articleHealth Services ResearchAug 11, 2005BRONZE OA

Measuring Diagnoses: ICD Code Accuracy

Pearson (United States) · Michael E. DeBakey VA Medical Center · +1 more institution

PubMed
Indexed incrossrefpubmed

Abstract

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.

Citation impact

1,114
total citations
FWCI
13.01
Percentile
100%
References
36
Citations per year

Authors

6

Topics & keywords

Keywords
  • Computer science
  • Coding (social sciences)
  • Diagnosis code
  • Medical diagnosis
  • Health care
  • Source code
  • Process (computing)
  • Data mining
UN Sustainable Development Goals
  • Good health and well-being
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