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Market-Based Solutions to Vital Economic Issues


Kenan Institute 2024 Grand Challenge: Business Resilience
Market-Based Solutions to Vital Economic Issues



UNC-Chapel Hill professor and Kenan Institute expert Iheoma U. Iruka took part in a roundtable discussion on the "childcare cliff" on PBS NC’s “State Lines” July 5. The episode is available online.

The EHR revolution has significantly transformed healthcare work and the flow of information, but it hasn't come without costs, measured in increased administrative burden and the accompanying stress for healthcare professionals. Can generative AI help?

Supreme Court decisions on reproductive rights and affirmative action inadvertently afford the nursing profession a propitious opportunity to capitalize on the nation’s rich mosaic of iceberg demographic identities—inherited and acquired traits that may not be visibly apparent—to address pressing worker shortages and other workplace conundrums. 

Taming the rising costs of prescription drugs has been a focus of U.S. healthcare reform for the past decade. High drug prices limit patient access while also contributing to higher overall healthcare costs. Recently, issues of how drug list prices are set, who reaps the benefits, and how those costs are passed on to patients have come under increased scrutiny.

As healthcare costs continue to rise, many Americans are looking to artificial intelligence to provide cost-reducing solutions. At the 13th annual UNC Business of Healthcare Conference, a panel of experts separated the AI hype from reality in a discussion of the limitations, risks and ethical questions surrounding AI solutions in healthcare.

Join the Center for the Business of Health for sessions including the rising price of drugs, the influence of consolidation on healthcare prices and costs, and the AI boom and reducing healthcare prices. Meals are included for in-person attendees.

Please join the Center for the Business of Health and the Kenan Institute for an exclusive lunchtime conversation with Dr. Craig Albanese and Dr. Wesley Burks, joined by Kody Kinsley. The Dean's Speaker Series talk is on Friday, Nov. 3 at 12:30 p.m.

This paper presents the development, validation, and implementation of a data-driven optimization model designed to dynamically plan the assignment of anesthesiologists across multiple hospital locations within a large multi-specialty healthcare system. We formulate the problem as a multi-stage robust mixed-integer program incorporating on-call flexibility to address demand uncertainty. The optimized dynamic staffing plan has been successfully implemented in the University of Pittsburgh Medical Center healthcare system, leading to estimated annual cost savings of 12\% compared to current practice, or about \$800,000 annually.

Health care costs in the United States make up a larger proportion of gross domestic product (GDP) than in any other developed country and continue to rise. We examine whether the use of consistent metrics in costing information systems across hospitals provides one avenue to reduce these costs. We refer to such consistency as “costing information consistency” or CIC and empirically measure it by identifying whether hospitals in a multihospital system share the same costing system vendor. Using M&A activity among vendors as an instrument for exogenous changes in hospital CIC, we find that CIC is associated with a 13.3% reduction in operating expenses, suggesting that increased cost comparability from CIC helps hospitals identify ways to reduce operating expenses by identifying clinical and administrative best practices.

About 27% of diabetics also suffer from depression, and the presence of co-morbid depression could increase the cost of care for diabetes by up to 100%. Several randomized clinical trials have demonstrated that physical and mental health are more likely to improve for diabetes patients suffering from depression when regular treatment for depression is provided in a primary care setting (called Collaborative Care). An important operational lever in managing Collaborative Care is the allocation of the care manager's time to enrolled patients based on their requirements, which in turn influences the revenue, costs, and patient health outcomes. We present a mathematical modeling approach that determines the optimal allocation of care manager's time and quantifies the costs and benefits of Collaborative Care.

With direct care facilities and workers in crisis, we explore trends behind the labor shortages in the industry as well as a menu of solutions that could possibly alleviate the issue.

How will sweeping changes in primary care services and providers affect the primary care workforce? We examine this question as well as how well the increasing demand for these services can be met in the future.