Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2015 Dec;76(12):707-12.
doi: 10.12968/hmed.2015.76.12.707.

Improving the accuracy of HES comorbidity codes by better documentation in surgical admission proforma

Affiliations

Improving the accuracy of HES comorbidity codes by better documentation in surgical admission proforma

Ahmad Navid et al. Br J Hosp Med (Lond). 2015 Dec.

Abstract

Background: Poor documentation in medical notes can affect the quality of the source document for coding which can lead to inaccurate coding. This study aimed to determine the accuracy of Hospital Episode Statistics (HES) data for comorbidities and to establish whether better documentation in admission clerking proforma can improve the accuracy of codes for comorbidities in general surgical patients.

Methods: A clinical audit was conducted to assess the accuracy, sensitivity, specificity, positive predictive value and negative predictive value of HES codes for comorbidities in general surgical patients before and after implementing better documentation in admission clerking proforma. The following comorbidities were included: hypertension, ischaemic heart disease, diabetes, asthma, chronic obstructive pulmonary disease, cerebrovascular disease, chronic kidney disease and hypercholesterolaemia. Medical notes were used as reference standard and a target standard of 98% was determined for the above measures.

Results: Overall, on the initial audit, HES codes had substandard accuracy (90.5%, kappa = 0.599), sensitivity (47.71%, 95% confidence interval 38.05-57.49%) and negative predictive value (89.60%, 95% confidence interval 86.73-92.03%). HES codes for comorbidities were 100% specific with positive predictive value of 100%. Implementing better documentation in the admission clerking proforma improved the accuracy (99.67%, kappa = 0.985), sensitivity (97.4%, 95% confidence interval 90.93-99.68%) and negative predictive value (99.62%, 95% confidence interval 98.63-99.95%) significantly from the baseline (P<0.0001).

Conclusions: Although HES codes can confidently predict the actual presence of the comorbidities, they have substandard accuracy and ability to rule out the presence of the comorbidities. Better documentation in clerking proforma can improve the accuracy and 'ruling out' ability of the HES codes. This can be achieved by improving knowledge and accountability of clinicians about documenting comorbidities.

PubMed Disclaimer

LinkOut - more resources