A theoretical framework for linking hospitals longitudinally: demonstrated using German Hospital Quality Reports 2016–2020

Abstract

Abstract Background In longitudinal health services research, hospital identification using an ID code, often supplemented with several additional variables, lacks clarity regarding representativeness and variable influence. This study presents an operational method for hospital identity delimitation and a novel longitudinal identification approach, demonstrated using a case study. Methods The conceptualisation considers hospitals as evolving entities, identifying “similar enough” pairs across two time points using an automated similarity matrix. This method comprises key variable selection, similarity scoring, and tolerance threshold definition, tailored to data source characteristics and clinical relevance. This linking method is tested by applying the identification of minimum caseload requirements-related German hospitals, utilizing German Hospital Quality Reports (GHQR) 2016–2020. Results The method achieved a success rate (min: 97.9% - max: 100%, mean: 99.9%) surpassing traditional hospital ID-code linkage (min: 91.5% - max: 98.8%, mean: 96.6%), with a remarkable 99% reduction in manual work through automation. Conclusions This method, rooted in a comprehensive understanding of hospital identities, offers an operational, automated, and customisable process serving diverse clinical topics. This approach has the advantage of simultaneously considering multiple variables and systematically observing temporal changes in hospitals. It also enhances the precision and efficiency of longitudinal hospital identification in health services research.

Keywords

• Longitudinal data linkage
• Linking hospital-level data
• Hospital identity
• Hospital variables
• Similarity matrix
• German hospital quality report