ArticleBMC bioinformatics2022
Implementation of machine learning in the clinic: challenges and lessons in prospective deployment from the System for High Intensity EvaLuation During Radiation Therapy (SHIELD-RT) randomized controlled study.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04277650 (System for High Intensity EvaLuation During Radiation Therapy), which is not on this map. Cited by 7 papers.
What it found
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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.
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.
System for High Intensity EvaLuation During Radiation Therapy (SHIELD-RT): A Prospective Randomized Study of Machine Learning-directed Clinical Evaluations During Outpatient Cancer Radiation and Chemoradiation
Who cites it
7 citing papers in PubMed, 10 citations in OpenAlex.
- Healthcare provider evaluation of machine learning-directed care: reactions to deployment on a randomised controlled study.BMJ health & care informatics · 2023Trial
- Development and evaluation of a machine learning model to predict acute care for opioid use disorder among Medicaid enrollees engaged in a community-based treatment program.Addiction (Abingdon, England) · 2025Article
- Transforming Prostate Cancer Care: Innovations in Diagnosis, Treatment, and Future Directions.International journal of molecular sciences · 2025Review
- Identifying Facilitators and Barriers to Implementation of AI-Assisted Clinical Decision Support in an Electronic Health Record System.Journal of medical systems · 2024Article
- Article
- Automation in Contemporary Clinical Information Systems: a Survey of AI in Healthcare Settings.Yearbook of medical informatics · 2023Article
- Enhancing clinical reliability in pressure injury prediction: A conformal prediction approach with machine learning models.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) and machine learning (ML) have resulted in significant enthusiasm for their promise in healthcare. Despite this, prospective randomized controlled trials and successful clinical implementation remain limited. One clinical application of ML is mitigation of the increased risk for acute care during outpatient cancer therapy. We previously reported the results of the System for High Intensity EvaLuation During Radiation Therapy (SHIELD-RT) study (NCT04277650), which was a prospective, randomized quality improvement study demonstrating that ML based on electronic health record (EHR) data can direct supplemental clinical evaluations and reduce the rate of acute care during cancer radiotherapy with and without chemotherapy. The objective of this study is to report the workflow and operational challenges encountered during ML implementation on the SHIELD-RT study.
resultsData extraction and manual review steps in the workflow represented significant time commitments for implementation of clinical ML on a prospective, randomized study. Barriers include limited data availability through the standard clinical workflow and commercial products, the need to aggregate data from multiple sources, and logistical challenges from altering the standard clinical workflow to deliver adaptive care.
conclusionsThe SHIELD-RT study was an early randomized controlled study which enabled assessment of barriers to clinical ML implementation, specifically those which leverage the EHR. These challenges build on a growing body of literature and may provide lessons for future healthcare ML adoption.
trial registrationNCT04277650. Registered 20 February 2020. Retrospectively registered quality improvement study.
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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 OpenQuestion graph.