Statistical Linkage of Treatment to Diagnosis for Research and Monitoring of Practice Patterns

Summary

Objectives: At each patient contact general practitioners enter information about the diagnoses and the interventions in the electronic medical record (EMR) system. If there is only one diagnosis during a single patient-physician contact, then a causal connection between the diagnosis and the intervention is established. Otherwise it is uncertain what may have been the cause of the intervention.

Methods: Ideally the general practitioners would record a match between each intervention and the diagnosis that justifies it. However, most EMR software is not capable of recording explanatory matches. Furthermore, supplying the matches is a resource-demanding task. In this study the general practitioners were supplied with a matching module for the EMR, so data with full matching between intervention and diagnosis was collected (our “gold standard“). The study models how close the full matching can be recreated by model linkage using different kinds of simple assumptions.

Results: The modeling demonstrates a raise in the measure of prediction (sensitivity) from 41.9 percent for a completely random linkage to 90.9 percent based on simple assumptions in the model. The small substantial potential further gain makes it less attractive to apply more intricate assumptions or use more complex modeling (including neural networks).

Conclusions: The perspective of the study lies in the support of the general practitioners with software for comparison of their decisions with those of their peers and also with guidelines. Thus a system for simple quality assurance and awareness to untypical decisions could be incorporated into the electronic medical record systems.

Keywords

General practitioner - family practice - classification - quality assurance - linkage - EMR data

• References

• 1 Bell RM, Keesey J, Richards T. The urge to merge: linking vital statistics records and Medicaid claims. Medical Care 1994; 32: 1004-18.
• 2 Schroll H. Metoder til registrering og analyse af diagnoseklassificerede kontaktforløb i almen praksis. København: Månedskr Prakt Lægegern. 2002
• 3 Rasmussen KB. Statistical linkage of treatment and diagnosis. Report for The County of Funen. 1999: 24
• 4 www.medcom.dk
• 5 Bentzen N, Schroll H. ICPC International klassifikation for den primære sundhedstjeneste. Dansk selskab for almen medicin, Månedskr Prakt Lægegern. 1991
• 6 Lorentzen EF. International Classification of primary Care converted to ICD-10: Extended Danish ICPC. Amsterdam: Medical Informatics Europe; 1996: 188-92.
• 7 The WHO Collaborating Centre for Drug Statistics Methodology’s homepage. www.legemiddelforbruk.no/dok/ATCclassification.htm
• 8 Copas JB, Hilton FJ. Record Linkage: Statistical Models for Matching Computer Records. Journal of the Royal Statistical Society A 1990; 153: 287-320.
• 9 Roos LL, Walld R, Wajda A, Bond R, Hartford K. Record Linkage Strategies, Outpatient Procedures, and Administrative Data. Medical Care 1996; 34: 570-82.
• 10 Piattelli-Palmarini M. Inevitable illusions: how mistakes of reason rule our minds. John Wiley & Sons, Inc; 1994
• 11 Linoff GS, Berry MJ. Mining the web. Wiley. 2002
• 12 Bridges-Webb C. The increasing complexity in general practice in Australia. Aust Fam Psysician 1982; 11 (11) 840