Trial reportJournal of medical Internet research2019
Improving Electronic Health Record Note Comprehension With NoteAid: Randomized Trial of Electronic Health Record Note Comprehension Interventions With Crowdsourced Workers.
Trial report in Journal of medical Internet research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
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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
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Improving Clinical Documentation with Artificial Intelligence: A Systematic Review.Perspectives in health information management · 2024Pooled it
- Evaluating the efficacy of NoteAid on EHR note comprehension among US Veterans through Amazon Mechanical Turk.International journal of medical informatics · 2023Trial
- Evaluating the Effectiveness of NoteAid in a Community Hospital Setting: Randomized Trial of Electronic Health Record Note Comprehension Interventions With Patients.Journal of medical Internet research · 2021Trial
- Individual Factors That Affect Laypeople's Understanding of Definitions of Medical Jargon.Health policy and technology · 2024Article
- Evaluating Expert-Layperson Agreement in Identifying Jargon Terms in Electronic Health Record Notes: Observational Study.Journal of medical Internet research · 2024Observational
- Biomedical text readability after hypernym substitution with fine-tuned large language models.PLOS digital health · 2024Article
- Evaluation of Patient-Friendly Diagnosis Clarifications in a Hospital Patient Portal.Applied clinical informatics · 2023Article
- Patients' Experiences of Web-Based Access to Electronic Health Records in Finland: Cross-sectional Survey.Journal of medical Internet research · 2022Article
- Can sharing clinic notes improve communication and promote self-management? A qualitative study of patients with COPD.Patient education and counseling · 2022Article
- Patient Rationales Against the Use of Patient-Accessible Electronic Health Records: Qualitative Study.Journal of medical Internet research · 2021Article
- Social and Behavioral Determinants of Health in the Era of Artificial Intelligence with Electronic Health Records: A Scoping Review.Health data science · 2021Article
- Evaluation of lexical clarification by patients reading their clinical notes: a quasi-experimental interview study.BMC medical informatics and decision making · 2020Article
- BENTO: A Visual Platform for Building Clinical NLP Pipelines Based on CodaLab.Proceedings of the conference. Association for Computational Linguistics. Meeting · 2020Article
- Development and Validation of a Comprehensive Well-Being Scale for People in the University Environment (Pitt Wellness Scale) Using a Crowdsourcing Approach: Cross-Sectional Study.Journal of medical Internet research · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundPatient portals are becoming more common, and with them, the ability of patients to access their personal electronic health records (EHRs). EHRs, in particular the free-text EHR notes, often contain medical jargon and terms that are difficult for laypersons to understand. There are many Web-based resources for learning more about particular diseases or conditions, including systems that directly link to lay definitions or educational materials for medical concepts.
objectiveOur goal is to determine whether use of one such tool, NoteAid, leads to higher EHR note comprehension ability. We use a new EHR note comprehension assessment tool instead of patient self-reported scores.
methodsIn this work, we compare a passive, self-service educational resource (MedlinePlus) with an active resource (NoteAid) where definitions are provided to the user for medical concepts that the system identifies. We use Amazon Mechanical Turk (AMT) to recruit individuals to complete ComprehENotes, a new test of EHR note comprehension.
resultsMean scores for individuals with access to NoteAid are significantly higher than the mean baseline scores, both for raw scores (P=.008) and estimated ability (P=.02).
conclusionsIn our experiments, we show that the active intervention leads to significantly higher scores on the comprehension test as compared with a baseline group with no resources provided. In contrast, there is no significant difference between the group that was provided with the passive intervention and the baseline group. Finally, we analyze the demographics of the individuals who participated in our AMT task and show differences between groups that align with the current understanding of health literacy between populations. This is the first work to show improvements in comprehension using tools such as NoteAid as measured by an EHR note comprehension assessment tool as opposed to patient self-reported scores.
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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.