ArticleApplied clinical informatics2020
Graphical Presentations of Clinical Data in a Learning Electronic Medical Record.
Article in Applied clinical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- Supporting clinical reasoning through visual summarization and presentation of patient data: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2025Pooled it
- Development of a digital tool to support overview in complex patient cases: Which information elements support the clinical workflow?BMC health services research · 2026Article
- User-centred prototyping solutions to solve adult critical care issues: a scoping review.BMJ health & care informatics · 2026Article
- Data Representation Structure to Support Clinical Decision-Making in the Pediatric Intensive Care Unit: Interview Study and Preliminary Decision Support Interface Design.JMIR formative research · 2024Article
- Engaging Multidisciplinary Clinical Users in the Design of an Artificial Intelligence-Powered Graphical User Interface for Intensive Care Unit Instability Decision Support.Applied clinical informatics · 2023Article
- Involving Health Care Professionals in the Development of Electronic Health Records: Scoping Review.JMIR human factors · 2023Article
- Intelligent Clinical Decision Support.Sensors (Basel, Switzerland) · 2022Article
- Enhancing Diagnosis Through Technology: Decision Support, Artificial Intelligence, and Beyond.Critical care clinics · 2022Review
- Building a Learning Health System: Creating an Analytical Workflow for Evidence Generation to Inform Institutional Clinical Care Guidelines.Applied clinical informatics · 2022Article
- Human Factors and Organizational Issues in Health Informatics: Innovations and Opportunities.Yearbook of medical informatics · 2021Review
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 at 1 institution in 1 country.
Funding
Abstract
backgroundComplex electronic medical records (EMRs) presenting large amounts of data create risks of cognitive overload. We are designing a Learning EMR (LEMR) system that utilizes models of intensive care unit (ICU) physicians' data access patterns to identify and then highlight the most relevant data for each patient.
objectivesWe used insights from literature and feedback from potential users to inform the design of an EMR display capable of highlighting relevant information.
methodsWe used a review of relevant literature to guide the design of preliminary paper prototypes of the LEMR user interface. We observed five ICU physicians using their current EMR systems in preparation for morning rounds. Participants were interviewed and asked to explain their interactions and challenges with the EMR systems. Findings informed the revision of our prototypes. Finally, we conducted a focus group with five ICU physicians to elicit feedback on our designs and to generate ideas for our final prototypes using participatory design methods.
resultsParticipating physicians expressed support for the LEMR system. Identified design requirements included the display of data essential for every patient together with diagnosis-specific data and new or significantly changed information. Respondents expressed preferences for fishbones to organize labs, mouseovers to access additional details, and unobtrusive alerts minimizing color-coding. To address the concern about possible physician overreliance on highlighting, participants suggested that non-highlighted data should remain accessible. Study findings led to revised prototypes, which will inform the development of a functional user interface.
conclusionIn the feedback we received, physicians supported pursuing the concept of a LEMR system. By introducing novel ways to support physicians' cognitive abilities, such a system has the potential to enhance physician EMR use and lead to better patient outcomes. Future plans include laboratory studies of both the utility of the proposed designs on decision-making, and the possible impact of any automation bias.
Indexed as
Identifiers
What 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.