Trial reportInternational journal of medical informatics2023
Evaluating the efficacy of NoteAid on EHR note comprehension among US Veterans through Amazon Mechanical Turk.
Trial report in International journal of medical informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed, 1 citations in OpenAlex.
- 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 4 institutions in 1 country.
Funding
Abstract
objectiveLow health literacy is a concern among US Veterans. In this study, we evaluated NoteAid, a system that provides lay definitions to medical jargon terms in EHR notes to help Veterans comprehend EHR notes. We expected that low initial scores for Veterans would be improved by using NoteAid. MATERIALS AND
methodsWe recruited Veterans from the Amazon Mechanical Turk crowd work platform (MTurk). We also recruited non-Veterans from MTurk as a control group for comparison. We randomly split recruited MTurk Veteran participants into control and intervention groups. We recruited non-Veteran participants into mutually exclusive control or intervention tasks on the MTurk platform. We showed participants de-identified EHR notes and asked them to answer comprehension questions related to the notes. We provided participants in the intervention group with EHR note content processed with NoteAid, while NoteAid was not available for participants in the control group.
resultsWe recruited 94 Veterans and 181 non-Veterans. NoteAid leads to a significant improvement for non-Veterans but not for Veterans. Comparing Veterans recruited via MTurk with non-Veterans recruited via MTurk, we found that without NoteAid, Veterans have significantly higher raw scores than non-Veterans. This difference is not significant with NoteAid. DISCUSSION: That Veterans outperform a comparable population of non-Veterans is a surprising outcome. Without NoteAid, scores on the test are already high for Veterans, therefore, minimizing the ability of an intervention such as NoteAid to improve performance. With regards to Veterans, understanding the health literacy of Veterans has been an open question. We show here that Veterans score higher than a comparable, non-Veteran population.
conclusionVeterans on MTurk do not see improved scores when using NoteAid, but they already score high on the test, significantly higher than non-Veterans. When evaluating NoteAid, population specifics need to be considered, as performance may vary across groups. Future work investigating the effectiveness of NoteAid on improving comprehension with local Veterans and developing a more difficult test to assess groups with higher health literacy is needed.
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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.