Algorithm linking patients and general practices in Denmark using the Danish National Health Service Register

Abstract

Maiken Ina Siegismund Kjaersgaard,1 Peter Vedsted,2 Erik Thorlund Parner,1 Bodil Hammer Bech,3 Mogens Vestergaard,2,4 Kaare Rud Flarup,2 Morten Fenger-Grøn,2 1Section for Biostatistics, 2Research Unit for General Practice, 3Section for Epidemiology, 4Section for General Medical Practice, Department of Public Health, Aarhus University, Aarhus, Denmark Background: The patient list system in Denmark assigns virtually all residents to a general practice. Nevertheless, historical information on this link between patient and general practice is not readily available for research purposes. Objectives: To develop, implement, and evaluate the performance of an algorithm linking individual patients to their general practice by using information from the Danish National Health Service Register and the Danish Civil Registration System. Materials and methods: The National Health Service Register contains information on all services provided by general practitioners from 1990 and onward. On the basis of these data and information on migration history and death obtained from the Civil Registration System, we developed an algorithm that allocated patients to a general practice on a monthly basis. We evaluated the performance of the algorithm between 2002 and 2007. During this time period, we had access to information on the link between patients and general practices. Agreement was assessed by the proportion of months for which the algorithm allocated patients to the correct general practice. We also assessed the proportion of all patients in the patient list system for which the algorithm was able to suggest an allocation. Results: The overall agreement between algorithm and patient lists was 98.6%. We found slightly higher agreement for women (98.8%) than for men (98.4%) and lower agreement in the age group 18–34 years (97.1%) compared to all other age groups (≥98.6%). The algorithm had assigned 83% of all patients in the patient list system after 1 year of follow-up, 91% after 2 years of follow-up, and peaked at 94% during the fourth year. Conclusion: We developed an algorithm that enables valid and nearly complete linkage between patients and general practices. The algorithm performs better in subgroups of patients with high health care needs. The algorithm constitutes a valuable tool for primary health care research. Keywords: general practice, medical record linkage, patient list, primary care, registers

Keywords

• general practice
• medical record linkage
• patient list
• primary care
• registers