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Randomized Controlled Trial
. 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

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Free PMC article
Randomized Controlled Trial

Improving Electronic Health Record Note Comprehension With NoteAid: Randomized Trial of Electronic Health Record Note Comprehension Interventions With Crowdsourced Workers

John P Lalor et al. J Med Internet Res. .
Free PMC article

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.

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

Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Flowchart describing our experiment. Amazon Mechanical Turk workers were randomly assigned to one of three tasks on the platform. They completed the ComprehENotes test with the use of the provided external tool. All scores were then collected, and ability estimated were obtained using Item Response Theory (IRT).
Figure 2
Figure 2
Example showing NoteAid simplified text.
Figure 3
Figure 3
Equations for Item Response Theory 3-parameter logistic models.
Figure 4
Figure 4
Box plot of raw scores for baseline and treatment Turker groups. The treatment groups were able to use MedlinePlus and NoteAid, respectively, when taking the ComprehENotes test.
Figure 5
Figure 5
Box plot of ability estimates for baseline and treatment Turker groups. The treatment groups MLP and NA were able to use MedlinePlus and NoteAid, respectively, when taking the ComprehENotes test. IRT: Item Response Theory.

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