ReviewJournal of medical Internet research2024
Usability Evaluation Methods Used in Electronic Discharge Summaries: Literature Review.
Review in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Automated Brief Hospital Course Summarization in Cardiac Surgery Using a Lightweight Large Language Model-Based Framework: Development and Evaluation Study on the Medical Information Mart for Intensive Care-IV.Journal of medical Internet research · 2026Article
- One template does not fit all: where next to improve hospital discharge communication to primary care?Primary health care research & development · 2025Article
- Evaluating the impact of the Hospital Information System (HIS) on staff performance in teaching hospitals of Kerman, Iran.Digital healthArticle
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWith the widespread adoption of digital health records, including electronic discharge summaries (eDS), it is important to assess their usability in order to understand whether they meet the needs of the end users. While there are established approaches for evaluating the usability of electronic health records, there is a lack of knowledge regarding suitable evaluation methods specifically for eDS.
objectiveThis literature review aims to identify the usability evaluation approaches used in eDS.
methodsWe conducted a comprehensive search of PubMed, CINAHL, Web of Science, ACM Digital Library, MEDLINE, and ProQuest databases from their inception until July 2023. The study information was extracted and reported in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). We included studies that assessed the usability of eDS, and the systems used to display eDS.
resultsA total of 12 records, including 11 studies and 1 thesis, met the inclusion criteria. The included studies used qualitative, quantitative, or mixed methods approaches and reported the use of various usability evaluation methods. Heuristic evaluation was the most used method to assess the usability of eDS systems (n=7), followed by the think-aloud approach (n=5) and laboratory testing (n=3). These methods were used either individually or in combination with usability questionnaires (n=3) and qualitative semistructured interviews (n=4) for evaluating eDS usability issues. The evaluation processes incorporated usability metrics such as user performance, satisfaction, efficiency, and impact rating.
conclusionsThere are a limited number of studies focusing on usability evaluations of eDS. The identified studies used expert-based and user-centered approaches, which can be used either individually or in combination to identify usability issues. However, further research is needed to determine the most appropriate evaluation method which can assess the fitness for purpose of discharge summaries.
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.