Evaluating the Effectiveness of NoteAid in a Community Hospital Setting: Randomized Trial of Electronic Health Record Note Comprehension Interventions With Patients. Lalor, J. P., Hu, W., Tran, M., Wu, H., Mazor, K. M., & Yu, H. Journal of Medical Internet Research, 23(5):e26354, May, 2021. doi abstract bibtex BACKGROUND: Interventions to define medical jargon have been shown to improve electronic health record (EHR) note comprehension among crowdsourced participants on Amazon Mechanical Turk (AMT). However, AMT participants may not be representative of the general population or patients who are most at-risk for low health literacy. OBJECTIVE: In this work, we assessed the efficacy of an intervention (NoteAid) for EHR note comprehension among participants in a community hospital setting. METHODS: Participants were recruited from Lowell General Hospital (LGH), a community hospital in Massachusetts, to take the ComprehENotes test, a web-based test of EHR note comprehension. Participants were randomly assigned to control (n=85) or intervention (n=89) groups to take the test without or with NoteAid, respectively. For comparison, we used a sample of 200 participants recruited from AMT to take the ComprehENotes test (100 in the control group and 100 in the intervention group). RESULTS: A total of 174 participants were recruited from LGH, and 200 participants were recruited from AMT. Participants in both intervention groups (community hospital and AMT) scored significantly higher than participants in the control groups (P\textless.001). The average score for the community hospital participants was significantly lower than the average score for the AMT participants (P\textless.001), consistent with the lower education levels in the community hospital sample. Education level had a significant effect on scores for the community hospital participants (P\textless.001). CONCLUSIONS: Use of NoteAid was associated with significantly improved EHR note comprehension in both community hospital and AMT samples. Our results demonstrate the generalizability of ComprehENotes as a test of EHR note comprehension and the effectiveness of NoteAid for improving EHR note comprehension.
@article{lalor_evaluating_2021,
title = {Evaluating the {Effectiveness} of {NoteAid} in a {Community} {Hospital} {Setting}: {Randomized} {Trial} of {Electronic} {Health} {Record} {Note} {Comprehension} {Interventions} {With} {Patients}},
volume = {23},
issn = {1438-8871},
shorttitle = {Evaluating the {Effectiveness} of {NoteAid} in a {Community} {Hospital} {Setting}},
doi = {10.2196/26354},
abstract = {BACKGROUND: Interventions to define medical jargon have been shown to improve electronic health record (EHR) note comprehension among crowdsourced participants on Amazon Mechanical Turk (AMT). However, AMT participants may not be representative of the general population or patients who are most at-risk for low health literacy.
OBJECTIVE: In this work, we assessed the efficacy of an intervention (NoteAid) for EHR note comprehension among participants in a community hospital setting.
METHODS: Participants were recruited from Lowell General Hospital (LGH), a community hospital in Massachusetts, to take the ComprehENotes test, a web-based test of EHR note comprehension. Participants were randomly assigned to control (n=85) or intervention (n=89) groups to take the test without or with NoteAid, respectively. For comparison, we used a sample of 200 participants recruited from AMT to take the ComprehENotes test (100 in the control group and 100 in the intervention group).
RESULTS: A total of 174 participants were recruited from LGH, and 200 participants were recruited from AMT. Participants in both intervention groups (community hospital and AMT) scored significantly higher than participants in the control groups (P{\textless}.001). The average score for the community hospital participants was significantly lower than the average score for the AMT participants (P{\textless}.001), consistent with the lower education levels in the community hospital sample. Education level had a significant effect on scores for the community hospital participants (P{\textless}.001).
CONCLUSIONS: Use of NoteAid was associated with significantly improved EHR note comprehension in both community hospital and AMT samples. Our results demonstrate the generalizability of ComprehENotes as a test of EHR note comprehension and the effectiveness of NoteAid for improving EHR note comprehension.},
language = {eng},
number = {5},
journal = {Journal of Medical Internet Research},
author = {Lalor, John P. and Hu, Wen and Tran, Matthew and Wu, Hao and Mazor, Kathleen M. and Yu, Hong},
month = may,
year = {2021},
pmid = {33983124},
pmcid = {PMC8160802},
keywords = {Comprehension, Electronic Health Records, Hospitals, Community, Humans, comprehension, crowdsourcing, efficacy, electronic health record, health literacy, information storage and retrieval, intervention, literacy, natural language processing, psychometrics},
pages = {e26354},
}
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{"_id":"eoy4K5wdyYnRAar5T","bibbaseid":"lalor-hu-tran-wu-mazor-yu-evaluatingtheeffectivenessofnoteaidinacommunityhospitalsettingrandomizedtrialofelectronichealthrecordnotecomprehensioninterventionswithpatients-2021","author_short":["Lalor, J. P.","Hu, W.","Tran, M.","Wu, H.","Mazor, K. M.","Yu, H."],"bibdata":{"bibtype":"article","type":"article","title":"Evaluating the Effectiveness of NoteAid in a Community Hospital Setting: Randomized Trial of Electronic Health Record Note Comprehension Interventions With Patients","volume":"23","issn":"1438-8871","shorttitle":"Evaluating the Effectiveness of NoteAid in a Community Hospital Setting","doi":"10.2196/26354","abstract":"BACKGROUND: Interventions to define medical jargon have been shown to improve electronic health record (EHR) note comprehension among crowdsourced participants on Amazon Mechanical Turk (AMT). However, AMT participants may not be representative of the general population or patients who are most at-risk for low health literacy. OBJECTIVE: In this work, we assessed the efficacy of an intervention (NoteAid) for EHR note comprehension among participants in a community hospital setting. METHODS: Participants were recruited from Lowell General Hospital (LGH), a community hospital in Massachusetts, to take the ComprehENotes test, a web-based test of EHR note comprehension. Participants were randomly assigned to control (n=85) or intervention (n=89) groups to take the test without or with NoteAid, respectively. For comparison, we used a sample of 200 participants recruited from AMT to take the ComprehENotes test (100 in the control group and 100 in the intervention group). RESULTS: A total of 174 participants were recruited from LGH, and 200 participants were recruited from AMT. Participants in both intervention groups (community hospital and AMT) scored significantly higher than participants in the control groups (P\\textless.001). The average score for the community hospital participants was significantly lower than the average score for the AMT participants (P\\textless.001), consistent with the lower education levels in the community hospital sample. Education level had a significant effect on scores for the community hospital participants (P\\textless.001). CONCLUSIONS: Use of NoteAid was associated with significantly improved EHR note comprehension in both community hospital and AMT samples. Our results demonstrate the generalizability of ComprehENotes as a test of EHR note comprehension and the effectiveness of NoteAid for improving EHR note comprehension.","language":"eng","number":"5","journal":"Journal of Medical Internet Research","author":[{"propositions":[],"lastnames":["Lalor"],"firstnames":["John","P."],"suffixes":[]},{"propositions":[],"lastnames":["Hu"],"firstnames":["Wen"],"suffixes":[]},{"propositions":[],"lastnames":["Tran"],"firstnames":["Matthew"],"suffixes":[]},{"propositions":[],"lastnames":["Wu"],"firstnames":["Hao"],"suffixes":[]},{"propositions":[],"lastnames":["Mazor"],"firstnames":["Kathleen","M."],"suffixes":[]},{"propositions":[],"lastnames":["Yu"],"firstnames":["Hong"],"suffixes":[]}],"month":"May","year":"2021","pmid":"33983124","pmcid":"PMC8160802","keywords":"Comprehension, Electronic Health Records, Hospitals, Community, Humans, comprehension, crowdsourcing, efficacy, electronic health record, health literacy, information storage and retrieval, intervention, literacy, natural language processing, psychometrics","pages":"e26354","bibtex":"@article{lalor_evaluating_2021,\n\ttitle = {Evaluating the {Effectiveness} of {NoteAid} in a {Community} {Hospital} {Setting}: {Randomized} {Trial} of {Electronic} {Health} {Record} {Note} {Comprehension} {Interventions} {With} {Patients}},\n\tvolume = {23},\n\tissn = {1438-8871},\n\tshorttitle = {Evaluating the {Effectiveness} of {NoteAid} in a {Community} {Hospital} {Setting}},\n\tdoi = {10.2196/26354},\n\tabstract = {BACKGROUND: Interventions to define medical jargon have been shown to improve electronic health record (EHR) note comprehension among crowdsourced participants on Amazon Mechanical Turk (AMT). However, AMT participants may not be representative of the general population or patients who are most at-risk for low health literacy.\nOBJECTIVE: In this work, we assessed the efficacy of an intervention (NoteAid) for EHR note comprehension among participants in a community hospital setting.\nMETHODS: Participants were recruited from Lowell General Hospital (LGH), a community hospital in Massachusetts, to take the ComprehENotes test, a web-based test of EHR note comprehension. Participants were randomly assigned to control (n=85) or intervention (n=89) groups to take the test without or with NoteAid, respectively. For comparison, we used a sample of 200 participants recruited from AMT to take the ComprehENotes test (100 in the control group and 100 in the intervention group).\nRESULTS: A total of 174 participants were recruited from LGH, and 200 participants were recruited from AMT. Participants in both intervention groups (community hospital and AMT) scored significantly higher than participants in the control groups (P{\\textless}.001). The average score for the community hospital participants was significantly lower than the average score for the AMT participants (P{\\textless}.001), consistent with the lower education levels in the community hospital sample. Education level had a significant effect on scores for the community hospital participants (P{\\textless}.001).\nCONCLUSIONS: Use of NoteAid was associated with significantly improved EHR note comprehension in both community hospital and AMT samples. Our results demonstrate the generalizability of ComprehENotes as a test of EHR note comprehension and the effectiveness of NoteAid for improving EHR note comprehension.},\n\tlanguage = {eng},\n\tnumber = {5},\n\tjournal = {Journal of Medical Internet Research},\n\tauthor = {Lalor, John P. and Hu, Wen and Tran, Matthew and Wu, Hao and Mazor, Kathleen M. and Yu, Hong},\n\tmonth = may,\n\tyear = {2021},\n\tpmid = {33983124},\n\tpmcid = {PMC8160802},\n\tkeywords = {Comprehension, Electronic Health Records, Hospitals, Community, Humans, comprehension, crowdsourcing, efficacy, electronic health record, health literacy, information storage and retrieval, intervention, literacy, natural language processing, psychometrics},\n\tpages = {e26354},\n}\n\n","author_short":["Lalor, J. P.","Hu, W.","Tran, M.","Wu, H.","Mazor, K. 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