ArticleJournal of medical Internet research2020
Development, Implementation, and Evaluation of a Personalized Machine Learning Algorithm for Clinical Decision Support: Case Study With Shingles Vaccination.
Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Advancements in Herpes Zoster Diagnosis, Treatment, and Management: Systematic Review of Artificial Intelligence Applications.Journal of medical Internet research · 2025Pooled it
- Digital integration between hospitals and local health authorities for enhanced vaccination coverage among frail patients: the CareVax study protocol.Frontiers in public health · 2025Article
- An integrated pathway for influenza vaccination across primary and secondary care using a clinical decision support system: model definition and predictive impact analysis.European journal of public health · 2024Article
- Applying Machine Learning Techniques to Implementation Science.Online journal of public health informatics · 2024Article
- Dynamic clustering via branched deep learning enhances personalization of stress prediction from mobile sensor data.Scientific reports · 2024Article
- Research on prediction model of adolescent suicide and self-injury behavior based on machine learning algorithm.Frontiers in psychiatry · 2024Article
- Effects of machine learning-based clinical decision support systems on decision-making, care delivery, and patient outcomes: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Systematic review and meta-analysis comparing educational and reminder digital interventions for promoting HPV vaccination uptake.NPJ digital medicine · 2023Article
- A survey of extant organizational and computational setups for deploying predictive models in health systems.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- How artificial intelligence can help us 'Choose Wisely'.Bioelectronic medicine · 2021Article
- Real-Time Clinical Decision Support Based on Recurrent Neural Networks for In-Hospital Acute Kidney Injury: External Validation and Model Interpretation.Journal of medical Internet research · 2021Article
- Use of machine learning to predict clinical decision support compliance, reduce alert burden, and evaluate duplicate laboratory test ordering alerts.JAMIA open · 2021Article
- Reducing Alert Fatigue by Sharing Low-Level Alerts With Patients and Enhancing Collaborative Decision Making Using Blockchain Technology: Scoping Review and Proposed Framework (MedAlert).Journal of medical Internet research · 2020Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlthough clinical decision support (CDS) alerts are effective reminders of best practices, their effectiveness is blunted by clinicians who fail to respond to an overabundance of inappropriate alerts. An electronic health record (EHR)-integrated machine learning (ML) algorithm is a potentially powerful tool to increase the signal-to-noise ratio of CDS alerts and positively impact the clinician's interaction with these alerts in general.
objectiveThis study aimed to describe the development and implementation of an ML-based signal-to-noise optimization system (SmartCDS) to increase the signal of alerts by decreasing the volume of low-value herpes zoster (shingles) vaccination alerts.
methodsWe built and deployed SmartCDS, which builds personalized user activity profiles to suppress shingles vaccination alerts unlikely to yield a clinician's interaction. We extracted all records of shingles alerts from January 2017 to March 2019 from our EHR system, including 327,737 encounters, 780 providers, and 144,438 patients.
resultsDuring the 6 weeks of pilot deployment, the SmartCDS system suppressed an average of 43.67% (15,425/35,315) potential shingles alerts (appointments) and maintained stable counts of weekly shingles vaccination orders (326.3 with system active vs 331.3 in the control group; P=.38) and weekly user-alert interactions (1118.3 with system active vs 1166.3 in the control group; P=.20).
conclusionsAll key statistics remained stable while the system was turned on. Although the results are promising, the characteristics of the system can be subject to future data shifts, which require automated logging and monitoring. We demonstrated that an automated, ML-based method and data architecture to suppress alerts are feasible without detriment to overall order rates. This work is the first alert suppression ML-based model deployed in practice and serves as foundational work in encounter-level customization of alert display to maximize effectiveness.
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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.