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Linking inpatient clinical registry data to Medicare claims data using indirect identifiers
Open accessAbstract
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 6581 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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Cited by
Heart Failure With Preserved, Borderline, and Reduced Ejection Fraction
Shah, K. S., Xu, H., Matsouaka, R. A., Bhatt, D. L., Heidenreich, P. A., Hernandez, A. F., Devore, A. D., Yancy, C. W., & Fonarow, G. C. (2017) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2017.08.074 · Cited by 1,110
Self-citationHernandez, A. F. (2010) · JAMA · doi:10.1001/jama.2010.533 · Cited by 1,032
Self-citationRegistries for Evaluating Patient Outcomes: A User’s Guide
(2020) · doi:10.23970/ahrqepcregistries4 · Cited by 960
Gupta, A., Allen, L. A., Bhatt, D. L., Cox, M., DeVore, A. D., Heidenreich, P. A., Hernandez, A. F., Peterson, E. D., Matsouaka, R. A., Yancy, C. W., et al. (2018) · JAMA Cardiology · doi:10.1001/jamacardio.2017.4265 · Cited by 425
Self-citationFrizzell, J. D., Liang, L., Schulte, P. J., Yancy, C. W., Heidenreich, P. A., Hernandez, A. F., Bhatt, D. L., Fonarow, G. C., & Laskey, W. K. (2017) · JAMA Cardiology · doi:10.1001/jamacardio.2016.3956 · Cited by 361
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Metadata
- Authors
- Hammill, B. G., Hernandez, A. F., Peterson, E. D., Fonarow, G. C., Schulman, K. A., & Curtis, L. H.
- Venue
- American Heart Journal
- Publisher
- Elsevier BV
- Published
- 2009-06
- Volume
- 157
- Issue
- 6
- Pages
- 995-1000
- ISSN
- 0002-8703 (ISSN-L)
- OpenAlex
- W2028120249
- Cited by
- 389
- References
- 0
- FWCI
- 27.51
- Type
- journal article
- Language
- en
- Open access
- green
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- Hammill, B. G., Hernandez, A. F., Peterson, E. D., Fonarow, G. C., Schulman, K. A., & Curtis, L. H. (2009). Linking inpatient clinical registry data to Medicare claims data using indirect identifiers. American Heart Journal. https://doi.org/10.1016/j.ahj.2009.04.002
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.