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Currently accepted at: JMIR Formative Research

Date Submitted: Oct 13, 2019
Open Peer Review Period: Oct 13, 2019 - Nov 11, 2019
Date Accepted: Mar 29, 2020
Date Submitted to PubMed: May 22, 2020
(closed for review but you can still tweet)

This paper has been accepted and is currently in production.

It will appear shortly on 10.2196/16670

The final accepted version (not copyedited yet) is in this tab.

An "ahead-of-print" version has been submitted to Pubmed, see PMID: 32442148

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Patient Perception of Plain Language Medical Notes Generated with Artificial Intelligence Software: A Pilot Study

  • Sandeep Bala; 
  • Angela Keniston; 
  • Marisha Burden; 

ABSTRACT

Background:

Providing patients access to their medical notes has been demonstrated to offer many benefits for patients and providers. [1] This has led to a rapidly expanding national movement, OpenNotes, which provides resources to clinicians who desire to share medical notes with their patients. [2, 3] However, a significant barrier to the widespread adoption of OpenNotes are clinician’s concerns that the medical terminology in such notes may confuse patients. [4]

Objective:

Artificial intelligence (AI) software may provide the opportunity to rapidly simplify medical notes to plain language through natural language processing. This offers the potential to resolve concerns over medical terminology and patient confusion. This pilot study assesses patient’s perception of AI-simplified plain language medical notes.

Methods:

Patient’s perception of notes was studied through comprehension questionnaires and guided interviews with subsequent thematic analysis. Study participants were recruited from patients hospitalized at the University of Colorado Hospital. A standardized cardiology patient’s note was generated using a synthetic patient generator which served as an original template. AI software produced a simplified version. Patients were randomly assigned to first read either the original note or the simplified version. Participants then completed a set of seven comprehension assessment questions to assess for comprehension of their respective note. Subsequently, patients reviewed the opposite version of the note and participated in a guided interview to discuss their thoughts on these notes. Participant responses were then thematically analyzed.

Results:

Twenty patients agreed to participate. The study was found to be underpowered to detect statistical significance of the impact of simplified notes on participant comprehension. Though the mean number of comprehension assessment questions answered correctly was found to be higher in the simplified note group at 4.7 as compared to 3.9 in the unsimplified note group, this was found to be non-significant (p=0.32). Guided interviews found that AI simplified open notes were perceived as desirable and beneficial by participants. Thematic analysis identified that simplified medical notes may (1) be more useable than unsimplified notes, (2) improve the patient-provider relationship, and (3) empower patients through an enhanced understanding of their conditions and management. Participant’s recommendations highlighted the need to reduce lengthy plain-language phrases and to target the level of simplification to each patient’s health literacy.

Conclusions:

Simplified notes were well received by participants, who expressed a desire to have access to such notes for their own medical conditions. This study illustrates the potential for artificial intelligence software to quickly generate plain language medical notes that are useful for patients and their providers. Feedback from participants in this study should be used to improve the simplification of notes. Larger studies should be conducted with heed to the insight gained from this pilot study.


 Citation

Please cite as:

Bala S, Keniston A, Burden M

Patient Perception of Plain Language Medical Notes Generated with Artificial Intelligence Software: A Pilot Study

JMIR Preprints. 13/10/2019:16670

DOI: 10.2196/preprints.16670

URL: https://preprints.jmir.org/preprint/16670

PMID: 32442148

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