Enhancing the adaptability of healthcare delivery as a complex system

Healthcare systems, by their very nature, are complex adaptive systems. For example, not only must a hospital adapt to the emergent evolution of patients' health, but it is also organized into many self-driven and autonomous departments that must interact to deliver quality healthcare to the patient population. The pressing question is, can we enhance hospital adaptability to improve patient outcomes at an individual and population level? Current approaches to adaptive healthcare delivery have significant limitations. They often rely on coarse aggregate measures calculated at the whole hospital level. These measures, typically no more than a single numerical value, provide insufficient feedback to enhance the adaptation. This underscores the urgent need for a new, more effective methodology. Our methodology is transdisciplinary in bringing engineering and non-engineering experts and merging their methodology and domain expertise to understand system behavior and feedback information to enhance adaptability. In this paper, we present a methodology that uses the same input data used to calculate aggregate measures but instead produces dynamic hetero-functional networks that provide feedback about who, what, when, and where care was provided to elucidate emergent behaviors within the hospital system. We use an illustrative example of end-of-life care delivery for patients with poor prognosis cancers.