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

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

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Patient Satisfaction; Quality of Care; Physician Satisfaction
• Intervention / Treatment: Other: AI-Powered Physicians' Assistant

• Study Type: Interventional
• Enrollment (Estimated): 180
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Dileep Kumar; Phone Number: +923332168948; Email: dileep.kumar@aku.edu
• Study Contact Backup: Name: Shifa Habib; Email: shifa.habib@aku.edu

Study Locations

• Pakistan
  • Sindh
    • Karachi, Sindh, Pakistan, 74800
      • Aga Khan University Hospital
      • Contact: Dileep Kumar; Phone Number: +923332168948; Email: dileep.kumar@aku.edu
      • Contact: Shifa Habib; Email: shifa.habib@aku.edu
      • Principal Investigator: Dileep Kumar

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

Description

Patients and Patients' Proxies

Inclusion Criteria:

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

Exclusion Criteria:

• Emergency Care patients
• Follow-up patients

Physicians:

Inclusion Criteria

• Attending or Consultant Physician
• Consent to participate
• Workflow integrated with AI Assistant

Exclusion Criteria

- Residents and Senior Medical Officers

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Other
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Single

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: AI-Assisted Care; 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; 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.
No Intervention: Standard Care; 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
Daily Physician Satisfaction and Workflow Efficiency Score (End-of-Day Physician Survey)	Physician satisfaction and workflow efficiency will be evaluated using an end-of-day survey administered to physicians whose patients were recruited into the study. Evaluated using a two-part structured questionnaire: Section 1 (Comparative Workflow): Binary comparisons across 5 domains (consultation duration, patient engagement, documentation efficiency, cognitive workload, and focus on patient care), with 3-point follow-up sub-questions for favorable intervention responses. Section 2 (Intervention Utility): 6 intervention-specific items assessing accuracy/trust, workflow efficiency, workload relief, and overall satisfaction (5-point ordinal scales; 1 = Strongly Disagree, 5 = Strongly Agree). Daily composite mean scores are calculated from Sections 1 and 2 (range: 1.0-5.0). Higher scores indicate greater satisfaction.	From enrollment to the end of the study, for each physician, for 8 weeks
Total Physician Consultation Duration (Stopwatch Timestamps)	Evaluates total physician consultation duration (in minutes) for each patient during their outpatient clinic encounter. Consultation time refers to the active time spent by the physician while the patient is inside the consultation room. It encompasses the time taken to inquire about symptoms, conduct physical examinations, prescribe treatments, and provide counseling. Duration is objectively measured in minutes using manual stopwatch timestamps, initiated the moment the patient enters the consultation room and concluded when the patient departs. Mean total consultation times will be calculated per patient and compared between the intervention and standard care arms. Lower or more optimized consultation durations reflect enhanced workflow efficiency.	From enrollment to the end of the study, for each patient, for 8 weeks

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
Process flow evaluation outcome - Mean Queuing Time (Timestamp Tracking)	Recorded using objective timestamps across two sequential intervals: Interval 1: Completion of counter registration to initiation of vital signs measurement. Interval 2: Completion of vital signs measurement to patient entry into the physician consultation room. Intervals are combined into a single aggregated total queuing time per patient visit. Lower mean queuing times indicate improved operational workflow efficiency.	From enrollment to the end of the study, for each patient, for 8 weeks

Collaborators and Investigators

• Sponsor: Aga Khan University
• Investigators: Principal Investigator: Dileep Kumar, Aga Khan University

Study record dates

Study Major Dates

• Study Start (Estimated): August 30, 2026
• Primary Completion (Estimated): October 30, 2026
• Study Completion (Estimated): October 30, 2026

Study Registration Dates

• First Submitted: July 28, 2026
• First Submitted That Met QC Criteria: July 31, 2026
• First Posted (Actual): August 3, 2026

Study Record Updates

• Last Update Posted (Actual): August 3, 2026
• Last Update Submitted That Met QC Criteria: July 31, 2026
• Last Verified: July 1, 2026

More Information

Terms related to this study

• Keywords: Artificial Intelligence; Quality of Care; Patient Satisfaction; Physician Satisfaction; Workflow Efficiency
• Additional Relevant MeSH Terms: Behavior; Treatment Adherence and Compliance; Health Behavior; Patient Satisfaction
• Other Study ID Numbers: 2026-12272-41803

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: The plan is to publish the study, and informed consent has been obtained from each participant. Each participant's data will be de-identified at the source level for both the application and survey.
• IPD Sharing Access Criteria: Through Publication Only
• IPD Sharing Supporting Information Type: SAP; CSR

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No