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

作者信息

Lalor John P, Woolf Beverly, Yu Hong

机构信息

College of Information and Computer Sciences, University of Massachusetts, Amherst, MA, United States.

Department of Computer Science, University of Massachusetts Lowell, Lowell, MA, United States.

出版信息

J Med Internet Res. 2019 Jan 16;21(1):e10793. doi: 10.2196/10793.


DOI:10.2196/10793
PMID:30664453
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6351990/
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.

摘要

相似文献

[1]
Improving Electronic Health Record Note Comprehension With NoteAid: Randomized Trial of Electronic Health Record Note Comprehension Interventions With Crowdsourced Workers.

J Med Internet Res. 2019-1-16

[2]
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J Med Internet Res. 2021-5-13

[3]
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Int J Med Inform. 2023-4

[4]
ComprehENotes, an Instrument to Assess Patient Reading Comprehension of Electronic Health Record Notes: Development and Validation.

J Med Internet Res. 2018-4-25

[5]
A Natural Language Processing System That Links Medical Terms in Electronic Health Record Notes to Lay Definitions: System Development Using Physician Reviews.

J Med Internet Res. 2018-1-22

[6]
Improving patients' electronic health record comprehension with NoteAid.

Stud Health Technol Inform. 2013

[7]
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[8]
Unsupervised ensemble ranking of terms in electronic health record notes based on their importance to patients.

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[9]
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[10]
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引用本文的文献

[1]
Improving Clinical Documentation with Artificial Intelligence: A Systematic Review.

Perspect Health Inf Manag. 2024-6-1

[2]
Individual Factors That Affect Laypeople's Understanding of Definitions of Medical Jargon.

Health Policy Technol. 2024-12

[3]
Evaluating Expert-Layperson Agreement in Identifying Jargon Terms in Electronic Health Record Notes: Observational Study.

J Med Internet Res. 2024-10-15

[4]
Biomedical text readability after hypernym substitution with fine-tuned large language models.

PLOS Digit Health. 2024-4-16

[5]
Social and Behavioral Determinants of Health in the Era of Artificial Intelligence with Electronic Health Records: A Scoping Review.

Health Data Sci. 2021-8-24

[6]
Evaluation of Patient-Friendly Diagnosis Clarifications in a Hospital Patient Portal.

Appl Clin Inform. 2023-5

[7]
Evaluating the efficacy of NoteAid on EHR note comprehension among US Veterans through Amazon Mechanical Turk.

Int J Med Inform. 2023-4

[8]
Patients' Experiences of Web-Based Access to Electronic Health Records in Finland: Cross-sectional Survey.

J Med Internet Res. 2022-6-6

[9]
Can sharing clinic notes improve communication and promote self-management? A qualitative study of patients with COPD.

Patient Educ Couns. 2022-3

[10]
Patient Rationales Against the Use of Patient-Accessible Electronic Health Records: Qualitative Study.

J Med Internet Res. 2021-5-28

本文引用的文献

[1]
Understanding Deep Learning Performance through an Examination of Test Set Difficulty: A Psychometric Case Study.

Proc Conf Empir Methods Nat Lang Process. 2018

[2]
ComprehENotes, an Instrument to Assess Patient Reading Comprehension of Electronic Health Record Notes: Development and Validation.

J Med Internet Res. 2018-4-25

[3]
Health Literacy and Awareness of Atrial Fibrillation.

J Am Heart Assoc. 2017-4-11

[4]
Building an Evaluation Scale using Item Response Theory.

Proc Conf Empir Methods Nat Lang Process. 2016-11

[5]
Health literacy and fear of cancer progression in elderly women newly diagnosed with breast cancer--A longitudinal analysis.

Patient Educ Couns. 2016-5

[6]
Ranking adverse drug reactions with crowdsourcing.

J Med Internet Res. 2015-3-23

[7]
Microtask crowdsourcing for disease mention annotation in PubMed abstracts.

Pac Symp Biocomput. 2015

[8]
Using the wisdom of the crowds to find critical errors in biomedical ontologies: a study of SNOMED CT.

J Am Med Inform Assoc. 2015-5

[9]
Improving patients' electronic health record comprehension with NoteAid.

Stud Health Technol Inform. 2013

[10]
Web 2.0-based crowdsourcing for high-quality gold standard development in clinical natural language processing.

J Med Internet Res. 2013-4-2

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