Original Medical Notes Versus AI Plain-Language Summaries

May 18, 2026 updated by: David Ring, University of Texas at Austin

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

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

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

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?

Study Type

Interventional

Enrollment (Estimated)

135

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Description

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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Health Services Research
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: Double

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: Intervention (LLM-simplified notes)
Participants randomized to the intervention arm will review a plain-language summary generated from a curated version of their prior musculoskeletal clinic note using a large language model (LLM). All protected health information (PHI) and identifying information will be removed prior to LLM processing. The summary will be designed to simplify medical terminology and improve readability while maintaining the original clinical meaning of the note. The specific LLM used has not yet been finalized but will likely consist of the most current version of ChatGPT and/or Perplexity available through institutionally approved platforms at the time of the study. Participants will review the summary on an iPad prior to their clinic visit and complete questionnaires assessing trust, experience, and emotional responses.
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 meaning while reducing jargon and improving readability. The summary will be concise, neutral in tone, and written in patient-friendly language without adding new medical information or altering clinical recommendations. The specific LLM platform is not yet finalized but will likely use the most current version of ChatGPT and/or Perplexity available through institutional access.
Active Comparator: 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.
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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Trust and Experience with the Clinician Scale (TRECS-7)
Time Frame: Immediately after visit

The Trust and Experience with the Clinician Scale (TRECS-7) is a validated 7-item scale that measures patients' trust in and experience with their clinician during a medical consultation. Designed to minimize ceiling effects, it enables more sensitive detection of variation in patient experience across different clinical interactions (Brinkman et al.). Each of 7 statements is scored from 0-4 (strongly disagree, disagree, neutral, agree, strongly agree), resulting in a total score between 0 and 28. Higher scores indicate greater perceived trust in the clinician. Source: 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.

Time Frame: Measured once, immediately following consultation with the musculoskeletal specialist
Immediately after visit

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Emotional response to medical note
Time Frame: Immediately after intervention
Emotional responses to viewing one's own medical record will be assessed using 0-100 sliding scales measuring emotional comfort, anxiety, perceived clinician caring, and perceived impact on communication with the clinician. Higher or lower scores will reflect participants' position between paired emotional anchors including sad/happy, worried/at ease, doctor is caring/doctor is not caring, uncomfortable/comfortable, and affects communication/does not affect communication.
Immediately after intervention
Themes identified by the LLM in verbatim text
Time Frame: Collected immediately after intervention/control
Themes identified from participant verbatim comments regarding their experience reading the study material. Participants will respond to the free-text prompt: "How did you feel reading your medical records before the visit with the clinician? (Please elaborate)" Responses will be analyzed using a large language model (LLM) to identify recurring themes related to understanding, emotional reactions, perceived trust, clarity of information, comfort or distress, and anticipated impact on communication with the clinician.
Collected immediately after intervention/control

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

University of Texas at Austin

Investigators

  • Principal Investigator: David Ring, MD, PhD, Dell Medical School, University of Texas at Austin, TX, United States

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

June 1, 2026

Primary Completion (Estimated)

December 1, 2026

Study Completion (Estimated)

February 1, 2027

Study Registration Dates

First Submitted

May 9, 2026

First Submitted That Met QC Criteria

May 18, 2026

First Posted (Actual)

May 22, 2026

Study Record Updates

Last Update Posted (Actual)

May 22, 2026

Last Update Submitted That Met QC Criteria

May 18, 2026

Last Verified

May 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • STUDY00008863

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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