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.
BMC Bioinformatics
; 23(Suppl 12): 408, 2022 Sep 30.
Article
in En
| MEDLINE
| ID: mdl-36180836
ABSTRACT
BACKGROUND:
Artificial 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.RESULTS:
Data 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.CONCLUSIONS:
The 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 REGISTRATION NCT04277650. Registered 20 February 2020. Retrospectively registered quality improvement study.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Artificial Intelligence
/
Neoplasms
Type of study:
Clinical_trials
/
Guideline
/
Observational_studies
/
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
BMC Bioinformatics
Journal subject:
INFORMATICA MEDICA
Year:
2022
Document type:
Article
Affiliation country:
United States