Evidence map›Paper›PMID 38697362›Full record

ArticleUrology2024

Clinical Decision Support for Surgery: A Mixed Methods Study on Design and Implementation Perspectives From Urologists.

Hung-Jui Tan, Brooke N Spratte, Allison M Deal, Hillary M Heiling, Elizabeth M Nazzal, William Meeks, Raymond Fang, Randall Teal, Maihan B Vu, Antonia V Bennett and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Urology, 2024. 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

5 · Who and what money

Authors and funding

17 authors.

Hung-Jui TanDepartment of Urology, School of Medicine, University of North Carolina, Chapel Hill, NC; Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC. Electronic address: ray_tan@med.unc.edu.
Brooke N SpratteDepartment of Urology, School of Medicine, University of North Carolina, Chapel Hill, NC.
Allison M DealLineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC.
Hillary M HeilingLineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC.
Elizabeth M NazzalDepartment of Urology, School of Medicine, University of North Carolina, Chapel Hill, NC.
William MeeksAmerican Urological Association Data Management and Statistical Services.
Raymond FangAmerican Urological Association Data Management and Statistical Services.
Randall TealLineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC; Connected Health Applications and Interventions Core, University of North Carolina, Chapel Hill, NC.
Maihan B VuConnected Health Applications and Interventions Core, University of North Carolina, Chapel Hill, NC; Center for Health Promotion and Disease Prevention, University of North Carolina, Chapel Hill, NC.
Antonia V BennettLineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC; Department of Health Policy & Management, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC.
Susan J BlalockPharmaceutical Outcomes & Policy, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC.
Arlene E ChungLineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC; Department of Bioinformatics, Duke University, Durham, NC.
David GotzLineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC; School of Information and Library Science, University of North Carolina, Chapel Hill, NC.
Matthew E NielsenDepartment of Urology, School of Medicine, University of North Carolina, Chapel Hill, NC; Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC; Department of Health Policy & Management, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC.
Daniel S ReulandLineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC; Department of Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC.
Alex Hs HarrisDepartment of Surgery, School of Medicine, Stanford University, Palo Alto, CA.
Ethan BaschLineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC; Department of Health Policy & Management, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC; Department of Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC.

Funding

Virology Research Program (Program 4)P30CA016086 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Deborah F. Tate · 1985 to 2026
$201.5M
NCI NIH HHS P30 CA016086NIA NIH HHS L30 AG051183
6 · The paper itself

Abstract

objectiveTo assess urologist attitudes toward clinical decision support (CDS) embedded into the electronic health record (EHR) and define design needs to facilitate implementation and impact. With recent advances in big data and artificial intelligence (AI), enthusiasm for personalized, data-driven tools to improve surgical decision-making has grown, but the impact of current tools remains limited.

methodsA sequential explanatory mixed methods study from 2019 to 2020 was performed. First, survey responses from the 2019 American Urological Association Annual Census evaluated attitudes toward an automatic CDS tool that would display risk/benefit data. This was followed by the purposeful sampling of 25 urologists and qualitative interviews assessing perspectives on CDS impact and design needs. Bivariable, multivariable, and coding-based thematic analysis were applied and integrated.

resultsAmong a weighted sample of 12,366 practicing urologists, the majority agreed CDS would help decision-making (70.9%, 95% CI 68.7%-73.2%), aid patient counseling (78.5%, 95% CI 76.5%-80.5%), save time (58.1%, 95% CI 55.7%-60.5%), and improve patient outcomes (42.9%, 95% CI 40.5%-45.4%). More years in practice was negatively associated with agreement (P <.001). Urologists described how CDS could bolster evidence-based care, personalized medicine, resource utilization, and patient experience. They also identified multiple implementation barriers and provided suggestions on form, functionality, and visual design to improve usefulness and ease of use.

conclusionUrologists have favorable attitudes toward the potential for clinical decision support in the EHR. Smart design will be critical to ensure effective implementation and impact.

Indexed as

Attitude of Health PersonnelDecision Support Systems, ClinicalUrologistsAdultElectronic Health RecordsFemaleHumansMaleMiddle AgedSurveys and QuestionnairesUrologic Surgical ProceduresUrology

Identifiers

PMID38697362
PMCPMC11344670

What OpenQuestion holds

Textmetadata
LicenceTDM
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.