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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Oliver Hirsch & Norbert Donner-Banzhoff & Maike Schulz & Michael Erhart, 2018. "Detecting and Visualizing Outliers in Provider Profiling Using Funnel Plots and Mixed Effects Models—An Example from Prescription Claims Data," IJERPH, MDPI, vol. 15(9), pages 1-11, September.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:9:p:2015-:d:170007
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    References listed on IDEAS

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    1. Manuel Gomes & Nils Gutacker & Chris Bojke & Andrew Street, 2016. "Addressing Missing Data in Patient‐Reported Outcome Measures (PROMS): Implications for the Use of PROMS for Comparing Provider Performance," Health Economics, John Wiley & Sons, Ltd., vol. 25(5), pages 515-528, May.
    2. Burgess Jr., James F. & Christiansen, Cindy L. & Michalak, Sarah E. & Morris, Carl N., 2000. "Medical profiling: improving standards and risk adjustments using hierarchical models," Journal of Health Economics, Elsevier, vol. 19(3), pages 291-309, May.
    3. Norbert Berthold & Nadya Kosturkova & Andreas Müller, 2010. "he German federal states in business location competition - yesterday, today and tomorrow," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 63(02), pages 15-20, January.
    4. David I. Ohlssen & Linda D. Sharples & David J. Spiegelhalter, 2007. "A hierarchical modelling framework for identifying unusual performance in health care providers," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(4), pages 865-890, October.
    5. Orzella, Letizia & Chini, Francesco & Giorgi Rossi, Paolo & Borgia, Piero, 2010. "Physician and patient characteristics associated with prescriptions and costs of drugs in the Lazio region of Italy," Health Policy, Elsevier, vol. 95(2-3), pages 236-244, May.
    6. Francesca Ieva & Anna Paganoni, 2015. "Detecting and visualizing outliers in provider profiling via funnel plots and mixed effect models," Health Care Management Science, Springer, vol. 18(2), pages 166-172, June.
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