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Benchmarking variation in coding accuracy across the United States.
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2003
Year
EngineeringInformation ManagersUnited StatesSoftware AnalysisData ScienceBenchmark StudyMedical CodingData CodingPublic HealthCoding TheoryHealth Services ResearchHealth InformaticsHealth PolicyOutcomes ResearchComputer ScienceClinical DataError Correction CodeNursingMedical RecordsHealth DataProgram AnalysisCoded Medical DataMedical Information SystemPatient SafetyClinical Database
As the United States moves toward an evidence‑based medicine environment, current patient data classification methods may be limited without greater attention to coding practices. The study aimed to assess the consistency of coded medical data by surveying information managers about overall coding error levels in patient records. A cross‑sectional design was used to examine the reported percentage of records containing coding errors significant enough to alter a diagnostic related group (DRG). The study found that coding error rates varied widely, with 87 % of respondents reporting less than 5 % errors, 9 % reporting 6–10 %, and 5 % reporting more than 10 %, and that significant variation in accuracy and consistency exists across demographic and organizational factors, leading some providers to distrust aggregated coded data.
The objective of this study was to measure the consistency of coded medical data through information managers' reports of the overall coding error level in patients' medical records. Using a cross-sectional design, we examined the reported percent of records containing coding errors significant enough to change a diagnostic related group (DRG). Results indicate about 87 percent, 9 percent, and 5 percent of respondents reported that significant coding errors existed in less than 5 percent, 6-10 percent, and greater than 10 percent of the medical records in their institutions, respectively. Significant variation was found in the accuracy and consistency of coding practice and associated data quality across key demographic and organizational variables. Significantly large error rates in coded data exist in some organizations. Given variations across key demographic characteristics, providers may tend to distrust all coded data, when aggregated. As the United States moves toward an evidence-based medicine environment, the use of current patient data classification methods may be of limited value without increased attention to coding practices.