Innovating Healthcare Delivery: Enhancing Patient Engagement with Rule-Based Machine Learning Solutions

Innovating Healthcare Delivery: Enhancing Patient Engagement with Rule-Based Machine Learning Solutions

Yash Sharma1, Shailendra Singh2 Senior IEEE Member

Department of Computer Science and Engineering /Oriental University, Indore/ India

1Yash.sharma.6510@gmail.com

2Shailendrasingh@orientaluniversity.in

Abstract— In contemporary healthcare, chatbots, driven by rule-based machine learning and sophisticated algorithms, are revolutionizing traditional models. This study explores their use in primary care triage, mental health support, chronic disease management, and telemedicine, highlighting their potential to enhance patient engagement, optimize clinical workflows, and improve care quality.

Despite their promise, chatbots face challenges such as safeguarding patient privacy, mitigating biases, ensuring regulatory compliance, and maintaining ethical standards. The paper emphasizes the need for interoperability, scalability, and usability for seamless integration into healthcare systems.

Future advancements include integrating advanced machine learning, evolving voice recognition, and personalized medicine approaches. By leveraging data analytics and predictive modelling, chatbots could proactively identify health risks, leading to a new era of preventive and personalized healthcare.

Overall, this research highlights chatbots' profound impact on promoting patient-centred care, initiative-taking health management, and data-driven decision-making, fostering equitable access to high-quality healthcare services.

Keywords— Rule-based chatbots, Machine Learning, Healthcare, Natural Language Processing, Patient Engagement, Clinical Support, Case Studies, Prospects.