Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Monday, March 11, 2019 at 4:00 PM to 4:30 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Advertisement

Currently submitted to: JMIR Formative Research

Date Submitted: Oct 13, 2019
Open Peer Review Period: Oct 13, 2019 - Nov 11, 2019
(closed for review but you can still tweet)

NOTE: This is an unreviewed Preprint

Warning: This is a unreviewed preprint (What is a preprint?). Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn (a note “no longer under consideration” will appear above).

Peer-review me: Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period (in this case, a “Peer-Review Me” button to sign up as reviewer is displayed above). All preprints currently open for review are listed here. Outside of the formal open peer-review period we encourage you to tweet about the preprint.

Citation: Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author).

Final version: If our system detects a final peer-reviewed “version of record” (VoR) published in any journal, a link to that VoR will appear below. Readers are then encourage to cite the VoR instead of this preprint.

Settings: If you are the author, you can login and change the preprint display settings, but the preprint URL/DOI is supposed to be stable and citable, so it should not be removed once posted.

Submit: To post your own preprint, simply submit to any JMIR journal, and choose the appropriate settings to expose your submitted version as preprint.

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


Request queued. Please wait while the file is being generated. It may take some time.

© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.