Musculoskeletal DiseaseNCT07602725
Original Medical Notes Versus AI Plain-Language Summaries
A study testing LLM-Generated Medical Note in people with Musculoskeletal Disease and Orthopedic Patients, run by University of Texas at Austin.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 to 89 years
- Conditions the study is for
- Musculoskeletal Disease, Orthopedic Patients
- What is being tested?
- LLM-Generated Medical Note
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 135 participants
- Where
- No sites listed yet
- Study dates
- 1 Jun 2026to 1 Feb 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 89 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Adult (18-89)
- Seeking outpatient musculoskeletal specialty care
- English language literacy
- Return patient to the clinic
Who cannot join
- Any impairment precluding completion of a survey on a tablet
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: 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:
Read more
- Does reading a plain-language summary change how patients feel about their doctor or their clinic experience?
- 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
Read the 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?
- Conditions
- Musculoskeletal Disease, Orthopedic Patients
- Being tested
- LLM-Generated Medical Note, Control (Original Medical Note)
- Keywords
- Artificial intelligence, Musculoskeletal Care, Patient experience, Medical documentation
- Medical subject headings
- Musculoskeletal Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
LLM-Generated Medical NoteBehavioural
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.
Control (Original Medical Note)Behavioural
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.
| Study group | Role | What they receive |
|---|---|---|
| Intervention (LLM-simplified notes) | Experimental | 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. Receives: Behavioral: LLM-Generated Medical Note |
| Control (Original medical note) | Active comparator | 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. Receives: Behavioral: Control (Original Medical Note) |
What is being measured
The primary outcome is the main question the study is designed to answer.
Trust and Experience with the Clinician Scale (TRECS-7)
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
Measured: Immediately after visit
Secondary outcomes (2)
Emotional response to medical note
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.
Measured: Immediately after intervention
Themes identified by the LLM in verbatim text
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.
Measured: Collected immediately after intervention/control
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 May 2026
- Study start1 Jun 2026 (estimated)
- Primary completion1 Dec 2026 (estimated)
- Study completion1 Feb 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (11)Papers the sponsor has linked to this study.
- Other trials for Musculoskeletal DiseaseStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind
- Who is masked
- Care provider, Outcomes assessor
- Enrolment
- 135 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Texas at Austin
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, David Ring, University of Texas at Austin
- Investigators
- David Ring, MD, PhD, Principal investigator, Dell Medical School, University of Texas at Austin, TX, United States
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07602725
- Sponsor's protocol ID
- STUDY00008863
- Status last verified
- May 2026
- First submitted
- 9 May 2026
- Last updated
- 22 May 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
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.
PubMed 34259849Delbanco 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.
PubMed 23027317Walker 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.
PubMed 31066717Gerard 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.
PubMed 29793900Bell 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.
PubMed 27193032Vranceanu 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.
PubMed 23997735Bala 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.
PubMed 32442148Brinkman 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.
PubMed 39466401Santesso N, Rader T, Wells GA, O'Connor AM, Brooks PM, Driedger M, Gallois C, Kristjansson E, Lyddiatt A, O'Leary G, Prince M, Stacey D, Wale J, Welch V, Wilson AJ, Tugwell PS. Responsiveness of the Effective Consumer Scale (EC-17). J Rheumatol. 2009 Sep;36(9):2087-91. doi: 10.3899/jrheum.090363.
PubMed 19738218Brinkman N, Broekman M, Teunis T, Choi S, Ring D, Jayakumar P. A New Measure of Quantified Social Health Is Associated With Levels of Discomfort, Capability, and Mental and General Health Among Patients Seeking Musculoskeletal Specialty Care. Clin Orthop Relat Res. 2025 Apr 1;483(4):647-663. doi: 10.1097/CORR.0000000000003394. Epub 2025 Feb 5.
PubMed 39915110Teunis T, Al Salman A, Koenig K, Ring D, Fatehi A. Unhelpful Thoughts and Distress Regarding Symptoms Limit Accommodation of Musculoskeletal Pain. Clin Orthop Relat Res. 2022 Feb 1;480(2):276-283. doi: 10.1097/CORR.0000000000002006.
PubMed 34652286
Study at a glance
From the registry record, last updated by the sponsor on 22 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Patient Experience and Trust When Viewing Original Medical Notes Versus Large Language Model (LLM)-Generated Plain Language Summaries
- ClinicalTrials.gov ID
- NCT07602725
- Acronym / sponsor ID
- STUDY00008863
- Conditions
- Musculoskeletal Disease, Orthopedic Patients
- Keywords
- Artificial intelligence, Musculoskeletal Care, Patient experience, Medical documentation
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 135 participants (estimated)
- Lead sponsor
- University of Texas at AustinUniversity, hospital or other
- Study start
- 1 Jun 2026 (estimated)
- Primary completion
- 1 Dec 2026 (estimated)
- Study completion
- 1 Feb 2027 (estimated)
- First posted
- 22 May 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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:
- Does reading a plain-language summary change how patients feel about their doctor or their clinic experience?
- 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?
Study design
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind (care provider, outcomes assessor)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Intervention (LLM-simplified notes) | Experimental | Behavioral: LLM-Generated Medical NoteParticipants 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. |
| Control (Original medical note) | Active comparator | 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. |
Who may qualify
- Ages
- 18 to 89 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- Adult (18-89)
- Seeking outpatient musculoskeletal specialty care
- English language literacy
- Return patient to the clinic
Key exclusion criteria (1 listed)
- Any impairment precluding completion of a survey on a tablet
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| LLM-Generated Medical NoteThe 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. | Behavioural | Intervention (LLM-simplified notes) |
| 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. | Behavioural | Control (Original medical note) |
Primary outcome
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
2 secondary outcomes
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
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 May 2026.
- Background: PubMed 34259849Park 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.
- Background: PubMed 23027317Delbanco 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.
- Background: PubMed 31066717Walker 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.
- Background: PubMed 29793900Gerard 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.
- Background: PubMed 27193032Bell 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.
- Background: PubMed 23997735Vranceanu 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.
- Background: PubMed 32442148Bala 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.
- Background: PubMed 39466401Brinkman 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.
- Background: PubMed 19738218Santesso N, Rader T, Wells GA, O'Connor AM, Brooks PM, Driedger M, Gallois C, Kristjansson E, Lyddiatt A, O'Leary G, Prince M, Stacey D, Wale J, Welch V, Wilson AJ, Tugwell PS. Responsiveness of the Effective Consumer Scale (EC-17). J Rheumatol. 2009 Sep;36(9):2087-91. doi: 10.3899/jrheum.090363.
- Background: PubMed 39915110Brinkman N, Broekman M, Teunis T, Choi S, Ring D, Jayakumar P. A New Measure of Quantified Social Health Is Associated With Levels of Discomfort, Capability, and Mental and General Health Among Patients Seeking Musculoskeletal Specialty Care. Clin Orthop Relat Res. 2025 Apr 1;483(4):647-663. doi: 10.1097/CORR.0000000000003394. Epub 2025 Feb 5.
- Background: PubMed 34652286Teunis T, Al Salman A, Koenig K, Ring D, Fatehi A. Unhelpful Thoughts and Distress Regarding Symptoms Limit Accommodation of Musculoskeletal Pain. Clin Orthop Relat Res. 2022 Feb 1;480(2):276-283. doi: 10.1097/CORR.0000000000002006.
- Individual participant dataSharing planned: No
Related trials for Musculoskeletal Disease
Active, not recruitingPhase 3
A Research Study to Evaluate the Effects of a New Oral Medicine Called Cenerimod in Adults With Systemic Lupus Erythematosus
Lupus Erythematosus, Systemic
Active, not recruitingPhase 1
Safety and Pharmacokinetics of LPX-TI641 in Rheumatoid Arthritis and Psoriatic Arthritis
Rheumatoid Arthritis (RA), Psoriatic Arthritis (PsA)
Active, not recruitingPhase 3
A Research Study to Evaluate the Efficacy and Safety of Cenerimod in Subjects Suffering From Systemic Lupus Erythematosus
Lupus Erythematosus, Systemic
Not yet recruitingObservational study
Clinical Genetics Branch Eligibility Screening Survey
Melanoma, Li-Fraumeni Syndrome
Recruiting
Oral Nutritional Optimization in Total Joint Arthroplasty
Osteoarthritis
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
David Ring, MD, PhD principal investigator
Dell Medical School, University of Texas at Austin, TX, United States
Questions about this trial
What is this clinical trial studying?
A study testing LLM-Generated Medical Note in people with Musculoskeletal Disease and Orthopedic Patients, run by University of Texas at Austin.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 22 May 2026. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Emily H Jaarsma, MD (7472879601, emily.jaarsma@austin.utexas.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Trust and Experience with the Clinician Scale (TRECS-7) (time frame: Immediately after visit). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07602725, which also lists 11 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 22 May 2026.