ArticleJournal of biomedical informatics2019
Using machine learning to selectively highlight patient information.
Article in Journal of biomedical informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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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.
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Who cites it
16 citing papers in PubMed, 28 citations in OpenAlex.
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- Machine learning to predict notes for chart review in the oncology setting: a proof of concept strategy for improving clinician note-writing.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Modelling of physicians' clinical information-seeking behaviour in Iran: a grounded theory study.BMJ open · 2024Article
- A voice-based digital assistant for intelligent prompting of evidence-based practices during ICU rounds.Journal of biomedical informatics · 2023Article
- The Role of Data Science in Closing the Implementation Gap.Critical care clinics · 2023Review
- Implementation of a Patient Summary Web Application According to the International Patient Summary and Validation in Common Use Cases in Japan.Journal of medical systems · 2023Article
- Involving Health Care Professionals in the Development of Electronic Health Records: Scoping Review.JMIR human factors · 2023Article
- Novel architecture for gated recurrent unit autoencoder trained on time series from electronic health records enables detection of ICU patient subgroups.Scientific reports · 2023Article
- Machine learning-based prediction of low-value care for hospitalized patients.Intelligence-based medicine · 2023Article
- Initial Development of an Automated Platform for Assessing Trainee Performance on Case Presentations.ATS scholar · 2022Article
- An atomic approach to the design and implementation of a research data warehouse.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Enhancing Diagnosis Through Technology: Decision Support, Artificial Intelligence, and Beyond.Critical care clinics · 2022Review
- 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
- Leveraging Eye Tracking to Prioritize Relevant Medical Record Data: Comparative Machine Learning Study.Journal of medical Internet research · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundElectronic medical record (EMR) systems need functionality that decreases cognitive overload by drawing the clinician's attention to the right data, at the right time. We developed a Learning EMR (LEMR) system that learns statistical models of clinician information-seeking behavior and applies those models to direct the display of data in future patients. We evaluated the performance of the system in identifying relevant patient data in intensive care unit (ICU) patient cases.
methodsTo capture information-seeking behavior, we enlisted critical care medicine physicians who reviewed a set of patient cases and selected data items relevant to the task of presenting at morning rounds. Using patient EMR data as predictors, we built machine learning models to predict their relevancy. We prospectively evaluated the predictions of a set of high performing models.
resultsOn an independent evaluation data set, 25 models achieved precision of 0.52, 95% CI [0.49, 0.54] and recall of 0.77, 95% CI [0.75, 0.80] in identifying relevant patient data items. For data items missed by the system, the reviewers rated the effect of not seeing those data from no impact to minor impact on patient care in about 82% of the cases.
conclusionData-driven approaches for adaptively displaying data in EMR systems, like the LEMR system, show promise in using information-seeking behavior of clinicians to identify and highlight relevant patient data.
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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.