Adopting Artificial Intelligence in OPD: A Study on Diagnostic Support and Decision Making

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Project Title: Adopting Artificial Intelligence in OPD: A Study on Diagnostic Support and Decision Making

Academic Institution: Parul University Accreditation: NAAC A++ Accredited University Faculty: Faculty of Management Studies Department: Healthcare Management Degree Program: Master of Business Administration (Healthcare Management)

Submitted By:

• Abhishekh Choubisa (Enrolment No: 2406142000197)
• Varun Kumar (Enrolment No: 2406142000195)

Under the Guidance of:

• Bharat Patil (PIFT, Parul University)

Location: P.O. Limda, Tal: Waghodia, Dist: Vadodara, Gujarat State, India-391760 Date of Submission: April, 2026

Abstract:

The integration of Artificial Intelligence (AI) into the healthcare sector represents a transformative paradigm shift, particularly within the Outpatient Department (OPD), which serves as the primary gateway for patient interaction in any healthcare system. This comprehensive research report examines the adoption of AI technologies, focusing on their role in enhancing diagnostic support and clinical decision-making. As OPDs globally, and specifically in India, grapple with escalating patient volumes, administrative inefficiencies, and the inherent risks of diagnostic error, AI-driven solutions offer a promising path toward optimization.

This study utilizes a dual-methodological approach, synthesizing extensive literature from seminal researchers such as Topol, Esteva, and Rajkomar with a primary empirical study conducted among healthcare professionals in Vadodara, Gujarat. The primary research evaluates the awareness, perception, and trust levels of doctors, nurses, and administrators regarding AI tools such as Natural Language Processing (NLP) for clinical documentation and Machine Learning (ML) for diagnostic imaging.

The findings indicate a moderate level of awareness (43.05%) and a cautious acceptance of AI as a supportive rather than a replacement tool. While the potential for AI to reduce workload (45.98%) and assist in data analysis (50.36%) is recognized, significant barriers—including concerns over data privacy, high implementation costs, and a "trust gap" in algorithmic reliability—persist. The report concludes that while AI significantly augments diagnostic accuracy and operational efficiency, its success depends on a collaborative framework where human clinical judgment remains central. Recommendations emphasize the need for specialized training, government-led infrastructure support, and robust ethical guidelines to facilitate a seamless transition into an AI-augmented healthcare future.

Keywords:

Artificial Intelligence, Outpatient Department (OPD), Clinical Decision Support Systems (CDSS), Diagnostic Accuracy, Machine Learning, Healthcare Management, Gujarat Healthcare Market, Natural Language Processing, Patient Triage, Digital Health Transformation.