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.

N/ANot Yet RecruitingNCT07756632

Aga Khan University367 enrolled

Overview

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 Type

INTERVENTIONAL

Allocation

RANDOMIZED

Purpose

OTHER

Masking

SINGLE

Enrollment

367

Conditions

Patient Centered Care Integration in Clinical Workflows

Interventions

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.

Eligibility

Sex: ALLMin age: 18 YearsHealthy volunteers:

Medical Language ↔ Plain English

Inclusion Criteria (Patients): * 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 Inclusion Criteria (Physicians): * Informed Consent * Agree to include AI physician assistant in their workflows Exclusion Criteria (Patients): * Patients requiring emergency care * Patients who refuse to complete the history process with the AI physician assistant. Exclusion Criteria (Physicians): \- Physicians from non-surgical specialties

Outcomes

Primary Outcomes

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

Secondary Outcomes

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