Patient Centered CareNCT07756632
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.
A study testing AI Physician Assistant in people with Patient Centered Care and Integration in Clinical Workflows, run by Aga Khan University at 1 site in Pakistan.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Patient Centered Care, Integration in Clinical Workflows
- What is being tested?
- AI Physician Assistant
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 367 participants
- Where
- 1 sitePakistan
- Study dates
- 1 Sep 2026to 1 Nov 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
- 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
Who cannot join
- Patients requiring emergency care
- Patients who refuse to complete the history process with the AI physician assistant.
- Physicians from non-surgical specialties
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Pakistan1 site
Aga Khan University Hospital
Karachi
Saqib Bakhshi, +923062750710, saqib.dow@gmail.com
Hamdan Pasha, +923333129014, hamdan.pasha@aku.edu
About this study
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.
- Being tested
- AI Physician Assistant
- Keywords
- Patient Satisfaction, Physician Satisfaction, Quality of Care, Workflows
- 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 Physician AssistantOther
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Every day from each patient for a period of 2 months
Secondary outcomes (2)
Physician Satisfaction
It will be assessed with regards to integration of an AI-powered physician assistant, focusing on usability, impact on workflow efficiency, evidence based treatment recommendations and improved patient-physician interaction.
Physician's satisfaction will be calculated utilizing mean scoring system, where each question will be scored on a 5 point Likert scale (Strongly Agree to Strongly Disagree). Additionally, we will ask one open-ended question at the end of the survey as part of physician satisfaction. This tool will be made exclusively for this study and will undergo content validation.
Measured: From each physician at the end of each day for two months.
Mean consultation time
Consultation time (calculated in minutes) refers to the time taken by the physician while the patient is in the physician's room and the time taken by the physician for each of the following: to inquire about symptoms, conduct an examination, prescribe treatment, and provide counselling.
It will be measured using timestamps from a stopwatch from the time the patient enters the consultation room till the time they leave.
Measured: Every day for each patient consultation for a period of 2 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Aug 2026
- Study start1 Sep 2026 (estimated)
- Primary completion1 Nov 2026 (estimated)
- Study completion1 Nov 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Patient Centered 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
- 367 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Aga Khan University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Saqib Bakhshi, Aga Khan University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07756632
- Sponsor's protocol ID
- 2026-12176-39541
- Status last verified
- Jan 2026
- First submitted
- 7 Aug 2026
- Last updated
- 10 Aug 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 10 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 Physician's Satisfaction in an Outpatient Setting of a Tertiary Care Hospital.
- ClinicalTrials.gov ID
- NCT07756632
- Acronym / sponsor ID
- 2026-12176-39541
- Keywords
- Patient Satisfaction, Physician Satisfaction, Quality of Care, Workflows
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 367 participants (estimated)
- Lead sponsor
- Aga Khan UniversityUniversity, hospital or other
- Study start
- 1 Sep 2026 (estimated)
- Primary completion
- 1 Nov 2026 (estimated)
- Study completion
- 1 Nov 2026 (estimated)
- First posted
- 10 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.
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.
Study design
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (outcomes assessor)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| AI Physician Assistant | Experimental | Other: AI Physician AssistantThe 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. |
| 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (7 listed)
- 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
Key exclusion criteria (3 listed)
- Patients requiring emergency care
- Patients who refuse to complete the history process with the AI physician assistant.
- Physicians from non-surgical specialties
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 Physician AssistantThe 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. | Other | AI Physician Assistant |
Primary outcome
Patient's satisfaction
Time frame: Every day from each patient for a period of 2 months
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.
2 secondary outcomes
Physician Satisfaction
Time frame: From each physician at the end of each day for two months.
Mean consultation time
Time frame: Every day for each patient consultation for a period of 2 months
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 10 Aug 2026.
- Individual participant dataSharing planned: No
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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.
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 Physician Assistant in people with Patient Centered Care and Integration in Clinical Workflows, 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 7 inclusion and 3 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 10 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 Saqib Bakhshi (+923062750710, saqib.dow@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Patient's satisfaction (time frame: Every day from each patient for a period of 2 months). 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 10 Aug 2026.