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Despite consensus about the importance of measuring quality of diabetes care and the widespread use of self-reports and medical records to assess quality, little is known about the degree of agreement between these data sources.To evaluate agreement between self-reported and medical record data on annual eye examinations and to identify factors associated with agreement.Data from interviews and medical records were available for 8409 adults with diabetes who participated in the baseline round of the Translating Research Into Action for Diabetes (TRIAD) Study.Agreement between self-reports and medical records was evaluated as concordance and Cohen's kappa coefficient.Self-reports indicated a higher performance of annual dilated eye examinations than did medical records (75.9% vs. 38.8%). Concordance between the data sources was 57.9%. Agreement was only fair (kappa coefficient = 0.25; 95% confidence interval, 0.23-0.26). Nearly two-thirds (64.6%) of discordance was due to lack of evidence in the medical record to support self-reported performance of the procedure. After adjustment, agreement was most strongly related to health plan (chi = 977.9, df = 9; P0.0001), and remained significantly better for 3 of the 10 health plans (P0.00001) and for persons younger than 45 years of age (P = 0.00002).The low level of agreement between self-report and medical records suggests that many providers of diabetes care do not have easily available accurate information on the eye examination status of their patients.

Certain hospitals play a central role in ensuring Medicaid-insured patients' access to care. Their quality of care is critical to evaluate.To determine whether hospitals for which Medicaid patients represent a high percentage of total discharges provide a different quality of care than other hospitals.Cross-sectional analysis.Acute care hospitals participating in the first Hospital Compare public report (released November 2004) and the 2004 American Hospital Association hospital survey.Hospitals serving 1 standard deviation above the national mean percentage of Medicaid patients were designated high Medicaid hospitals. Performance was assessed using percent compliance with 10 processes of care for 3 conditions: myocardial infarction, congestive heart failure, and community-acquired pneumonia.Among the 2874 nonteaching hospitals, high Medicaid hospitals had lower adherence (P0.01) than other nonteaching hospitals on all 10 indicators. Of particular clinical importance, high Medicaid nonteaching hospitals less frequently prescribed beta-blockers (83% vs. 90%%, P0.0001) and aspirin at discharge in myocardial infarction (85% vs. 91%%, P0.0001), and administered antibiotics on time in CAP (68% vs. 75%, P0.0001). Among teaching institutions, there were few differences between high Medicaid and other hospitals.Among hospitals publicly reporting on the Hospital Compare Web site, nonteaching hospitals treating a high percentage of Medicaid patients had lower adherence to quality indicators than other nonteaching hospitals on 10 indicators. Further research is needed to determine what factors contribute to differences in reported quality.

Background. There remains considerable uncertainty about whether prospective or concurrent risk adjustment (RA) is preferable. Although concurrent models have better predictive power than prospective models, the large payments associated with concurrent RA create incentives for fraudulent coding. A

Background: Measures for pay-for-performance and public reporting programs may be based on clinical practice guidelines. The impact of guideline changes over time—and whether evolving clinical evidence can render measures based on prior guidelines misleading—is not known. Objective: To assess the impact of using different percutaneous coronary intervention (PCI) guidelines when evaluating whether PCI was indicated. Research Design: PCIs from the National Cardiovascular Data Registry's CathPCI registry performed in 2003-2004 were categorized into indication classes (Class I, IIa, IIb, III), using 2001 American College of Cardiology/American Heart Association guidelines for PCI, the guidelines available at the time of the procedures. The same procedures were recategorized using 2005 guidelines, which reflect the best evidence available to clinicians at the time of PCI. Procedures unable to be categorized were labeled as "Not Certain." Subjects: Patients undergoing PCI for stable or unstable angina in 394 hospitals. Measures: Number of procedures changing classification categories using 2001 versus 2005 guidelines. Results: A total of 345,779 PCIs were evaluated. Applying 2001 guidelines, 47.9% had Class I indications; 33.3% Class IIa; 5.9% Class IIb; 3.7% Class III; and 9.2% Not Certain. Applying 2005 guidelines to the same procedures, 25.1% had Class I indications; 57.5% Class IIa; 5.5% Class IIb; 3.7% Class III; and 8.3% Not Certain; 41.1% of procedures changed the classification overall. Conclusions: The changes in guidelines resulted in a marked shift in whether PCIs done in 2003-2004 were considered indicated. Guideline-based performance measures should be carefully evaluated before implementation to avoid incorrect assessments of quality of care.

Current intensive care unit performance measures include in-hospital mortality after intensive care unit admission. This measure does not account for deaths occurring after transfer to another hospital or soon after discharge and therefore, may be biased.Determine how transfer rates to other acute care hospitals and early post-discharge mortality rates impact hospital performance assessments using an in-hospital mortality model.Data were retrospectively collected on 10,502 eligible intensive care unit patients across 35 California hospitals between 2001 and 2004.We calculated the rates of acute care hospital transfers and early post-discharge mortality (30-day overall mortality-30-day in-hospital mortality) for each hospital. We assessed hospital performance with standardized mortality ratios (SMRs) using the Mortality Probability Model III. Using regression models, we explored the relationship between in-hospital SMRs and the rates of hospital transfers or early post-discharge mortality. We explored the same relationship using a 30-day SMR.In multivariable models, for each 1% increase in patients transferred to another acute care hospital, there was an in-hospital SMR reduction of -0.021 (-0.040-0.001). Additionally, a 1% increase in early post-discharge mortality was associated with an in-hospital SMR reduction of -0.049 (-0.142-0.045). Assessing hospital performance based upon 30-day mortality end point resulted in SMRs closer to 1.0 for hospitals at high and low ends of in-hospital mortality performance.Variations in transfer rates and potentially discharge timing appear to bias in-hospital SMR calculations. A 30-day mortality model is a potential alternative that may limit this bias.

Background: Maintaining independence in daily functioning is an important health outcome in older adults. A key measure of functional independence in elders is the ability to do activities of daily living (ADL) without the assistance of another person. However, few prognostic indices have been developed that stratify elders into groups at variable risk for developing ADL dependence. Objective: We sought to develop and validate a prognostic index that distinguishes between elders at different risk of ADL dependence. Research Design, Subjects, and Measures: We studied subjects enrolled in Asset and Health Dynamics Among the Oldest Old (AHEAD), a nationally representative cohort of elders older than the age of 70. We included 5239 subjects (mean age, 77) reporting that they could do each of 5 ADL (bathing, dressing, toileting, transferring, and eating) without the assistance of another person at baseline. Subjects were divided into development (n 3245) and validation (n 1994) samples based on region of the United States. Our primary outcome was the need for help (dependence) with at least one ADL at 2 years. We used logistic regression to select among predictor variables encompassing several domains: demographic characteristics, comorbid conditions, functional status, cognitive status, and general health indicators. Results: The 9 independent predictors of 2-year ADL dependence were age older than 80, diabetes, difficulty walking several blocks, difficulty bathing or dressing, need for help with personal finances, difficulty lifting 10 pounds, inability to name the Vice President, history of falling, and low body mass index. We created a risk score by assigning 1 point to each risk factor. In the development sample, rates of 2-year ADL dependence in subjects with 0, 1, 2, 3, 4, and 5 or more risk factors were 1.3%, 2.8%, 3.8%, 10%, 22%, and 33%, respectively (P 0.001, roc area 0.79). In the validation sample, the rates were 0.7%, 4.3%, 8.7%, 11%, 18%, and 40% (P 0.001, roc area 0.77). The risk score also discriminated between subjects at variable risk for a combined outcome of either ADL decline or death (4.3%, 7.6%, 15%, 21%, 30%, and 47%). Conclusion: Using data available from patient reports, we validated a simple risk index that distinguished between elders at variable risk of ADL dependence. This index may be useful for identifying elders at high risk of poor outcomes or for risk adjustment.