Effects of resident duty hour reform on surgical and procedural patient safety indicators among hospitalized Veterans Health Administration and Medicare patients.

OBJECTIVE: Improving patient safety was a strong motivation behind duty hour regulations implemented by Accreditation Council for Graduate Medical Education on July 1, 2003. We investigated whether rates of patient safety indicators (PSIs) changed after these reforms. RESEARCH DESIGN: Observational study of patients admitted to Veterans Health Administration (VA) (N = 826,047) and Medicare (N = 13,367,273) acute-care hospitals from July 1, 2000 to June 30, 2005. We examined changes in patient safety events in more versus less teaching-intensive hospitals before (2000-2003) and after (2003-2005) duty hour reform, using conditional logistic regression, adjusting for patient age, gender, comorbidities, secular trends, baseline severity, and hospital site. MEASURES: Ten PSIs were aggregated into 3 composite measures based on factor analyses: "Continuity of Care," "Technical Care," and "Other" composites. RESULTS: Continuity of Care composite rates showed no significant changes postreform in hospitals of different teaching intensity in either VA or Medicare. In the VA, there were no significant changes postreform for the technical care composite. In Medicare, the odds of a Technical Care PSI event in more versus less teaching-intensive hospitals in postreform year 1 were 1.12 (95% CI; 1.01-1.25); there were no significant relative changes in postreform year 2. Other composite rates increased in VA in postreform year 2 in more versus less teaching-intensive hospitals (odds ratio, 1.63; 95% CI; 1.10-2.41), but not in Medicare in either postreform year. CONCLUSIONS: Duty hour reform had no systematic impact on PSI rates. In the few cases where there were statistically significant increases in the relative odds of developing a PSI, the magnitude of the absolute increases were too small to be clinically meaningful.

Do different case-mix measures affect assessments of provider efficiency? Lessons from the Department of Veterans Affairs.

Although case-mix adjustment is critical for provider profiling, little is known regarding whether different case-mix measures affect assessments of provider efficiency. We examine whether two case-mix measures, Adjusted Clinical Groups (ACGs) and Diagnostic Cost Groups (DCGs), result in different assessments of efficiency across service networks within the Department of Veterans Affairs (VA). Three profiling indicators examine variation in resource use. Although results from the ACGs and DCGs generally agree on which networks have greater or lesser efficiency than average, assessments of individual network efficiency vary depending upon the case-mix measure used. This suggests that caution should be used so that providers are not misclassified based on reported efficiency.

Profiling resource use: do different outcomes affect assessments of provider efficiency?

OBJECTIVES: To examine whether 2 outcome measures result in different assessments of efficiency across 22 service networks within the Department of Veterans Affairs (VA). STUDY DESIGN: A retrospective analysis using VA inpatient and outpatient administrative databases. METHODS: A 60% random sample of veterans who used healthcare services during fiscal year 1997 was split into a 40% sample (n = 1,046,803) for development and a 20% sample (n = 524,461) for validation. Weighted concurrent case-mix models using adjusted clinical groups were developed to explain variation in 2 outcomes: "days of care"--the sum of a patient's inpatient and outpatient annual visit days, and "average accounting costs"--the sum of the average service costs multiplied by the units of service for each patient. Two profiling indicators were calculated for each outcome: an unadjusted efficiency index and an adjusted efficiency index. These indices were compared to examine network efficiency. RESULTS: Although about half the networks were identified as "efficient" before and after case-mix adjustment, assessments of individual network efficiency were affected by the adjustment. The 2 outcomes differed on which networks were efficient. For example, 4 networks that appeared as efficient based on days of care appeared as inefficient based on average costs. CONCLUSIONS: Assessments of provider efficiency across the 22 networks depended on the outcome measure used. Knowledge about the extent to which assessments of provider efficiency depend on the outcome measure used is an important step toward improved and more equitable comparisons across providers.

Diagnostic cost groups (DCGs) and concurrent utilization among patients with substance abuse disorders.

OBJECTIVE: To assess the performance of Diagnostic Cost Groups (DCGs) in explaining variation in concurrent utilization for a defined subgroup, patients with substance abuse (SA) disorders, within the Department of Veterans Affairs (VA). DATA SOURCES: A 60 percent random sample of veterans who used health care services during Fiscal Year (FY) 1997 was obtained from VA administrative databases. Patients with SA disorders (13.3 percent) were identified from primary and secondary ICD-9-CM diagnosis codes. STUDY DESIGN: Concurrent risk adjustment models were fitted and tested using the DCG/HCC model. Three outcome measures were defined: (1) "service days" (the sum of a patient's inpatient and outpatient visit days), (2) mental health/substance abuse (MH/SA) service days, and (3) ambulatory provider encounters. To improve model performance, we ran three DCG/HCC models with additional indicators for patients with SA disorders. DATA COLLECTION: To create a single file of veterans who used health care services in FY 1997, we merged records from all VA inpatient and outpatient files. PRINCIPAL FINDINGS: Adding indicators for patients with mild/moderate SA disorders did not appreciably improve the R-squares for any of the outcome measures. When indicators were added for patients with severe SA who were in the most costly category, the explanatory ability of the models was modestly improved for all three outcomes. CONCLUSIONS: Modifying the DCG/HCC model with additional markers for SA modestly improved homogeneity and model prediction. Because considerable variation still remained after modeling, we conclude that health care systems should evaluate "off-the-shelf" risk adjustment systems before applying them to their own populations.