This paper is in the following e-collection/theme issue:

Personal Health Records, Patient-Accessible Electronic Health Records, Patient Portals (542) Participatory Medicine & E-Patients (758) Electronic Health Records (966) Crowdsourcing and Mechanical Turks (91) Linguistics, Comprehension, and Learning (52)

Published on in Vol 21, No 1 (2019): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/10793, first published .
Improving Electronic Health Record Note Comprehension With NoteAid: Randomized Trial of Electronic Health Record Note Comprehension Interventions With Crowdsourced Workers

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

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

Authors of this article:

John P Lalor1 Author Orcid Image ;   Beverly Woolf1 Author Orcid Image ;   Hong Yu1, 2, 3, 4 Author Orcid Image
Article Authors Cited by (15) Tweetations (7) Metrics

John P Lalor   1 , MS ;   Beverly Woolf   1 , PhD ;   Hong Yu   1, 2, 3, 4 , PhD, FACMI

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

Corresponding Author:

  • Hong Yu, PhD, FACMI
  • Department of Computer Science
  • University of Massachusetts Lowell
  • 1 University Avenue
  • Lowell, MA
  • United States
  • Phone: 1 508 612 7292
  • Email: hong.yu@umassmed.edu