Evidence map›Paper›PMID 32845984›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2020

Envisioning an artificial intelligence documentation assistant for future primary care consultations: A co-design study with general practitioners.

A Baki Kocaballi, Kiran Ijaz, Liliana Laranjo, Juan C Quiroz, Dana Rezazadegan, Huong Ly Tong, Simon Willcock, Shlomo Berkovsky, Enrico Coiera

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In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

40 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

A Baki KocaballiAustralian Institute of Health Innovation, Macquarie University, Sydney, Australia.
Kiran IjazAustralian Institute of Health Innovation, Macquarie University, Sydney, Australia.
Liliana LaranjoAustralian Institute of Health Innovation, Macquarie University, Sydney, Australia.
Juan C QuirozAustralian Institute of Health Innovation, Macquarie University, Sydney, Australia.
Dana RezazadeganAustralian Institute of Health Innovation, Macquarie University, Sydney, Australia.
Huong Ly TongAustralian Institute of Health Innovation, Macquarie University, Sydney, Australia.
Simon WillcockHealth Sciences Centre, Macquarie University, Sydney, Australia.
Shlomo BerkovskyAustralian Institute of Health Innovation, Macquarie University, Sydney, Australia.
Enrico CoieraAustralian Institute of Health Innovation, Macquarie University, Sydney, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe study sought to understand the potential roles of a future artificial intelligence (AI) documentation assistant in primary care consultations and to identify implications for doctors, patients, healthcare system, and technology design from the perspective of general practitioners. MATERIALS AND

methodsCo-design workshops with general practitioners were conducted. The workshops focused on (1) understanding the current consultation context and identifying existing problems, (2) ideating future solutions to these problems, and (3) discussing future roles for AI in primary care. The workshop activities included affinity diagramming, brainwriting, and video prototyping methods. The workshops were audio-recorded and transcribed verbatim. Inductive thematic analysis of the transcripts of conversations was performed.

resultsTwo researchers facilitated 3 co-design workshops with 16 general practitioners. Three main themes emerged: professional autonomy, human-AI collaboration, and new models of care. Major implications identified within these themes included (1) concerns with medico-legal aspects arising from constant recording and accessibility of full consultation records, (2) future consultations taking place out of the exam rooms in a distributed system involving empowered patients, (3) human conversation and empathy remaining the core tasks of doctors in any future AI-enabled consultations, and (4) questioning the current focus of AI initiatives on improved efficiency as opposed to patient care.

conclusionsAI documentation assistants will likely to be integral to the future primary care consultations. However, these technologies will still need to be supervised by a human until strong evidence for reliable autonomous performance is available. Therefore, different human-AI collaboration models will need to be designed and evaluated to ensure patient safety, quality of care, doctor safety, and doctor autonomy.

Indexed as

Artificial IntelligenceAttitude of Health PersonnelDocumentationGeneral PractitionersPrimary Health CareProfessional AutonomyAttitude to ComputersDecision Making, Computer-AssistedElectronic Health RecordsForecastingHumansReferral and ConsultationUser-Computer Interfaceartificial intelligencegeneral practitionersmedical informaticsprimary health carequalitative study

Identifiers

PMID32845984
PMCPMC7671614

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.