Ingrid M. Nembhard

Qualitative and Mixed Methods Provide Unique Contributions to Outcomes Research

Outcomes research examines the effects of medical care interventions and policies on the health outcomes of individuals and society.1 Investigators conducting outcomes research seek to inform the development of clinical practice guidelines, to evaluate the quality of medical care, and to foster effective interventions to improve the quality of care.2 Outcomes research has traditionally used quantitative sciences to examine the utilization, cost, and clinical effectiveness of medical care through randomized and nonrandomized experimental designs. Quantitative methods are not as well suited to measure other complex aspects of the healthcare delivery system, such as organizational change, clinical leadership in implementing evidence-based guidelines, and patient perceptions of quality of care, which are also critical issues in outcomes research.3–7 These more nuanced aspects of healthcare delivery may be most appropriately examined with qualitative research methods.8–10nnQualitative approaches are becoming more common in clinical medicine and health services research.5,11–15 Federal encouragement of qualitative research is regularly reflected in funding program announcements issued by the National Institutes of Health.16 For more than a decade, federal agencies and foundations such as the National Science Foundation have demonstrated a commitment to supporting qualitative research through funding scientific conferences, workshops, and monographs on this field of inquiry.17–20 Despite this steady growth in qualitative research, outcomes investigators in cardiology have relatively little guidance on when and how best to implement these methods in their investigations.nnThe purpose of the present report is to introduce qualitative methods as providing unique and critical contributions to outcomes research. This report will describe the situations in which qualitative approaches are most helpful; summarize the primary principles and practices in study design, sampling, data collection, and data analysis for qualitative studies; present representative examples of cardiovascular outcomes research that uses qualitative methods; and synthesize current standards for …

Research in action: using positive deviance to improve quality of health care

BackgroundDespite decades of efforts to improve quality of health care, poor performance persists in many aspects of care. Less than 1% of the enormous national investment in medical research is focused on improving health care delivery. Furthermore, when effective innovations in clinical care are discovered, uptake of these innovations is often delayed and incomplete. In this paper, we build on the established principle of positive deviance to propose an approach to identifying practices that improve health care quality.MethodsWe synthesize existing literature on positive deviance, describe major alternative approaches, propose benefits and limitations of a positive deviance approach for research directed toward improving quality of health care, and describe an application of this approach in improving hospital care for patients with acute myocardial infarction.ResultsThe positive deviance approach, as adapted for use in health care, presumes that the knowledge about what works is available in existing organizations that demonstrate consistently exceptional performance. Steps in this approach: identify positive deviants, i.e., organizations that consistently demonstrate exceptionally high performance in the area of interest (e.g., proper medication use, timeliness of care); study the organizations in-depth using qualitative methods to generate hypotheses about practices that allow organizations to achieve top performance; test hypotheses statistically in larger, representative samples of organizations; and work in partnership with key stakeholders, including potential adopters, to disseminate the evidence about newly characterized best practices. The approach is particularly appropriate in situations where organizations can be ranked reliably based on valid performance measures, where there is substantial natural variation in performance within an industry, when openness about practices to achieve exceptional performance exists, and where there is an engaged constituency to promote uptake of discovered practices.ConclusionThe identification and examination of health care organizations that demonstrate positive deviance provides an opportunity to characterize and disseminate strategies for improving quality.

Assessing the evidence of Six Sigma and Lean in the health care industry.

Background Popular quality improvement tools such as Six Sigma and Lean Systems (SS/L) claim to provide health care managers the opportunity to improve health care quality on the basis of sound methodology and data. However, it is unclear whether these 2 quality improvement tools actually improve health care quality. Methods The authors conducted a comprehensive literature review to assess the empirical evidence relating SS/L to improved clinical outcomes, processes of care, and financial performance of health care organizations. Results The authors identified 177 articles on SS/L published in the last 10 years. However, only 34 of them reported any outcomes of the SS/L projects studied, and less than one-third of these articles included statistical analyses to test for significant changes in outcomes. Conclusions This review demonstrates that there are significant gaps in the SS/L health care quality improvement literature and very weak evidence that SS/L improve health care quality.

National Efforts to Improve Door-to-Balloon Time. Results From the Door-to-Balloon Alliance

OBJECTIVESnThe purpose of this study was to determine if enrollment in the Door-to-Balloon (D2B) Alliance, a national quality campaign sponsored by the American College of Cardiology and 38 partner organizations, was associated with increased likelihood of patients who received primary percutaneous coronary intervention for ST-segment elevation myocardial infarction (STEMI) being treated within 90 min of hospital presentation.nnnBACKGROUNDnThe D2B Alliance, launched in November 2006, sought to achieve the goal of having 75% of patients with STEMI treated within 90 min of hospital presentation.nnnMETHODSnWe conducted a longitudinal study of D2B times in 831 hospitals participating in the National Cardiovascular Data Registry (NCDR) CathPCI Registry, April 1, 2005, to March 31, 2008.nnnRESULTSnBy March 2008, >75% of patients had D2B times of < or = 90 min, compared with only about one-half of patients with D2B times within 90 min in April 2005. Trends since the launch of the D2B Alliance showed that patients treated in hospitals enrolled in the D2B Alliance for at least 3 months were significantly more likely than patients treated in nonenrolled hospitals to have D2B times within 90 min, although the magnitude of the difference was modest (odds ratio: 1.16; 95% confidence interval: 1.07 to 1.27).nnnCONCLUSIONSnThe D2B Alliance reached its goal of 75% of patients with STEMI having D2B times within 90 min by 2008.

Why Does the Quality of Health Care Continue to Lag? Insights from Management Research.

Executive Overview The United States health care system is plagued with quality problems. We draw on management research to explain why it has been so difficult to improve quality of care, despite a burgeoning body of scientific evidence on practices that result in better outcomes and the widespread adoption of quality-improving innovations based on those practices. Our analysis points to the prevalence of innovation implementation failure—organizational members’ inconsistent or improper use of innovations—as a primary cause. In our review, we identify the organizational sources of this failure and offer six strategies for avoiding innovation implementation failure in health care.

Measuring Teamwork in Health Care Settings: A Review of Survey Instruments

Background:Teamwork in health care settings is widely recognized as an important factor in providing high-quality patient care. However, the behaviors that comprise effective teamwork, the organizational factors that support teamwork, and the relationship between teamwork and patient outcomes remain empirical questions in need of rigorous study. Objective:To identify and review survey instruments used to assess dimensions of teamwork so as to facilitate high-quality research on this topic. Research Design:We conducted a systematic review of articles published before September 2012 to identify survey instruments used to measure teamwork and to assess their conceptual content, psychometric validity, and relationships to outcomes of interest. We searched the ISI Web of Knowledge database, and identified relevant articles using the search terms team, teamwork, or collaboration in combination with survey, scale, measure, or questionnaire. Results:We found 39 surveys that measured teamwork. Surveys assessed different dimensions of teamwork. The most commonly assessed dimensions were communication, coordination, and respect. Of the 39 surveys, 10 met all of the criteria for psychometric validity, and 14 showed significant relationships to nonself-report outcomes. Conclusions:Evidence of psychometric validity is lacking for many teamwork survey instruments. However, several psychometrically valid instruments are available. Researchers aiming to advance research on teamwork in health care should consider using or adapting one of these instruments before creating a new one. Because instruments vary considerably in the behavioral processes and emergent states of teamwork that they capture, researchers must carefully evaluate the conceptual consistency between instrument, research question, and context.

Learning and Improving in Quality Improvement Collaboratives: Which Collaborative Features Do Participants Value Most?

OBJECTIVEnTo understand participants views on the relative helpfulness of various features of collaboratives, why each feature was helpful and which features the most successful participants viewed as most central to their success.nnnDATA SOURCESnPrimary data collected from 53 teams in four 2004-2005 Institute for Healthcare Improvement (IHI) Breakthrough Series collaboratives; secondary data from IHI and demographic sources.nnnSTUDY DESIGNnCross-sectional analyses were conducted to assess participants views of 12 features, and the relationship between their views and performance improvement.nnnDATA COLLECTION METHODSnParticipants views on features were obtained via self-administered surveys and semi-structured telephone interviews. Performance improvement data were obtained from IHI and demographic data from secondary sources.nnnPRINCIPAL FINDINGSnParticipants viewed six features as most helpful for advancing their improvement efforts overall and knowledge acquisition in particular: collaborative faculty, solicitation of their staffs ideas, change package, Plan-Do-Study-Act cycles, Learning Session interactions, and collaborative extranet. These features also provided participants with motivation, social support, and project management skills. Features enabling interorganizational learning were rated higher by teams whose organizations improved significantly than by other teams.nnnCONCLUSIONSnFindings identify features of collaborative design and implementation that participants view as most helpful and highlight the importance of interorganizational features, at least for those organizations that most improve.

Deliberate Learning to Improve Performance in Dynamic Service Settings: Evidence from Hospital Intensive Care Units

Dynamic service settings---characterized by workers who interact with customers to deliver services in a rapidly changing, uncertain, and complex environment (e.g., hospitals)---play an important role in the economy. Organizational learning studies in these settings have largely investigated autonomous learning via cumulative experience as a strategy for performance improvement. Whether induced learning through the use of deliberate learning activities provides additional performance benefits has been neglected. We argue that the use of deliberate learning activities offers performance benefits beyond those of cumulative experience because these activities counter the learning challenges presented by rapid knowledge growth, uncertainty, and complexity in dynamic settings. We test whether there are additional performance benefits to using deliberate learning activities and whether the effectiveness of these activities depends on interdisciplinary collaboration in the workgroup. We test our hypotheses in a study of 23 hospital neonatal intensive care units (NICUs) involved in a quality improvement collaborative. We find that using deliberate learning activities is associated with better workgroup performance, as measured by NICUs risk-adjusted mortality rates for 2159 infant patients, but only after two years. In the shorter term, using these activities is associated with worse performance. By the third year, the positive impact of using deliberate learning activities is similar to the benefit of cumulative experience (18% and 20% reduction in odds of mortality, respectively). Contrary to prediction, interdisciplinary collaboration mediates, rather than moderates, the relationship between using deliberate learning activities and workgroup performance. Thus, our data suggest that using deliberate learning activities fosters interdisciplinary collaboration.

All teach, all learn, all improve?: the role of interorganizational learning in quality improvement collaboratives.

Background: Quality improvement collaboratives are an increasingly common strategy for implementing evidence-based practices in health care. However, research shows that many participating organizations do not achieve the level of performance improvement desired. Purpose: This study examined the use of interorganizational learning activities (inter-OLAs) as an explanation for mixed performance improvement among collaborative participants. We tested the hypotheses that inter-OLA use is positively associated with participants’ performance improvement and that this relationship is moderated by the use of intraorganizational learning activities (intra-OLAs) and quality-focused human resource (Q-HR) practices. Methodology: We conducted a survey of organizational teams participating in 4 Institute for Healthcare Improvement Breakthrough Series collaboratives. Survey responses from 52 teams, regarding the use of inter-OLAs, intra-OLAs and Q-HR practices, were linked to performance improvement data obtained from the Institute for Healthcare Improvement and demographic data obtained from secondary sources. Findings: The more collaborative teams used inter-OLAs, the more their organizations’ performance improved. Contrary to our hypothesis, the use of intra-OLAs did not moderate this relationship; teams’ use of intra-OLAs added to, but did not multiply, the effect of inter-OLA use. In contrast, an organization’s use of Q-HR practices multiplied the performance benefit of inter-OLA use. Practice Implications: Our findings suggest that organizations that participate in collaboratives are more likely to improve their performance if they use the inter-OLAs offered by the collaborative. Our results also suggest that complementing high use of inter-OLAs with intra-OLA use and Q-HR practices enhances performance improvement. For collaborative sponsors, our findings imply that including activities that facilitate interorganizational and intraorganizational learning are worthwhile.

Inside the Organizational Learning Curve: Understanding the Organizational Learning Process

In this work, we aim to provide an in-depth understanding of the organizational learning curve and why significant differences in the rate of learning exist across organizations. We review what is known about organizational learning curves as well as what is unknown. In sum, much is known and much remains unknown. Few studies have stepped inside the learning curve to provide greater understanding of the organizational learning process underlying the learning curve. We contend that this understanding is essential for helping organizations learn better and faster, and thus, operate more effectively and efficiently in a dynamic world. Therefore, not only do we examine what is known about organizational learning curves, but also what is known about the organizational learning process. Much of the former research has been conducted by operations scholars, while much of the latter has been conducted by organizational behavior scholars. By integrating research from both (of our) disciplines, we hope to provide a more comprehensive understanding of organizational learning and the venerable organizational learning curve.