Evidence map›Paper›PMID 29695372›Full record

ArticleJournal of medical Internet research2018

ComprehENotes, an Instrument to Assess Patient Reading Comprehension of Electronic Health Record Notes: Development and Validation.

John P Lalor, Hao Wu, Li Chen, Kathleen M Mazor, Hong Yu

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

  1. Trial
  2. Trial
  3. Trial
  4. Review
  5. Article
  6. 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 · 2024
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Is research on patient portals attuned to health equity? A scoping review.Journal of the American Medical Informatics Association : JAMIA · 2019
    Article
  12. Article
  13. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

John P LalorCollege of Information and Computer Sciences, University of Massachusetts, Amherst, MA, United States.ORCID 0000-0003-0848-4786
Hao WuPsychology Department, Boston College, Chestnut Hill, MA, United States.ORCID 0000-0001-6471-1774
Li ChenPsychology Department, Boston College, Chestnut Hill, MA, United States.ORCID 0000-0001-6876-4398
Kathleen M MazorMeyers Primary Care Institute, University of Massachusetts Medical School / Reliant Medical Group / Fallon Health, Worcester, MA, United States.ORCID 0000-0002-9491-9872
Hong YuCollege of Information and Computer Sciences, University of Massachusetts, Amherst, MA, United States.ORCID 0000-0001-9263-5035

Funding

Resource Curation and Evaluation for EHR Note ComprehensionR01LM012817 · NLM · UNIVERSITY OF MASSACHUSETTS LOWELL · PI YU, HONG · 2018 to 2020
$1.0M
Systems for Helping Veterans Comprehend Electronic Health Record NotesI01HX001457 · VA · NORTHAMPTON VA MEDICAL CENTER · PI YU, HONG · 2015 to 2020
–
HSRD VA I01 HX001457NLM NIH HHS R01 LM012817
6 · The paper itself

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.

Indexed as

ReadingComprehensionElectronic Health RecordsFemaleHumansMaleValidation Studies as Topiccrowdsourcingelectronic health recordshealth literacypsychometrics

Identifiers

PMID29695372
PMCPMC5943623

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

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.