The way the novel coronavirus tore through the U.S. healthcare system and threatened to overwhelm it in places has brought into sharp relief the need to apply data-driven analytics and optimization in order to hone healthcare practices and service delivery for the next wave of challenges. Healthcare in the U.S. is characterized by innumerable variables that make it difficult to fully understand, much less improve, the system. That’s where data and modeling come in.
Our Biomedical Analytics and Systems Optimization (BASO) research lab is developing healthcare analytics models, techniques, and tools, which are critical to smart and connected health. These innovations can be used to address problems in healthcare service, clinical practice and public health via technology-based solutions and community-engaged deployment.
Smart, connected health aims to develop groundbreaking approaches to help transform healthcare from reactive and hospital-centered to proactive, preventive, evidence-based and person-centered — that is, focused on well-being, rather than disease. These inventive technologies will provide the next-generation solutions and breakthrough ideas in healthcare.
In the past, there’s been a reluctance to tackle healthcare renovation because it is so complex. The key to success is developing computational modeling and optimization techniques for complex, uncertain, fragmented systems like those in healthcare, for which we lack the in-depth, detailed knowledge and the accurate, ample measurements needed for data-driven modeling. In the absence of confidence in the data, we model and optimize probabilities for various outcomes, assigning a value to each probability to reflect its likelihood of occurring.
Healthcare systems analytics is an emerging area that holds great potential to harness big data for advances in personalized, precision healthcare. It includes descriptive, predictive and prescriptive analytics to sift through huge, often poorly structured datasets to discover complex patterns, and then use those patterns to forecast and manage future events.
Our research team focuses on healthcare operations management and clinical decision-making, biomedicine, and healthcare analytics. These areas address such needs as trauma/emergency care, infectious disease control, rehabilitative/long-term care, cancer screening, the opioid crisis, and mental health.
For example, care network design and capacity management drive operational decisions involving staff scheduling, referral coordination, and emergency logistics — all crucial during the COVID-19 surge. With these approaches, we’ve uncovered insights to enable care organizations to streamline patient flow and manage staff workload more efficiently for the inpatient discharge process. We also have developed novel stochastic (probabilistic) programming models to reduce preventable hospital readmissions.
Additionally, we’ve investigated system design and operations management challenges around access to care in care organizations with a tiered-care delivery system — central hospitals and many more satellite clinics — in which there is a geographic mismatch between care need and timely provision.
Moreover, an important area ripe for innovation is staffing planning at nursing homes and other long-term care facilities. For these environments, we’ve developed novel stochastic programming models and methods for analyzing and optimizing staffing decisions that balance trade-offs between a resident-centered approach, the employee experience, and operating cost. Another area requiring an upgrade is network design for emergency care. To address this issue, our team has created novel bi-level integer programming models and methods to simultaneously boost health and well-being and hospital network profitability.
Our smart, connected healthcare engineering projects are well-placed at the interface of computational modeling, data science, and clinical engineering, with equal emphasis on basic model/methodology development, research translation, and community engagement. Lab members come from disciplines including biomedical, industrial and chemical engineering, as well as computer science, mathematics, and statistics. We actively collaborate with researchers from medicine, nursing, pharmacy and management.
It’s all hands on deck to find ways to transform healthcare practices and delivery so we can be better prepared for the next wave, whatever it is.
Nan Kong, PhD
Associate Professor, Weldon School of Biomedical Engineering
College of Engineering, Purdue University