Search

Background: Inappropriate use of acute hospital beds is a major topic in health politics. We present here a new approach to measure unnecessary hospitalizations in Medicine and Pediatrics., Methods: The necessity of a hospital admission was determined using explicit criteria related to the recorded diagnoses. Two indicators (i.e. "unjustified" and "sometimes justified" stays) were applied to more than 800,000 hospital stays and a random sample of 200 of them was analyzed by two clinicians, using routine data available in medical statistics. The validation of the indicators focused on their precision, validity and adjustment, as well as their usefulness (i.e. interest and risk of abuse)., Results: Rates, adjusted for case mix (i.e. age of patient, admission planned or not), showed statistically significant differences among hospitals. Only 6.5% of false positives were observed for "unjustified stays" and 17% for "sometimes justified stays". Respectively 7 and 12% of stays had an unknown status, due to a lack of sufficiently precise data. Considering true positives only, almost one third of medical and pediatric stays were classified as not strictly justified from a medical point of view in Switzerland. Among these stays, about one fifth could have probably been avoided without risk. To enable a larger ambulatory shift, recommendations were made to strengthen the ambulatory care, notably regarding post-emergency follow-up, cardiac and pulmonary functions' monitoring, pain management, falls prevention, and specialized at-home services that should be offered., Conclusion: We recommend using "unjustified stays" and "sometimes justified stays" indicators to monitor inappropriate hospitalizations. The latter could help the planning of reinforced ambulatory care measures to pursue the ambulatory shift. Nonetheless, we clearly advise against the use of these two indicators for hospitals financing purposes., (© 2022. The Author(s).)

Outcome indicators are routinely used to compare hospitals with respect to quality of care. Indicators might be based on observed proportions of adverse events (binary outcomes) or observed averages of e.g. lengths or costs of hospital stays (continuous outcomes). These observed values are compared with expected ones in an average hospital, which might be estimated from a reference sample and should be appropriately adjusted for the case mix of patients. One possibility to achieve a reliable adjustment is to stratify the patients according to their risks, where each patient belongs to one and only one stratum. Control limits calculated under the null hypothesis of an average hospital, allowing to decide whether a discrepancy between an observed and an expected value might be explained by chance or not, are then plotted around the indicator, such that hospitals falling above those control limits are detected as being statistically worse than an average hospital. Calculation of valid control limits is however not always obvious. In this article, we propose a simple and unified framework to calculate such control limits when adjustment is based on stratification, where we allow to distinguish and disentangle the variability explained by stratification and the variability due to chance, where we take into account the uncertainty about the estimation of the expected values, and where it is possible not only to detect those hospitals which are statistically worse, but also those which are statistically much worse than an average hospital. The method applies both to binary and continuous outcomes and is illustrated on Swiss hospital discharge data.

Objectives: For certain major operations, inpatient mortality risk is lower in high-volume hospitals than those in low-volume hospitals. Extending the analysis to a broader range of interventions and outcomes is necessary before adopting policies based on minimum volume thresholds., Methods: Using the United States 2004 Nationwide Inpatient Sample, we assessed the effect of intervention-specific and overall hospital volume on surgical complications, potentially avoidable reoperations, and deaths across 1.4 million interventions in 353 hospitals. Outcome variations across hospitals were analyzed through a 3-level hierarchical logistic regression model (patients, surgical interventions, and hospitals), which took into account interventions on multiple organs, 144 intervention categories, and structural hospital characteristics. Discriminative performance and calibration were good., Results: Hospitals with more experience in a given intervention had similar reoperation rates but lower mortality and complication rates: odds ratio per volume deciles 0.93 and 0.97. However, the benefit was limited to heart surgery and a small number of other operations. Risks were higher for hospitals that performed more interventions overall: odds ratio per 1000 for each event was approximately 1.02. Even after adjustment for specific volume, mortality varied substantially across both high- and low-volume hospitals., Conclusion: Although the link between specific volume and certain inpatient outcomes suggests that specialization might help improve surgical safety, the variable magnitude of this link and the heterogeneity of hospital effect do not support the systematic use of volume-based referrals. It may be more efficient to monitor risk-adjusted postoperative outcomes and to investigate facilities with worse than expected outcomes.

Objectives: Reoperations may reflect a suboptimal initial surgical treatment. The study aimed to develop a screening algorithm for those potentially avoidable, using only routinely collected hospital data and a prediction model to adjust rates for case-mix., Study Design and Setting: Data of a 3-year random sample of 7,370 therapeutic operations on inpatients, among which 833 were followed-up by a reoperation during the same stay. A review of medical records identified clearly avoidable and other potentially avoidable reoperations to develop and test the screening algorithm. A logistic prediction model of potentially avoidable reoperations was developed on one randomly chosen half of the data (about 9,000 interventions) and tested on the other half (cross-validation)., Results: Two hundred thirty-seven interventions (3%) were followed by a potentially avoidable reoperation, among which 144 were clearly avoidable. The screening algorithm had a sensitivity of 75% and a specificity of 72%. Predictors of potentially avoidable reoperations were surgery categories, diagnosis related conditions, and experiencing prior surgery. The risk score, based on these variables, showed at once a satisfactory discriminative performance (C-statistic=0.76) and goodness-of-fit measure on the validation set., Conclusion: The adjusted rate of potentially avoidable reoperations should be included in internal reporting of hospital quality indicators, but further validated in various settings.