Evidence map›Paper›PMID 31399099›Full record

ArticleBMC medical informatics and decision making2019

Collective intelligence in medical decision-making: a systematic scoping review.

Kate Radcliffe, Helena C Lyson, Jill Barr-Walker, Urmimala Sarkar

Open access · goldAbstract readScoping Review
In one paragraph

Article in BMC medical informatics and decision making, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 1 pooled it
5.7field-weighted citation impact, top 4% 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

26 citing papers in PubMed, 1 synthesis or guideline pooled it, 64 citations in OpenAlex.

  1. Pooled it
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  8. Emerging trends in managed care pharmacy: A mixed-method study.Journal of managed care & specialty pharmacy · 2025
    Article
  9. Review
  10. Ten misconceptions regarding decision-making in critical care.World journal of critical care medicine · 2024
    Review
  11. Collective Intelligence Increases Diagnostic Accuracy in a General Practice Setting.Medical decision making : an international journal of the Society for Medical Decision Making · 2024
    Article
  12. Article
  13. Article
  14. Experimental evidence for structured information-sharing networks reducing medical errors.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  15. Article
  16. Article
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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 1 country.

Kate RadcliffeCenter for Vulnerable Populations, University of California, San Francisco, USA.
Helena C LysonCenter for Vulnerable Populations, University of California, San Francisco, USA.
Jill Barr-WalkerZuckerberg San Francisco General Hospital Library, University of California, San Francisco, San Francisco, CA, USA.
Urmimala SarkarCenter for Vulnerable Populations, University of California, San Francisco, USA. Urmimala.sarkar@ucsf.edu.ORCID 0000-0003-4213-4405
University of California, San Francisco · USSan Francisco General Hospital · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCollective intelligence, facilitated by information technology or manual techniques, refers to the collective insight of groups working on a task and has the potential to generate more accurate information or decisions than individuals can make alone. This concept is gaining traction in healthcare and has potential in enhancing diagnostic accuracy. We aim to characterize the current state of research with respect to collective intelligence in medical decision-making and describe a framework for diverse studies in this topic.

methodsFor this systematic scoping review, we conducted a systematic search for published literature using PubMed, Embase, Web of Science, and CINAHL on August 8, 2017. We included studies that combined the insights of two or more medical experts to make decisions related to patient care. Studies that examined medical decisions such as diagnosis, treatment, and management in the context of an actual or theoretical patient case were included. We include studies of complex medical decision-making rather than identification of a visual finding, as in radiology or pathology. We differentiate between medical decisions, in which synthesis of multiple types of information is required over time, and studies of radiological scans or pathological specimens, in which objective identification of a visual finding is performed. Two reviewers performed article screening, data extraction, and final inclusion for analysis.

resultsOf 3303 original articles, 15 were included. Each study examined the medical decisions of two or more individuals; however, studies were heterogeneous in their methods and outcomes. We present a framework to characterize these diverse studies, and future investigations, based on how they operationalize collective intelligence for medical decision-making: 1) how the initial decision task was completed (group vs. individual), 2) how opinions were synthesized (information technology vs. manual vs. in-person), and 3) the availability of collective intelligence to participants. DISCUSSION: Collective intelligence in medical decision-making is gaining popularity to advance medical decision-making and holds promise to improve patient outcomes. However, heterogeneous methods and outcomes make it difficult to assess the utility of collective intelligence approaches across settings and studies. A better understanding of collective intelligence and its applications to medicine may improve medical decision-making.

Indexed as

Clinical Decision-MakingDelivery of Health CareGroup ProcessesIntelligenceHumansMass ScreeningRadiology

Identifiers

PMID31399099
PMCPMC6688241
OpenAlexW2967990436

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.