Quality of CareNCT07743320
Evaluating the Effectiveness of an AI-Powered Physician Assistant in Improving Patients' and Physicians' Satisfaction in Anaesthesiology Clinics
A study testing AI-Powered Physicians' Assistant in people with Quality of Care and Patient Satisfaction, run by Aga Khan University at 1 site in Pakistan.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Quality of Care, Patient Satisfaction, Physician Satisfaction
- What is being tested?
- AI-Powered Physicians' Assistant
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 180 participants
- Where
- 1 sitePakistan
- Study dates
- 30 Aug 2026to 30 Oct 2026
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Consent to participate
- Age 18 years and above
- Initial patients
- Possession of a phone
- Read and write Urdu and/or English
- Attending or Consultant Physician
- Consent to participate
- Workflow integrated with AI Assistant
Who cannot join
- Emergency Care patients
- Follow-up patients
- Physicians:
- Residents and Senior Medical Officers
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Shifa Habib contact
Pakistan1 site
Aga Khan University Hospital
Karachi, Sindh, 74800
Dileep Kumar, +923332168948, dileep.kumar@aku.edu
Shifa Habib, shifa.habib@aku.edu
About this study
Official title: 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
- What is the effect of an AI-powered Physician Assistant on patients' satisfaction with their quality of care compared to the standard care?
Read more
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.
- Being tested
- AI-Powered Physicians' Assistant
- Keywords
- Artificial Intelligence, Patient Satisfaction, Quality of Care, Physician Satisfaction, Workflow Efficiency
- Medical subject headings
- Patient Satisfaction
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-Powered Physicians' AssistantOther
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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)
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.
Measured: 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.
Measured: From enrollment to the end of the study, for each patient, for 8 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted3 Aug 2026
- Study start30 Aug 2026 (estimated)
- Primary completion30 Oct 2026 (estimated)
- Study completion30 Oct 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Quality of CareStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind
- Who is masked
- Outcomes assessor
- Enrolment
- 180 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Aga Khan University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Dileep Kumar, Aga Khan University
- Investigators
- Dileep Kumar, Principal investigator, Aga Khan University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07743320
- Sponsor's protocol ID
- 2026-12272-41803
- Status last verified
- Jul 2026
- First submitted
- 28 Jul 2026
- Last updated
- 3 Aug 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: 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.
Study at a glance
From the registry record, last updated by the sponsor on 3 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluating the Effectiveness of an AI-Powered Physician Assistant in Improving Patients' and Physicians' Satisfaction in Anaesthesiology Clinics of a Tertiary Care Hospital
- ClinicalTrials.gov ID
- NCT07743320
- Acronym / sponsor ID
- 2026-12272-41803
- Keywords
- Artificial Intelligence, Patient Satisfaction, Quality of Care, Physician Satisfaction, Workflow Efficiency
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 180 participants (estimated)
- Lead sponsor
- Aga Khan UniversityUniversity, hospital or other
- Study start
- 30 Aug 2026 (estimated)
- Primary completion
- 30 Oct 2026 (estimated)
- Study completion
- 30 Oct 2026 (estimated)
- First posted
- 3 Aug 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
- 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 design
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (outcomes assessor)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| AI-Assisted Care | Experimental | Other: AI-Powered Physicians' AssistantThe 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. |
| 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (8 listed)
- Consent to participate
- Age 18 years and above
- Initial patients
- Possession of a phone
- Read and write Urdu and/or English
Key exclusion criteria (4 listed)
- Emergency Care patients
- Follow-up patients
- Residents and Senior Medical Officers
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-Powered Physicians' AssistantThe 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. | Other | AI-Assisted Care |
Primary outcome
Patient Satisfaction and Outpatient Experience Score (Adapted NHS Outpatient Survey - Patient Questionnaire)
Time frame: From enrollment to the end of the study, for each patient, for 8 weeks
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.
2 secondary outcomes
Daily Physician Satisfaction and Workflow Efficiency Score (End-of-Day Physician Survey)
Time frame: From enrollment to the end of the study, for each physician, for 8 weeks
Total Physician Consultation Duration (Stopwatch Timestamps)
Time frame: From enrollment to the end of the study, for each patient, for 8 weeks
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 3 Aug 2026.
- Individual participant dataSharing planned: Yes. 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Shifa Habib contact
Dileep Kumar principal investigator
Aga Khan University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing AI-Powered Physicians' Assistant in people with Quality of Care and Patient Satisfaction, run by Aga Khan University at 1 site in Pakistan.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 8 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 3 Aug 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Pakistan. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Dileep Kumar (+923332168948, dileep.kumar@aku.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Patient Satisfaction and Outpatient Experience Score (Adapted NHS Outpatient Survey - Patient Questionnaire) (time frame: From enrollment to the end of the study, for each patient, for 8 weeks). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 3 Aug 2026.