The impact of three discharge coding methods on the accuracy of diagnostic coding and hospital reimbursement for inpatient medical care

Tsopra, Rosy; Peckham, Daniel; Beirne, Paul; Rodger, Kirsty; Callister, Matthew; White, Helen; Jaïs, Jean Philippe; Ghosh, Dipansu; Whitaker, Paul; Clifton, Ian; Wyatt, Jeremy C.

doi:10.1016/j.ijmedinf.2018.03.015

Cited by 20 publications

(19 citation statements)

References 30 publications

(26 reference statements)

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“…The Read codes seemed to suggest the opposite problem; there are a large number of codes for recording a CF condition, suspected or otherwise but given the wide choice, a simple CF diagnosis code was often chosen. The use of coding systems in a consistent manner and the limitations apparent in some coding systems, is a recognised concern in the use of EHR [10,30] and the reasons for the misclassified cases seemed consistent with this concern.…”

Section: Discussionmentioning

confidence: 93%

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Identifying children with Cystic Fibrosis in population-scale routinely collected data in Wales

Griffiths

Schlüter

Akbari

et al. 2020

IJPDS

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Introduction The challenges in identifying a cohort of people with a rare condition can be addressed by routinely collected, population-scale electronic health record (eHR) data, which provide large volumes of data at a national level. This paper describes the challenges of accurately identifying a cohort of children with Cystic Fibrosis (CF) using eHR and their validation against the UK CF Registry. Objectives To establish a proof of principle and provide insight into the merits of linked data in CF research and the benefits of access to multiple data sources. In particular, the UK CF Registry data, and to demonstrate the opportunity it represents as a resource for future CF research. Method Three eHR data sources were used to identify children with CF born in Wales between 1st January 1998 and 31st August 2015 within the Secure Anonymised Information Linkage (SAIL) Databank. The UK CF Registry was later acquired by SAIL and linked to the eHR cohort to validate the cases and explore the reasons for misclassifications. Results We identified 352 children with CF in the three eHR data sources. This was greater than expected based on historical incidence rates in Wales. Subsequent validation using the UK CF Registry found that 257 (73%) of these were true cases. Over 98% of individuals identified as CF cases in all three eHR data sources were confirmed as true cases; but this was only the case for 19.8% of those identified in a single data source. Conclusion Identifying health conditions in eHR data can be challenging, so data quality assurance and validation is important or the merit of the research is undermined. This retrospective review identifies some of the challenges in identifying CF cases and demonstrates the benefits of linking cases across multiple data sources to improve quality.

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Section: Discussionmentioning

confidence: 93%

“…Whilst there is considerable value to be gained from the use of routine EHR for research, it also poses a number of challenges [9][10][11][12]. These can occur because routine EHR rely on data collected for administrative purposes rather than clinical research, so identifying clinical outcomes or decisions can be more difficult.…”

Section: Introductionmentioning

confidence: 99%

Identifying children with Cystic Fibrosis in population-scale routinely collected data in Wales

Griffiths

Schlüter

Akbari

et al. 2020

IJPDS

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“…Tsopra et al suggested that if the data needed to summarize patient discharge by specialist physicians were based on patient records, reports, and patient notes, it could reduce hospital costs by 1.8-16.5 million pounds (27); nonetheless, the results of different studies may not actually be comparable. Overall, it seems that the accuracy of the diagnostic codes or the errors in diagnostic coding in nine educational hospitals affiliated to Shahid Beheshti University of Medical Sciences is not significantly different from other studies.…”

Section: Discussionmentioning

confidence: 99%

Auditing the Accuracy of Medical Diagnostic Coding based on International Classification of Diseases, Tenth Revision

Mirhashemi

Ramezanghorbani

Asadi

et al. 2020

ircmj

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Background: Medical diagnostic coding is used for the ease of retrieval and accuracy of medical information classification in health information systems. This information is the main source of decision making for health managers and policymakers in planning, epidemiological, and medical research at different levels. Objectives: The present study aimed to audit the accuracy of the ICD-10 (International Classification of Diseases, Tenth Revision) medical diagnosis code. Materials and Methods: The present cross-sectional study was performed on a sample of 692 hospitalized cases in 9 educational centers affiliated to Shahid Beheshti University of Medical Sciences in the first half of 2020. The content validity of the checklist was determined in this study, and the obtained data were analyzed in SPSS software using descriptive statistics. Results: The average accuracy of coding for the main medical diagnoses across all subjects was 70%, signifying that 30% of medical records contain coding errors. The highest and least accuracy values of diagnostic coding were 80% and 47%, respectively. The application of standard abbreviations and file legibility were recognized as variables affecting code accuracy. The highest precision percentage of codes attributed to other medical diagnoses, including ICD-10-based comorbidity and complication, was in 84%-85% of the participants. Conclusion: Given the importance of all-encompassing coding in retrieving medical information, research, and macro-health policymaking, the coding accuracy audit must be conducted on a regular basis. The interaction between coders and healthcare providers, coders' training, and improving the documentation process exerts a significant impact on the enhancement of coding accuracy.

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“… 18 The application of machine learning in EHR is of great value. First, predictive modelling with EHR data can save labour, enhance efficiency, increase accuracy 19 , 20 and promote personalized medicine. 21 Second, machine-learning techniques can transform a large amount of textual information and unstructured data from patient records into structured data.…”

Section: Artificial Intelligence and Electronic Health Recordsmentioning

confidence: 99%

Artificial intelligence-aided decision support in paediatrics clinical diagnosis: development and future prospects

Zhang

Yang

et al. 2020

J Int Med Res

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Artificial intelligence (AI)-aided decision support has developed rapidly to meet the needs for effective analysis of substantial data sets from electronic medical records and medical images generated daily, and computer-assisted intelligent drug design. In clinical practice, paediatricians make medical decisions after obtaining a large amount of information about symptoms, physical examinations, laboratory test indicators, special examinations and treatments. This information is used in combination with paediatricians’ knowledge and experience to form the basis of clinical decisions. This diagnosis and therapeutic strategy development based on large amounts of information storage can be applied to both large clinical databases and data for individual patients. To date, AI applications have been of great value in intelligent diagnosis and treatment, intelligent image recognition, research and development of intelligent drugs and intelligent health management. This review aims to summarize recent advances in the research and clinical use of AI in paediatrics.

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The impact of three discharge coding methods on the accuracy of diagnostic coding and hospital reimbursement for inpatient medical care

Abstract: 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.

Cited by 20 publications

References 30 publications

Identifying children with Cystic Fibrosis in population-scale routinely collected data in Wales

Identifying children with Cystic Fibrosis in population-scale routinely collected data in Wales

Auditing the Accuracy of Medical Diagnostic Coding based on International Classification of Diseases, Tenth Revision

Artificial intelligence-aided decision support in paediatrics clinical diagnosis: development and future prospects

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