ArticleApplied clinical informatics2025
Information Prioritization and Reading Patterns in Electronic Health Record Nursing Summaries: An Eye-Tracking Case Study.
Article in Applied clinical informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
While nursing summaries in electronic health records are used for initial orientation to a patient's status, research on nurses' use of these summaries remains scarce. This case study conducted an eye-tracking simulation to identify (1) key information types (orders, vital signs, etc.), (2) frequently paired information types, and (3) common sequential patterns of information types within nursing summaries as nurses review simulated patient cases.We recruited 33 medical-surgical nurses from a university hospital. As part of an eye-tracking simulation, they reviewed three simulated patients' nursing summaries. A screen-based eye-tracker was used to capture participants' gaze fixation on different information types. For analysis, we used discrete-time Markov chains and sequential pattern mining.The average total gaze fixation time was 1.77 minutes from 26 analyzed participants' eye gaze data. Most of this time was spent shifting between information types or making notes. "Orders" and "Sidebar" (mini summary of demographics and health status) were the information types that consistently emerged as key areas of focus. Participants tended to read specific information types in pairs and followed a top-to-bottom order of reading on the screen.When reviewing unfamiliar patient cases, nurses prefer to construct a comprehensive patient narrative. Nursing summaries can be redesigned by prioritizing key information types, grouping relevant information pairs, and arranging information in a top-to-bottom manner based on relevance. We recommend that hospitals and EHR vendors prioritize the customization of nursing summaries to align with nurses' information needs and workflows. Tailored summary layout improvements beyond a one-size-fits-all design, informed by interdisciplinary collaboration, can enhance information reading efficiency.
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.