The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Measuring Diagnoses: ICD Code Accuracy
Open accessAbstract
Objective. To examine potential sources of errors at each step of the described inpatient International Classification of Diseases (ICD) coding process. Data Sources/Study Setting. The use of disease codes from the ICD has expanded from classifying morbidity and mortality information for statistical purposes to diverse sets of applications in research, health care policy, and health care finance. By describing a brief history of ICD coding, detailing the process for assigning codes, identifying where errors can be introduced into the process, and reviewing methods for examining code accuracy, we help code users more systematically evaluate code accuracy for their particular applications. Study Design/Methods. We summarize the inpatient ICD diagnostic coding process from patient admission to diagnostic code assignment. We examine potential sources of errors at each step and offer code users a tool for systematically evaluating code accuracy. Principle Findings. Main error sources along the “patient trajectory” include amount and quality of information at admission, communication among patients and providers, the clinician's knowledge and experience with the illness, and the clinician's attention to detail. Main error sources along the “paper trail” include variance in the electronic and written records, coder training and experience, facility quality‐control efforts, and unintentional and intentional coder errors, such as misspecification, unbundling, and upcoding. Conclusions. By clearly specifying the code assignment process and heightening their awareness of potential error sources, code users can better evaluate the applicability and limitations of codes for their particular situations. ICD codes can then be used in the most appropriate ways.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Diagnostic and Statistical Manual of Mental Disorders
American Psychiatric Association (2022) · doi:10.1176/appi.books.9780890425787 · Cited by 23,476
Overweight, obesity, and mortality from cancer in a prospectively studied cohort of U.S. Adults
Calle, E. E., Rodriguez, C., Walker-Thurmond, K., & Thun, M. J. (2003) · New England Journal of Medicine · doi:10.1056/nejmoa021423 · Cited by 7,696
The epidemiology of sepsis in the United States from 1979 through 2000
Martin, G. S., Mannino, D. M., Eaton, S., & Moss, M. (2003) · New England Journal of Medicine · doi:10.1056/nejmoa022139 · Cited by 5,841
Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative
Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P. P., Irwig, L. M., Lijmer, J. G., Moher, D., Rennie, D., & de Vet, H. C. W. (2003) · BMJ · doi:10.1136/bmj.326.7379.41 · Cited by 3,215
Towards Complete and Accurate Reporting of Studies of Diagnostic Accuracy: The STARD Initiative
Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P. P., Irwig, L. M., Lijmer, J. G., Moher, D., Rennie, D., & de Vet, H. C. W. (2003) · Radiology · doi:10.1148/radiol.2261021292 · Cited by 3,215
Cited by
Do no harm: a roadmap for responsible machine learning for health care
Wiens, J., Saria, S., Sendak, M., Ghassemi, M., Liu, V. X., Doshi-Velez, F., Jung, K., Heller, K., Kale, D., Saeed, M., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0548-6 · Cited by 1,266
Walley, A. Y., Xuan, Z., Hackman, H. H., Quinn, E., Doe-Simkins, M., Sorensen-Alawad, A., Ruiz, S., & Ozonoff, A. (2013) · BMJ · doi:10.1136/bmj.f174 · Cited by 999
Hersh, W. R., Weiner, M. G., Embi, P. J., Logan, J. R., Payne, P. R. O., Bernstam, E. V., Lehmann, H. P., Hripcsak, G., Hartzog, T. H., Cimino, J. J., et al. (2013) · Medical Care · doi:10.1097/mlr.0b013e31829b1dbd · Cited by 567
Brogan, T. V., Thiagarajan, R. R., Rycus, P. T., Bartlett, R. H., & Bratton, S. L. (2009) · Intensive Care Medicine · doi:10.1007/s00134-009-1661-7 · Cited by 441
Zeng, Q. T., Goryachev, S., Weiss, S., Sordo, M., Murphy, S. N., & Lazarus, R. (2006) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-6-30 · Cited by 360
Metadata
- Authors
- O'Malley, K. J., Cook, K. F., Price, M. D., Wildes, K. R., Hurdle, J. F., & Ashton, C. M.
- Venue
- Health Services Research
- Publisher
- Wiley
- Published
- 2005-08-11
- Volume
- 40
- Issue
- 5p2
- Pages
- 1620-1639
- ISSN
- 0017-9124 (ISSN-L)
- 1475-6773
- OpenAlex
- W1997057722
- Cited by
- 1,116
- References
- 5
- FWCI
- 15.41
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Measuring Diagnoses: ICD Code Accuracy. https://app.cochat.ai/papers/10.1111/j.1475-6773.2005.00444.x
- The paper
- O'Malley, K. J., Cook, K. F., Price, M. D., Wildes, K. R., Hurdle, J. F., & Ashton, C. M. (2005). Measuring Diagnoses: ICD Code Accuracy. Health Services Research. https://doi.org/10.1111/j.1475-6773.2005.00444.x
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.