Evidence map›Paper›PMID 36764680›Full record

Trial reportBMJ health & care informatics2023

Healthcare provider evaluation of machine learning-directed care: reactions to deployment on a randomised controlled study.

Julian C Hong, Pranalee Patel, Neville C W Eclov, Sarah J Stephens, Yvonne M Mowery, Jessica D Tenenbaum, Manisha Palta

Registry-linked trialOpen access · goldAbstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in BMJ health & care informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03775265 (Phase III Randomized Trial of Concurrent Chemoradiotherapy With or Without Atezolizumab in Localized Muscle Invasive Bladder Cancer), which is not on this map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 40% 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.

NCT03775265 phase3active not recruitingnot on this map

Phase III Randomized Trial of Concurrent Chemoradiotherapy With or Without Atezolizumab in Localized Muscle Invasive Bladder Cancer

TypeinterventionalSponsorNational Cancer Institute (NCI)Ran2019 to 2027Enrolled475ConditionsBladder Urothelial Carcinoma, Muscle Invasive Bladder Carcinoma, Stage II Bladder Cancer AJCC v8, Stage IIIA Bladder Cancer AJCC v8ArmsAtezolizumab, Biopsy of Bladder, Cisplatin, Computed Tomography, Cystoscopy
3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 5 citations in OpenAlex.

  1. Article
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

7 authors at 2 institutions in 1 country.

Julian C HongDepartment of Radiation Oncology, University of California San Francisco, San Francisco, California, USA julian.hong@ucsf.edu.
Pranalee PatelDepartment of Radiation Oncology, Duke University, Durham, North Carolina, USA.
Neville C W EclovDepartment of Radiation Oncology, Duke University, Durham, North Carolina, USA.
Sarah J StephensDepartment of Radiation Oncology, Duke University, Durham, North Carolina, USA.
Yvonne M MoweryDepartment of Radiation Oncology, Duke University, Durham, North Carolina, USA.
Jessica D TenenbaumDepartment of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA.
Manisha PaltaDepartment of Radiation Oncology, Duke University, Durham, North Carolina, USA.
Duke University · USUniversity of California, Berkeley · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesClinical artificial intelligence and machine learning (ML) face barriers related to implementation and trust. There have been few prospective opportunities to evaluate these concerns. System for High Intensity EvaLuation During Radiotherapy (NCT03775265) was a randomised controlled study demonstrating that ML accurately directed clinical evaluations to reduce acute care during cancer radiotherapy. We characterised subsequent perceptions and barriers to implementation.

methodsAn anonymous 7-question Likert-type scale survey with optional free text was administered to multidisciplinary staff focused on workflow, agreement with ML and patient experience.

results59/71 (83%) responded. 81% disagreed/strongly disagreed their workflow was disrupted. 67% agreed/strongly agreed patients undergoing intervention were high risk. 75% agreed/strongly agreed they would implement the ML approach routinely if the study was positive. Free-text feedback focused on patient education and ML predictions.

conclusionsRandomised data and firsthand experience support positive reception of clinical ML. Providers highlighted future priorities, including patient counselling and workflow optimisation.

Indexed as

Artificial IntelligenceHealth PersonnelHumansMachine LearningProspective StudiesSurveys and QuestionnairesDelivery of Health CareMachine Learning

Identifiers

PMID36764680
PMCPMC9923272
OpenAlexW4320032560

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

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 Socratic graph.