Evidence map›Paper›PMID 38498022›Full record

ArticleJMIR human factors2024

Physicians' and Patients' Expectations From Digital Agents for Consultations: Interview Study Among Physicians and Patients.

Andri Färber, Christiane Schwabe, Philipp H Stalder, Mateusz Dolata, Gerhard Schwabe

Abstract read
In one paragraph

Article in JMIR human factors, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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

2 citing papers in PubMed.

  1. Article
  2. Rewriting Doom: Serial Pathways from eHealth Use to Reduced Cancer Fatalism in Family Cancer History Groups.Journal of cancer education : the official journal of the American Association for Cancer Education · 2026
    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

5 authors.

Andri FärberZHAW School of Management and Law, Zurich University of Applied Sciences, Winterthur, Switzerland.ORCID 0000-0002-9748-7817
Christiane SchwabeDepartment of Informatics, University of Zurich, Zurich, Switzerland.ORCID 0000-0002-8495-4930
Philipp H StalderZHAW School of Management and Law, Zurich University of Applied Sciences, Winterthur, Switzerland.ORCID 0000-0002-9910-3733
Mateusz DolataDepartment of Informatics, University of Zurich, Zurich, Switzerland.ORCID 0000-0002-2732-4465
Gerhard SchwabeDepartment of Informatics, University of Zurich, Zurich, Switzerland.ORCID 0000-0002-0453-9762

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPhysicians are currently overwhelmed by administrative tasks and spend very little time in consultations with patients, which hampers health literacy, shared decision-making, and treatment adherence.

objectiveThis study aims to examine whether digital agents constructed using fast-evolving generative artificial intelligence, such as ChatGPT, have the potential to improve consultations, adherence to treatment, and health literacy. We interviewed patients and physicians to obtain their opinions about 3 digital agents-a silent digital expert, a communicative digital expert, and a digital companion (DC).

methodsWe conducted in-depth interviews with 25 patients and 22 physicians from a purposeful sample, with the patients having a wide age range and coming from different educational backgrounds and the physicians having different medical specialties. Transcripts of the interviews were deductively coded using MAXQDA (VERBI Software GmbH) and then summarized according to code and interview before being clustered for interpretation.

resultsStatements from patients and physicians were categorized according to three consultation phases: (1) silent and communicative digital experts that are part of the consultation, (2) digital experts that hand over to a DC, and (3) DCs that support patients in the period between consultations. Overall, patients and physicians were open to these forms of digital support but had reservations about all 3 agents.

conclusionsUltimately, we derived 9 requirements for designing digital agents to support consultations, treatment adherence, and health literacy based on the literature and our qualitative findings.

Indexed as

Artificial IntelligencePhysiciansHumansMotivationQualitative ResearchReferral and Consultationadherence to treatmentdigital agentseHealthelectronic medical recordshealth literacymHealthmobile healthmobile phone

Identifiers

PMID38498022
PMCPMC10985611

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.