Evidence map›Paper›PMID 39042600›Full record

ArticlePLOS digital health2024

The First Generative AI Prompt-A-Thon in Healthcare: A Novel Approach to Workforce Engagement with a Private Instance of ChatGPT.

William R Small, Kiran Malhotra, Vincent J Major, Batia Wiesenfeld, Marisa Lewis, Himanshu Grover, Huming Tang, Arnab Banerjee, Michael J Jabbour, Yindalon Aphinyanaphongs and 2 more

Abstract read
In one paragraph

Article in PLOS digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

12 authors.

William R SmallDepartment of Health Informatics, NYU Langone Health, New York, New York, United States of America.ORCID https://orcid.org/0009-0007-3105-6495
Kiran MalhotraDepartment of Health Informatics, NYU Langone Health, New York, New York, United States of America.
Vincent J MajorDepartment of Health Informatics, NYU Langone Health, New York, New York, United States of America.
Batia WiesenfeldDepartment of Management and Organizations, NYU Stern School of Business, New York, New York, United States of America.
Marisa LewisDepartment of Health Informatics, NYU Langone Health, New York, New York, United States of America.
Himanshu GroverDepartment of Health Informatics, NYU Langone Health, New York, New York, United States of America.
Huming TangDepartment of Health Informatics, NYU Langone Health, New York, New York, United States of America.
Arnab BanerjeeMicrosoft, Seattle, Washington, United States of America.
Michael J JabbourMicrosoft, Seattle, Washington, United States of America.
Yindalon AphinyanaphongsDepartment of Health Informatics, NYU Langone Health, New York, New York, United States of America.
Paul TestaDepartment of Health Informatics, NYU Langone Health, New York, New York, United States of America.
Jonathan S AustrianDepartment of Health Informatics, NYU Langone Health, New York, New York, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHealthcare crowdsourcing events (e.g. hackathons) facilitate interdisciplinary collaboration and encourage innovation. Peer-reviewed research has not yet considered a healthcare crowdsourcing event focusing on generative artificial intelligence (GenAI), which generates text in response to detailed prompts and has vast potential for improving the efficiency of healthcare organizations. Our event, the New York University Langone Health (NYULH) Prompt-a-thon, primarily sought to inspire and build AI fluency within our diverse NYULH community, and foster collaboration and innovation. Secondarily, we sought to analyze how participants' experience was influenced by their prior GenAI exposure and whether they received sample prompts during the workshop.

methodsExecuting the event required the assembly of an expert planning committee, who recruited diverse participants, anticipated technological challenges, and prepared the event. The event was composed of didactics and workshop sessions, which educated and allowed participants to experiment with using GenAI on real healthcare data. Participants were given novel "project cards" associated with each dataset that illuminated the tasks GenAI could perform and, for a random set of teams, sample prompts to help them achieve each task (the public repository of project cards can be found at https://github.com/smallw03/NYULH-Generative-AI-Prompt-a-thon-Project-Cards). Afterwards, participants were asked to fill out a survey with 7-point Likert-style questions.

resultsOur event was successful in educating and inspiring hundreds of enthusiastic in-person and virtual participants across our organization on the responsible use of GenAI in a low-cost and technologically feasible manner. All participants responded positively, on average, to each of the survey questions (e.g., confidence in their ability to use and trust GenAI). Critically, participants reported a self-perceived increase in their likelihood of using and promoting colleagues' use of GenAI for their daily work. No significant differences were seen in the surveys of those who received sample prompts with their project task descriptions.

conclusionThe first healthcare Prompt-a-thon was an overwhelming success, with minimal technological failures, positive responses from diverse participants and staff, and evidence of post-event engagement. These findings will be integral to planning future events at our institution, and to others looking to engage their workforce in utilizing GenAI.

Identifiers

PMID39042600
PMCPMC11265701

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.