CONFIGURABILITY CHALLENGES OF HEALTHCARE RECOMMENDER SYSTEMS

Abstract

Recommender systems play a pivotal role in modern healthcare by offering personalized recommendations for patient diagnosis, treatment, and healthcare providers. This paper discusses about the challenges associated with configurability in Healthcare Recommender Systems (HRS) and articulates the critical considerations essential for their development and acceptance. By examining key facets of configurability, the paper aims to raise awareness among practitioners and users. These challenges impact the product owners, developers, as well as the users, and is thus necessary to be addressed. The objectives encompass raising awareness about the multifaceted challenges posed by configurability, informing decision-making processes for practitioners, empowering users with insights into the implications of configuration choices, stressing the importance of rigorous testing and validation, and proposing considerations for best practices. The outcome of addressing these challenges is expected to have a positive impact on the efficiency, accuracy, and user experience of Healthcare Recommender Systems. This paper serves as a guide for navigating the complexities of configurability in healthcare, fostering informed decision-making, responsible system development, and user empowerment. Through this exploration, the research lays the groundwork for addressing configurability challenges, aiming to contribute to the creation of more robust and error-resistant configuration processes in recommendation systems within the healthcare domain.