American Heart Journal
Volume 157, Issue 6 , Pages 995-1000, June 2009

Linking inpatient clinical registry data to Medicare claims data using indirect identifiers

  • Bradley G. Hammill, MS

      Affiliations

    • Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC
  • ,
  • Adrian F. Hernandez, MD, MHS

      Affiliations

    • Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC
    • Department of Medicine, Duke University School of Medicine, Durham, NC
  • ,
  • Eric D. Peterson, MD, MPH

      Affiliations

    • Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC
    • Department of Medicine, Duke University School of Medicine, Durham, NC
  • ,
  • Gregg C. Fonarow, MD

      Affiliations

    • University of California, Los Angeles Medical Center, Los Angeles, CA
  • ,
  • Kevin A. Schulman, MD

      Affiliations

    • Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC
    • Department of Medicine, Duke University School of Medicine, Durham, NC
  • ,
  • Lesley H. Curtis, PhD

      Affiliations

    • Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC
    • Department of Medicine, Duke University School of Medicine, Durham, NC
    • Corresponding Author InformationReprint requests: Lesley H. Curtis, PhD, Center for Clinical and Genetic Economics, Duke Clinical Research Institute, PO Box 17969, Durham, NC 27715.

Received 16 January 2009; accepted 3 April 2009.

Background

Inpatient clinical registries generally have limited ability to provide a longitudinal perspective on care beyond the acute episode. We present a method to link hospitalization records from registries with Medicare inpatient claims data, without using direct identifiers, to create a unique data source that pairs rich clinical data with long-term outcome data.

Methods and Results

The method takes advantage of the hospital clustering observed in each database by demonstrating that different combinations of indirect identifiers within hospitals yield a large proportion of unique patient records. This high level of uniqueness also allows linking without advance knowledge of the Medicare provider number of each registry hospital. We applied this method to 2 inpatient databases and were able to identify 81% of 39,178 records in a large clinical registry of patients with heart failure and 91% of 6,581 heart failure records from a hospital inpatient database. The quality of the link is high, and reasons for incomplete linkage are explored. Finally, we discuss the unique opportunities afforded by combining claims and clinical data for specific analyses.

Conclusions

In the absence of direct identifiers, it is possible to create a high-quality link between inpatient clinical registry data and Medicare claims data. The method will allow researchers to use existing data to create a linked claims-clinical database that capitalizes on the strengths of both types of data sources.

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 This work was supported by grant U18HS10548 from the Agency for Healthcare Research and Quality (Rockville, MD) and a research agreement between GlaxoSmithKline (Research Triangle Park, NC) and Duke University (Durham, NC). Dr Hernandez is a recipient of an American Heart Association Pharmaceutical Roundtable grant (0675060N). Drs Curtis and Schulman were supported in part by grants U01HL066461 from the National Heart, Lung, and Blood Institute and R01AG026038 from the National Institute on Aging. Dr Fonarow is supported by the Ahmanson Foundation (Beverly Hills, CA) and the Corday Family Foundation (Los Angeles, CA). The OPTIMIZE-HF registry is registered at clinicaltrials.gov as study number NCT00344513.

 David J. Cohen, MD, MSc served as guest editor on this manuscript.

PII: S0002-8703(09)00269-5

doi:10.1016/j.ahj.2009.04.002

American Heart Journal
Volume 157, Issue 6 , Pages 995-1000, June 2009