When AI challenges the encoding of medical information. Performance and uncertainty of medical information encoding in a hospital

Le codage de l’information médicale à l’épreuve de l’IA

Loubna Echajari, Hugo Jeanningros () and Myriam Lewkowicz ()
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Loubna Echajari: LIST3N - TPRA - LIST3N - Technologies et pratiques - LIST3N - Laboratoire Informatique et Société Numérique - UTT - Université de Technologie de Troyes
Hugo Jeanningros: LIST3N - TPRA - LIST3N - Technologies et pratiques - LIST3N - Laboratoire Informatique et Société Numérique - UTT - Université de Technologie de Troyes
Myriam Lewkowicz: LIST3N - TPRA - LIST3N - Technologies et pratiques - LIST3N - Laboratoire Informatique et Société Numérique - UTT - Université de Technologie de Troyes

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Abstract: The deployment of artificial intelligence (AI) in healthcare organizations is not limited to the fields of research and care; the encoding of medical data, an essential part of hospital administration, is also challenged by AI, in the framework of activity-based pricing. Drawing on an interview and observation survey in a French hospital, we analyse the methods and effects of introducing AI into a Medical Information Department. While AI currently has only a marginal impact on professional and communication practices and procedures within this department, we show that the main reason for integrating AI is the financial efficiency of encoding in the context of activity-based pricing. The article establishes not only that AI does not reduce uncertainty in the encoding process, but also that it is likely to generate uncertainty about the quality of the encoded data, whereas it is an essential resource for medical research.

Date: 2025-01-14
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Published in Réseaux : communication, technologie, société, 2025, N° 248 (6), pp.153-191. ⟨10.3917/res.248.0153⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04890946

DOI: 10.3917/res.248.0153

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