Rosy Tsopra1, Daniel Peckham2, Paul Beirne3, Kirsty Rodger4, Matthew Callister5, Helen White6, Jean-Philippe Jais7, Dipansu Ghosh8, Paul Whitaker9, Ian J Clifton10, Jeremy C Wyatt11. 1. Leeds Centre for Respiratory Medicine, St James's University Hospital, Beckett Street, Leeds LS9 7TF, United Kingdom; Leeds Institute of Health Sciences, 101 Clarendon Rd, Leeds LS2 9LJ, United Kingdom; LIMICS, INSERM, U1142, Université Paris 13, Sorbonne Paris Cité, F75006 Paris, France; AP-HP, Assistance Publique des Hôpitaux de Paris, Paris, France. Electronic address: rosy.tsopra@aphp.fr. 2. Leeds Centre for Respiratory Medicine, St James's University Hospital, Beckett Street, Leeds LS9 7TF, United Kingdom. Electronic address: daniel.peckham@nhs.net. 3. Leeds Centre for Respiratory Medicine, St James's University Hospital, Beckett Street, Leeds LS9 7TF, United Kingdom. Electronic address: p.beirne@nhs.net. 4. Leeds Centre for Respiratory Medicine, St James's University Hospital, Beckett Street, Leeds LS9 7TF, United Kingdom. Electronic address: kirsty.rodger1@nhs.net. 5. Leeds Centre for Respiratory Medicine, St James's University Hospital, Beckett Street, Leeds LS9 7TF, United Kingdom. Electronic address: matthew.callister@nhs.net. 6. School of Clinical and Applied Science, Leeds Beckett University, Leeds LS1 3HE, United Kingdom. Electronic address: H.White@leedsbeckett.ac.uk. 7. INSERM UMRS 1138 Team 22, Paris Descartes University, Sorbonne Paris Cité, Assistance Publique Hôpitaux de Paris, Necker Enfants Malades Hospital, Biostatistics Unit, Paris, France. 8. Leeds Centre for Respiratory Medicine, St James's University Hospital, Beckett Street, Leeds LS9 7TF, United Kingdom. Electronic address: dipansughosh@nhs.net. 9. Leeds Centre for Respiratory Medicine, St James's University Hospital, Beckett Street, Leeds LS9 7TF, United Kingdom. Electronic address: p.whitaker@nhs.net. 10. Leeds Centre for Respiratory Medicine, St James's University Hospital, Beckett Street, Leeds LS9 7TF, United Kingdom. Electronic address: i.clifton@nhs.net. 11. Wessex Institute of Health & Research, Faculty of Medicine, University of Southampton, SO16 7NS, United Kingdom. Electronic address: j.c.wyatt@soton.ac.uk.
Abstract
BACKGROUND: Coding of diagnoses is important for patient care, hospital management and research. However coding accuracy is often poor and may reflect methods of coding. This study investigates the impact of three alternative coding methods on the inaccuracy of diagnosis codes and hospital reimbursement. METHODS: Comparisons of coding inaccuracy were made between a list of coded diagnoses obtained by a coder using (i)the discharge summary alone, (ii)case notes and discharge summary, and (iii)discharge summary with the addition of medical input. For each method, inaccuracy was determined for the primary, secondary diagnoses, Healthcare Resource Group (HRG) and estimated hospital reimbursement. These data were then compared with a gold standard derived by a consultant and coder. RESULTS: 107 consecutive patient discharges were analysed. Inaccuracy of diagnosis codes was highest when a coder used the discharge summary alone, and decreased significantly when the coder used the case notes (70% vs 58% respectively, p < 0.0001) or coded from the discharge summary with medical support (70% vs 60% respectively, p < 0.0001). When compared with the gold standard, the percentage of incorrect HRGs was 42% for discharge summary alone, 31% for coding with case notes, and 35% for coding with medical support. The three coding methods resulted in an annual estimated loss of hospital remuneration of between £1.8 M and £16.5 M. CONCLUSION: The accuracy of diagnosis codes and percentage of correct HRGs improved when coders used either case notes or medical support in addition to the discharge summary. Further emphasis needs to be placed on improving the standard of information recorded in discharge summaries.
BACKGROUND: Coding of diagnoses is important for patient care, hospital management and research. However coding accuracy is often poor and may reflect methods of coding. This study investigates the impact of three alternative coding methods on the inaccuracy of diagnosis codes and hospital reimbursement. METHODS: Comparisons of coding inaccuracy were made between a list of coded diagnoses obtained by a coder using (i)the discharge summary alone, (ii)case notes and discharge summary, and (iii)discharge summary with the addition of medical input. For each method, inaccuracy was determined for the primary, secondary diagnoses, Healthcare Resource Group (HRG) and estimated hospital reimbursement. These data were then compared with a gold standard derived by a consultant and coder. RESULTS: 107 consecutive patient discharges were analysed. Inaccuracy of diagnosis codes was highest when a coder used the discharge summary alone, and decreased significantly when the coder used the case notes (70% vs 58% respectively, p < 0.0001) or coded from the discharge summary with medical support (70% vs 60% respectively, p < 0.0001). When compared with the gold standard, the percentage of incorrect HRGs was 42% for discharge summary alone, 31% for coding with case notes, and 35% for coding with medical support. The three coding methods resulted in an annual estimated loss of hospital remuneration of between £1.8 M and £16.5 M. CONCLUSION: The accuracy of diagnosis codes and percentage of correct HRGs improved when coders used either case notes or medical support in addition to the discharge summary. Further emphasis needs to be placed on improving the standard of information recorded in discharge summaries.
Authors: Vera Alonso; João Vasco Santos; Marta Pinto; Joana Ferreira; Isabel Lema; Fernando Lopes; Alberto Freitas Journal: J Med Syst Date: 2020-02-08 Impact factor: 4.460