dynamics, we are told, managers must be intuitive, innovate, and spot emergent strategies
without recourse to analytic crutches (Rosenhead, 2001).
Tools and techniques dealing
with system complexity are nothing new.
Authors like Vickers (1965) and Schon
(1973,1983) took related systems
concepts into broader social discourse
decades ago. Etzioni (1971) also
anticipated some of the complexity-based
insights. His mixed-scanning approach to
planning is an attempt to encompass the
paradox that organizations need both
control and innovation. Managers are not confronted with problems that are independent
of each other, but with dynamic situations that consist of complex systems of changing
problems that interact with each other. Sterman (2000) suggests scenario planning provides
managerial assistance to administrators as they operate in this uncertain wodd. The different
scenarios represent alternative possible and desirable futures for the organization's context
and serves as a stimulus for a debate on strategy. Planning and design are predominantly
synthesizing rather than analytical activities, they involve putting things together rather than
taking them apart (Ackoff, 1979).
Table 3.3:Analysis Summary
________________(Stacey, 1991)___________
-analysis loses its primacy
-contingency (cause and effect) loses its
-long-term planning becomes impossible
-consensus and strong cultures become
-statistical relationships become dubious

Bobrow and Dryzek in Poky Analysis by Design (1987) continue to stress the post-
positivist approach by articulating that models need to address values. The modeler selects
and applies the appropriate approach but also by necessity captures the context. The
modeler must understand or recognize the context, that is, the situations complexity,
uncertainty, feedback loops, audience, and stability. There is a need to view the wholeness
or comprehensiveness of die circumstances in order to develop models that will assist
scholars or administrators in solving complex problems beyond the limitations inherent in
muddling through whether in the public, private or non-profit sector.
Nonlinear dynamics can provide a descriptive foundation of the behavior of
complex systems that is supported by much of current thinking about management and
organizational change (Kiel, 1994). With this possession of new mental models and
visions, techniques we have used in the past need to be expanded. Managers dealing with
interconnected systems and environmental turbulence need state-of-the art- resources.
Nonlinear dynamics is proposed as a means to offer organizations faced with these
uncertainties a variety of helpful analytic tools and techniques.
Approaches and Tools
As we embrace organizations as complex systems, we need to move from
theoretical generalizations about these systems to practical tools and processes to assist
administrators. An important tangent to general systems theory is the development of the
system dynamics approach. System dynamics is a method to enhance learning in complex
systems (Forrester 1961). Because we are concerned with the behavior of complex systems,
system dynamics is grounded in the theory of nonlinear dynamics and feedback control
(Forrester, [1961]; Sterman [2000]).
The area of inquiry had its origins in Jay Forresters Industrial Dynamics (1961). The
application and methods went beyond industry and the name evolved to the more general
term system dynamics. The label is thought to be misleading, suggesting incorrectly that
this approach is linked to various system philosophies, such as general systems theory.
System dynamics has roots in

advances in computing technology,
growing experience with computer simulation improved understanding of
strategic decision making, and
developments in the understanding of the role of feedback in complex
systems (Richardson, 1996).
The central feature of this method is to study complex interactions of variables.
Richardson (1991), in his history of feedback concepts in the social sciences, shows how in
the 1940s, social scientists recognized that the concept of feedback applied not only to
mechanisms but to human decision-making and social settings. Forrester (1961) asserted
that all decisions (including learning) takes {dace in die context of feedback loops. John
Dewey, Schon (1992) asserted recognized die feedback loop character of learning beginning
in the 20th century when he described learning as an iterative cycle of invention, observation,
reflection and action. Urban planners, industrial engineers and technology disciplines have
been using system dynamics as a modeling tool for decades (Senge, 1990).
For example, an ambitious study published by The Qub of Rome, in 1972 under
the tide The Limits to Growth was based on system dynamics. A large-scale computer model
was constructed to simulate die likely future outcomes of the wodd economy using
feedback loops to explain behavior. The primary aim was not to make a prediction but to
improve insight.
Several years after the first phase of environmental awareness and shortiy before
die first oil crisis (1973), the study brought the message that the wodd was heading for
disaster because of unfettered population growth and industrial expansion, exhaustion of
stocks of natural resources, environmental destruction, and food shortages. When the
nonrenewable resources (on which the industrial base depends) have been depleted, a
precipitous collapse of the economic system will occur. The characteristic behavior of die
system is overshoot and collapse. The study was widely publicized and caused concern
woddwide. It had few direct policy impacts, despite the Qub of Romes promotional
campaign to governments. It did, however, have a large inpact on researchers, creating
coundess studies to confirm or disprove the reports hypotheses and methodology.

Grizzle and Pettijohn (2002) suggest that the nonlinear feedback loop approach
encourages the ability to see the interdependencies between variables and the ripple effect
when a change occurs in one or more variables. This leads to better understanding which
variables are more critical to the success of the system as a whole and enables managers to
forecast possible future changes to their environment
Organizations may want to focus on studying their subsystems and associated
feedback loops rather than focusing on internal discrete tasks. Neither behavior of
autonomous agents nor the resulting systems is the appropriate focal point. Rather, it is the
patterns of interaction that matter most (Epstein and Ax tell 1996).
This refocusing can be accomplished by building a complex system model. Models
can be created that simulate the processes that the organizational system will gp through as it
transforms. Model building and problem identification demonstrates die potential use for
nonlinear dynamics in the organization.
Social science scholars have begun to explore the usefulness of nonlinear system
dynamics in social systems. These include Priesmeyer (1992), who demonstrated through
time series financial data that basic organizational processes are nonlinear dynamic
systems. He and other scholars have discovered how management decisions,
organizational functions, and strategic planning inadvertendy fail to recognize complexity
in an organizational setting (Baumol and Benhabib, [1989]; Daneke, [1994], [1999];
Overman and Lorraine, [1994]; Kiel, [1994]; Priestmeyer, [1992]).
Many of the insights that are emerging about systems thinking in the corporate
and public sectors are grounded in simulation modeling. Richardson (1996) argues that die
perspective of systems thinking to complex systems cannot be understood without the aid
of formal (i.e., mathematical and graphical) models.3 5 Model-based support can
significandy contribute to the potential insightfulness and effectiveness of management
Levy (1994) has used nonlinear simulation of international supply chains to
demonstrate that managers might underestimate the costs of international production
35 Sharon Samsons (1998) dissertation explored Information Complexity in Higher Education
Policy through die design, creation and simulation of a policy model.

resulting in disruptions and volatility in the production function. Levy also demonstrated
that managers should be able to control the pertinent system process and shift this system
itself back into a stable state.
We are challenged in both our learning and managing abilities of complex inhibiting
systems. Feedback loops by which we may learn can be weakened or disrupted by a variety
of structures. Some are physical or institutional features of the environment Some are
consequences of our culture, group processes and inquiry skills. Others are fundamental
bounds on human cognition, particularly the poor quality of our mental maps and our
inability to make correct inferences about the dynamics of complex nonlinear systems
(Richardson, 1996).
Change describes the environment in which the present day the health care industry
operates. Organizations need to explore new approaches and enlarge their analytic toolbox
as they operate in their complex environment. Beginning with the introduction of die
Medicare prospective payment system in 1983, the growth of managed care and continuing
through the Balanced Budget Act of 1997, health care has been under constant pressure to
adapt to environmental change.
This was surmised more than a decade ago as the health care environment changed
from a cloistered state to a more competitive turbulent state (Subramanian et al., 2002). The
techniques for managing the organization must change to remain both effective and
efficient. The traditional reliance on static statistical methods is questioned when the
dynamism of organizational reality operating in a turbulent environment is fully appreciated
(Deming, 1993).
Model-based support can significantly contribute to the potential insightfulness and
effectiveness of management decisions. Richardson (1996) contends that formal models
have the potential to reduce uncertainty, disagreement and complexity. Each different
model application has distinctive strengths. Uncertainty can be reduced by statistical
techniques such as time series analysis, statistical inference and risk analysis. Disagreement

can be addressed with tools such as decision trees and optimizing techniques such as linear
and dynamic programming. Problems with understanding, reducing and managing
complexity can be aided with formal computer simulation modeling.
By themselves, these formal models and modeling techniques can improve ones
understanding of the structure and dynamic behavior of complex systems. Used with other
tools, these simulation models contribute to a group of formal techniques that can support
management thinking and decision-making.
If the dynamic of a successful organization is complex and nonlinear, as this diesis
argues, then managers cannot rely on mental models and static, linear plans to handle open-
ended, long-term futures. Nor can they rely on personal visions, shared values, and trial-
and-error actions to secure stability in open-ended situations. There is an alternative for
effective control and development. This alternative proposes to understand and make more
effective the political learning process through which managers continually develop new
mental models, build strategic issue agendas, use multi-variant analytic tools, and take steps
to create their own environments.
Accepting the causal feedback nature of complex, dynamic organizations forces one
to recognize some weaknesses in our abilities to predict or understand die behavior of the
systems we hope to influence. The use of rationality is bounded not merely because there
are limits to how much information it can deal with but also because there are unanticipated
consequences following die decisions due to the difficulty in reasoning in circular causal
Effective decision-making and learning in a world of growing dynamic complexity
requires contemporary decision makers to become system thinkers. They need to expand
their boundaries of our mental models and develop and use tools to understand how the
structure of complex systems creates their behavior. Nonlinear system dynamics presents
both a perspective and also a set of conceptual tools that enables the construction of formal
computer simulations of organizational complex systems.
These computer simulations are used to design and manage improved policies,
operational procedures and ultimately organizations. As Sterman (2000, p.l) states .. these
tools allow us to create management flight simulators -micro-worlds, where space and time

can be compressed and slowed so we can experience the long term effects of decisions,
speed learning, and develop our understanding of complex systems and design structures
and strategies for improvement
Based upon the literature review, the following null hypotheses are proposed:
Hlo : There is no difference between die use of limited mental models and formal
dynamic models in managing organizational processes.
Much of the research discussed suggests that mental models shape our culture,
institutions and guide how we manage (Senge, [1990]; Choi, [1993]; Richardson, [1996]).
Administrators are faced with the overwhelming complexity of die real wodd, time pressure
and limited cognitive capabilities (i.e., bounded rationality). Managers are forced to fall back
on standard operating procedures, habits, rules of thumb and simple mental models to make
decisions (Simon, 1957). Sterman (2000) suggests these mental models are dynamically
deficient and organizations should turn to formal (i.e., simulation) modeling to improve
organizational performance.
While there has been extensive research and utilization of formal models in other
disciplines, there is a need for further research in the use formal models in the delivery of
primary care health services. A formal model offers health care administrators the potential
to simulate future alternatives by inputting changes individually to key variables influencing
organizational processes such as resource allocation, administrative policies and productivity.
As variable inputs are changed, the formal model is expected to return non-proportional
results that in turn provide administrators with superior information to their current mental
models to improve health care organizational processes.
H2o : There is no difference between linear and nonlinear tools in planning for
improved cost performance within organizations.
The literature review supports the idea that organizations are complex and
nonlinear (Kiel, [1993]; Overman, [1996]; Mitroff, [1998]). The traditional organizational
theories and practices, measures of performance, and service delivery based on mechanistic
design and linear techniques in organizations cannot necessarily rely on linear plans to

handle open-ended, long-term futures. Linear approaches and tools do not appear to be in
unison in a complex environment and limited in their analysis of complex systems. An
alternative to analyze complex systems for effective control and development is required.
The use of the feedback concept permits administrators to learn about the structure and
dynamics of their complex organization. These feedback processes determine the dynamics
of the system under study (Grizzle and Pettijohn [2002]; Moorecraft, [1988]; Richardson,
The addition of nonlinear tools to die health care administrators current linear
toolbox, potentially offers the opportunity to visually scrutinize organizational structure and
its corresponding behavior in the effort to improve primary care service delivery. A variety
of sensitivity (Svhat-if) scenarios have the potential to reduce uncertainty, disagreement
and complexity (Richardson 1996). As multiple organizational process improvement
scenarios are simulated (guided by our observations in hypothesis one) and compared, it is
expected that die improved management of key organizational processes will result in a
nonlinear relationship between the proportion of inputs and outputs resulting in enhanced
potential cost performance over current performance.
H3o : There is no difference between linear and nonlinear analysis for managers in
revealing patterns of behavior in organizational systems or subsystems.
Epstein and Axrtell (1996) suggest that organizations may want to focus on
studying their subsystems and associated feedback loops rather than focusing exclusively on
die resulting systems. It is these patterns of interaction that matter most, when analyzing
system or subsystem behavior. Priestmeyer (1992), Kiel (1993), and Daneke (1994,1999)
among others suggest that basic organizational processes are nonlinear dynamic systems and
have demonstrated complex administrative systems by graphically quantifying work
processes. Litde research in the analysis of subsystem components in die delivery of health
care services has been documented.
Traditional methods such as time series and linear regression may fail to illustrate
complex patterns. Qualitative measures such as phase portraits offer a framework for

illustrating a subsystem change over time. These measures reveal different patterns of
behavior than linear measures such as regression analysis. Subsystem components are
compared through both linear and nonlinear measures for actual and simulated performance
data. It is expected that patterns of behavior will differ between measures and performance

This chapter describes the methodological framework used in this dissertation
examining nonlinear behavior in health care delivery systems. The modeling approach
follows Sterman (2000), and is comprised of five stages: boundary selection; diagramming of
selected systems and subsystems; the formulation of a simulation model; testing the model;
and scenario building for future alternatives. Boundary selection or model conceptualization
is discussed in detail. The remaining four stages are summarized in this section and are
talked about at greater length in Chapter Five. Issues regarding model validity, reliability and
limitation issues with the models source data conclude this chapter.
Boundary selection orients the reader to the general problem area, establishes the
objective of the study, provides research questions to be addressed, and conceptually
describes the selected basic system using a causal diagram (Randers, [1996]; Sterman,
Boundary Selection
As previously discussed in Chapter One, the present health care system in the U.S.,
a mixture between public, non-profit and private sectors- is challenged by a combination
of rising medical costs, an aging population, increased number of uninsured patients, and a
delivery strain put upon primary care physicians and services. As organizations strive to
meet their private and public population needs more exactly, traditional analysis techniques
do not always offer a full understanding of the complexities of the primary care
environment. These complex organizations want to attain higher levels of provider/clinic
productivity without increasing medical costs, especially since we know costs will grow even
if we hold personnel costs constant
This is especially important for M+ C plans as depicted in Chapter Two. These
managed care plans are operating in an unstable market environment confronted with

providing medical services effectively and efficiently to a high utilizing population. To assist
health care administrators in this endeavor, the use of demand planning tools and
techniques must also improve. Nonlinear dynamic approaches may offer health care
administrators a guide to understanding and managing complexity in their health care
System dynamics is a computer-aided approach applied to dynamic problems -
problems that involve multiple change over time -arising in complex social, managerial,
economic or ecological systems (Richardson, 1996). Using this approach, Senge (1990)
suggests, enables one to meaningfully understand systems, their patterns of behavior and
their interrelatedness while dismissing the belief that complex problems are more
manageable by breaking the whole into parts.
The concept of endogenous change is fundamental to the system dynamics
approach (Richardson 1996). Looking internally at this system and its structure as it evolves
over-time may empower managers to focus their attention on those aspects that they may
have some control to improve performance. The goal is to derive the essential dynamic
characteristics of a system from the internal workings of die system itself. An equal objective
is to view problems as consequences of interacting subsystems of a complex system.
There has been improvement in simulation models leading to better insight into
dynamic behavior. Computer simulation tools can help picture a dynamic social system
characterized by interdependence, mutual interaction, and information feedback.
Simulation modeling encourages management discourse between mental models and their
corresponding dynamic social systems, in this particular case the health care delivery
system (Morecroft 1988). The use of simulation in administrative tasks is more recent and
less widely adopted (Sterman, 2000). Yet these are precisely the settings where dynamic
complexity is most problematic, where learning feedbacks are the least effective and where
the stakes are highest.
Such simulations offer administrators the ability to test a variety of strategies that
may lead to improve or worse performance. Often pushing a system into extreme
conditions reveals more about its structure and dynamics than incremental adjustments to

successful strategies. Simulations offer managers what Schon (1992) calls reflective
conversation with the situation
Simulation is ideally suited for addressing a wide range of issues in health care
organizations (Standridge 1999). Simulation applications are used in health care delivery
including public policy (Standridge et al., [1978]; Yesalis, et al., [1982]; Pritsker, et al., [1995]),
patient treatment processes (Vennix and Gubbels, [1994]; Kleinmuntz and Thomas, [1987]),
capital expenditure requirements (Barnes and Quiason, [1997]) and provider operating
policies (Butler et al., [1992]; Qian and Metzer, [1993]; Dittus et al., [1996]; Su ,[2003]).
Research Objective
This dissertations primary interest is to understand incrementally the nonlinear
dynamic interaction between patients demand for primary care services and the health care
provider workforce in the public arena. The heart of this dissertation examines the
Medicare patient subpopulations behavior at one Kaiser Permanente medical facility.
Understanding this populations unique medical demand for services offers clinic
administrators the ability to plan for the future with improved efficiency. While this
Medicare population comprises approximately 12 percent of Kaiser Permanentes
membership, it consumes 25 percent of the primary adult care physician workload and
nearly 40 percent of all health care expenses. The intent is to provide further insight to
public and non-profit managers to improve performance in the future delivery of primary
care medical services.
Traditional methods in analyzing Medicare patient demand for primary care
services and workforce interaction consist of discrete linear modeling. The linear model
includes tasks consisting of estimated future Medicare membership, productivity, visit rates
and budgeted labor. This method is presently standard for the Kaiser Permanente System
and is used at other regional areas. Qinic administrators are expected to use the resulting
information as a tool to plan for resources accordingly.
However, clinics have expenenced variations in actual patient visits on a month-to-
month basis. The impact of these variations created operational challenges to clinic

administrators. An increase in patient visits to the clinic for this subgroup creates demand
for services immediately and can overwhelm staff, while with a lack of patients valuable
staffing resources are idle. Monthly variations can incur additional expenses for the clinic,
frustration (at best) for patients, and a growing dissatisfaction with public health provisions.
The administrator in die short term chooses strategies to deal with the increased
demand that may include: recruiting additional support staff from the corporate pool of
individuals; diverting resources internally to this specific department; requesting assistance
from other clinics; increasing patient wait time for appointments; treating individuals over
the telephone if possible; encourage members to seek services elsewhere at another facility;
or more expensive venues such as urgent care centers or the emergency department
Long-term solutions include hiring additional permanent employees, implementing
programs that may decrease utilization and improve health status such as group visits. In
addition to the pressure put on direct staffing, overhead and indirect staffing expenses are
affected. This is especially critical where Medicare expenditures are under serious budgetary
constraints. The additional visits this population creates affects not only the staffing level,
but the physical limitation of the facilities themselves. Exam rooms that do not turn over
quickly enough will cause longer waits for patients. Indirect costs result because
appointment schedulers at centralized locations are forced to deal with the increased
volume. Also, the scheduling of appointments for non-Medicare members may have to
endure longer wait times for appointments.
But how do managers at the operational level best prepare, track and become
more knowledgeable about potential monthly instability (i.e., patient visits and supporting
staff) in their respective medical facilities. Clinic administrators have the responsibility to
provide a somewhat stable environment (reduce variability), not only to meet customer
expectations but their employees as well.
This diesis posits that health care organizations are complex systems consisting of
interdependencies, mutual interactions and information feedback that require system
dynamics modeling. Specifically, the computer simulation software package, Vensim, is
used to construct selected systems and replace these qualitative representations with
mathematical equations. Next the model is tested and simulates Svhat-if scenarios. This

systems dynamic method offers clinic managers an increased understanding of behavioral
interactions between Medicare patients and providers of health care in the effort to improve
future service delivery. This thesis is primarily theoretical in nature and not a precise model
of the structure of the Kaiser Permanente appointment- health care labor workflow.
Although any model is necessarily an abstraction of reality, here it contains general
characteristics that are observed in practice in the health care industry. The goal of this
model -indeed, any model is to keep the model simple and to strike a balance between the
selected structures and management levers.
Research Questions and Hypotheses
To understand the interaction between patient medical demands for access to
primary care services (via the appointment system) and the health care provider workforce,
this research explores the structure and behavior of this system, its subsystems and their
primary components. Specifically, it asks:
1) Does system dynamics modeling, specifically a causal-feedback
simulation model, improve a health care administrators understanding of
patient care services for future improvement?
2) Can a system dynamics modeled appointment process provide more
efficiencies (i.e., reduce cost) than are currently available from a linear
3) How does a system dynamics model offer administrators new insight
about how health care organizations work?
The focus of the dissertation is limited to the Medicare population (approximately
5,800 members) receiving primary care services at one Kaiser Permanente medical facility
that serves about 48,000 total members over a 21-month time frame (1995-1997). The
modeling will concentrate on the labor and appointment dynamics by examining the
relationships between patient behavior patterns, workforce behavior patterns and
appointment (access) changes using a casual feedback simulation model. Actual Medicare