Article in The Journal of Medical Practice Management

PRACTICE MANAGEMENT

A Case Study of Quality Improvement Methods for Complex Adaptive Systems Applied to an Academic Hepatology Program John Fontanesi, PhD,* Anthony Martinez, MD,† Toritsesan O. Boyo, MPH,‡ and Robert Gish, MD§ Although demands for greater access to hepatology services that are less costly and achieve better outcomes have led to numerous quality improvement initiatives, traditional quality management methods may be inappropriate for hepatology. We empirically tested a model for conducting quality improvement in an academic hepatology program using methods developed to analyze and improve complex adaptive systems. We achieved a 25% increase in volume using 15% more clinical sessions with no change in staff or faculty FTEs, generating a positive margin of 50%. Wait times for next available appointments were reduced from five months to two weeks; unscheduled appointment slots dropped from 7% to less than 1%; “no-show” rates dropped to less than 10%; Press-Ganey scores increased to the 100th percentile. We conclude that framing hepatology as a complex adaptive system may improve our understanding of the complex, interdependent actions required to improve quality of care, patient satisfaction, and cost-effectiveness. KEY WORDS: Hepatology; operations; time-motion; adaptive; clinic.

H

ealthcare delivery in America is fragmented,1,2 resulting in substantial waste3,4 and unacceptable patient outcomes.5 Despite numerous national and regional initiatives and the allocation of substantial resources, much of the extant data and research reflect substantial shortcomings in the provision of effective and reliable care.6,7 The introduction of quality improvement methods has done little to mitigate emergency department overcrowding and ambulance diversion,8,9 improve surgical suite underutilization and decrease excessive staff overtime,10 or ensure that electronic medical records improve care,11 reduce errors,12-14 increase patientprovider communication,15,16 or improve patient access to care. Spending more than twice as much on healthcare as other industrialized countries has been ineffective at reducing disparities or mortality related to medical error.17 A number of authors have suggested that a reliance on traditional industrial-quality improvement techniques may

*Director, Center for Management Science in Health, and Department of Medicine, Division of General Internal Medicine, and Departments of Family and Preventive Medicine and Pediatrics, University of California, San Diego School of Medicine, 9500 Gilman Drive, #8415, La Jolla, CA 92093-0821; phone: 619-543-3886; e-mail: [email protected]; †Division of Gastroenterology, Hepatology and Nutrition, University at Buffalo, Buffalo General Medical Center, Erie County Medical Center, Buffalo, New York. ‡Director of Ambulatory Services, San Mateo Medical Center, San Mateo, California, and Division of Gastroenterology and Hepatology, Stanford University, Stanford, California; §Division of G ­ astroenterology and Hepatology, Stanford University, Stanford, California, and Robert G. Gish Consultants LLC, San Diego, California. Copyright © 2015 by Greenbranch Publishing LLC.

not be appropriate in light of the complexity of healthcare delivery systems.18-20 The basic argument is that methods such as benchmarking, failure mode analysis, Lean, and Six Sigma are inherently reductionistic as they decompose work into subtasks and subroutines with the goal of identifying and then reducing variation and waste. There is an explicit assumption that the processes under review are deterministic and linear, with minimally complex multivariate conditions for which there will be “one best way” independent of context. Information used to identify opportunities for improvement tends to use averages of aggregated data, collected over extended time horizons that are difficult to contextualize at the point of service. Medical providers typically work in a world of probabilities composed of dynamic treatment options, system resources, and patient circumstances that require judgment about the various trade-offs in possible combinations of treatment and lifestyle recommendations unique for a

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324   Medical Practice Management |  March/April 2015 given patient who can then elect to accept or reject any or all of those recommendations. This is the antithesis of a deterministic, well-behaved linear system. Providing care requires the coordination of multiple entities, operationally managed across administratively distinct groups, each of which functions semi-autonomously with its own set of regulations, culture, and logistical requirements united primarily by the desire to provide quality care to an individual patient. Indeed, a healthcare delivery system possesses all the attributes of a complex adaptive system, as defined by Nobel Laureate Murray Gell-Mann of the Santa Fe Institute21 and further refined for healthcare by Rouse19: that of a dynamic, evolving, nonlinear set of interactions without centralized control. Numerous researchers in industries such as computer sciences, 22, 23 economics, 24 social sciences, 25 and aerospace26 have explored the requirements for effective quality management of complex adaptive systems. These generally include the following: 77 Noncentralized control requires the locus of leadership to switch from role-based actions of an individual to team-level relationships in which teams: —— Organize around a few simple principles focused on customer goals. Patient-centered care is an example of organizing around “customer needs.” —— Are encouraged to experiment and innovate in achieving “customer” goals. This includes the opportunity to “fail” as long as the “failing” is recognized quickly. —— Receive timely information on individual customer needs rather than aggregated data. An example is the difference between receiving a report on the percentage of hypercholesterolemic patients prescribed statins or a report showing that the current patient is a 90-year-old hypercholesterolemic patient with Alzheimer disease and prostate cancer. 77 Complex adaptive systems are sensitive to their starting conditions. Small disturbances can have a surprisingly profound impact on overall system behavior as the initial disturbance amplifies and propagates “downstream.” A common example in healthcare occurs when a patient (or key staff member) is late for the first appointment of the day. Therefore, it is critical to find methods to “control” starting conditions. 77 Complex adaptive systems are context sensitive. For example, a recent meta-analysis of adoption of “evidenced-based medicine” found that small hospitals did best when focused on specific practices, whereas larger hospital systems did better when efforts were directed to enhancing the process of culture change. 77 The meaning of data changes at different levels of organization. Using the example cited earlier, the percent of hypercholesterolemic patients prescribed statins has different values and meanings at the patient–provider interface, provider–health system interface, health

system–regulatory/payer interface, and regulator/ payer–societal interface.

METHODS To explore how these concepts might be operationalized, we empirically tested a model for conducting quality improvement in a complex adaptive system using an academic hepatology program. Hepatology was chosen in part because of the willingness of the leadership and in part because of the rapidly changing “best practices” and multiple specialties involved in treating patients with liver disease. Chronic liver disease (CLD) is on the increase, currently affecting an estimated 15% of the U.S. population and expected to continue to rise.27 It is the eighth leading cause of death in the United States,28 with an estimated direct cost of $9.1 billion dollars annually.29 New treatment breakthroughs that can reverse or at least flatten the trajectory of CLD progression can occur only if a potential patient can receive timely and appropriate services. The University of California, San Diego Hepatology Program, as part of the Division of Gastroenterology, is a nationally recognized program that trains new physicians, conducts a broad array of research and clinical trials, and serves as the largest regional provider of care for patients suffering acute and chronic liver diseases. The mission statement of the hepatology program is “to be the complex liver disease center for the Imperial, Kern, Orange, Riverside, San Bernardino and San Diego counties and Southwest Arizona Region.” At the time we began our assessment, the average wait for a first appointment was five months, but 7% of all appointment slots went unfilled. The “no show” rates exceeded 10%, and patient satisfaction was low. Staff members, organized along a number of different administrative structures, were highly professional but focused on their individual organizational imperatives rather than on the purpose of the program. The resulting “silos” hindered continuity of action and information flow, resulting in burdensome and redundant documentation requirements where patients were asked the same questions multiple times, leaving the impression of fragmented and uncoordinated care. Quarterly “quality” reports often provided information about processes over which no individual type of staff member had control (e.g., patient’s satisfaction with facility cleanliness) and served to increase isolation between types of staff with the secondary consequence of suppressing teamwork and innovation. The scheduling schema was static in the sense that the same number and types of appointments were available on the same days of each month regardless of varying demand. Furthermore, the schema used traditional proportional appointment allocation methodology (fixed appointment durations based on clinical characteristics such as a new patient or a patient post-transplant) and assumed that tardiness followed a normal Gaussian distribution.

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Fontanesi, Martinez, Boyo, and Gish | Quality Improvement Methods    325

Table 1. Effects of Queueing on Key Clinic Functions Activity Registration* Receptionist verifies contact and insurance information* Time in waiting room* Medications checked by staff* Direct patient:provider time Percent of provider recommendations recalled at end of visit* Average time in clinic* Patient receives after-visit summary*

Early in Clinic Session

Later in Clinic Session

5 minutes

2 minutes

93%

70%

2 minutes

19 minutes

48%

22%

37 minutes

22 minutes

75%

25%

62 minutes

58 minutes

100%

32%

* p = .01

Table 2. Analysis of Referral Patterns p Value* Normal Distribution Shapiro-Wilk

.006

Kolmogorov-Smirnov test

.0097

Anderson-Darling

.005

Shapiro-Wilk

.023

Kolmogorov-Smirnov test

.049

Anderson-Darling

.045

Shapiro-Wilk

.512

Kolmogorov-Smirnov test

.831

Anderson-Darling

.678

Johnson Distribution

Weibull Distribution

*If p ≤ .05, then the distribution family does not fit the observed data.

The result of these assumptions produced queues that increased as the daily clinic progressed. Receptionists responded to the queues by skipping verification items such as a patient’s current contact and insurance information, which, in turn, affected schedulers’ ability to contact patients and increased the rate of insurance denial of benefits. Similarly, medical assistants were less likely to initiate medication reconciliation, resulting in physicians spending more time during the exam collecting basic information and less time explaining treatment options and patients, as objectively measured at the end of each visit, being less knowledgeable about their disease and treatment options compared to the times when queues did not exist. These complex interactions underscored the need for quality management appropriate to a complex adaptive system (Table 1).

INITIATION Accordingly, under the Medical Director’s direction, the first step was to review and clarify the mission statement. This

statement became the fundamental organizing principle to harmonize the various staff’s activities and realign roles and responsibilities in creating treatment teams. Activities related to direct patient care for activities for each team were directed by the physician leader, while purely administrative tasks such as human resource documentation requirements remained with the original supervisors for specific types of personnel. The hepatologists took existing generic treatment protocols and translated them to the specific activities required for individual patients. For example, initial consults for patients with decompensated cirrhosis were conducted by the hepatologist who, with the team, constructed a conditional treatment course for a specific time horizon that included diagnostic tests and procedures, and follow-up monitoring visits with the nurse practitioner. As part of organizing around patient needs, “team huddles” were initiated to prepare for upcoming patients and to better coordinate care. These fundamental changes were supported by changes in the scheduling schema. Shapiro-Wilk, KolmogorovSmirnov, and Anderson-Darling tests for normalcy found service demand nonconforming to normal Gaussian assumptions (Table 2). This meant scheduling schemas could not control service demand. However, Hurst exponents yielded an index of 0.37, a nonrandom pattern to service demand suggesting that a flexible scheduling schema could anticipate and adjust to demand efficiently. Congestion in clinic due to queue development was analyzed with the primary “driver” being that very few patients actually arrived “on time.” Most arrived early, but a substantial number arrived late, resulting in “waves” or pulses of patients all arriving at the registration desk at the same time. Almost 20% of patients depended on family, friends, or public transportation to get to their appointments and thus did not have full control over their arrival time. Although analysis indicated that length of time with the provider in the exam room was driven, in part, by the type, stage, and degree of disease control, it also showed that two patients with clinically similar disease states could require significantly different amounts of time based on

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326   Medical Practice Management |  March/April 2015 Table 3. Change in Key Metrics PreIntervention

PostIntervention

5 months

2 weeks

Unfilled appointment slots*

7%

≤1%

No-shows

17%

9%

Next appointment scheduled prior to leaving clinic*

50%

93%

Press-Ganey scores*

50%

100%

Activity Wait until next available appointment*

*p = .01

their psychosocial and cognitive characteristics. Most importantly, the hepatologists and their team could easily “predict” which patients would require longer visit times. This foreknowledge was used in a Bin-Packing scheduling paradigm such that “easy” patients were scheduled in the beginning of each clinic session with more “needy” patients scheduled for longer appointments later in the session.

RESULTS This initiative resulted in a 25% increase in volume, representing a total of 9500 visits. There were 2800 consults and 1400 procedures utilizing 15% more clinical sessions with no change in either staff or faculty FTEs and generating a positive margin of 50%. Additionally, wait times for next available appointments were reduced from five months to two weeks; unscheduled appointment slots dropped from 7% to less than 1%; “no-show” rates dropped to less than 10%; and Press-Ganey scores increased from the low 50th percentile to the 90th to 100th percentile. Additionally, a 40% reduction in patient wait and staff idle times was noted along with a four-fold increase in new referrals (Table 3).

CONCLUSION The healthcare system is buffeted by often competing demands for greater access to more personalized care that both is less costly and achieves better health outcomes. Hepatology services for patients with acute and chronic liver disease are no exception. The tendency has been to try to meet the demands by applying quality improvement management methods effective in other industries. However, patients with acute and chronic liver disease are cared for within a classically complex adaptive system for which traditional centralized “command and control” management techniques are inappropriate methodologies that risk “improvements” in one care domain at a cost to the total system of care. The improvements noted in this case study required considerable time, team effort, and, most importantly, cultural change. There is certainly risk associated with such change, but the results speak for themselves. It is

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