Download Characterizing and Reducing Avoidable Outside Utilization

NSF CHOT IUCRC PROGRESS REPORT – 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 “leakage”, 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
system’s 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.
𝑃( 𝐷|𝑋 → 𝑌) ∗ P(𝑋 → 𝑌)
𝑃(𝑋 → 𝑌 | 𝐷) =
𝑃(𝐷)
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
• Network structure analysis
• Data mining to identify
signals of costly referrals
• Predictive modeling of
patient referral pathways
Obj. 2: Preventing
Leakage
•
•
•
•
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
 Developed a system dynamics model
for the system of factors that cause
leakage
 Illustrated network analysis approach to
better understand referral patterns
 Created Monte Carlo model that
simulates a given network-scenario to
estimate total costs of the scenario
 Developed models for all four network
interdiction optimization methods
 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
 Test algorithms on a wider range of
possible input data to identify networks
for which more advanced algorithms
would be most valuable
 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