Detecting and Visualizing Outliers in Provider Profiling Using Funnel Plots and Mixed Effects Models—An Example from Prescription Claims Data

Oliver Hirsch, Norbert Donner-Banzhoff, Maike Schulz and Michael Erhart
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Oliver Hirsch: Department of General Practice/Family Medicine, Philipps University Marburg, Karl-von-Frisch-Str.4, 35043 Marburg, Germany
Norbert Donner-Banzhoff: Department of General Practice/Family Medicine, Philipps University Marburg, Karl-von-Frisch-Str.4, 35043 Marburg, Germany
Maike Schulz: Central Research Institute of Ambulatory Health Care in Germany (ZI), Salzufer 8, 10587 Berlin, Germany
Michael Erhart: Central Research Institute of Ambulatory Health Care in Germany (ZI), Salzufer 8, 10587 Berlin, Germany

IJERPH, 2018, vol. 15, issue 9, 1-11

Abstract: When prescribing a drug for a patient, a physician also has to consider economic aspects. We were interested in the feasibility and validity of profiling based on funnel plots and mixed effect models for the surveillance of German ambulatory care physicians’ prescribing. We analyzed prescriptions issued to patients with a health insurance card attending neurologists’ and psychiatrists’ ambulatory practices in the German federal state of Saarland. The German National Association of Statutory Health Insurance Physicians developed a prescribing assessment scheme (PAS) which contains a systematic appraisal of the benefit of drugs for so far 12 different indications. The drugs have been classified on the basis of their clinical evidence as “standard”, “reserve” or “third level” medication. We had 152.583 prescriptions in 56 practices available for analysis. A total of 38.796 patients received these prescriptions. The funnel plot approach with additive correction for overdispersion was almost equivalent to a mixed effects model which directly took the multilevel structure of the data into account. In the first case three practices were labeled as outliers, the mixed effects model resulted in two outliers. We suggest that both techniques should be routinely applied within a surveillance system of prescription claims data.

Keywords: drug prescriptions; ambulatory care; statistical data interpretation; reference standards (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2018
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