Evaluating the Effectiveness of an AI-Powered Physician Assistant in Improving Patients' and Physicians' Satisfaction in Anaesthesiology Clinics

NCT07743320 | Not Yet Recruiting

• 1 other identifier: 2026-12272-41803
• Study Type: interventional
• Target: 180
• Locations: 1 country
• Sites: 1

Brief Summary

This trial aimed to evaluate the effectiveness of an AI-Powered Physician Assistant in improving patients' satisfaction with the quality of care. It also aims to evaluate physicians' satisfaction with the integration of a Physician Assistant into their clinical workflows. The primary research question is A. What is the effect of an AI-powered Physician Assistant on patients' satisfaction with their quality of care compared to the standard care? The secondary questions are as follows: B1. How satisfied are physicians with integrating an AI-powered physician assistant into their daily clinical workflows? B2. Which factors are significantly associated with patient satisfaction regarding the AI-Powered Physician Assistant? B3. Is there a statistically significant difference in mean consultation time per patient between those receiving AI-assisted care and those receiving only standard of careonly ? Participants will be enrolled from pre-operative outpatient clinics, including patients attending clinic for anaesthetic clearance prior to surgery and consultant anaesthetists providing care. Patients will serve as the unit of randomization and will be assigned to one of two study arms on each clinic day. On each clinic day, the first 8 eligible patients presenting for consultation will be randomly assigned to the intervention group or the control group in a 1:1 ratio. Intervention patients will proceed to a dedicated waiting room for structured digital intake via an AI platform (demographics, symptoms, history, clinical data) and receive AI-assisted care. Control group patients will undergo routine standard care protocols. The consultant physician will evaluate both arms during each clinic session, reviewing physician assistant-generated patient summaries and charts for patients in both the intervention and control groups. Each patient will be asked to fill out the survey at the end of the consultation with the physician. The consultants will be requested to fill out a survey at the end of the day.

Conditions

Quality of Care; Patient Satisfaction; Physician Satisfaction

Keywords

Artificial Intelligence; Patient Satisfaction; Quality of Care; Physician Satisfaction; Workflow Efficiency

Outcome Measures

Primary Outcomes (1)

• Patient Satisfaction and Outpatient Experience Score (Adapted NHS Outpatient Survey - Patient Questionnaire)

  Patient satisfaction will be evaluated across two domains: (1) effective utilization of waiting time and (2) receipt of patient-centered care during the outpatient visit. Evaluated post-consultation using a structured survey adapted from the NHS Outpatient Survey: Section A (Wait Time): Wait duration (A1) and wait time utility (A2). Section B (Doctor Interaction): Patient-centered care, consultation duration, listening, and clarity (B2-B7). Section C (AI Assistant - Intervention Arm Only): Perceived listening, trust, and privacy (C1-C6). Section D (Overall Impression): Respect, dignity, and care- quality (D1-D3). Ordinal items are assigned numerical scores to calculate a composite mean score and domain sub-scores (range: 1.0-5.0). Higher scores indicate greater satisfaction.

  From enrollment to the end of the study, for each patient, for 8 weeks

Secondary Outcomes (2)

• Daily Physician Satisfaction and Workflow Efficiency Score (End-of-Day Physician Survey)

  From enrollment to the end of the study, for each physician, for 8 weeks

• Total Physician Consultation Duration (Stopwatch Timestamps)

  From enrollment to the end of the study, for each patient, for 8 weeks

Other Outcomes (1)

• Process flow evaluation outcome - Mean Queuing Time (Timestamp Tracking)

  From enrollment to the end of the study, for each patient, for 8 weeks

Study Arms (2)

AI-Assisted Care

EXPERIMENTAL

The patients in this arm will receive AI-assisted care in addition to the standard care. The AI platform will take the patient's history, and then the patient will talk to the consultant as part of the routine flow.

Other: AI-Powered Physicians' Assistant

Standard Care

NO INTERVENTION

The patients in this arm will receive standard care. The standard of care for our study is what is usually applied in Anaesthesiology outpatient clinics, with consultants seeing the patients after the resident.

Interventions

AI-Powered Physicians' Assistant OTHER

The study participant allocated to the intervention arm will interact with the AI-physician assistant application before they consult with the physician. The application will collect medical history of the patient. This will then be followed by an AI-generated clinical summary, which their physicians will receive before the consultation begins. Physicians will review this summary and ask further questions of patients if required. Any additions and changes in the patient's history will also be made. Physicians will subsequently conduct a physical examination of the patient. After this, the physician will be able to view AI- and guideline-based suggestions for the patient's assessment and pre-operative management. The recommendations can be selected, modified, or not used as per the physician's expertise. All additions within the application can be either typed manually or verbalised via an AI-assisted scribe within the application.

AI-Assisted Care

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Consent to participate
• Age 18 years and above
• Initial patients
• Possession of a phone
• Read and write Urdu and/or English

You may not qualify if:

• Emergency Care patients
• Follow-up patients
• Physicians:
• Attending or Consultant Physician
• Consent to participate
• Workflow integrated with AI Assistant
• \- Residents and Senior Medical Officers

Sponsors & Collaborators

• Aga Khan University: lead

Study Sites (1)

Aga Khan University Hospital

Karachi, Sindh, 74800, Pakistan

Location

MeSH Terms

Conditions

Patient Satisfaction

Condition Hierarchy (Ancestors)

Treatment Adherence and Compliance; Health Behavior; Behavior

Study Officials

• Dileep Kumar

  Aga Khan University

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Dileep Kumar

CONTACT

+923332168948; dileep.kumar@aku.edu

Shifa Habib

CONTACT

shifa.habib@aku.edu

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: OUTCOMES ASSESSOR
• Masking Details: Both the person allocating the patient to the intervention and control arms and the outcomes assessor will be masked
• Purpose: OTHER
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Assistant Professor and Section Head

Study Record Dates

• First Submitted: July 28, 2026
• First Posted: August 3, 2026
• Study Start (Estimated): August 30, 2026
• Primary Completion (Estimated): October 30, 2026
• Study Completion (Estimated): October 30, 2026
• Last Updated: August 3, 2026

Record last verified: 2026-07

Data Sharing

• IPD Sharing: Will share
• Shared Documents: SAP, CSR
• Access Criteria: Through Publication Only

Locations

PA (1)