Hospitals Must Rethink Operations Before Integrating AI
Chicago, Monday, 30 June 2025.
Hospitals seeking to adopt AI for operational efficiency should prioritize foundational changes, such as change management and model redesign, for successful and sustainable AI integration.
Strategic Operational Changes: A Prerequisite for AI
To effectively integrate artificial intelligence in healthcare, hospitals must undertake significant operational adjustments. Prashant Karamchandani from Chartis underscores the need for thoughtful change management and a redesign of existing operational models, which align technological advancements with healthcare workflows. This dual approach aids in mitigating risks associated with AI implementations and fosters better efficiency and patient care outcomes [1].
Enhancing Revenue Cycle Management with AI
One area particularly ripe for AI integration is revenue cycle management. Nearly 15% of medical claims submitted to private payers are initially denied, showcasing a clear inefficiency that AI can address through automation and error reduction [2]. Karamchandani advocates for a strategic allocation of tasks between human and automated systems to optimize performance and financial outcomes [1].
Expanding Care Delivery Models
As hospitals face financial pressures, many are transforming their operational models by consolidating service lines and shifting care to outpatient or virtual settings. This transformation is not only critical for cutting labor costs but also necessary for adapting to the increasingly digital landscape in which healthcare operates [1][3]. AI can facilitate these changes by providing data-driven insights that enhance decision-making and patient care [4].
AI’s Role in Personalized Medicine
AI’s potential in personalized medicine is vast, extending from predictive modeling to the development of treatment protocols tailored to an individual’s unique genetic makeup and health history. By harnessing AI’s capabilities, healthcare providers can improve survival rates and patient satisfaction through more targeted and effective treatments [2]. However, ethical concerns such as data privacy and bias must be addressed to realize these benefits fully [3].