An interventional study of AI Physician Assistant in Patient Centered Care and Integration in Clinical Workflows, sponsored by Aga Khan University. Not yet recruiting at 1 site in Pakistan. Open to participants aged 18 Years and older, including healthy volunteers. Per ClinicalTrials.gov, last updated 2026-08-10.
Sponsored by Aga Khan University · Not applicable, Interventional, and Other
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.
877 studies on the registry are indexed under Patient Satisfaction; 202 are open to participants now.
This study's planned enrollment of 367 is above the median of 96 across 624 interventional studies indexed under Patient Satisfaction.
Browse Patient Satisfaction studies →Aga Khan University is the lead sponsor of 294 studies on the registry; 43 are open to participants now.
Counted across the registry records on this site, refreshed daily.
Inclusion Criteria (Patients):
Inclusion Criteria (Physicians):
Exclusion Criteria (Patients):
Exclusion Criteria (Physicians):
- Physicians from non-surgical specialties
Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Informed consent
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
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
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.
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.
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.
Time frame: Every day from each patient for a period of 2 months
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.
Time frame: 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.
Time frame: Every day for each patient consultation for a period of 2 months
Process flow evaluation outcome - Mean queuing time
Mean queuing time for each patient before the consultation process begins. Queuing time (calculated in minutes) refers to the time spent by a patient in the waiting area after their registration has been completed till the start of their consultation. It will be recorded using timestamps in two steps: one starting from the registration till the vitals are taken, secondly after vitals have been recorded till the patient visit the physician. These timings will be combined into a single aggregated time and will be calculated once for each patient.
Time frame: Every day for each patient visit for a period of 2 months
Plan to share: No
No publications or documents are linked to this record.
Start is estimated for Sep 2026; primary completion for Nov 2026, 2 months later.
Of 368 non-industry interventional studies with no phase under Patient Satisfaction, half reached primary completion within 12 months of starting (the median), counting those still running; a quarter within 6 and three quarters within 23. This study's planned 2 months is below that.
Counting every sponsor, half of 370 studies reached it within 12 months; a quarter within 6 and three quarters within 23.
How these figures are worked out, and which studies are left out of them.
This study is not yet recruiting, as verified in Jan 2026. You cannot join it, but the record below documents what was studied.
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