Pre-Visit AI Symptom-Checking and Shared Decision-Making in Spine Physical Therapy

July 24, 2026 updated by: Mariam ibrahim, Assiut University

When AI Is the First Clinician: Impact of Pre-Visit AI Use on Presentation, Diagnostic Expectations, and Shared Decision-Making in Spine Physical Therapy

Artificial intelligence (AI) symptom-checking tools, including large language models such as ChatGPT, are increasingly used by patients before they seek care. These tools may shape patients' beliefs about their diagnosis, how serious they think their condition is, and when they decide to seek treatment. It is not yet known how this pre-visit AI use affects the initial physical therapy encounter for spine-related problems.

This prospective observational cohort study examines whether prior use of AI symptom-checking tools influences the first physical therapy evaluation in adults presenting with spine-related musculoskeletal complaints (neck, thoracic, or low back pain, with or without radicular symptoms). Consecutive patients attending an outpatient physical therapy clinic for a new evaluation are grouped as AI users or non-AI users based on whether they used such a tool for their current complaint in the previous 30 days.

The primary outcome is shared decision-making, measured with the SDM-Q-9 immediately after the initial evaluation. Secondary outcomes include stage of presentation, agreement between the patient's expected diagnosis and the clinician's classification, baseline pain and disability, functional performance, and clinical outcomes at 2 and 6 weeks. The investigators hypothesize that prior AI use is associated with differences in shared decision-making and in how patients present for care.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

Background and Rationale AI-based symptom-checking tools, including large language models, are increasingly used by patients as a first point of clinical interpretation before seeking care. Available evidence suggests these tools show moderate and variable diagnostic accuracy and can influence patients' diagnostic beliefs, perceived symptom severity, and healthcare-seeking behavior, raising concerns about pre-diagnostic anchoring. In spine and musculoskeletal care, early presentation, accurate classification, and patient engagement are important determinants of outcomes, and current practice relies on history, physical examination, and shared decision-making under the assumption that patients present without strongly preformed diagnostic expectations. The growing use of AI may alter this dynamic. Evidence on the real-world impact of pre-visit AI use on the physical therapy encounter is currently limited.

Objective The primary objective is to determine whether prior use of AI symptom-checking tools influences shared decision-making during the initial physical therapy evaluation in patients presenting with spine-related musculoskeletal conditions. Secondary objectives examine associations between prior AI use and stage of presentation, agreement between patient-expected and clinician diagnosis, baseline clinical status, functional performance, health-seeking behavior, and short-term clinical outcomes.

Design and Setting This is a prospective observational cohort study conducted in outpatient physical therapy clinics. No study-specific interventions are introduced; all participants receive standard physical therapy care determined by their treating clinician.

Participants and Grouping Consecutive adults (≥18 years) presenting for a new evaluation for a spine-related musculoskeletal complaint (neck, thoracic, or low back pain, with or without radicular symptoms) who can provide informed consent and complete study questionnaires in English are eligible. Patients are excluded for recent spinal surgery within the past 3 months, serious spinal pathology under active medical management (e.g., malignancy, spinal infection, acute fracture), cognitive impairment limiting consent or reliable completion of measures, or current enrollment in another study that may influence clinical decision-making or spine-related outcomes. Participants are classified into two groups based on whether they used an AI symptom-checking tool for their current complaint within the prior 30 days: AI users and non-AI users.

Study Instruments and Measures AI exposure is captured at baseline via a questionnaire assessing AI use (yes/no), tool type, frequency, degree of personalization, and reported influence on care-seeking. Clinical measures include symptom duration, pain intensity (Numeric Pain Rating Scale, 0-10), region-specific disability (Oswestry Disability Index for low back pain or Neck Disability Index for neck pain), and the Five Times Sit-to-Stand test. Diagnostic expectation is assessed by comparing the patient-expected diagnosis with the clinician's classification, categorized as match, partial match, or mismatch. Shared decision-making is measured with the SDM-Q-9 (score range 0-45; higher scores indicate greater patient involvement) immediately after the initial evaluation. Follow-up outcomes at 2 and 6 weeks include pain, disability (ODI/NDI), Global Rating of Change, self-reported adherence, and healthcare utilization.

Procedures At baseline (pre-visit), AI exposure, symptom duration, pain, and disability are collected. During the initial evaluation, the clinician performs a standard physical therapy assessment and classification, and patient expectations are recorded. Immediately post-visit, participants complete the SDM-Q-9. At 2-week and 6-week follow-up, clinical outcomes and adherence are assessed and AI use is re-evaluated. Data are recorded on standardized forms and stored securely using coded identifiers.

Sample Size The primary outcome is SDM-Q-9 following the initial evaluation. Using G*Power (v3.1.9.7), a sample of 128 participants (64 per group) provides 80% power to detect a moderate between-group difference (Cohen's d = 0.5) at a two-sided alpha of 0.05. To account for potential group imbalance and an anticipated attrition of 15-20%, approximately 200 consecutive patients will be recruited.

Statistical Analysis Continuous variables are summarized as mean ± standard deviation and categorical variables as frequencies and percentages. AI users and non-AI users are compared using independent-samples t-tests for continuous variables and chi-square tests for categorical variables. Statistical significance is set at p ≤ 0.05.

Study Type

Observational

Enrollment (Estimated)

200

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

Study Locations

  • Egypt
      • Asyut, Egypt
        • Recruiting
        • Faculty of Medicine, Assiut University, Egypt
        • 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

Sampling Method

Non-Probability Sample

Study Population

Adults aged 18 years or older presenting for a new evaluation at outpatient physical therapy clinics for a spine-related musculoskeletal complaint - neck, thoracic, or low back pain, with or without radicular symptoms. Consecutive eligible patients are enrolled and grouped by their use of AI-based symptom-checking tools for the current complaint in the 30 days preceding the visit (AI users vs. non-AI users). Patients with recent spinal surgery, serious spinal pathology under active management, cognitive impairment limiting participation, or concurrent enrollment in a study affecting spine-related decision-making are not included.

Description

Inclusion Criteria:

  • Age: 18 years or older
  • Presenting for a new evaluation at an outpatient physical therapy clinic for a spine-related musculoskeletal complaint, including Neck, Thoracic, and Low back pain With or without radicular symptoms
  • Able to provide informed consent.
  • Able to read, understand, and complete study questionnaires

Exclusion Criteria:

  • Recent spinal surgery within the past 3 months, due to differing clinical pathways and management strategies.
  • Presence of serious spinal pathology under active medical management, such as:

Malignancy (e.g., metastatic disease) Spinal infection Acute fracture

  • Cognitive impairment or other conditions that limit the ability to provide informed consent or reliably complete study measures.
  • Patients currently enrolled in another study that may influence clinical decision-making or outcomes related to spine care.

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

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
AI Users
Patients who reported using an AI-based symptom-checking tool (e.g., a large language model such as ChatGPT) for their current spine-related complaint within the 30 days before their initial physical therapy evaluation.
Other: re-visit AI symptom-checker use
Self-reported use of an AI-based symptom-checking tool (e.g., a large language model such as ChatGPT) for the current spine-related complaint during the 30 days before the initial physical therapy evaluation. This exposure occurs naturally prior to presentation and is not assigned by the investigator. Exposure status is ascertained at baseline via a questionnaire capturing whether AI was used (yes/no), the type of tool, frequency of use, degree of personalization, and the reported influence of AI use on care-seeking timing.
Non-AI Users
Patients who reported no use of any AI-based symptom-checking tool for their current spine-related complaint within the 30 days before their initial physical therapy evaluation.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Shared Decision-Making (SDM-Q-9)
Time Frame: Immediately after the initial physical therapy evaluation (Day 0)
Patient-perceived involvement in shared decision-making during the initial physical therapy evaluation, measured with the 9-item Shared Decision-Making Questionnaire (SDM-Q-9). Raw total score ranges from 0 to 45; higher scores indicate greater perceived patient involvement in decision-making.
Immediately after the initial physical therapy evaluation (Day 0)

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Stage of Presentation (Symptom Duration)
Time Frame: At initial evaluation (Day 0)
Self-reported duration of the current spine-related symptoms, in days, recorded at the initial evaluation as an indicator of timeliness/stage of presentation.
At initial evaluation (Day 0)
Diagnostic Agreement (Patient-Clinician Concordance)
Time Frame: At initial evaluation (Day 0)
Concordance between the patient's expected diagnosis and the clinician's classification, categorized as 0 = match, 1 = partial match, 2 = mismatch.
At initial evaluation (Day 0)
Pain Intensity (Numeric Pain Rating Scale)
Time Frame: Day 0, 2 weeks, 6 weeks
Self-reported pain intensity on the 11-point Numeric Pain Rating Scale (0 = no pain, 10 = worst imaginable pain); higher scores indicate greater pain.
Day 0, 2 weeks, 6 weeks
Disability (ODI or NDI)
Time Frame: Day 0, 2 weeks, 6 weeks
Region-specific self-reported disability, using the Oswestry Disability Index for low back pain or the Neck Disability Index for neck pain. Both are scored 0-100% (0 = no disability; higher scores indicate greater disability).
Day 0, 2 weeks, 6 weeks
Health-Seeking Behavior (AI Influence on Care Timing)
Time Frame: At initial evaluation (Day 0)
Self-reported influence of prior AI symptom-checker use on the timing of seeking care, categorized as earlier, delayed, or no change.
At initial evaluation (Day 0)
Adherence and Healthcare Utilization
Time Frame: 2 weeks, 6 weeks
Self-reported adherence to the recommended treatment plan.
2 weeks, 6 weeks

Collaborators and Investigators

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

Sponsor

Assiut University

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 (Actual)

June 10, 2026

Primary Completion (Estimated)

December 10, 2026

Study Completion (Estimated)

February 10, 2027

Study Registration Dates

First Submitted

July 24, 2026

First Submitted That Met QC Criteria

July 24, 2026

First Posted (Actual)

July 29, 2026

Study Record Updates

Last Update Posted (Actual)

July 29, 2026

Last Update Submitted That Met QC Criteria

July 24, 2026

Last Verified

June 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 04-2026-300876

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.

Subscribe