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NSF CHOT IUCRC PROGRESS REPORT β PROJECT # 1 Characterizing and Reducing Avoidable Outside Utilization Research team James Benneyan, Hande Musdal; Parth Vadera, Cory Stasko, Anne-Marie Chouinard Description The objectives of this project are to: 1) to explore the utility of a variety of analytic methods to help understand, characterize, and describe referrals and leakage patterns and 2) to help reduce, disrupt, or prevent leakage. Outside referrals, or βleakageβ, is a ubiquitous problem for many health systems, especially accountable care organizations and other health systems with risk-sharing contracts. Leakage occurs when patients within a health systemβs population are referred to or otherwise receive care outside that system, with both cost and continuity implications. For various reasons an index referral leads to a chain of additional referrals with unclear patterns and visibility as to how these referrals are occurring. In characterizing leakage, this work develops a flexible multi-phase Bayesian methodology capable of inferring a network from time series patient visit data, with additional phase(s) based on the type and specificity of data available. π( π·|π β π) β P(π β π) π(π β π | π·) = π(π·) Comparison of Improvement Approaches In reducing leakage, four approaches are compared ranging from a naïve greedy algorithm that would be easily implemented to more difficult to implement genetic algorithms. Project Framework Obj. 1: Characterizing Leakage β’ Network structure analysis β’ Data mining to identify signals of costly referrals β’ Predictive modeling of patient referral pathways Obj. 2: Preventing Leakage β’ β’ β’ β’ System dynamics model Simulation of flows Network interdiction Comparison of algorithm accuracy and feasibility How is this different than related research? This is the first work of this type in characterizing and preventing outside utilization, using analytical methods from industrial engineering and operations research. Most approaches to managing outside utilization focus on methods to identify inappropriate referrals without considering the complex network flows involved. Other previous work has studied ways of educating providers or effectively introducing new contractual mechanisms. Our project complements this domain of work by applying operations research methods to achieve a network-based understanding of how to characterize, prevent, and minimize leakage. Bayesian Update for Timing and Frequency Data Milestones achieved to date ο· Developed a system dynamics model for the system of factors that cause leakage ο· Illustrated network analysis approach to better understand referral patterns ο· Created Monte Carlo model that simulates a given network-scenario to estimate total costs of the scenario ο· Developed models for all four network interdiction optimization methods ο· Compared performance of the four models for various levels of data specificity in terms of leakage reduction, model run time, and model complexity Next steps ο· Test algorithms on a wider range of possible input data to identify networks for which more advanced algorithms would be most valuable ο· Identify and partner with health systems to validate and apply both the leakage characterization and reduction models Potential member benefits 1. Better understanding of how and why leakage occurs 2. Identification of potential sources and patterns of avoidable leakage 3. Approaches to detect, prevent, and mitigate avoidable out-of-network referrals