ArticleApplied clinical informatics2017
Health IT Usability Focus Section: Adapting EHR-Based Medication Instructions to Comply with Plain Language Guidance-A Randomized Experiment.
Article in Applied clinical informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Evidence-Based Design of Prescription Medication Information: An Updated Scoping Review.Drug safety · 2025Article
- Improving Patient-Centered Communication about Sudden Unexpected Death in Epilepsy through Computerized Clinical Decision Support.Applied clinical informatics · 2021Article
- Patients Are Knowledge Workers in the Clinical Information Space.Applied clinical informatics · 2021Article
- Developing Infographics to Facilitate HIV-Related Patient-Provider Communication in a Limited-Resource Setting.Applied clinical informatics · 2019Article
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
Objective Patient instructions are generally written by clinicians. However, clinician-centered language is challenging for patients to understand; in the case of pediatric medication instructions, consequences can be serious. Using examples of clinician-written medication instructions from an electronic health record, we conducted an experiment to determine whether parental misinterpretations would be reduced by instructions that followed best practices for plain language. Methods We selected examples of dosing instructions from after-visit summaries in a commercial electronic health record. A demographically diverse sample of parents and adult caregivers was recruited from an online panel to participate in an English-language experiment, in which they received a comprehension questionnaire with either original after-visit summary instructions or instructions revised to comply with federal and other sources of plain-language guidance. Results Nine-hundred and fifty-one respondents completed the experiment; 50% were women, the mean age was 36 years, and 38% had less than a 4-year college education. The revisions were associated with an 8 percentage point increase in correct answers overall (from 55% to 63%, p < 0.001), although revisions were not equally effective for all instructions. Health literacy and health numeracy were strong and independent predictors of comprehension. Overall, mistakes on comprehension questions were common, with respondents missing an average of 41% (6.1 of 15) of questions. Conclusion In this experimental study, a relatively simple intervention of revising text was associated with a modest reduction in frequency of misinterpretations of medication instructions. As a supplement to more intensive high-touch interventions, revising electronic health record output to replace complex language with patient-centered language in an automated fashion is a potentially scalable solution that could reduce medication administration errors by parents.
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.