A Qualitative Study of Increasing β-Blocker Use After Myocardial Infarction:  Why Do Some Hospitals Succeed? FREE

Randomized clinical trials have demonstrated the efficacy of β-blocker use in reducing mortality and future cardiac events after acute myocardial infarction (AMI).¹ Based on substantial evidence, clinical practice guidelines for AMI published by the American College of Cardiology and the American Heart Association² strongly recommend the use of β-blockers for secondary prevention after AMI.

Despite the evidence and the published guidelines, studies have repeatedly demonstrated wide variation and underuse of β-blockers.^3- 13 The American Medical Association has reminded physicians of the importance of β-blocker use after AMI,¹⁴ and both the Health Care Financing Administration and the National Committee for Quality Assurance have adopted β-blocker use after AMI as a quality-of-care indicator. Physicians and hospitals thus have considerable motivation to increase the use of β-blockers after AMI. Yet factors associated with successful improvement efforts to increase their use over time in the hospital setting are poorly understood.

Identifying factors associated with successful performance improvement in the use of β-blockers after AMI can provide guidance to hospitals that have been less successful in increasing their use over time. Also, the lessons learned from improving the use of β-blockers may be relevant to other efforts to improve clinical care and patient outcomes. The first step toward understanding the strategies used by hospitals is to carefully and systematically collect information about their approaches, with particular attention to factors associated with success. Despite the substantial attention given to changing physicians' practices^15- 19 and improving quality in clinical care,^20- 25 efforts to classify the essential characteristics of initiatives for improvement in AMI care are limited, and few studies have systematically explored the factors that are common to the more successful improvement efforts directed at increasing β-blocker use after AMI.

To address this topic, we designed a qualitative study intended to provide an in-depth perspective on the ways that hospitals are improving care for patients with AMI. The objectives of this study were to develop a taxonomy that can be used to classify and evaluate hospital-based performance improvement efforts in the care of patients with AMI with particular focus on β-blocker use, and to explore how essential factors varied among higher- and lower-performing hospitals.

We undertook a qualitative study, based on open-ended interviews conducted with clinical and administrative staff during hospital site visits from March through June of 2000. The qualitative approach was chosen for 2 reasons. First, with the exception of a recent randomized controlled trial,²⁴ few studies have investigated factors specific to improvements either in the care of patients with AMI or in β-blocker use. Qualitative research is particularly well suited for exploratory studies for which previous literature is limited.²⁶ Such studies are useful for generating hypotheses that can later be tested with quantitative data.^26- 27 Second, we anticipated that some factors, such as administrative philosophy and physician leadership, may be multifaceted and difficult to measure. Again, qualitative research provides a method for describing the diverse facets and dimensions of such factors.^26,28

As is standard in qualitative research,^26- 27 we chose sites using purposeful sampling to ensure that we included a diverse set of hospitals. Study hospitals were selected to reflect a range of geographical regions, hospital size, AMI volume, and improvement or decline in β-blocker use over time. Hospitals were not selected based on knowledge of their approach to measuring and improving care. Additional hospitals were selected and visited until no new concepts were identified by the additional interviews, ie, until the point of theoretical saturation. This occurred after the eighth hospital site visit and 45 interviews. The research team was blinded to hospital rates of β-blocker use until the completion of the data collection and coding. The characteristics of the study hospitals are displayed in Table 1.

Eligible hospitals were those that participated in the National Registry of Myocardial Infarction (NRMI)¹² continuously for at least 30 months during the 36-month (October 1996-September 1999) observation period. In addition, eligible hospitals had to report at least 40 patients with AMI on an annual basis during the study or observation period. The cut point between the baseline and follow-up periods was chosen as the midpoint between the time of the first and last patient submission to the NRMI during the observation period. While NRMI does not include all hospitals in the country, it does include a broad spectrum of facilities and is the largest ongoing registry with detailed clinical and medication information. The chosen baseline and follow-up periods reflect time before and after the dissemination of new data demonstrating the effectiveness of β-blockers for patients with AMI.^11,14 The mean improvement in β-blocker use from baseline to follow-up was 6 percentage points.

To ensure that the study hospitals reflected a range of β-blocker use rates, we arrayed all eligible hospitals into 20 quantiles according to their change in β-blocker use (mean [SD] β-blocker use rate of all patients with AMI: 54.2% [14.1%] during baseline vs 60.0% [12.7%] during follow-up). We randomly selected hospitals from the lowest 2 quantiles (representing declines in β-blocker use rates ranging from −22 and −6 percentage points), the middle 2 quantiles (representing increases in use rates ranging from 5-7 percentage points), and the highest 2 quantiles (representing increases in use rates from 19-35 percentage points). In 2 cases, randomly selected hospitals did not meet other selection criteria (ie, they did not reflect a range of sizes or geographical regions) and thus were replaced with other randomly selected hospitals from the same quantiles.

In-depth, open-ended interviews^27,29 were conducted in person with physician, nursing, quality management, and administrative staff described by directors of quality assurance or quality management as key staff involved with improving the care of patients with AMI. Between 4 and 7 individuals considered to be key respondents were interviewed at each hospital, for a total number of 45 key respondents interviewed. These included 8 cardiologists, 4 internists, 2 emergency medicine physicians, 15 members of nursing staff, 11 members of quality management staff, and 5 members of senior administrative staff. Interviews were generally conducted with a single respondent, except when sites preferred to have multiple respondents participating together. Interviews were each 1 to 1½ hours in length, as is typical with in-depth interviewing.²⁹ Interviews were generally conducted by at least 2 members of the research team, which included investigators with diverse backgrounds in internal medicine and cardiology, epidemiology, health administration, and nursing. All researchers had substantial backgrounds and expertise in quality improvement. At least 2 interviewers were present at nearly all interviews; all interviews were audiotaped and transcribed by independent professional transcriptionists.

Interviews were conducted using a standard interview guide, with probes for clarification and additional detail. The interview guide began with the standard "grand tour"²⁷ question, "In the last 3 years, please describe the major initiatives your hospital has undertaken to improve care of patients with AMI." Specific probes concerning initiatives targeted at β-blocker use followed this question. For each initiative, respondents were asked to describe specific instances of difficulty and of success in implementing change in their hospital. In addition, respondents were asked about their experiences related to data monitoring and feedback, physician leadership, and sustaining change. For all areas of inquiry, respondents were encouraged to illustrate their experiences with specific stories or vignettes.

Transcribed data were analyzed using common coding techniques for qualitative data^28,30 and the constant comparative method of qualitative data analysis.²⁷ Coding of the data was accomplished in a series of iterative steps. An initial code list was used to organize transcripts of the first 2 site visits and was then refined during review and analysis of transcripts from subsequent site visits. During its development, the code structure was reviewed 3 times by the full research team for logic and breadth. The process of refining the code structure included adding and reconstructing codes as new insights emerged and identifying new relationships within units of a given category. A total of 72 specific codes organized within several broad themes were ultimately developed and served as the basis for final text review and organization of the transcript data.

Using this final version of the code structure, members of the research team (E.H.B., J.A.M., S.A.R.) independently coded all transcripts, then met as a group to code in several joint sessions, achieving consensus and assigning codes to observations by a negotiated, group process. Coded data were entered into a software package designed to handle unstructured qualitative data (NUD*IST 4, Sage Publications Software, Thousand Oaks, Calif) to assist in reporting recurrent themes, links among the themes, and supporting quotations. Specific analysis was conducted to identify distinctions in common themes between higher-performing hospitals (those in which ≥65% vs those in which <65% of all patients with AMI were prescribed β-blockers at discharge during the follow-up period).

Several techniques were used to ensure that data analysis was systematic and verifiable, as commonly recommended by experts in qualitative research.²⁸ These included consistent use of the discussion guide, audiotaping and independent professional preparation of the transcripts, standardized coding and analysis of the data, and the creation of an analysis audit trail to document analytic decisions. The interviewers, the individuals who listened to and transcribed the interview tapes, and those coding the transcript data were blinded to the rates of β-blocker use among the study hospitals.

After review of the interview data, respondents' comments were organized into 6 broad factors that formed the basis of the taxonomy for classifying efforts to improve AMI care in general and β-blocker use specifically. These broad factors were: (1) goals for improvement, (2) administrative support for improvement efforts, (3) support among clinicians for such efforts, (4) design and implementation of initiatives for performance improvement, (5) use of data concerning β-blocker use, and (6) a set of contextual variables. Each broad factor has a set of dimensions that further describe its meaning. Table 2 summarizes the resulting taxonomy including the 6 broad factors, their dimensions, and examples of concepts underlying the dimensions. The BOX lists direct quotations to illustrate selected factors and related dimensions (a longer list of illustrative quotations is available online as an Appendix).

Respondents identified 4 distinct dimensions of goals, including goal content, goal specificity, goal challenge, and the degree to which goals were shared by staff throughout the organization. Improvement of patient care was the most commonly mentioned goal underlying performance improvement efforts, although maintaining financial position and improving hospital or professional reputation were also described in several hospitals. A range of specificity of goals was also described. Some hospitals stated specific goals, such as "our target is 80% β-blocker use"; others, less specific, stated that "the goal was to get β-blocker use within an acceptable range." Goal challenge varied from what was described as a "zero-defect approach" (the most challenging) to more lenient goals of having some portion of physicians, rather than all physicians, use a clinical pathway. Finally, the degree to which the goals for improvement were shared throughout the organization varied substantially. For example, in some hospitals staff described widespread buy-in and agreement with organizational goals for improvement. In contrast, respondents in other hospitals described having goals for one's own clinical practice but no overall organizational goals concerning AMI that were widely known and agreed upon by clinical and administrative staff.

Support from the senior administration was viewed in several hospitals as the most important factor in the success of performance improvement efforts for β-blocker use. Administrative support encompassed not only the organizational philosophy toward performance improvement but also the procurement of needed resources to sustain improvement in care (eg, care coordinators, chart abstractors, computer and analytical support, and quality improvement training). In some hospitals, staff reported that senior administration was focused on and committed to performance improvement. In other hospitals, administration was reported to be uninvolved in or less supportive of improvement initiatives, and resources devoted to quality improvement were inadequate.

Nearly all respondents emphasized that effective support from clinicians was essential to the success or failure of initiatives to improve the use of β-blockers or other treatments for AMI. Dimensions of this factor included the types of clinician (ie, physician, nurse, ancillary support staff), the degree of clinician engagement in the performance improvement effort, and clinicians' ability to lead change. Several respondents also described the importance of support from nursing and other clinical staff, although physician leadership was perceived as a dominant success factor for enhancing β-blocker use.

A single physician who was leading AMI improvement efforts, often termed a physician "champion" by respondents, was identified in several of the hospitals. Characteristics that enhanced physicians' effectiveness included being highly respected as busy practitioners and expert clinicians, being committed to promoting β-blocker use themselves, and having consensus-building skills to resolve conflicting views of clinicians. Expertise and previous training in quality improvement techniques generally were not viewed as essential for physician effectiveness in this capacity.

The dimensions of performance improvement initiatives identified included the type of initiative and the style of implementation. All hospitals described a variety of initiatives to improve β-blocker use at discharge. These included ongoing education (by internal or external experts or both) of physicians about the benefits of β-blockers, development of pathways and/or protocols that included β-blocker use, implementation of standing discharge orders with β-blockers, hiring care coordinators to remind physicians to consider β-blockers for specific patients or to document why β-blockers were contraindicated, chart-based reminders to prompt β-blocker use, and providing data feedback to physicians about their β-blocker use. Nearly all hospitals reported that changes were slow to be implemented and that continued attention to β-blocker use through education, reminders, and data feedback to physicians was necessary to sustain high performance.

The implementation style of performance improvement efforts encompassed several aspects of managing change. Primary aspects of implementation included the degree to which participatory teams were used, whether the initiative was aimed at improvement or fault finding, and methods used to promote or ensure adherence to new standards.

A central factor described by respondents at all hospitals was the use of data in improvement efforts. Data were reported to be critical in 2 ways. First, availability and acceptance of the evidence based on credible research regarding the benefits of β-blocker use were essential in the early stages to attain physicians' commitment to change practice, in this case to increase their use of β-blockers for patients with AMI. Respondents reported that without credible empirical evidence to support the recommended practice, physician behavior was unlikely to change. Hence, transmitting such evidence was an important early step in effective improvement efforts.

Second, data feedback on physicians' use rates of β-blockers was also reported to be essential for effective improvement efforts. However, such data feedback efforts were described as successful only if the data were perceived by physicians to be valid, reflective of current practice, and benchmarked against a reasonable comparison group. In some hospitals, this comparison group was performance at similar hospitals; in others the comparison was to national standards; in still others, physicians within a hospital were compared with each other. In all cases, the credibility of the data was cited as a critical ingredient in making data feedback effective.

Respondents described a set of variables that characterized the context within which quality improvement efforts occurred. These included hospital size, health system affiliation, ownership type, financial and market constraints, and organizational turbulence. Staff in smaller hospitals described interdepartmental communication as easier due to smaller size. Those affiliated with health systems described the ability to benchmark performance data against other hospitals in the system as an advantage. Several respondents in markets they perceived to be highly competitive noted that the competition had encouraged performance improvement efforts. In contrast, some staff in nonprofit settings described their nonprofit status as limiting the pressure to improve performance. Finally, organizational turbulence (eg, turnover of senior administrative or clinical staff, large financial losses, recent unionization) was described as slowing quality improvement efforts due to necessary focus on issues that might jeopardize the organization's stability or survival.

Several of the factors and their related dimensions from the taxonomy differed between the higher-performing hospitals (those in which ≥65% of patients with AMI received β-blockers at discharge) and the lower-performing hospitals (those in which <65% of patients with AMI received β-blockers at discharge), although statistical associations could not be tested based on the study design. Nevertheless, findings from these qualitative data may help frame hypotheses to be formally tested in future quantitative studies.

Four characteristics were apparent in the higher-performing hospitals and were absent in the lower-performing hospitals: (1) high degree of goal sharedness, (2) substantial level of administrative support, (3) strong physician leadership, and (4) high-quality data feedback.

First, higher-performing hospitals described performance goals as shared and agreed upon by staff throughout the organization. Higher-performing hospitals did not all have explicitly challenging goals, but all had substantial goal agreement. In the case of β-blocker use, higher-performing hospitals described substantial buy-in among clinical staff that use should increase. In contrast, ambivalence toward use of β-blockers or ambiguity in terms of whether that goal was a priority was apparent in lower-performing hospitals. In the lowest-performing hospital, physician leaders described explicit disagreement that increased β-blocker use improved AMI outcomes.

Second, higher-performing hospitals reported extensive administrative support. This support was manifest in the chief executive officers and governing boards requesting performance data, senior administrators participating in quality improvement teams, and provision of staff and technical resources to implement improvement initiatives. Respondents in lower-performing hospitals did not describe such administrative support.

A third distinction was in the area of physician leadership. Higher-performing hospitals described the presence of a physician leader who was committed to increasing β-blocker use among his or her peers. In lower-performing hospitals, physician participants in improvement efforts were described as weak or nonexistent.

The final distinction was in the ongoing monitoring and feedback of valid data concerning current practice. Higher-performing hospitals reported their data as credible to physicians; lower-performing hospitals either did not routinely report current data on β-blocker use to physician staff or reported it in ways that were perceived as not helpful, eg, reporting data on patients from a year previous or reporting data that did not adequately account for contraindications.

Equally important as the ways in which the higher- and lower-performing hospitals differed are the ways in which they appeared similar. Somewhat unexpectedly, the ways in which the higher- and lower-performing hospitals were similar included the type of β-blocker initiatives and the style of the implementation of such initiatives.

First, both higher- and lower-performing hospitals described a myriad of quality-improvement efforts in the care of patients with AMI. Contrary to our expectations, higher-performing hospitals did not describe innovative quality initiatives that were not found in the lower-performing hospitals. The technical and programmatic innovations did not differ substantially among the higher- and lower-performing hospitals.

Second, nearly all hospitals described an implementation process that included the use of multidisciplinary teams and participatory management techniques. Furthermore, both higher- and lower-performing hospitals described change as taking place slowly. Both higher- and lower-performers reported difficulty in sustaining improvements already made, regardless of the initial enthusiasm to improve. A cycle of change, characterized by substantial inertia in the beginning, a learning curve, accelerated improvement, a plateau, and then either decline or maintenance, was commonplace. Although the shape and timing of this trajectory varied across hospitals, it did not distinguish higher- vs lower-performing hospitals. Even the higher-performing hospitals experienced inertia and plateaus over time.

These findings suggest a taxonomy of factors and their dimensions that may influence the success of efforts to increase β-blocker use. Previous studies have measured improvement efforts by the level of involvement of physicians and senior administrative staff,^22,25,31 the training of staff in quality improvement techniques,^23,32 and support for total quality management in general in the organization.^21,33 Our taxonomy suggests that these measures may have omitted key factors that characterize improvement efforts. Previous evidence that has failed to demonstrate a significant association between quality improvement and performance may be attributed to imprecise characterizations of the improvement efforts.

In addition to proposing the taxonomy for classifying improvement efforts, we propose 4 factors that may distinguish the higher-performing hospitals in β-blocker use: the presence of shared goals for improvement, administrative support, physician leadership, and credible data feedback. The first factor and its related dimensions are consistent with previous research in clinical medicine³⁴ and organizational theory³⁵ that identifies these as important elements in performance improvement. However, the taxonomy expands and extends previous research on total quality management and continuous quality improvement in important ways.

First, the findings highlight physician leadership, rather than merely physician participation. Although this is consistent with the 1 randomized controlled trial assessing the role of physician opinion leaders in the care of patients with AMI,²⁴ most have focused on physician participation^21- 22,25 rather than physician leadership and may therefore have neglected the more central aspect of physician involvement.

Second, the types of initiatives undertaken to increase β-blocker use were notably similar across the study hospitals, and the type of initiatives did not appear to distinguish higher- vs lower-performing hospitals. This was unexpected given the considerable literature focusing on systems and process changes for improving performance in the clinical setting.^20- 23,34

This finding has several alternative interpretations. Hospital staff may have reported efforts that were not actually occurring at the hospitals, due to pressures to appear progressive in this area. Nevertheless, the use of in-depth, qualitative interviewing of multiple respondents in each setting limits respondents' tendencies to overstate the truth. Another interpretation is that the hospitals in this study were limited in the scope of improvement efforts they conducted, and thus the efforts reported did not distinguish the more successful hospitals from the less successful ones. Interviewing more hospitals might reveal innovative systems changes not found in this study; however, our study did include hospitals that had high β-blocker use rates for patients with AMI and thus might be expected to have among the more sophisticated approaches to achieving those rates. Finally, the finding may suggest that the presence of quality-improvement initiatives and skilled approaches to implementation is not sufficient to guarantee increased use of β-blockers. Additional factors identified in our taxonomy, such as shared goals, administrative support, physician leadership, and effective data feedback, may be necessary to effect increased β-blocker use. This observation raises the question of whether the roles of factors in the taxonomy vary depending on the targeted area of clinical practice. In areas marked by physician judgment and clinical uncertainty, physician leadership may be especially important. In other areas marked by complex workflow processes involving multiple departments (eg, time to primary angioplasty), system redesign initiatives may be more important.

The study has several important limitations. First, the study was conducted among a limited set of hospitals, purposefully selected to reflect a range of performance levels, geographical regions, and organizational sizes, and the findings may differ in other settings. Second, self-reporting of performance improvement efforts may have been influenced by respondents' knowledge of their β-blocker use rates. When asked about their rates, however, respondents commonly were unable to report change in β-blocker rates at their hospital.

Third, a careful look at Table 1 reveals that the 2 hospitals with the highest baseline use of β-blockers experienced the greatest decline in use, and regression to the mean may have played a role in this change. Our interviews revealed an absence of the factors identified as important in higher-performing hospitals, but no other distinguishing features. The next step in this research is to identify the factors associated with such improvement or decline in hospital performance using a larger, randomly selected sample of hospitals.

Finally, this qualitative study was designed to generate, not to test, hypotheses. Future research based on larger samples of hospitals treating patients with AMI should be conducted to test these hypotheses. Such research will require adequate measurement of complex factors such as administrative support and physician leadership.

The application of rigorous qualitative methods has provided an in-depth view of a complex area of clinical care that has not been adequately explored with traditional methods. The interventions to improve care and the context in which they occur are complex and not easily understood by focusing on a single facet of the process. Our taxonomy can promote more comprehensive and sensitive measurement of quality-improvement efforts, so that their role in clinical practice can be adequately assessed. Moreover, the identification of essential elements of improvement efforts can help clinicians and researchers plan effective interventions, focusing on areas that may have been previously overlooked. This study is an important, though early, step in developing an evidence-based approach to improving the translation of research into practice.