Original Medical Notes Versus AI Plain-Language Summaries

Official title

Patient Experience and Trust When Viewing Original Medical Notes Versus Large Language Model (LLM)-Generated Plain Language Summaries

Overview

The goal of this clinical trial is to learn how patients feel when reading their medical notes. The study compares reading the original doctor's note with reading a simpler, Artificial Intelligence (AI)-generated version written in plain language in adults receiving musculoskeletal specialty care. The main questions the study aims to answer are: 1. Does reading a plain-language summary change how patients feel about their doctor or their clinic experience? 2. Does the type of note affect how comfortable, reassured, or worried patients feel? Researchers will compare patients who read their original clinic note with patients who read an Artificial Intelligence (AI)-generated plain-language summary to see whether simpler language changes patient understanding, trust, or emotional responses. Participants will: * Read either their original clinic note or a plain-language summary of the note * Complete short questionnaires about their experience, emotions, and trust in their clinician * Optionally provide written comments about how it felt to read the information

Detailed description

Background As patient access to electronic medical records becomes more widespread, understanding how individuals emotionally respond to their clinical documentation has become increasingly important. A qualitative study analyzing 600 medical encounter notes authored by 138 clinicians identified distinct patterns of language reflecting clinicians' attitudes toward patients. Five categories of negative language were described (questioning credibility, disapproval, stereotyping, labeling patients as "difficult," and unilateral decision-making), alongside six categories of positive language (compliments, approval, self-disclosure, minimizing blame, personalization, and collaborative decision-making). Prior research has demonstrated that access to clinical notes ('open notes') can influence patient experience, with potential benefits including improved understanding, greater trust, enhanced perceived quality of care, and increased engagement in self-care and health management. These benefits appear particularly pronounced with patients with lower education attainment or those from ethnic minority groups. Despite this, fewer than half of clinicians routinely discuss shared notes with patients during visits.

Rationale Language choices within clinical documentation may meaningfully shape how patients perceive their diagnosis, their relationship with healthcare providers, and their emotional well-being. For example, in a survey study of 100 healthy companions of orthopedic hand surgery patients, participants evaluated 19 commonly used medical terms and their synonyms using the Self-Assessment Manikin (SAM). Terms such as "pain" was rated more negatively than alternatives such as "discomfort" or "ache", while "rupture" elicited a more negative response than "tear" or "defect". These findings suggest that frequently used clinical terminology may also unintentionally elicit negative emotional reactions. Patients may be particularly vulnerable to language-related distress when reading their own medical records. The tone, complexity, and structure of these notes, especially in surgical specialties, may impact patient trust and overall comfort, and, in some cases, may unintentionally erode trust or intensify anxiety. Emerging evidence suggests that plain-language summaries might mitigate these effects. In a small study of 20 participants, artificial intelligence was used to generate plain-language medical notes, which were perceived as more useful, supportive of the patient-clinician relationship, and empowering for patients' understanding in their health. Although the importance and benefits of accessible visit notes have been well established, there remains limited empirical evidence regarding the optimal tone, structure, and language of medical documentation from the patient perspective.

Hypotheses

Primary hypothesis:

There are no factors associated with patient ratings of trust and experience with the clinician (TRECS), including whether patients viewed their original medical record entry or an LLM-generated plain language summary based on a curated version of the record (where PHI is removed) prior to the visit and other patient factors (demographics, mental health clusters).

Secondary hypotheses:

There are no factors associated with emotional responses to viewing one's own medical record entry, including whether patients view the original entry or an LLM-generated plain language summary based on a curated version of the record (where PHI is removed) prior to the visit and other patient factors (demographics, mental health clusters).

Qualitative hypothesis:

What themes does an LLM identify in patient verbatim comments regarding viewing their medical record entry or an LLM plain language summary?

Interventions

• Behavioral LLM-Generated Medical Note
  The intervention consists of presenting participants with an LLM-generated plain-language summary of a prior musculoskeletal clinic note. The summary will be produced from a curated version of the original documentation after removal of all protected health information (PHI), macros, and administrative content. Physical therapy notes will be excluded, and normal examination or imaging findings may be simplified (e.g., "Exam otherwise normal"). The LLM will be instructed to preserve clinical mean
• Behavioral Control (Original Medical Note)
  Participants randomized to the control arm will review the original clinical note from their prior musculoskeletal clinic visit. The note will be presented in its original format without language simplification. Participants will review the note on an iPad prior to their clinic visit and complete questionnaires assessing trust, experience, and emotional responses.

Primary outcome measures

• Trust and Experience with the Clinician Scale (TRECS-7) [Time frame: Immediately after visit]

Secondary outcome measures (2)

• Emotional response to medical note [Time frame: Immediately after intervention]
• Themes identified by the LLM in verbatim text [Time frame: Collected immediately after intervention/control]

Eligibility criteria

Inclusion criteria

• Adult (18-89)
• Seeking outpatient musculoskeletal specialty care
• English language literacy
• Return patient to the clinic

Exclusion criteria

\- Any impairment precluding completion of a survey on a tablet

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Allocation

Randomized

Model

Parallel assignment

Masking

Double blind

Primary purpose

Health services research

Study locations

Center list to be confirmed — check the primary protocol.

Publications

• Park J, Saha S, Chee B, Taylor J, Beach MC. Physician Use of Stigmatizing Language in Patient Medical Records. JAMA Netw Open. 2021 Jul 1;4(7):e2117052. doi: 10.1001/jamanetworkopen.2021.17052. PMID 34259849
• Delbanco T, Walker J, Bell SK, Darer JD, Elmore JG, Farag N, Feldman HJ, Mejilla R, Ngo L, Ralston JD, Ross SE, Trivedi N, Vodicka E, Leveille SG. Inviting patients to read their doctors' notes: a quasi-experimental study and a look ahead. Ann Intern Med. 2012 Oct 2;157(7):461-70. doi: 10.7326/0003-4819-157-7-201210020-00002. PMID 23027317
• Walker J, Leveille S, Bell S, Chimowitz H, Dong Z, Elmore JG, Fernandez L, Fossa A, Gerard M, Fitzgerald P, Harcourt K, Jackson S, Payne TH, Perez J, Shucard H, Stametz R, DesRoches C, Delbanco T. OpenNotes After 7 Years: Patient Experiences With Ongoing Access to Their Clinicians' Outpatient Visit Notes. J Med Internet Res. 2019 May 6;21(5):e13876. doi: 10.2196/13876. PMID 31066717
• Gerard M, Chimowitz H, Fossa A, Bourgeois F, Fernandez L, Bell SK. The Importance of Visit Notes on Patient Portals for Engaging Less Educated or Nonwhite Patients: Survey Study. J Med Internet Res. 2018 May 24;20(5):e191. doi: 10.2196/jmir.9196. PMID 29793900
• Bell SK, Mejilla R, Anselmo M, Darer JD, Elmore JG, Leveille S, Ngo L, Ralston JD, Delbanco T, Walker J. When doctors share visit notes with patients: a study of patient and doctor perceptions of documentation errors, safety opportunities and the patient-doctor relationship. BMJ Qual Saf. 2017 Apr;26(4):262-270. doi: 10.1136/bmjqs-2015-004697. Epub 2016 May 18. PMID 27193032
• Vranceanu AM, Elbon M, Adams M, Ring D. The emotive impact of medical language. Hand (N Y). 2012 Sep;7(3):293-6. doi: 10.1007/s11552-012-9419-z. PMID 23997735
• Bala S, Keniston A, Burden M. Patient Perception of Plain-Language Medical Notes Generated Using Artificial Intelligence Software: Pilot Mixed-Methods Study. JMIR Form Res. 2020 Jun 5;4(6):e16670. doi: 10.2196/16670. PMID 32442148
• Brinkman N, Looman R, Jayakumar P, Ring D, Choi S. Is It Possible to Develop a Patient-reported Experience Measure With Lower Ceiling Effect? Clin Orthop Relat Res. 2025 Apr 1;483(4):693-703. doi: 10.1097/CORR.0000000000003262. Epub 2024 Oct 25. PMID 39466401

Identifiers

NCT: NCT07602725 · STUDY00008863

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗