The Risk and Consequences of Clinical Miscoding Due to Inadequate Medical Documentation: A Case Study of the Impact on Health Services Funding

To examine potential sources of errors at each step of the described inpatient International Classification of Diseases (ICD) coding process. 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. 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. 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. 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.

The objectives of this study were to assess the accuracy of cause-of-death coding, determine the extent to which coders follow the selection rules of coding set out in the International Classification of Diseases, 9th Revision (ICD-9), and the effects of miscoding on mortality statistics in Taiwan. A systematic sample of 5621 death certificates was reviewed. The underlying cause of death (UCD) selected by the reviewer for each death certificate was compared with that selected by the original coder. The UCD was selected according to ACME (Automated Classification of Medical Entities) Decision Tables. The overall agreement rates between the reviewer and coders according to the three-digit and two-digit categories of ICD-9 were 80.9% and 83.9%, respectively. Good agreement was found for malignant neoplasms (kappa = 0.94) and injuries and poisoning (kappa = 0.97), but there was poor agreement for nephrotic diseases (kappa = 0.74), hypertension-related diseases (kappa = 0.74), and cerebral infarction (kappa = 0.77). Reasons for disagreements included disagreement in nomenclature (42.8%), inappropriate judgement of causal relationships (41.5%), and incorrect interpretation of Selection Rule 3 and Modification Rules (15.7%). This study showed various levels of agreement for different diseases between the reviewer and the original coders in selection of the UCD. Owing to the 'compensatory effect of errors', the national mortality statistics were not affected significantly. The national administration should undertake routine internal studies to control the quality of UCD coding practices.

Summary Estimating the true burden of influenza is problematic because relatively few hospitalisations or deaths are specifically coded as influenza related. Statistical regression techniques using influenza and respiratory syncytial virus surveillance data were used to estimate the number of excess hospitalisations and deaths attributable to influenza. Several International Classification of Diseases 10th Revision (ICD-10) groupings were used for both hospitalisation and mortality estimates, including influenza and pneumonia, other respiratory disorders, and circulatory disorders. For Australians aged 50–64 years, the annual excess hospitalisations attributable to influenza were 33.3 (95%CI: 23.2–43.4) per 100,000 for influenza and pneumonia and 57.6 (95%CI: 32.5–82.8) per 100,000 for other respiratory disorders. For Australians aged ≥65 years, the annual excess hospitalisations attributable to influenza were 157.4 (95%CI: 108.4–206.5) per 100,000 for influenza and pneumonia and 282.0 (95%CI: 183.7–380.3) per 100,000 for other respiratory disorders. The annual excess all-cause mortality attributable to influenza was 6.4 (95%CI: 2.6–10.2) per 100,000 and 116.4 (95%CI: 71.3–161.5) per 100,000, for Australians aged 50–64 years and those aged ≥65 years, respectively. In the age-group ≥65 years, a significant association was found between influenza activity and circulatory mortality. We conclude that influenza is responsible for a substantial amount of mortality and morbidity, over and above that which is directly diagnosed as influenza in Australians aged ≥50 years.