Evidence map›Paper›PMID 40425478›Full record

ArticleAnnals of family medicine2025

Seven Opportunities for Artificial Intelligence in Primary Care Electronic Visits: Qualitative Study of Staff and Patient Views.

Susan Moschogianis, Sarah Darley, Tessa Coulson, Niels Peek, Sudeh Cheraghi-Sohi, Benjamin C Brown

Abstract read
In one paragraph

Article in Annals of family medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

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

  1. Pooled it
  2. Overdiagnosis in atrial fibrillation screening with wearables.Scandinavian journal of primary health care · 2026
    Article
  3. Ambient scribes in general practice - help or hindrance?The British journal of general practice : the journal of the Royal College of General Practitioners · 2026
    Article
  4. Article
  5. Article
  6. 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

6 authors.

Susan MoschogianisSchool of Health Sciences, Division of Population Health, Health Services Research and Primary Care, University of Manchester, Manchester, United Kingdom.
Sarah DarleySchool of Health Sciences, Division of Population Health, Health Services Research and Primary Care, University of Manchester, Manchester, United Kingdom.
Tessa CoulsonNational Health Service Salford Clinical Commissioning Group, Salford, United Kingdom.
Niels PeekThe Healthcare Improvement Institute (THIS), Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom.
Sudeh Cheraghi-SohiSchool of Health Sciences, Division of Population Health, Health Services Research and Primary Care, University of Manchester, Manchester, United Kingdom.
Benjamin C BrownSchool of Health Sciences, Division of Population Health, Health Services Research and Primary Care, University of Manchester, Manchester, United Kingdom benjamin.brown@manchester.ac.uk.

Funding

Wellcome Trust
6 · The paper itself

Abstract

purposeIncreased workload associated with electronic visits (eVisits) in primary care could potentially be decreased by the use of artificial intelligence (AI); however, it is unknown whether this use of AI would be acceptable to staff and patients. We explored patient and primary care staff views on the use of and opportunities for AI during eVisits.

methodsWe conducted semistructured interviews and focus groups with primary care staff (n = 16) and patients (n = 37) from primary care practices in northwest England and London (n = 14) using the Patchs eVisits system (Patchs Health Limited;

resultsMisconceptions regarding AI were common, which led to initial reservations on its use during eVisits. Perceived potential AI benefits included decreased staff workload and faster response times for patients. Safety concerns stemmed from the complexity of primary care and fears of depersonalized service. The following 7 opportunities for AI during eVisits were identified: workflow, directing, prioritization, asking questions, writing assistance, providing self-help information, and face-to-face appointment booking. Despite staff concerns regarding patient acceptability, most patients welcomed the use of AI if it were used as an adjunct to (not replacement for) clinical judgment and could support them in getting help more quickly. Retention of clinical oversight and ongoing evaluation was key to staff acceptability.

conclusionsPatients and staff welcomed the use of AI and identified 7 potential uses during eVisits to decrease staff workload and improve patient safety. Successful implementation will depend on clear communication from practices, demonstrating and monitoring safety, clarifying misconceptions, and reassuring that it will not replace humans.

Indexed as

Artificial IntelligenceAttitude of Health PersonnelPrimary Health CareAdultAgedEnglandFemaleFocus GroupsHumansInterviews as TopicLondonMaleMiddle AgedQualitative ResearchWorkloadartificial intelligence (AI)electronic visits (eVisits)general practiceprimary health carequalitative research

Identifiers

PMID40425478
PMCPMC12120151

What OpenQuestion holds

Textmetadata
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.