Ann-Sophie Buchardt, Jacob Bodilsen, Lene Wohlfahrt Dreyer, Kirsten Skjærbæk Duch, Pi Vejsig Madsen, Bergur Magnussen, Rasmus Gregersen Mottlau, Jens-Jakob Møller, Flemming Skjøth, Marie Villumsen, Andreas Jensen
The Danish National Patient Register (DNPR) is an important data source for register-based health-related research, providing detailed information on all hospital contacts in Denmark. With the transition from the second version of the DNPR (DNPR2) to the third version (DNPR3) in early 2019, the variable distinguishing inpatient and outpatient contacts was discontinued, introducing substantial methodological challenges for epidemiological research. This perspective provides a narrative synthesis of current approaches to classifying hospital stays in DNPR3 and highlights key epidemiological considerations. Two main methodological approaches were identified: consensus-based frameworks, which emphasize interpretability and clinical reasoning and data-driven algorithms, which leverage patterns in register data to improve classification performance. Existing methods generally provide reasonable classification for inpatient and elective outpatient contacts. However, classification of acute outpatient visits remains a persistent and unresolved challenge, reflecting heterogeneous registration practices and the absence of a gold-standard reference in DNPR3. Further, current methodological approaches are fragmented and context-dependent, limiting comparability across studies and over time. We therefore call for coordinated efforts to develop transparent, validated, and consensus-driven classification frameworks. Such efforts are essential to ensure valid, reproducible, and interpretable epidemiological research using DNPR data.