ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2015
Development and Preliminary Evaluation of a Prototype of a Learning Electronic Medical Record System.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review of applied usability metrics within usability evaluation methods for hospital electronic healthcare record systems: Metrics and Evaluation Methods for eHealth Systems.Journal of evaluation in clinical practice · 2021Pooled it
- 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
- 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
- Using machine learning to selectively highlight patient information.Journal of biomedical informatics · 2019Article
- Using Machine Learning to Predict the Information Seeking Behavior of Clinicians Using an Electronic Medical Record System.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- Eye-tracking for clinical decision support: A method to capture automatically what physicians are viewing in the EMR.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2017Article
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.
Funding
Abstract
Electronic medical records (EMRs) are capturing increasing amounts of data per patient. For clinicians to efficiently and accurately understand a patient's clinical state, better ways are needed to determine when and how to display EMR data. We built a prototype system that records how physicians view EMR data, which we used to train models that predict which EMR data will be relevant in a given patient. We call this approach a Learning EMR (LEMR). A physician used the prototype to review 59 intensive care unit (ICU) patient cases. We used the data-access patterns from these cases to train logistic regression models that, when evaluated, had AUROC values as high as 0.92 and that averaged 0.73, supporting that the approach is promising. A preliminary usability study identified advantages of the system and a few concerns about implementation. Overall, 3 of 4 ICU physicians were enthusiastic about features of the prototype.
Indexed as
Identifiers
26958296PMC4765593What 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.