Evaluating the Effectiveness of an AI-powered Physician Assistant in Improving Patients' and Physician's Satisfaction in an Outpatient Setting of a Tertiary Care Hospital.

NCT07756632 | Not Yet Recruiting DMC

• 1 other identifier: 2026-12176-39541
• Study Type: interventional
• Target: 367
• Locations: 1 country
• Sites: 1

Brief Summary

Patients' satisfaction depends on several factors, including health care costs, access to care, and the waiting time to see a healthcare professional. In Pakistan, hospitals face overcrowding, which in turn results in long waiting times, particularly in outpatient departments. Longer waiting times not only hurt patients' experience and hospitals' performance but also increase stress on the physicians. These challenges can be addressed with the effective use of Artificial Intelligence (AI) and related technologies. By leveraging machine learning algorithms and advanced data prediction models, AI can augment healthcare providers in clinical decision-making and streamline their work processes. However, these applications are largely studied and implemented in high-income countries, creating a lack of evidence from low- and middle-income countries. Hence, a randomized controlled trial will be conducted to assess the effectiveness of an AI physician assistant in improving patient and physician satisfaction within outpateint clincis of a resource constrained setting.

Conditions

Patient Centered Care; Integration in Clinical Workflows

Keywords

Patient Satisfaction; Physician Satisfaction; Quality of Care; Workflows

Outcome Measures

Primary Outcomes (1)

• Patient's satisfaction

  Patient satisfaction is conceptualized through the lens of perceived quality of care, which is influenced by the effective utilization of waiting time and the provision of patient-centred care. Effective utilization of waiting time refers to patients' perceptions regarding whether their waiting time was used meaningfully during the visit. The domains of patient-centred care have been adapted from the Institute of Medicine (IOM) framework and include respect for patients' values and preferences, coordinated and integrated care, adequacy of information and communication, emotional support, involvement of family and friends, and physical comfort. These questions have been adapted based on the study objectives. The questionnaire will include demographic questions and five-point Likert-scale items (Strongly Agree to Strongly Disagree) and one open-ended question to obtain additional feedback regarding patients' experiences and satisfaction.

  Every day from each patient for a period of 2 months

Secondary Outcomes (2)

• Physician Satisfaction

  From each physician at the end of each day for two months.

• Mean consultation time

  Every day for each patient consultation for a period of 2 months

Other Outcomes (1)

• Process flow evaluation outcome - Mean queuing time

  Every day for each patient visit for a period of 2 months

Study Arms (2)

AI Physician Assistant

EXPERIMENTAL

The intervention group will comprise participants enrolled in the application (AI physician assistant) in addition to the standard of care The study participant allocated to the intervention will interact with the AI-physician assistant application "Hami" before they consult with the physician. The application will collect the 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, and update the patient's record through an inbuilt scribe feature in the application.

Other: AI Physician Assistant

Standard of Care

NO INTERVENTION

The arm will comprise participants who receive standard care. In surgical clinics, standard care involves residents seeing the patients before the physicians. However, as part of the study, we will include physicians who agree to see patients without residents taking the history first. Hence, the trial uses the term 'physician' as part of the control group or standard care terminology.

Interventions

AI Physician Assistant OTHER

The intervention evaluated here is an AI Physician Assistant. The assistant takes the patient's history using a specialty-specific line of questioning. Once the interaction ends, the application converts the information into an AI-generated clinical summary for physicians to review. The physician reviews the summary and asks the patient additional questions, if required. Any additions or changes to the patient's history are recorded in the application. The physician then conducts a physical examination and can view AI-generated and guideline-based recommendations for assessment and treatment within the application. These recommendations may be selected, modified, or disregarded according to the physician's clinical expertise. All additions to the patient's record can be entered manually or dictated verbally and automatically added through the application's ambient scribe feature. Once the treatment plan has been documented, the application generates a SOAP note.

AI Physician Assistant

Eligibility Criteria

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

You may qualify if:

• Informed consent before enrolment.
• Adults aged 18 years and above.
• Initial patients registering at the clinic during the entire trial duration.
• Possession of a digital device for an OTP (one-time password)
• Can read and write Urdu and/or English
• Informed Consent
• Agree to include AI physician assistant in their workflows

You may not qualify if:

• Patients requiring emergency care
• Patients who refuse to complete the history process with the AI physician assistant.
• \- Physicians from non-surgical specialties

Sponsors & Collaborators

• Aga Khan University: lead

Study Sites (1)

Aga Khan University Hospital

Karachi, Pakistan

Location

MeSH Terms

Conditions

Patient Satisfaction

Condition Hierarchy (Ancestors)

Treatment Adherence and Compliance; Health Behavior; Behavior

Central Study Contacts

Saqib Bakhshi

CONTACT

+923062750710; saqib.dow@gmail.com

Shifa Habib

CONTACT

+923018222783; shifa.habib@aku.edu

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: OUTCOMES ASSESSOR
• Masking Details: The person assigning the intervention and the outcomes assessor will be blinded
• Purpose: OTHER
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Assistant Professor

Study Record Dates

• First Submitted: August 7, 2026
• First Posted: August 10, 2026
• Study Start (Estimated): September 1, 2026
• Primary Completion (Estimated): November 1, 2026
• Study Completion (Estimated): November 1, 2026
• Last Updated: August 10, 2026

Record last verified: 2026-01

Data Sharing

• IPD Sharing: Will not share

Locations

PA (1)