Nick A Heywood1, Michael D Gill2, Natasha Charlwood3, Rachel Brindle4, Cliona C Kirwan5. 1. Department of General Surgery, University Hospitals of South Manchester, Manchester, UK. Electronic address: Nick.heywood@nhs.net. 2. Department of Surgery, Royal Oldham Hospital, Pennine Acute Hospitals NHS Foundation Trust, Oldham, UK. 3. Department of Surgery, Stockport NHS Foundation Trust, Stockport, UK. 4. Department of Surgery, Salford Royal NHS Foundation Trust, Manchester, UK. 5. Institute of Cancer Sciences, University of Manchester & University Hospital South Manchester, Manchester, UK.
Abstract
BACKGROUND: Clinical coding data provide the basis for Hospital Episode Statistics and Healthcare Resource Group codes. High accuracy of this information is required for payment by results, allocation of health and research resources, and public health data and planning. We sought to identify the level of accuracy of clinical coding in general surgical admissions across hospitals in the Northwest of England. METHOD: Clinical coding departments identified a total of 208 emergency general surgical patients discharged between 1st March and 15th August 2013 from seven hospital trusts (median = 20, range = 16-60). Blinded re-coding was performed by a senior clinical coder and clinician, with results compared with the original coding outcome. Recorded codes were generated from OPCS-4 & ICD-10. RESULTS: Of all cases, 194 of 208 (93.3%) had at least one coding error and 9 of 208 (4.3%) had errors in both primary diagnosis and primary procedure. Errors were found in 64 of 208 (30.8%) of primary diagnoses and 30 of 137 (21.9%) of primary procedure codes. Median tariff using original codes was £1411.50 (range, £409-9138). Re-calculation using updated clinical codes showed a median tariff of £1387.50, P = 0.997 (range, £406-10,102). The most frequent reasons for incorrect coding were "coder error" and a requirement for "clinical interpretation of notes". CONCLUSIONS: Errors in clinical coding are multifactorial and have significant impact on primary diagnosis, potentially affecting the accuracy of Hospital Episode Statistics data and in turn the allocation of health care resources and public health planning. As we move toward surgeon specific outcomes, surgeons should increase collaboration with coding departments to ensure the system is robust.
BACKGROUND: Clinical coding data provide the basis for Hospital Episode Statistics and Healthcare Resource Group codes. High accuracy of this information is required for payment by results, allocation of health and research resources, and public health data and planning. We sought to identify the level of accuracy of clinical coding in general surgical admissions across hospitals in the Northwest of England. METHOD: Clinical coding departments identified a total of 208 emergency general surgical patients discharged between 1st March and 15th August 2013 from seven hospital trusts (median = 20, range = 16-60). Blinded re-coding was performed by a senior clinical coder and clinician, with results compared with the original coding outcome. Recorded codes were generated from OPCS-4 & ICD-10. RESULTS: Of all cases, 194 of 208 (93.3%) had at least one coding error and 9 of 208 (4.3%) had errors in both primary diagnosis and primary procedure. Errors were found in 64 of 208 (30.8%) of primary diagnoses and 30 of 137 (21.9%) of primary procedure codes. Median tariff using original codes was £1411.50 (range, £409-9138). Re-calculation using updated clinical codes showed a median tariff of £1387.50, P = 0.997 (range, £406-10,102). The most frequent reasons for incorrect coding were "coder error" and a requirement for "clinical interpretation of notes". CONCLUSIONS: Errors in clinical coding are multifactorial and have significant impact on primary diagnosis, potentially affecting the accuracy of Hospital Episode Statistics data and in turn the allocation of health care resources and public health planning. As we move toward surgeon specific outcomes, surgeons should increase collaboration with coding departments to ensure the system is robust.
Authors: Loreen Straub; Joshua J Gagne; Judith C Maro; Michael D Nguyen; Nicolas Beaulieu; Jeffrey S Brown; Adee Kennedy; Margaret Johnson; Adam Wright; Li Zhou; Shirley V Wang Journal: Drug Saf Date: 2019-09 Impact factor: 5.606
Authors: Yannik C Layer; Jan Menzenbach; Yonah L Layer; Andreas Mayr; Tobias Hilbert; Markus Velten; Andreas Hoeft; Maria Wittmann Journal: PLoS One Date: 2021-01-27 Impact factor: 3.240