Evidence map›Paper›PMID 39353696›Full record

ArticleBMJ open2024

Prospectively investigating the impact of AI onshared decision-making in post kidney transplant care (PRIMA-AI): protocol for a longitudinal qualitative study among patients, their support persons and treating physicians at a tertiary care centre.

Zeineb Sassi, Sascha Eickmann, Roland Roller, Bilgin Osmanodja, Aljoscha Burchardt, David Samhammer, Peter Dabrock, Sebastian Möller, Klemens Budde, Anne Herrmann

Abstract read
In one paragraph

Article in BMJ open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
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

10 authors.

Zeineb SassiDepartment of Epidemiology and Preventive Medicine, Medical Sociology, University of Regensburg, Regensburg, Germany zeineb.sassi@klinik.uni-regensburg.de.ORCID 0009-0004-9720-9528
Sascha EickmannDepartment of Epidemiology and Preventive Medicine, Medical Sociology, University of Regensburg, Regensburg, Germany.
Roland RollerGerman Research Center for Artificial Intelligence, DFKI, Berlin, Germany.
Bilgin OsmanodjaDepartment of Nephrology and Medical Intensive Care, Charité - Universitätsmedizin Berlin, Corporate Member of Free University of Berlin, Berlin Institute of Health, Humboldt-University of Berlin, Berlin, Germany.
Aljoscha BurchardtGerman Research Center for Artificial Intelligence, DFKI, Berlin, Germany.
David SamhammerInstitute for Systematic Theology II (Ethics), Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany.
Peter DabrockInstitute for Systematic Theology II (Ethics), Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany.
Sebastian MöllerQuality and Usability Lab, Technische Universität Berlin, Berlin, Germany.
Klemens BuddeDepartment of Nephrology and Medical Intensive Care, Charité - Universitätsmedizin Berlin, Corporate Member of Free University of Berlin, Berlin Institute of Health, Humboldt-University of Berlin, Berlin, Germany.ORCID 0000-0002-7929-5942
Anne HerrmannDepartment of Epidemiology and Preventive Medicine, Medical Sociology, University of Regensburg, Regensburg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAs healthcare is shifting from a paternalistic to a patient-centred approach, medical decision making becomes more collaborative involving patients, their support persons (SPs) and physicians. Implementing shared decision-making (SDM) into clinical practice can be challenging and becomes even more complex with the introduction of artificial intelligence (AI) as a potential actant in the communicative network. Although there is more empirical research on patients' and physicians' perceptions of AI, little is known about the impact of AI on SDM. This study will help to fill this gap. To the best of our knowledge, this is the first systematic empirical investigation to prospectively assess the views of patients, their SPs and physicians on how AI affects SDM in physician-patient communication after kidney transplantation. Using a transdisciplinary approach, this study will explore the role and impact of an AI-decision support system (DSS) designed to assist with medical decision making in the clinical encounter. METHODS AND ANALYSIS: This is a plan to roll out a 2 year, longitudinal qualitative interview study in a German kidney transplant centre. Semi-structured interviews with patients, SPs and physicians will be conducted at baseline and in 3-, 6-, 12- and 24-month follow-up. A total of 50 patient-SP dyads and their treating physicians will be recruited at baseline. Assuming a dropout rate of 20% per year, it is anticipated that 30 patient-SP dyads will be included in the last follow-up with the aim of achieving data saturation. Interviews will be audio-recorded and transcribed verbatim. Transcripts will be analysed using framework analysis. Participants will be asked to report on their (a) communication experiences and preferences, (b) views on the influence of the AI-based DSS on the normative foundations of the use of AI in medical decision-making, focusing on agency along with trustworthiness, transparency and responsibility and (c) perceptions of the use of the AI-based DSS, as well as barriers and facilitators to its implementation into routine care. ETHICS AND DISSEMINATION: Approval has been granted by the local ethics committee of Charité-Universitätsmedizin Berlin (EA1/177/23 on 08 August 2023). This research will be conducted in accordance with the principles of the Declaration of Helsinki (1996). The study findings will be used to develop communication guidance for physicians on how to introduce and sustainably implement AI-assisted SDM. The study results will also be used to develop lay language patient information on AI-assisted SDM. A broad dissemination strategy will help communicate the results of this research to a variety of target groups, including scientific and non-scientific audiences, to allow for a more informed discourse among different actors from policy, science and society on the role and impact of AI in physician-patient communication.

Indexed as

Artificial IntelligenceDecision Making, SharedKidney TransplantationPhysician-Patient RelationsQualitative ResearchTertiary Care CentersCommunicationGermanyHumansLongitudinal StudiesMalePatient ParticipationProspective StudiesResearch DesignClinical Decision-MakingHealth informaticsNephrologyPatient ParticipationTransplant medicine

Identifiers

PMID39353696
PMCPMC11448240

What OpenQuestion holds

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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.