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Background:The hospital readmission rate has been proposed as an important outcome indicator computable from routine statistics. However, most commonly used measures raise conceptual issues. Objectives: We sought to evaluate the usefulness of the computerized algorithm for identifying avoidable readmissions on the basis of minimum bias, criterion validity, and measurement precision. Research Design and Subjects: A total of 131,809 hospitalizations of patients discharged alive from 49 hospitals were used to compare the predictive performance of risk adjustment methods. A subset of a random sample of 570 medical records of discharge/readmission pairs in 12 hospitals were reviewed to estimate the predictive value of the screening of potentially avoidable readmissions. Measures: Potentially avoidable readmissions, defined as readmissions related to a condition of the previous hospitalization and not expected as part of a program of care and occurring within 30 days after the previous discharge, were identified by a computerized algorithm. Unavoidable readmissions were considered as censored events. Results: A total of 5.2% of hospitalizations were followed by a potentially avoidable readmission, 17% of them in a different hospital. The predictive value of the screen was 78%; 27% of screened readmissions were judged clearly avoidable. The correlation between the hospital rate of clearly avoidable readmission and all readmissions rate, potentially avoidable readmissions rate or the ratio of observed to expected readmissions were respectively 0.42, 0.56 and 0.66. Adjustment models using clinical information performed better. Conclusion: Adjusted rates of potentially avoidable readmissions are scientifically sound enough to warrant their inclusion in hospital quality surveillance.

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BackgroundSince generic drugs have the same therapeutic effect as the original formulation but at generally lower costs, their use should be more heavily promoted. However, a considerable number of barriers to their wider use have been observed in many countries. The present study examines the influence of patients, physicians and certain characteristics of the generics' market on generic substitution in Switzerland.MethodsWe used reimbursement claims' data submitted to a large health insurer by insured individuals living in one of Switzerland's three linguistic regions during 2003. All dispensed drugs studied here were substitutable. The outcome (use of a generic or not) was modelled by logistic regression, adjusted for patients' characteristics (gender, age, treatment complexity, substitution groups) and with several variables describing reimbursement incentives (deductible, co-payments) and the generics' market (prices, packaging, co-branded original, number of available generics, etc.).ResultsThe overall generics' substitution rate for 173,212 dispensed prescriptions was 31%, though this varied considerably across cantons. Poor health status (older patients, complex treatments) was associated with lower generic use. Higher rates were associated with higher out-of-pocket costs, greater price differences between the original and the generic, and with the number of generics on the market, while reformulation and repackaging were associated with lower rates. The substitution rate was 13% lower among hospital physicians. The adoption of the prescribing practices of the canton with the highest substitution rate would increase substitution in other cantons to as much as 26%.ConclusionsPatient health status explained a part of the reluctance to substitute an original formulation by a generic. Economic incentives were efficient, but with a moderate global effect. The huge interregional differences indicated that prescribing behaviours and beliefs are probably the main determinant of generic substitution.

BackgroundRegional rates of hospitalization for ambulatory care sensitive conditions (ACSC) are used to compare the availability and quality of ambulatory care but the risk adjustment for population health status is often minimal. The objectives of the study was to examine the impact of more extensive risk adjustment on regional comparisons and to investigate the relationship between various area-level factors and the properly adjusted rates.MethodsOur study is an observational study based on routine data of 2 million anonymous insured in 26 Swiss cantons followed over one or two years. A binomial negative regression was modeled with increasingly detailed information on health status (age and gender only, inpatient diagnoses, outpatient conditions inferred from dispensed drugs and frequency of physician visits). Hospitalizations for ACSC were identified from principal diagnoses detecting 19 conditions, with an updated list of ICD-10 diagnostic codes. Co-morbidities and surgical procedures were used as exclusion criteria to improve the specificity of the detection of potentially avoidable hospitalizations. The impact of the adjustment approaches was measured by changes in the standardized ratios calculated with and without other data besides age and gender.Results25% of cases identified by inpatient main diagnoses were removed by applying exclusion criteria. Cantonal ACSC hospitalizations rates varied from to 1.4 to 8.9 per 1,000 insured, per year. Morbidity inferred from diagnoses and drugs dramatically increased the predictive performance, the greatest effect found for conditions linked to an ACSC. More visits were associated with fewer PAH although very high users were at greater risk and subjects who had not consulted at negligible risk. By maximizing health status adjustment, two thirds of the cantons changed their adjusted ratio by more than 10 percent. Cantonal variations remained substantial but unexplained by supply or demand.ConclusionAdditional adjustment for health status is required when using ACSC to monitor ambulatory care. Drug-inferred morbidities are a promising approach.