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Lalor JP, Woolf B, Yu H. Improving Electronic Health Record Note Comprehension With NoteAid: Randomized Trial of Electronic Health Record Note Comprehension Interventions With Crowdsourced Workers. J Med Internet Res. 2019;21(1):e10793doi: 10.2196/10793.
Lalor, J. P., Woolf, B., & Yu, H. (2019). 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, 21(1), e10793. https://doi.org/10.2196/10793
Lalor, John P, et al. "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 vol. 21,1 (2019): e10793. doi: https://doi.org/10.2196/10793
Lalor JP, Woolf B, Yu H. Improving Electronic Health Record Note Comprehension With NoteAid: Randomized Trial of Electronic Health Record Note Comprehension Interventions With Crowdsourced Workers. J Med Internet Res. 2019 Jan 16;21(1):e10793. doi: 10.2196/10793. PMID: 30664453; PMCID: PMC6351990.
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J Med Internet Res. 2019 Jan 16;21(1):e10793. doi: 10.2196/10793.

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

John P Lalor, Beverly Woolf, Hong Yu

Affiliations

  1. College of Information and Computer Sciences, University of Massachusetts, Amherst, MA, United States.
  2. Department of Computer Science, University of Massachusetts Lowell, Lowell, MA, United States.
  3. Department of Medicine, University of Massachusetts Medical School, Worcester, MA, United States.
  4. Bedford Veterans Affairs Medical Center, Center for Healthcare Organization and Implementation Research, Bedford, MA, United States.

PMID: 30664453 PMCID: PMC6351990 DOI: 10.2196/10793

Abstract

BACKGROUND: Patient 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.

OBJECTIVE: Our 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.

METHODS: In 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.

RESULTS: Mean 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).

CONCLUSIONS: In 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.

©John P Lalor, Beverly Woolf, Hong Yu. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 16.01.2019.

Keywords: MedlinePlus; crowdsourcing; health literacy; information storage and retrieval; natural language processing; psychometrics

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