Separating the wheat from the chaff: How to measure hospital quality in routine data?

• Published in: Health services research 2024-04, Vol.59 (2), p.e14282-n/a
• Main Authors: Bilger, Jana, Pletscher, Mark, Müller, Tobias
• Format: Article
• Language: English
• Subjects: Acute services; Analysis; Data collection; Data mining; Data warehousing/data mining; Delivery of Health Care; Evaluation; Extraction; Germany; Health authorities; Hernia; Heterogeneity; Hip replacement; hospital; Hospitals; Humans; Indicators; Inpatient care; Logistic Models; Logit models; Machine learning; Measurement; Medical care; Medical care quality; Medical diagnosis; Methods; Patients; Public health; Public health administration; Quality management; Quality of care; Random effects; Regression analysis; Regression models; Risk Adjustment; risk adjustment for clinical outcomes; Statistical analysis; Statistics; Supervised learning; Surgery; United States; Wheat
• ISSN: 0017-9124; 1475-6773; 1475-6773
• DOI: 10.1111/1475-6773.14282

Objective To measure hospital quality based on routine data available in many health care systems including the United States, Germany, the United Kingdom, Scandinavia, and Switzerland. Data Sources and Study Setting We use the Swiss Medical Statistics of Hospitals, an administrative hospital dataset of all inpatient stays in acute care hospitals in Switzerland for the years 2017–2019. Study Design We study hospital quality based on quality indicators used by leading agencies in five countries (the United States, the United Kingdom, Germany, Austria, and Switzerland) for two high‐volume elective procedures: inguinal hernia repair and hip replacement surgery. We assess how least absolute shrinkage and selection operator (LASSO), a supervised machine learning technique for variable selection, and Mundlak corrections that account for unobserved heterogeneity between hospitals can be used to improve risk adjustment and correct for imbalances in patient risks across hospitals. Data Collection/Extraction Methods The Swiss Federal Statistical Office collects annual data on all acute care inpatient stays including basic socio‐demographic patient attributes and case‐level diagnosis and procedure codes. Principal Findings We find that LASSO‐selected and Mundlak‐corrected hospital random effects logit models outperform common practice logistic regression models used for risk adjustment. Besides the more favorable statistical properties, they have superior in‐ and out‐of‐sample explanatory power. Moreover, we find that Mundlak‐corrected logits and the more complex LASSO‐selected models identify the same hospitals as high or low‐quality offering public health authorities a valuable alternative to standard logistic regression models. Our analysis shows that hospitals vary considerably in the quality they provide to patients. Conclusion We find that routine hospital data can be used to measure clinically relevant quality indicators that help patients make informed hospital choices.