Identifying optimal survey-based algorithms to distinguish diabetes type among adults with diabetes.

OBJECTIVES: Surveys for U.S. diabetes surveillance do not reliably distinguish between type 1 and type 2 diabetes, potentially obscuring trends in type 1 among adults. To validate survey-based algorithms for distinguishing diabetes type, we linked survey data collected from adult patients with diabetes to a gold standard diabetes type. RESEARCH DESIGN AND METHODS: We collected data through a telephone survey of 771 adults with diabetes receiving care in a large healthcare system in North Carolina. We tested 34 survey classification algorithms utilizing information on respondents' report of physician-diagnosed diabetes type, age at onset, diabetes drug use, and body mass index. Algorithms were evaluated by calculating type 1 and type 2 sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) relative to a gold standard diagnosis of diabetes type determined through analysis of EHR data and endocrinologist review of selected cases. RESULTS: Algorithms based on self-reported type outperformed those based solely on other data elements. The top-performing algorithm classified as type 1 all respondents who reported type 1 and were prescribed insulin, as "other diabetes type" all respondents who reported "other," and as type 2 the remaining respondents (type 1 sensitivity 91.6%, type 1 specificity 98.9%, type 1 PPV 82.5%, type 1 NPV 99.5%). This algorithm performed well in most demographic subpopulations. CONCLUSIONS: The major federal health surveys should consider including self-reported diabetes type if they do not already, as the gains in the accuracy of typing are substantial compared to classifications based on other data elements. This study provides much-needed guidance on the accuracy of survey-based diabetes typing algorithms.

Use of Internal Performance Measurement to Guide Improvement Within Medical Groups.

BACKGROUND: Public reporting of provider performance currently encompasses a range of measures of quality, cost, and patient experience of care. However, little is known about how medical groups use measures for performance improvement. This information could help medical groups undertake internal measurement while helping payers, policy makers, and measurement experts develop more useful publicly reported measures and quality improvement strategies. METHODS: An exploratory, qualitative study was conducted of ambulatory care medical groups across the United States that currently gather their own performance data. RESULTS: Eighty-three interviews were conducted with 91 individuals representing 37 medical groups. Findings were distilled into three major themes: (1) measures used internally, (2) strategies for using internal measurement for performance improvement, and (3) other uses of internal measurement. Medical groups used both clinical and business process measures, including measures from external measure sets and internally derived measures. Strategies for using internal measurement for quality improvement included taking a gradual, iterative approach and setting clear goals with high priority, finding workable approaches to data sharing, and fostering engagement by focusing on actionable measures. Measurement was also used to check accuracy of external performance reports, clarify and manage conflicting external measurement requirements, and prepare for anticipated external measurement requirements. Respondents in most groups did not report a need to assess costs of internal measurement or the capacity to do so. CONCLUSION: Despite challenges and barriers, respondents found great value in conducting internal measurement. Their experiences may provide valuable lessons and knowledge for medical group leaders in earlier stages of establishing internal measurement programs.

Using Learning Communities to Support Adoption of Health Care Innovations.

BACKGROUND: Diffusion of innovations can be a slow process, posing a major challenge to quality improvement in health care. Learning communities can provide a rich, collaborative environment that supports the adoption of health care innovations and motivates organizational change. From 2014-2016, the Agency for Healthcare Research and Quality (AHRQ) Health Care Innovations Exchange established and supported three learning communities focused on adopting innovations in three high-priority areas: (1) advancing the practice of patient- and family-centered care in hospitals, (2) promoting medication therapy management for at-risk populations, and (3) reducing non-urgent emergency services. METHODS: Members of each learning community worked collaboratively in facilitated settings to adapt and implement strategies featured in the Health Care Innovations Exchange, receiving technical assistance from content experts. Project staff conducted a mixed methods evaluation of the initiative, both formative and summative. RESULTS: The activities and outcomes of the three learning communities provided insights about how this approach can support local implementation efforts, and about factors influencing innovation adoption. Using a qualitative synthesis method, lessons were identified related to learning community startup (recruitment and goal setting), learning community operations (engagement, collaborative decision-making, and sustainability), and innovation implementation (changing care delivery processes and/or policies). CONCLUSIONS: Findings from this work indicate that the learning community model of group learning can serve as an effective method to support dissemination and implementation of innovations, and to achieve desired outcomes in local settings.

State-level estimation of diabetes and prediabetes prevalence: Combining national and local survey data and clinical data.

Many statisticians and policy researchers are interested in using data generated through the normal delivery of health care services, rather than carefully designed and implemented population-representative surveys, to estimate disease prevalence. These larger databases allow for the estimation of smaller geographies, for example, states, at potentially lower expense. However, these health care records frequently do not cover all of the population of interest and may not collect some covariates that are important for accurate estimation. In a recent paper, the authors have described how to adjust for the incomplete coverage of administrative claims data and electronic health records at the state or local level. This article illustrates how to adjust and combine multiple data sets, namely, national surveys, state-level surveys, claims data, and electronic health record data, to improve estimates of diabetes and prediabetes prevalence, along with the estimates of the method's accuracy. We demonstrate and validate the method using data from three jurisdictions (Alabama, California, and New York City). This method can be applied more generally to other areas and other data sources.

Novel Methods and Data Sources for Surveillance of State-Level Diabetes and Prediabetes Prevalence.

States bear substantial responsibility for addressing the rising rates of diabetes and prediabetes in the United States. However, accurate state-level estimates of diabetes and prediabetes prevalence that include undiagnosed cases have been impossible to produce with traditional sources of state-level data. Various new and nontraditional sources for estimating state-level prevalence are now available. These include surveys with expanded samples that can support state-level estimation in some states and administrative and clinical data from insurance claims and electronic health records. These sources pose methodologic challenges because they typically cover partial, sometimes nonrandom subpopulations; they do not always use the same measurements for all individuals; and they use different and limited sets of variables for case finding and adjustment. We present an approach for adjusting new and nontraditional data sources for diabetes surveillance that addresses these limitations, and we present the results of our proposed approach for 2 states (Alabama and California) as a proof of concept. The method reweights surveys and other data sources with population undercoverage to make them more representative of state populations, and it adjusts for nonrandom use of laboratory testing in clinically generated data sets. These enhanced diabetes and prediabetes prevalence estimates can be used to better understand the total burden of diabetes and prediabetes at the state level and to guide policies and programs designed to prevent and control these chronic diseases.

Exploring relationships between patient safety culture and patients' assessments of hospital care.

OBJECTIVES: The purpose of this study was to examine relationships among 2 Agency for Healthcare Research and Quality measures of hospital patient safety and quality, which reflect different perspectives on hospital performance: the Hospital Survey on Patient Safety Culture (Hospital SOPS)--a hospital employee patient safety culture survey--and the Consumer Assessment of Healthcare Providers and Systems Hospital Survey (CAHPS Hospital Survey)--a survey of the experiences of adult inpatients with hospital care and services. Our hypothesis was that these 2 measures would be positively related. METHODS: We performed multiple regressions to examine the relationships between the Hospital SOPS measures and CAHPS Hospital Survey measures, controlling for hospital bed size and ownership. Analyses were conducted at the hospital level with each survey's measures using data from 73 hospitals that administered both surveys during similar periods. RESULTS: Higher overall Hospital SOPS composite average scores were associated with higher overall CAHPS Hospital Survey composite average scores (r = 0.41, P G 0.01). Twelve of 15 Hospital SOPS measures were positively related to the CAHPS Hospital Survey composite average score after controlling for bed size and ownership, with significant standardized regression coefficients ranging from 0.25 to 0.38. None of the Hospital SOPS measures were significantly correlated with either of the two single-item CAHPS Hospital Survey measures (hospital rating and willingness to recommend). CONCLUSIONS: This study found that hospitals where staff have more positive perceptions of patient safety culture tend to have more positive assessments of care from patients. This finding helps validate both surveys and suggests that improvements in patient safety culture may lead to improved patient experience with care. Further research is needed to determine the generalizability of these results to larger sets of hospitals, to hospital units, and to other settings of care.

Exploring relationships between patient safety culture and patients' assessments of hospital care.

OBJECTIVES: The purpose of this study was to examine relationships among 2 Agency for Healthcare Research and Quality measures of hospital patient safety and quality, which reflect different perspectives on hospital performance: the Hospital Survey on Patient Safety Culture (Hospital SOPS)-a hospital employee patient safety culture survey-and the Consumer Assessment of Healthcare Providers and Systems Hospital Survey (CAHPS Hospital Survey)-a survey of the experiences of adult inpatients with hospital care and services. Our hypothesis was that these 2 measures would be positively related. METHODS: We performed multiple regressions to examine the relationships between the Hospital SOPS measures and CAHPS Hospital Survey measures, controlling for hospital bed size and ownership. Analyses were conducted at the hospital level with each survey's measures using data from 73 hospitals that administered both surveys during similar periods. RESULTS: Higher overall Hospital SOPS composite average scores were associated with higher overall CAHPS Hospital Survey composite average scores (r = 0.41, P < 0.01). Twelve of 15 Hospital SOPS measures were positively related to the CAHPS Hospital Survey composite average score after controlling for bed size and ownership, with significant standardized regression coefficients ranging from 0.25 to 0.38. None of the Hospital SOPS measures were significantly correlated with either of the two single-item CAHPS Hospital Survey measures (hospital rating and willingness to recommend). CONCLUSIONS: This study found that hospitals where staff have more positive perceptions of patient safety culture tend to have more positive assessments of care from patients. This finding helps validate both surveys and suggests that improvements in patient safety culture may lead to improved patient experience with care. Further research is needed to determine the generalizability of these results to larger sets of hospitals, to hospital units, and to other settings of care.

Risk-adjusted mortality as an indicator of outcomes: comparison of the Medicare Advantage Program with the Veterans' Health Administration.

BACKGROUND: The Medicare Advantage Program (MAP) and the Veterans' Health Administration (VHA) currently provide many services that benefit the elderly, and a comparative study of their risk-adjusted mortality rates has the potential to provide important information regarding these 2 systems of care. OBJECTIVE: The objective of this retrospective study was to compare mortality rates between the MAP and the VHA after controlling for case-mix differences. SUBJECTS: This study consisted of 584,294 MAP patients and 420,514 VHA patients. MEASURES: We used the Death Master File to ascertain the vital status of each study subject over approximately 4 years. We used Cox regression models to estimate hazard ratios (HRs) with 95% confidence intervals (CIs) for the MAP compared with VHA patients. RESULTS: The average age for male MAP patients was 73.8 years (+/- 5.6) and for male VHA patients was 74.05 years (+/- 6.3). Unadjusted mortality rates of males for VHA and MAP were 25.7% and 22.8%, respectively, over approximately 4 years (P < 0.0001), respectively. The case-mix of VHA patients, however, was sicker than those from MAP. After adjusting for case-mix, the HR for mortality in the MAP was significantly higher than that in the VHA (HR, 1.404; 95% CI = 1.383-1.426). We obtained similar results when we compared the mortality rates of females for VHA and MAP. CONCLUSIONS: After adjusting for their higher prevalence of chronic disease and worse self-reported health, mortality rates were lower for patients cared for in the VHA compared with those in the MAP. Further studies should examine what differences in care structures and processes contribute to lower mortality in the VHA.