Collaboration, not Confrontation: Understanding General Practitioners’ Attitudes Towards Natural Language and Text Automation in Clinical Practice

Author:

Fraile Navarro David1ORCID,Kocaballi A. Baki2ORCID,Dras Mark3ORCID,Berkovsky Shlomo1ORCID

Affiliation:

1. Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, NSW, Australia

2. School of Computer Science, University of Technology Sydney, NSW, Australia

3. Department of Computing, Macquarie University, NSW, Australia

Abstract

General Practitioners are among the primary users and curators of textual electronic health records, highlighting the need for technologies supporting record access and administration. Recent advancements in natural language processing facilitate the development of clinical systems, automating some time-consuming record-keeping tasks. However, it remains unclear what automation tasks would benefit clinicians most, what features such automation should exhibit, and how clinicians will interact with the automation. We conducted semi-structured interviews with General Practitioners uncovering their views and attitudes toward text automation. The main emerging theme was doctor-AI collaboration, addressing a reciprocal clinician-technology relationship that does not threaten to substitute clinicians, but rather establishes a constructive synergistic relationship. Other themes included: (i) desired features for clinical text automation; (ii) concerns around clinical text automation; and (iii) the consultation of the future. Our findings will inform the design of future natural language processing systems, to be implemented in general practice.

Publisher

Association for Computing Machinery (ACM)

Subject

Human-Computer Interaction

Reference143 articles.

1. Peeking inside the black-box: A survey on explainable artificial intelligence (XAI);Adadi Amina;IEEE Access,2018

2. Emily Alsentzer, John Murphy, William Boag, Wei-Hung Weng, Di Jindi, Tristan Naumann, and Matthew McDermott. 2019. Publicly available clinical BERT Embeddings. In Proceedings of the 2nd Clinical Natural Language Processing Workshop, Association for Computational Linguistics, Minneapolis, Minnesota, 72--78. DOI:10.18653/v1/W19-1909

3. Explainability for artificial intelligence in healthcare: A multidisciplinary perspective;Amann Julia;BMC Med. Inform. Decis. Mak.,2020

4. Modernizing health information technology: Lessons from healthcare delivery systems;Amlung Joseph;JAMIA open,2020

5. Introduction to information extraction;Appelt Douglas E.;Ai Commun,1999

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