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

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).