Evidence map›Paper›PMID 34546948›Full record

ArticleAnnals of family medicine

Technology-Enabled and Artificial Intelligence Support for Pre-Visit Planning in Ambulatory Care: Findings From an Environmental Scan.

Laura M Holdsworth, Chance Park, Steven M Asch, Steven Lin

Open access · diamondAbstract read
In one paragraph

Article in Annals of family medicine. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.5field-weighted citation impact, top 28% of its field
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

4 citing papers in PubMed, 10 citations in OpenAlex.

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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

4 authors at 2 institutions in 2 countries.

Laura M HoldsworthDivision of Primary Care and Population Health, Stanford School of Medicine, Stanford, California l.holdsworth@stanford.edu.
Chance ParkUniversity of British Columbia, Vancouver, British Columbia, Canada.
Steven M AschDivision of Primary Care and Population Health, Stanford School of Medicine, Stanford, California.
Steven LinDivision of Primary Care and Population Health, Stanford School of Medicine, Stanford, California.
Stanford Medicine · USUniversity of British Columbia · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposePre-visit planning (PVP) is believed to improve effectiveness, efficiency, and experience of care, yet numerous implementation barriers exist. There are opportunities for technology-enabled and artificial intelligence (AI) support to augment existing human-driven PVP processes-from appointment reminders and pre-visit questionnaires to pre-visit order sets and care gap closures. This study aimed to explore the current state of PVP, barriers to implementation, evidence of impact, and potential use of non-AI and AI tools to support PVP.

methodsWe used an environmental scan approach involving: (1) literature review; (2) key informant interviews with PVP experts in ambulatory care; and (3) a search of the public domain for technology-enabled and AI solutions that support PVP. We then synthesized the findings using a qualitative matrix analysis.

resultsWe found 26 unique PVP implementations in the literature and conducted 16 key informant interviews. Demonstration of impact is typically limited to process outcomes, with improved patient outcomes remaining elusive. Our key informants reported that many PVP barriers are human effort-related and see potential for non-AI and AI technologies to support certain aspects of PVP. We identified 8 examples of commercially available technology-enabled tools that support PVP, some with AI capabilities; however, few of these have been independently evaluated.

conclusionsAs health systems transition toward value-based payment models in a world where the coronavirus disease 2019 pandemic has shifted patient care into the virtual space, PVP activities-driven by humans and supported by technology-may become more important and powerful and should be rigorously evaluated.

Indexed as

Ambulatory CareArtificial IntelligenceCOVID-19HumansSARS-CoV-2Technologyambulatory careartificial intelligencequalitative methods

Identifiers

PMID34546948
PMCPMC8437572
OpenAlexW3200024623

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.