Evidence map›Paper›PMID 40161559›Full record

ArticleFrontiers in digital health2025

Approaches to identify scenarios for data science implementations within healthcare settings: recommendations based on experiences at multiple academic institutions.

Lillian Sung, Michael Brudno, Michael C W Caesar, Amol A Verma, Brad Buchsbaum, Ravi Retnakaran, Vasily Giannakeas, Azadeh Kushki, Gary D Bader, Helen Lasthiotakis and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Lillian SungDepartment of Paediatrics, The Hospital for Sick Children, Institute of Health Policy Management & Evaluation, University of Toronto, Toronto, ON, Canada.
Michael BrudnoDepartment of Computer Science, Vector Institute for Artificial Intelligence, University Health Network, University of Toronto, Toronto, ON, Canada.
Michael C W CaesarInstitute of Health Policy Management & Evaluation, University Health Network, University of Toronto, Toronto, ON, Canada.
Amol A VermaDepartment of Medicine, Department of Laboratory Medicine and Pathobiology, and Institution of Health Policy Management & Evaluation; St. Michael's Hospital, Unity Health Toronto, University of Toronto, Toronto, ON, Canada.
Brad BuchsbaumDepartment of Psychology, Rotman Research Institute, Baycrest Centre, University of Toronto, Toronto, ON, Canada.
Ravi RetnakaranDivision of Endocrinology, Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada.
Vasily GiannakeasWomen's College Research Institute, Women's College Hospital, Toronto, ON, Canada.
Azadeh KushkiInstitute of Biomedical Engineering, Holland Bloorview Kids Rehabilitation Hospital, University of Toronto, Toronto, ON, Canada.
Gary D BaderDepartment of Molecular Genetics, Temerty Faculty of Medicine, Toronto, ON, Canada.
Helen LasthiotakisData Sciences Institute, University of Toronto, Toronto, ON, Canada.
Muhammad MamdaniTemerty Faculty of Medicine, Centre for Artificial Intelligence Education and Research in Medicine, Unity Health Toronto, University of Toronto, Toronto, ON, Canada.
Lisa StrugData Sciences Institute, University of Toronto, Toronto, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: To describe successful and unsuccessful approaches to identify scenarios for data science implementations within healthcare settings and to provide recommendations for future scenario identification procedures. Materials and methods: Representatives from seven Toronto academic healthcare institutions participated in a one-day workshop. Each institution was asked to provide an introduction to their clinical data science program and to provide an example of a successful and unsuccessful approach to scenario identification at their institution. Using content analysis, common observations were summarized. Results: Observations were coalesced to idea generation and value proposition, prioritization, approval and champions. Successful experiences included promoting a portfolio of ideas, articulating value proposition, ensuring alignment with organization priorities, ensuring approvers can adjudicate feasibility and identifying champions willing to take ownership over the projects. Conclusion: Based on academic healthcare data science program experiences, we provided recommendations for approaches to identify scenarios for data science implementations within healthcare settings.

Indexed as

data scienceshealthcaremachine learningscenariouse case

Identifiers

PMID40161559
PMCPMC11949942

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.