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J Ambulatory Care Manage
Vol. 36, No. 2, pp. 129–139
C 2013 Wolters Kluwer Health | Lippincott Williams & Wilkins
Copyright Modifying Physician Behavior
to Improve Cost-efficiency in
Safety-Net Ambulatory Settings
Nancy Borkowski, DBA, CPA, FACHE, FHFMA;
Gulcin Gumus, PhD; Gloria J. Deckard, PhD
Abstract: Change interventions in one form or another are viewed as important tools to reduce
variation in medical services, reduce costs, and improve quality of care. With the current focus on
efficient resource use, the successful design and implementation of change strategies are of utmost
importance for health care managers. We present a case study in which macro and micro level
change strategies were used to modify primary care physicians’ practice patterns of prescribing
diagnostic services in a safety-net’s ambulatory clinics. The findings suggest that health care
managers using evidence-based strategies can create a practice environment that reduces barriers
and facilitates change. Key words: ambulatory services, physician behavior, primary care,
safety-net
O
VER THE LAST few decades, widespread
variability in clinical practices has been
well documented in the United States. Such
variations persist in virtually all areas of medical practice, including rates of surgery, drug
Author Affiliations: Health Management Programs
(Dr Borkowski) and Department of Decision Science
and Information Systems (Dr Deckard), College of
Business Administration, Florida International
University, Miami; and Department of Management
Programs, College of Business, Florida Atlantic
University, Boca Raton (Dr Gumus).
The authors thank the following individuals at the
Jackson Health System in Miami-Dade County,
Florida, for their contributions: Lenworth L. Anglin,
MD, MPH, Medical Director, Ambulatory Managed
Care & Quality; Serge A. Boisette, MHSA,
Administrator, Ambulatory Managed Care &
Quality; and Paul Ling, Senior Systems Analyst,
Ambulatory Managed Care & Quality. They also
thank the anonymous reviewer for the comments
and constructive feedback.
The authors have disclosed that they have no
significant relationships with, or financial interest in,
any commercial companies pertaining to this article.
Correspondence: Nancy Borkowski, DBA, CPA,
FACHE, FHFMA, Health Management Programs,
College of Business Administration, Florida
International University, 1101 Brickell Ave, S Tower
306, Miami, FL 33131 ([email protected]).
DOI: 10.1097/JAC.0b013e318242fe67
prescription, diagnostic testing, hospitalization, and length of stays (Ashton et al., 1999;
Chassin et al., 1986; McGlynn et al., 2003).*
The noted variability suggests that some
patients end up with inappropriate and/or
overutilization of medical care that serves no
benefit for improving health outcomes but
contributes only to increasing health care
costs. These concerns over rising costs and
striking variations in physician practice patterns have led to a growing interest in the standardization of health care delivery through the
use of various change strategies (Baskerville
et al., 2001; Chilingerian & Sherman, 1990;
Harris, 1990; Main et al., 1995). With the focus on efficient and effective resource allocation, successful design and implementation
of change strategies are of utmost importance
for today’s health care managers.
The effectiveness of innovative organizational changes in policies, procedures, structure, as well as behavioral changes in practice patterns, is critical in particular for the
publicly funded safety-net hospitals and their
ambulatory clinics. Facing an ever-increasing
*See also the Dartmouth Atlas of Health Care.
129
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burden of uncompensated indigent care as the
number of uninsured individuals rise, safetynet providers are simultaneously experiencing reductions in both tax dollars and charitable contributions. Currently, about 1 in
5 nonelderly Americans is uninsured, which
creates challenges for this vulnerable population, the organizations that serve them,
and the US health care system overall (Kaiser
Family Foundation, 2010). Thus, the financial burden on states, localities, and safetynet providers must be met as best possible
through innovations in care delivery.
This article contributes to the research as
to what strategy or strategies are appropriate in a safety-net setting for modifying primary care physicians’ behavior regarding ambulatory services. We present a case study
in which multiple change strategies at the
macro and micro levels were implemented
in community-based clinics serving the members of Jackson Access Plus, an innovative
managed care program for the uninsured residents of Miami-Dade County in Florida. Our results indicate that significant reductions were
achieved and sustained in the cost per member per month (PMPM) for services delivered
to uninsured patients by tracking and monitoring utilization data and sharing of best practices among the employed physicians of the
safety-net’s ambulatory clinics.
BACKGROUND
Health care managers have been under increasing pressure to establish “best practices”
within their facilities in order to improve both
effectiveness and efficiency in the delivery of
medical services. Change strategies are important tools to assist in reducing variations in
physician practice patterns in an effort to eliminate unnecessary or inappropriate care and
thereby improving efficiency. Since reduced
variance of care can lead to clinical benefits
for patients and cost savings for health systems and payers, there is continuing interest
as to why some change strategies at the micro
level have been remarkably unsuccessful in influencing physician practice patterns (Smith,
2000).
Various studies report and/or evaluate the
utilization of organizational theories in change
efforts within hospitals, health maintenance
organizations, and other managed care organizations, as well as clinical practice settings
(Bazzoli et al., 2004; Bradley et al., 2004; Liebhaber et al., 2009; Miller, 1998; Moulding
et al., 1999). Moulding et al. (1999) found the
most crucial factor in successful intervention
to be a multifaceted dissemination and implementation strategy. However, Main et al.
(1995) point out that mutually reinforcing interventions may only have modest success because health care managers fail to consider
both environmental and physicians’ readiness
to change. As such, managers need to address the barriers to change while creating an
environment for minimizing conflicts associated with resistance to change. More recently,
evidence-based management has emerged as
an effective tool to guide behavioral change
and practice patterns (Kovner et al., 2009).
While health care managers are under increasing pressure to plan for and deliver
health care services in an efficient and costeffective manner to ensure the future survival
of their organizations, physicians are under
increasing pressure to justify their patterns of
clinical practice and productivity. Changes,
focused on reducing variations in health care
delivery, have at times resulted in confusion,
frustration, and conflict between physicians
and managers (Kleinke, 1997; O’Connor &
Lanning, 1992). Physicians may view an organizational change strategy as a perceived
threat to their professionalism (Stamps &
Boley Cruz, 1994).
Previous studies have reported that because physician autonomy is based on expert
knowledge and individual patient care rather
than on organizational accountability, physicians disfavor and will resist policies that constrain their practice patterns (Borkowski &
Allen, 2002, 2003; Ku & Fisher, 1990; Rappolt,
1997). Although some evidence suggests that
participation in decision making can lead to
significant reduction in resistance to organizational change, not enough is known about
successful modification of physician practice
patterns. To successfully implement change,
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managers need to effectively reduce the tension between the clinical autonomy and independence needs of physicians and the needs
of the organization to control resources and
variations in patient care (Marcus, 1985).
STRATEGIES FOR CHANGE
At the macro level, health care managers
need to first consider the driving and restraining forces for organizational change. The
force field model of Lewin (1947) provides
a framework to understand the forces that
will influence physicians’ adoption or nonadoption of change in their practice behaviors. Borkowski and Allen (2002) note that
a key restraining force would be physicians
viewing management-initiated change efforts
as inappropriate and unnecessary substitution
for their clinical judgment. Driving forces that
could encourage physicians to modify their
practice patterns would be (1) improved quality of care, (2) reallocation of resources/cost
containment, (3) legislative mandates, and (4)
financial penalties and/or incentives. To effectively initiate change, managers need to
assess the tension between the driving and
resisting forces and then alter the balance of
these forces by addressing individual physician and practice environment issues. Building on Lewin’s work, others such as Kotter
(1996) provide managers with the guidance
for successful organizational change at the
macro level. Kotter introduced an 8-step approach: the first 4 steps address changing the
status quo, steps 5 through 7 introduce new
policies and/or practice patterns, and step
8 institutionalizes the change (Borkowski,
2005).
At the micro level, Hersey and Blanchard (1993) state that reducing resistance to
change requires the involvement of the individual or group in direct participation for
selecting the best method for development
and implementation of change. A significant
advantage of using the participative change
cycle, although time consuming, is that once
the change is accepted, it tends to be long
lasting “since everyone has been involved in
the development of the change, each person
131
tends to be more highly committed to its implementation” (Hersey & Blanchard, 1993, p.
377).
Interestingly, though, evidence on the
effectiveness of participatory strategies by
physicians related to the development and
implementation of clinical practice changes
is mixed. For example, Ford et al. (1987)
document an unsuccessful attempt to implement practice changes that involved participation. However, studies by Spiegel et al.
(1989) and Gottlieb et al. (1990) imply that
participation in practice guideline development, along with other intervention strategies, increases the likelihood of successful implementation. Other intervention strategies,
such as the dissemination of changes by “opinion leaders,” that is practitioners recognized
by their peers as trusted sources of clinical information, appear to successfully alter physician practices (Liebhaber et al., 2009; Lomas
et al., 1991). Similarly, Taylor et al. (2010)
emphasize “transformational physician leadership” as a critical element in influencing the
behavior of other physicians in the group. As
such, physicians leading physicians appears to
be a critical strategy in efforts to after physician behavior.
Other researched interventions for modifying physician behavior include continuing
medical education strategies with or without
opinion leaders, administrative interventions,
feedback on practice norms with educational
reinforcement efforts, peer review programs,
financial incentives, and other reimbursement
arrangements.* Overall, there is no unifying
theory of physician behavior change (Smith,
2000), and a combination of strategies may
be more effective than a single intervention
(Teleki et al., 2006), including mutually reinforcing approaches such as local participation
of opinion leaders, didactic and interactive educational sessions, simulation training, audit
and feedback interventions, evidence-based
decision aids, and active system-oriented implementation. The mixed evidence on the
*See reviews by Borkowski and Allen (2003), Greco and
Eisenberg (1993), and Smith (2000).
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effectiveness of change strategies regarding
physicians’ clinical practice highlights the
need for continued research efforts to better understand physicians’ motivation or resistance for change.
CASE STUDY
Here we present a case study describing
the fiscal outcomes achieved by primary care
physicians after multifaceted change interventions were implemented to encourage more
efficient use of ambulatory services. The organizational change strategies emphasized the
use of Kotter’s step approach interchangeable with Hersey and Blanchard’s participative change cycle to modify salaried physicians’ prescribing of diagnostic services. The
settings were community-based primary care
clinics (PCCs) under the Jackson Access Plus
program, part of the Jackson Health System
(JHS) in Miami-Dade County in Florida. The
JHS’s Division of Ambulatory and Community
Health designed and implemented mutually
reinforcing interventions at the macro and
micro levels to influence primary care physicians’ clinical ordering behaviors. No financial bonuses or other targeted economic incentives were used to encourage particular
practice patterns.
Miami-Dade County
Miami-Dade County is home to a diverse
population of about 2.5 million individuals.
More than 60% of the county population is
Hispanic, the ethnic group identified by the
US Census data to have the highest uninsured rate nationwide (32.1% in 2007). The
same data source reflected that Miami-Dade’s
poverty level (15.3%) was above both state
and national levels (12.1% and 13.0%, respectively), and the county’s median household
income ($43,495) was below the state and
national levels ($47,804 and $50,665, respectively). In 2008, the Health Council of South
Florida estimated that the uninsured rate in
Miami-Dade was approximately 35%, up from
29% in 2004 as reported by the Florida Health
Insurance Study (Agency for Health Care Administration, 2004). Clearly, Miami-Dade is in
the forefront of localities struggling with their
responsibility to provide quality health care
for its indigent and uninsured populations.
The Jackson Health System
The JHS is an integrated health care delivery system that consists of multiple PCCs and
specialty care centers as well as a variety of
school-based clinics, 2 long-term care nursing facilities, a network of community mental
health facilities, clinics in the county’s correctional facilities, and 2 community hospitals. As the designated safety-net health care
provider, the JHS receives county funding
through a half-cent sales tax. In addition, the
JHS receives a portion of the county’s property tax proceeds. The JHS receives the same
amount of county funding regardless of the total amount of “free care” the system provides.
Given the recent economic downturn and
the associated growth in the uninsured population in Miami-Dade, the unfunded gap between charity/free care provided by the JHS
and its local tax support has widened over the
years, with the difference growing to $230
million in 2010. As partial response to the
widening of this financial gap, a multifaceted
approach was developed to change primary
care physician practice patterns to encourage
more efficient use of ambulatory services for
Jackson Access Plus program members.
The Jackson Access Plus program
Jackson Access Plus is a comprehensive
program that utilizes primary managed care
principles in the delivery of health care
services to Miami-Dade’s indigent/uninsured
population. The program was designed to improve access to the appropriate use of primary
care, provide patient education and care coordination, and encourage best clinical practices.
Jackson Access Plus program members are
residents of Miami-Dade County (1) who have
an income at or below 100% of the federal
poverty level and (2) are not eligible for any
other health care coverage. Program enrollment occurs at first contact with the primary care centers by the eligible individual.
All enrolled individuals were assigned to a
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specific physician within a designated JHS
clinic, to foster and encourage a long-term
patient-physician-center relationship. Qualifying residents remain enrolled until (1) other
health coverage is obtained, (2) the patient
no longer resides in Miami-Dade, or (3) no
services are provided within a 12-month
period.
Modifying primary care physician
practice patterns
Following Kotter’s (1996) step approach
for change, the administrative and physician
leaders of the Division of Ambulatory and
Community Health and the Department of
Ambulatory Managed Care and Quality* communicated the urgency of the need to change
current clinic-level practice patterns due to
unfunded gap affecting JHS’ fiscal solvency.
Media coverage of JHS’ financial challenges
highlighted the need to address the growing
deficit that was in part attributed to the ambulatory care clinics. The system’s financial
deficits provided the initial momentum for the
PCCs to examine the physicians’ practice patterns and identify potential inefficiencies in
the ordering of diagnostic services.
The division leadership formed a coalition to design and implement the necessary change program with strategies directed at both the organizational (macro) and
clinic/physician (micro) levels. All PCC medical directors joined the coalition and became
change agent champions within each clinic.
The coalition shared the goals of the change
program with staff physicians as well as the
managerial and other clinical PCC personnel:
(1) access to care for the indigent population,
(2) encouragement of the patient-physiciancenter relationship, (3) coordination of care,
(4) data-driven utilization management, and
(5) sharing best practices. Communicating the
vision of the change program to all individuals within the PCCs strengthened the overall
*Hereafter, this combined team of division and department administrative and physician leadership is referred
to as Division leadership.
133
commitment to the change and helped solidify a team approach for success.
Rather than adopting a prescriptive approach to change the physicians’ practice behavior, the division leadership provided each
PCC monthly diagnostic services utilization
reports for the top 25 diagnoses with peerreviewed evidence-based prescribing protocols. The sharing of information provided the
physicians an opportunity to examine the
costs and benefits associated with any incremental diagnostic service prescribed. The
transparency of the cost data allowed the
physicians to identify redundancies and inefficiencies. The decentralized decision making
to the PCC level empowered the physicians
to (1) identify inappropriate utilization patterns and (2) implement clinic-specific protocols and responses. Each clinic’s implementation strategies were designed within the overall framework of the program as determined
by the particular member and neighborhood
characteristics.
Consistent with the principles of evidencebased management (Cohn & Lambert, 2005;
Kovner et al., 2009; Sparer et al., 2002), the
PCCs were provided timely feedback, with
monthly data analysis identifying key trends in
membership, practice patterns, utilization of
care, and patient satisfaction. The PCCs held
monthly meetings to review the data that provided a forum to not only identify immediate
needed improvements but also opportunities
for continuous improvement. No specific decision support tools were introduced; instead,
improvements in the efficiency of care were
realized through physicians mutually reinforcing changes in the care processes.
Three to 4 actionable items were identified
by each PCC every month. Thereby, each PCC
identified and implemented new changes as
appropriate for the individual center based
upon the data reports and experiences. Best
practices across the PCCs were identified and
shared at medical directors’ meetings to encourage further improvements and maintain
momentum. The monthly data reports followed by scheduled meetings over the first
24 months of the change program’s deployment solidified physician involvement in the
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design and implementation of sustained
change, which facilitated institutionalization
of the approaches.
Data and analytic methods
To measure the impact of the various interventions for organizational and physician behavioral changes, we examined PCC-specific
data over a 2-year period. The data were obtained from 2 JHS database systems for the periods October 1, 2005, to September 30, 2006
(Y1), and October 1, 2006, to September 30,
2007 (Y2). Of the 12 PCCs in the JHS at the
start of the Jackson Access Plus program, 6
were included in the analyses whereas 6 others were excluded because of small or transient populations. The analysis is based on the
following centers and the respective deployment dates:
Juanita Mann (JM) October 1, 2005
Liberty City (LC) October 1, 2005
North Dade (ND) April 1, 2006
Penalver (PV) October 1, 2006
Jeff Reaves (JR) November 1, 2006
Rosie Lee (RL) February 1, 2007
The rollout time frame of the deployment
for the change program allows us to make
comparisons across the 6 PCCs in terms of
change outcome. Change outcome was measured as the cost PMPM unit of service at
the clinic level. As such, this required us
to first examine the membership growth for
each center during the study period and then
the change in ambulatory costs. The Jackson
Access Plus membership was defined as all
eligible individuals who received health services at specific centers over the previous 12
months. Given that all uninsured eligible patients were automatically enrolled in Jackson
Access Plus, the increase in membership is
not attributable to marketing but reflects more
uninsured patients seeking care from the
safety-net.
Figure 1 reflects the growth in membership
in each of the 6 centers during the study period. Each center’s membership figure at the
time of introducing the change program is
normalized to 100% and the number of members at any other month is represented as a
percentage of that benchmark figure.* Note
that the uncharacteristic dips in June to July
2007 are attributed to the introduction of a
new electronic medical record system that
caused inconsistencies in data collection during that period.
RESULTS
Membership growth
JM and LC centers deployed the change
program in October 2005. Although neither
center experienced major changes in membership, this was not the case for the other 4
centers. For example, the next center deployment was ND, which experienced a significant increase in the number of members. Expressed in terms of its membership figures as
of the deployment date, the number of members was 86% at the beginning of Y1 and increased to 130% at the end of Y2.
Following ND, the PV and the JR centers
initiated the change program, with both experiencing a rapid growth in membership after the deployment. The simultaneous deployment may have been a factor in explaining the
similarities in the trends we observe for these
2 PCCs. At the beginning of Y1, both started
around 75% of their deployment date membership. At the end of Y2, PV had reached
132% whereas JR reached 124%. PV experienced the most dramatic rise in membership
as compared with the other 5 centers. The last
center to introduce the change program was
RL in February 2007. Membership at RL grew
from 70% at the beginning of Y1 to almost
120% by the end of Y2.†
*Therefore, for any center, any percentages above (or
below) 100 represent increases (or declines) compared
to the membership at the deployment date.
†Given that we only have about half a year of data available for RL after its deployment date and considering the
potential inconsistencies in the data for July-August 2007,
the results for this specific center should be interpreted
with caution.
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135
Figure 1. Membership (as a percentage of the values at the deployment date for each individual center).
JR indicates Jeff Reaves; JM, Juanita Mann; LC, Liberty City; ND, North Dade; PV, Penalver; RL, Rosie Lee.
Ambulatory care costs
After reviewing the centers’ membership
growth, we examined the ambulatory care
cost figures. Because of the growing trends
in membership and utilization, we corrected
these figures for the number of members and
express them in PMPM units for each of the 6
centers. In addition, cost figures are corrected
for the rising costs of delivering service. The
corresponding health care inflation rate during this period, measured using the consumer
price index for medical care services, was
9.6%. The cost figures were deflated to correct for this fast increase in prices of medical
care services and the results reflect real cost
figures.
Figure 2 presents the trends in real ambulatory costs PMPM at each center. As in Figure 1,
we normalize each center’s deployment date
real costs to 100% and the monthly real costs
are represented as a percentage of that benchmark figure.* Although the cost figures in June
and July of 2007 might be atypical, the overall
trends over the 2-year period provide a consistent picture.
For the first 2 centers deployed (LC and
JM), significant increases in the real ambulatory costs PMPM were observed in the initial
months of implementation. However, by the
end of Y2, the centers were successful in lowering their ambulatory costs PMPM to 80% or
less compared with their starting figures. The
temporary increases in costs during the initial
months of implementation may be attributed
to a learning curve because LC and JM were
the first 2 centers deployed, and this pattern
*Thus, percentages above (or below) 100 represent increases (or declines) compared to the real cost of ambulatory care PMPM at the deployment date.
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Figure 2. Real ambulatory costs per member per month (as a percentage of the values at the deployment
date for each individual center). JR indicates Jeff Reaves; JM, Juanita Mann; LC, Liberty City; ND, North
Dade; PV, Penalver; RL, Rosie Lee.
in the cost figures is not observed for any of
the other 4 centers. As the new processes and
data feedback became more familiar, the capability to interpret, design, and implement
PCC-specific interventions became more effective.
ND was deployed in April 2006, and the
center experienced a significant decline in the
real ambulatory costs PMPM starting 2 months
after the deployment date. This finding may
suggest that the lessons learned at the first 2
centers had been put into use by the center
staff. Within 1 year, the cost figures declined
to about 60% of the deployment cost figures
and continued to decrease further until the
end of Y2 to about 50%. The next 2 centers
deployed about the same time are JR and PV,
which display similar patterns to ND in terms
of the steady decline in cost figures soon after
the deployment. By the end of Y2, within less
than 12 months since deployment, both JR
and PV managed to reduce their cost figures
down to about 60%. Finally, RL also displays
immediate reductions in the real ambulatory
costs PMPM while the graph oscillates around
the 100% figure throughout the period before
the deployment date of April 2007. Within a
few months, ambulatory costs at this center
were brought down to about 60%.
It is important to note that in the case of
all 4 centers for which we have data points
before the deployment dates, that is, ND, PV,
JR, and RL, the real ambulatory costs PMPM
are relatively stable up until the start of the
intervention. This stability despite the sharp
increases in the membership suggests that the
change strategies were being successfully implemented throughout the entire period, well
before the deployment dates. However, significant decreases in real ambulatory costs
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PMPM took place right after deployment dates
at each center, pointing to the impact or effectiveness of the additional interventions introduced during the period.
This case study has several limitations.
First, we relied on data that were collected for administrative purposes rather than
for research. Second, the multimethod approach prevents the measurement of the
impact of specific interventions. Third, our
analysis does not address other medical
services delivered within the PCCs. The separate and unlinked structure of the financial and clinical data systems prevented exploration of possible changes in the quality
of care or other costs associated with this
population.
DISCUSSION
The number of uninsured people in the
United States, Florida, and Miami-Dade continues to grow as funding continues to decrease. The funding gap requires constant and
creative efforts by the JHS to provide medical care. The Jackson Access Plus program is
an innovative strategy for providing primary
managed care to this population that was accompanied by an organizational strategy to encourage change in physician prescribing behavior.
This article points to a few conclusions regarding the effectiveness of change strategies.
To effectively influence physician behavior,
health care managers need to create a practice
environment that reduces barriers and facilitates change. As pointed out by Smith (2000),
development and implementation of change
strategies need to be theory driven and
137
evidence based. The JHS experience indicates
that the cost-effectiveness of health care delivery to a large urban uninsured population can
be improved by designing and implementing
change in concert with organizational theories of participative and change management.
Central to this achievement is the assignment of all uninsured patients to a designated
PCC and a physician that provide the patient
with a medical home and the use of evidence
(data)-based management of medical services
through regular and timely feedback of utilization data to the PCCs and physicians. As noted
earlier, the division leaders found collaboration across the system in various improvement
initiatives. Thus, continuing and broad initiatives that supported the goals of the Jackson
Access Plus change program became institutionalized across the community-based clinics. Our findings suggest that safety-net systems can effectively address cost-efficiency of
primary care services for uninsured patients
through a multimethod change strategy.
The analysis presented here used administrative data that were collected for purposes
other than research. As a result, we were unable to provide a complete assessment of the
impact of the interventions across the full continuum of health care utilization, quality of
care, and health outcomes, as well as overall cost impacts for this population under the
Jackson Access Program. Despite these limitations, the findings validate physician leadership and involvement as critical elements
in changing behavior and practice patterns.
As physicians and clinics improve data systems, the ability to link financial, clinical, and
quality data will allow more comprehensive
evaluation.
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