Seyed Ahmad Reza Nouraei1, Jagdeep Singh Virk2, Anita Hudovsky3, Christopher Wathen4, Ara Darzi5, Darren Parsons6. 1. Department of Ear Nose Throat Surgery, Imperial College Healthcare Trust, Charing Cross Hospital, London W6 8RF, UK National Institute for Health and Care Excellence (2013) Scholar, London W6 8RF, UK UCL Ear Institute, 332 Grays Inn Road, London WC1X 8EE, UK. 2. Department of ENT Surgery, Queen's Hospital, Romford, UK. 3. Department of Clinical Coding, Charing Cross Hospital, London, UK. 4. Department of Respiratory Medicine, Buckinghamshire Healthcare NHS Trust, Amersham, UK. 5. Academic Surgical Unit, Department of Surgery and Cancer, Imperial College Healthcare Trust, St Mary's Hospital, London, UK. 6. Directorate of Renal and Transplant Medicine, Imperial College Healthcare NHS Trust, Hammersmith Hospital, London, UK.
Abstract
BACKGROUND: We evaluated the accuracy, limitations and potential sources of improvement in the clinical utility of the administrative dataset for acute medicine admissions. METHODS: Accuracy of clinical coding in 8888 patient discharges following an emergency medical hospital admission to a teaching hospital and a district hospital over 3 years was ascertained by a coding accuracy audit team in respect of the primary and secondary diagnoses, morbidities and financial variance. RESULTS: There was at least one change to the original coding in 4889 admissions (55%) and to the primary diagnosis of at least one finished consultant episodes of 1496 spells (16.8%). There were significant changes in the number of secondary diagnoses and the Charlson morbidity index following the audit. Charlson score increased in 8.2% and decreased in 2.3% of patients. An income variance of £816 977 (+5.0%) or £91.92 per patient was observed. CONCLUSIONS: The importance and applications of coded healthcare big data within the NHS is increasing. The accuracy of coding is dependent on high-fidelity information transfer between clinicians and coders, which is prone to subjectivity, variability and error. We recommend greater involvement of clinicians as part of multidisciplinary teams to improve data accuracy, and urgent action to improve abstraction and clarity of assignment of strategic diagnoses like pneumonia and renal failure.
BACKGROUND: We evaluated the accuracy, limitations and potential sources of improvement in the clinical utility of the administrative dataset for acute medicine admissions. METHODS: Accuracy of clinical coding in 8888 patient discharges following an emergency medical hospital admission to a teaching hospital and a district hospital over 3 years was ascertained by a coding accuracy audit team in respect of the primary and secondary diagnoses, morbidities and financial variance. RESULTS: There was at least one change to the original coding in 4889 admissions (55%) and to the primary diagnosis of at least one finished consultant episodes of 1496 spells (16.8%). There were significant changes in the number of secondary diagnoses and the Charlson morbidity index following the audit. Charlson score increased in 8.2% and decreased in 2.3% of patients. An income variance of £816 977 (+5.0%) or £91.92 per patient was observed. CONCLUSIONS: The importance and applications of coded healthcare big data within the NHS is increasing. The accuracy of coding is dependent on high-fidelity information transfer between clinicians and coders, which is prone to subjectivity, variability and error. We recommend greater involvement of clinicians as part of multidisciplinary teams to improve data accuracy, and urgent action to improve abstraction and clarity of assignment of strategic diagnoses like pneumonia and renal failure.
Authors: Jonathan D Davis; Margaret A Olsen; Kerry Bommarito; Shane J LaRue; Mohammed Saeed; Michael W Rich; Justin M Vader Journal: Am J Med Date: 2016-08-31 Impact factor: 4.965
Authors: Jonathan Hinton; Mark Mariathas; Lavinia Gabara; Zoe Nicholas; Rick Allan; Sanjay Ramamoorthy; Mamas A Mamas; Michael Mahmoudi; Paul Cook; Nick Curzen Journal: Clin Med (Lond) Date: 2020-11 Impact factor: 2.659
Authors: Tiffany Pellathy; Melissa Saul; Gilles Clermont; Artur W Dubrawski; Michael R Pinsky; Marilyn Hravnak Journal: J Clin Monit Comput Date: 2021-02-08 Impact factor: 1.977
Authors: S A Zafirah; Amrizal Muhammad Nur; Sharifa Ezat Wan Puteh; Syed Mohamed Aljunid Journal: BMC Health Serv Res Date: 2018-01-25 Impact factor: 2.655