Evidence map›Paper›PMID 33742085›Full record

ArticleNPJ digital medicine2021

The potential of artificial intelligence to improve patient safety: a scoping review.

David W Bates, David Levine, Ania Syrowatka, Masha Kuznetsova, Kelly Jean Thomas Craig, Angela Rui, Gretchen Purcell Jackson, Kyu Rhee

Open access · goldAbstract readScoping Review
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 110 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
110citing papers in PubMed, 7 pooled it
33.5field-weighted citation impact, top 1% 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

110 citing papers in PubMed, 7 syntheses or guidelines pooled it, 271 citations in OpenAlex.

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  3. Effect of electronic drug-drug interaction alerts on patient and clinician outcomes: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2025
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  19. Reintegrating the Human in Health: A Triadic Blueprint for Whole-Person Care in the Age of AI.International journal of environmental research and public health · 2026
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50 more citing papers are in PubMed but not listed here.

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

8 authors at 3 institutions in 1 country.

David W BatesDivision of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA. dbates@bwh.harvard.edu.ORCID http://orcid.org/0000-0001-6268-1540
David LevineDivision of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Ania SyrowatkaDivision of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA.ORCID http://orcid.org/0000-0002-7161-9770
Masha KuznetsovaHarvard Business School, Harvard University, Boston, MA, USA.
Kelly Jean Thomas CraigIBM Watson Health, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-9954-2795
Angela RuiDivision of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Gretchen Purcell JacksonIBM Watson Health, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-3242-8058
Kyu RheeIBM Watson Health, Cambridge, MA, USA.
Brigham and Women's Hospital · USIBM (United States) · USHarvard University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) represents a valuable tool that could be used to improve the safety of care. Major adverse events in healthcare include: healthcare-associated infections, adverse drug events, venous thromboembolism, surgical complications, pressure ulcers, falls, decompensation, and diagnostic errors. The objective of this scoping review was to summarize the relevant literature and evaluate the potential of AI to improve patient safety in these eight harm domains. A structured search was used to query MEDLINE for relevant articles. The scoping review identified studies that described the application of AI for prediction, prevention, or early detection of adverse events in each of the harm domains. The AI literature was narratively synthesized for each domain, and findings were considered in the context of incidence, cost, and preventability to make projections about the likelihood of AI improving safety. Three-hundred and ninety-two studies were included in the scoping review. The literature provided numerous examples of how AI has been applied within each of the eight harm domains using various techniques. The most common novel data were collected using different types of sensing technologies: vital sign monitoring, wearables, pressure sensors, and computer vision. There are significant opportunities to leverage AI and novel data sources to reduce the frequency of harm across all domains. We expect AI to have the greatest impact in areas where current strategies are not effective, and integration and complex analysis of novel, unstructured data are necessary to make accurate predictions; this applies specifically to adverse drug events, decompensation, and diagnostic errors.

Identifiers

PMID33742085
PMCPMC7979747
OpenAlexW3138676254

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.