Supporting the billing process in outpatient medical care: Automated medical coding through machine learning


Oberste, Luis ; Finze, Nikola ; Hoffmann, Philipp ; Heinzl, Armin



URL: https://aisel.aisnet.org/ecis2022_rp/136/
Document Type: Conference or workshop publication
Year of publication: 2022
Book title: Proceedings of the 30th European Conference on Information Systems (ECIS): Timișoara, Romania, June 18-24, 2022
The title of a journal, publication series: Conference of the European Colloid and Interface Society (ECIS) : Research Papers
Volume: 2022, Paper 136
Page range: 1-18
Date of the conference: 18.-24.06.2022
Place of publication: Atlanta, GA
Publishing house: AISeL
Publication language: English
Institution: Außerfakultäre Einrichtungen > Institut für Enterprise Systems (InES)
Business School > ABWL, Organisation u. Wirtschaftsinformatik I (Heinzl)
Subject: 330 Economics
Abstract: Reimbursement in medical care implies significant administrative effort for medical staff. To bill the treatments or services provided, diagnosis and treatment codes must be assigned to patient records using standardized healthcare classification systems, which is a time-consuming and error-prone task. In contrast to ICD diagnosis codes used in most countries for inpatient care reimbursement, outpatient medical care often involves different reimbursement schemes. Following the Action Design Research methodology, we developed an NLP-based machine learning artifact in close collaboration with a general practitioner’s office in Germany, leveraging a dataset of over 5,600 patients with more than 63,000 billing codes. For the code prediction of most problematic treatments as well as a complete code prediction task, we achieved F1-scores of 93.60 % and 78.22 %, respectively. Throughout three iterations, we derived five meta requirements leading to three design principles for an automated coding system to support the reimbursement of outpatient medical care.

Dieser Eintrag ist Teil der Universitätsbibliographie.




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