ArticleJournal of medical Internet research2018
ComprehENotes, an Instrument to Assess Patient Reading Comprehension of Electronic Health Record Notes: Development and Validation.
Article in Journal of medical Internet research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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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.
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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.
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Who cites it
13 citing papers in PubMed.
- 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
- Improving Electronic Health Record Note Comprehension With NoteAid: Randomized Trial of Electronic Health Record Note Comprehension Interventions With Crowdsourced Workers.Journal of medical Internet research · 2019Trial
- Artificial Intelligence Techniques and Health Literacy: A Systematic Review.Mayo Clinic proceedings. Digital health · 2025Review
- Individual Factors That Affect Laypeople's Understanding of Definitions of Medical Jargon.Health policy and technology · 2024Article
- README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP.Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing · 2024Article
- Assessment Tools for Measuring Health Literacy and Digital Health Literacy in a Hospital Setting: A Scoping Review.Healthcare (Basel, Switzerland) · 2023Article
- A Smart Mobile App to Simplify Medical Documents and Improve Health Literacy: System Design and Feasibility Validation.JMIR formative research · 2022Article
- When text simplification is not enough: could a graph-based visualization facilitate consumers' comprehension of dietary supplement information?JAMIA open · 2021Article
- A hybrid approach toward biomedical relation extraction training corpora: combining distant supervision with crowdsourcing.Database : the journal of biological databases and curation · 2020Article
- Is research on patient portals attuned to health equity? A scoping review.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Common Laboratory Results Frequently Misunderstood by a Sample of Mechanical Turk Users.Applied clinical informatics · 2019Article
- QuikLitE, a Framework for Quick Literacy Evaluation in Medicine: Development and Validation.Journal of medical Internet research · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundPatient portals are widely adopted in the United States and allow millions of patients access to their electronic health records (EHRs), including their EHR clinical notes. A patient's ability to understand the information in the EHR is dependent on their overall health literacy. Although many tests of health literacy exist, none specifically focuses on EHR note comprehension.
objectiveThe aim of this paper was to develop an instrument to assess patients' EHR note comprehension.
methodsWe identified 6 common diseases or conditions (heart failure, diabetes, cancer, hypertension, chronic obstructive pulmonary disease, and liver failure) and selected 5 representative EHR notes for each disease or condition. One note that did not contain natural language text was removed. Questions were generated from these notes using Sentence Verification Technique and were analyzed using item response theory (IRT) to identify a set of questions that represent a good test of ability for EHR note comprehension.
resultsUsing Sentence Verification Technique, 154 questions were generated from the 29 EHR notes initially obtained. Of these, 83 were manually selected for inclusion in the Amazon Mechanical Turk crowdsourcing tasks and 55 were ultimately retained following IRT analysis. A follow-up validation with a second Amazon Mechanical Turk task and IRT analysis confirmed that the 55 questions test a latent ability dimension for EHR note comprehension. A short test of 14 items was created along with the 55-item test.
conclusionsWe developed ComprehENotes, an instrument for assessing EHR note comprehension from existing EHR notes, gathered responses using crowdsourcing, and used IRT to analyze those responses, thus resulting in a set of questions to measure EHR note comprehension. Crowdsourced responses from Amazon Mechanical Turk can be used to estimate item parameters and select a subset of items for inclusion in the test set using IRT. The final set of questions is the first test of EHR note comprehension.
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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.