The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Database and Registry Research in Orthopaedic Surgery: Part I
Abstract
The use of large-scale national databases for observational research in orthopaedic surgery has grown substantially in the last decade, and the data sets can be grossly categorized as either administrative claims or clinical registries. Administrative claims data comprise the billing records associated with the delivery of health-care services. Orthopaedic researchers have used both government and private claims to describe temporal trends, geographic variation, disparities, complications, outcomes, and resource utilization associated with both musculoskeletal disease and treatment. Medicare claims comprise one of the most robust data sets used to perform orthopaedic research, with >45 million beneficiaries. The U.S. government, through the Centers for Medicare & Medicaid Services, often uses these data to drive changes in health policy. Private claims data used in orthopaedic research often comprise more heterogeneous patient demographic samples, but allow longitudinal analysis similar to that offered by Medicare claims. Discharge databases, such as the U.S. National Inpatient Sample, provide a wide national sampling of inpatient hospital stays from all payers and allow analysis of associated adverse events and resource utilization. Administrative claims data benefit from the high patient numbers obtained through a majority of hospitals. Using claims, it is possible to follow patients longitudinally throughout encounters irrespective of the location of the institution delivering health care. Some disadvantages include lack of precision of ICD-9 (International Classification of Diseases, Ninth Revision) coding schemes. Much of these data are expensive to purchase, complicated to organize, and labor-intensive to manipulate--often requiring trained specialists for analysis. Given the changing health-care environment, it is likely that databases will provide valuable information that has the potential to influence clinical practice improvement and health policy for years to come.
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Cites
Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases
DEYO, R. (1992) · Journal of Clinical Epidemiology · doi:10.1016/0895-4356(92)90133-8 · Cited by 10,452
Murray, C. J. L., Vos, T., Lozano, R., Naghavi, M., Flaxman, A. D., Michaud, C., Ezzati, M., Shibuya, K., Salomon, J. A., Abdalla, S., et al. (2012) · The Lancet · doi:10.1016/s0140-6736(12)61689-4 · Cited by 8,888
Cited by
Herzog, M. M., Marshall, S. W., Lund, J. L., Pate, V., Mack, C. D., & Spang, J. T. (2018) · Sports Health: A Multidisciplinary Approach · doi:10.1177/1941738118803616 · Cited by 313
Surgical research using national databases
Alluri, R. K., Leland, H., & Heckmann, N. (2016) · Annals of Translational Medicine · doi:10.21037/atm.2016.10.49 · Cited by 266
Metadata
- Authors
- Pugely, A. J., Martin, C. T., Harwood, J., Ong, K. L., Bozic, K. J., & Callaghan, J. J.
- Venue
- The Journal of Bone and Joint Surgery
- Published
- 2015-08
- Volume
- 97
- Issue
- 15
- Pages
- 1278-1287
- ISSN
- 0021-9355 (ISSN-L)
- OpenAlex
- W1904726308
- Cited by
- 192
- References
- 2
- FWCI
- 31.87
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Database and Registry Research in Orthopaedic Surgery: Part I. https://app.cochat.ai/papers/10.2106/jbjs.n.01260
- The paper
- Pugely, A. J., Martin, C. T., Harwood, J., Ong, K. L., Bozic, K. J., & Callaghan, J. J. (2015). Database and Registry Research in Orthopaedic Surgery: Part I. The Journal of Bone and Joint Surgery. https://doi.org/10.2106/jbjs.n.01260
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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.