ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2018
Using Machine Learning to Predict the Information Seeking Behavior of Clinicians Using an Electronic Medical Record System.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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.
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.
Who cites it
13 citing papers in PubMed.
- OrderRex clinical user testing: a randomized trial of recommender system decision support on simulated cases.Journal of the American Medical Informatics Association : JAMIA · 2020Trial
- Comparison of Feature Selection Methods in Machine Learning Models of Cancer Information Seeking Among United States Adults: Cross-Sectional Study.JMIR medical informatics · 2026Article
- Digital Information Ecosystems in Modern Care Coordination and Patient Care Pathways and the Challenges and Opportunities for AI Solutions.Journal of medical Internet research · 2024Article
- Involving Health Care Professionals in the Development of Electronic Health Records: Scoping Review.JMIR human factors · 2023Article
- A simple electronic medical record system designed for research.JAMIA open · 2021Article
- Evaluation of eye tracking for a decision support application.JAMIA open · 2021Article
- Article
- Development and Verification of a Digital Twin Patient Model to Predict Specific Treatment Response During the First 24 Hours of Sepsis.Critical care explorations · 2020Article
- Graphical Presentations of Clinical Data in a Learning Electronic Medical Record.Applied clinical informatics · 2020Article
- Leveraging Eye Tracking to Prioritize Relevant Medical Record Data: Comparative Machine Learning Study.Journal of medical Internet research · 2020Article
- Assessment of Electronic Health Record Search Patterns and Practices by Practitioners in a Large Integrated Health Care System.JAMA network open · 2020Article
- Physician Usage and Acceptance of a Machine Learning Recommender System for Simulated Clinical Order Entry.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- Using machine learning to selectively highlight patient information.Journal of biomedical informatics · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Poor electronic medical record (EMR) usability is detrimental to both clinicians and patients. A better EMR would provide concise, context sensitive patient data, but doing so entails the difficult task of knowing which data are relevant. To determine the relevance of patient data in different contexts, we collect and model the information seeking behavior of clinicians using a learning EMR (LEMR) system. Sufficient data were collected to train predictive models for 80 different targets (e.g., glucose level, heparin administration) and 27 of them had AUROC values of greater than 0.7. These results are encouraging considering the high variation in information seeking behavior (intraclass correlation 0.40). We plan to apply these models to a new set of patient cases and adapt the LEMR interface to highlight relevant patient data, and thus provide concise, context sensitive data.
Indexed as
Identifiers
30815109PMC6371238What OpenQuestion holds
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.