Alleviating the Patient's Price of Privacy Through a Partially Observable Waiting List

Abstract

I n the United States, end-stage liver disease patients join a waiting list and then make accept/reject decisions for transplantation as deceased-donor organs are offered to them over time. These decisions are largely influenced by the patient's prospect for future offers, which can be ascertained most accurately by knowing the entire composition of the waiting list. Under the current transplantation system, however, the United Network for Organ Sharing (UNOS), in an effort to strike a balance between privacy and transparency, only publishes an aggregated version of the waiting list. However, it is not clear whether the published information is good enough (compared with perfect information) to help patients make optimal decisions that maximize their individual life expectancies. We provide a novel model of this accept/reject problem from an individual patient's perspective using a partially observed Markov decision process (POMDP) framework, which incorporates the imperfect waiting list information as published currently into the patient's decision making. We analyze structural properties of this model. In particular, we establish conditions that guarantee a monotone value function and a threshold-type optimal policy with respect to the partially observable rank state that captures the imperfect waiting list information. Furthermore, we develop an improved solution methodology to solve a generic POMDP model. This solution method guarantees, for any fixed grid, the best possible approximation to the optimal value function by solving linear programs to compute the optimal weights used for the approximation. Finally, we compare, in a clinically driven numerical study, the results of this model with those of an existing Markov decision process model that differs from our model in assuming the availability of perfect waiting list information. This comparison allows us to assess the quality of the published imperfect information as measured by a patient's so-called price of privacy (i.e., the opportunity loss in expected life days due to a lack of perfect waiting list information). Previous work estimates a significant loss in a patient's life expectancy, on average, when the patient has no waiting list information compared with full information. In this paper, we find that the currently published partial information is nearly sufficient to eliminate this loss, resulting in a negligible price of privacy and supporting current UNOS practice.

OrganJet: Overcoming Geographical Disparities in Access to Deceased Donor Kidneys in the United States

“…Organ transplant operations have received significant research attention in recent years. Several researchers study the patient's problem of accepting/rejecting an organ offer while waiting for a transplant; see David and Yechiali (1995), Ahn and Hornberger (1996), Hornberger and Ahn (1997), Howard (2002), Alagoz et al (2004Alagoz et al ( , 2007a, and Sandıkçi et al (2008Sandıkçi et al ( , 2013. Several others use simulation models to study the results of possible changes to the organ allocation policy or alternative policies; see CONSAD Research Corporation (1995), Pritsker et al (1995), Zenios et al (1999), Taranto et al (2000), Kreke et al (2002), and Shechter et al (2005).…”

A critical look at the U.S. deceased‐donor organ procurement and utilization system

“…Sandıkçı et al (2008) estimate a significant loss in a patient's life expectancy, on average, when the patient has no waiting list information compared with full information. In contrast, Sandıkçı et al (2013) find that the partial information that is usually available is nearly sufficient to eliminate this loss and they claim that negligible price of privacy supports current UNOS practice.…”

“…Sandıkçı, Maillart, Schaefer, and Roberts (2013) incorporate PTR location in the deceased‐donor liver wait list by modeling the accept/decline decision as a partially observed markov decision process (POMDP). Sandıkçı et al (2008) estimate a significant loss in a patient's life expectancy, on average, when the patient has no waiting list information compared with full information.…”

OM Forum—Healthcare Operations Management: A Snapshot of Emerging Research

“…The HEM decomposes the complex web of interconnections between various entities into four circles. In illustrating each circle, we use a concrete example related to an active area in the HOM literature-organ transplantation (see, e.g., Su and Zenios 2004;Bertsimas et al 2013;Sandikçi et al 2013;Ata et al 2017;Dai et al 2018), with a particular focus on the US kidney transplantation system.…”

“…For example, the concept of "delayed differentiation" in the operations management literature (e.g., Tang 1997, Swaminathan andTayur 1998) entails creatively revamping the business model of a manufacturer or service provider such that it focuses on the generic parts and does not commit to custom options until the last stages. If we view healthcare delivery as a supply-chain design problem, society has a choice in terms of the timing of offering common services (preventive care, screening, and Alizamir et al (2013); Angalakudati et al (2014); Ata et al (2017); Bertsimas et al (2013); ; Deo and Sohoni (2015); Deo et al (2013); ; Huh et al (2013); Mamani et al (2013); Natarajan and Swaminathan (2014); Sandikçi et al (2013) MeT2: Design of Delivery Gawande (2010aGawande ( , 2011aGawande ( , 2012; Makary (2013); Weick and Sutcliffe (2015) Berwick et al (2006); Blumenthal (1996);Brook et al (1996); Donabedian (1988); Grol and Grimshaw (2003); Newman-Toker and Pronovost (2009) health-related activities) and custom services (diagnostic and treatment services) to the population. Inspired by the concept of "delayed differentiation," Thompson et al (2018) empirically demonstrate the improvement in the value of care through implementing "temporal displacement of care" with a panel of 45,000 patients in Vermont, with an objective of shifting costly diagnostic and treatment services offered to sick patients to more intensive preventive services offered to healthy individuals.…”