Carri W. Chan

Optimizing Intensive Care Unit Discharge Decisions with Patient Readmissions

This work examines the impact of discharge decisions under uncertainty in a capacity-constrained high-risk setting: the intensive care unit ICU. New arrivals to an ICU are typically very high-priority patients and, should the ICU be full upon their arrival, discharging a patient currently residing in the ICU may be required to accommodate a newly admitted patient. Patients so discharged risk physiologic deterioration, which might ultimately require readmission; models of these risks are currently unavailable to providers. These readmissions in turn impose an additional load on the capacity-limited ICU resources. We study the impact of several different ICU discharge strategies on patient mortality and total readmission load. We focus on discharge rules that prioritize patients based on some measure of criticality assuming the availability of a model of readmission risk. We use empirical data from over 5,000 actual ICU patient flows to calibrate our model. The empirical study suggests that a predictive model of the readmission risks associated with discharge decisions, in tandem with simple index policies of the type proposed, can provide very meaningful throughput gains in actual ICUs while at the same time maintaining, or even improving upon, mortality rates. We explicitly provide a discharge policy that accomplishes this. In addition to our empirical work, we conduct a rigorous performance analysis for the family of discharge policies we consider. We show that our policy is optimal in certain regimes, and is otherwise guaranteed to incur readmission related costs no larger than a factor of

When to Use Speedup: An Examination of Service Systems with Returns

\hat{\rho}+1

Stochastic Depletion Problems: Effective Myopic Policies for a Class of Dynamic Optimization Problems

of an optimal discharge strategy, where

Optimal Power, Throughput and Routing for Wireless Link Arrays

\hat{\rho}

The Impact of Delays on Service Times in the Intensive Care Unit

is a certain natural measure of system utilization.

Scheduling algorithms for broadcasting media with multiple distortion measures

In a number of service systems, there can be substantial latitude to vary service rates. However, although speeding up service rate during periods of congestion may address a present congestion issue, it may actually exacerbate the problem by increasing the need for rework. We introduce a state-dependent queuing network where service times and return probabilities depend on the “overloaded” and “underloaded” state of the system. We use a fluid model to examine how different definitions of “overload” affect the long-term behavior of the system and provide insight into the impact of using speedup. We identify scenarios where speedup can be helpful to temporarily alleviate congestion and increase access to service. For such scenarios, we provide approximations for the likelihood of speedup to service. We also identify scenarios where speedup should never be used; moreover, in such a situation, an interesting bi-stability arises, such that the system shifts randomly between two equilibria states. Hence, our analysis sheds light on the potential benefits and pitfalls of using speedup when the subsequent returns may be unavoidable.

Multiple Distortion Measures for Packetized Scalable Media

This paper presents a general class of dynamic stochastic optimization problems we refer to as stochastic depletion problems. A number of challenging dynamic optimization problems of practical interest are stochastic depletion problems. Optimal solutions for such problems are difficult to obtain, both from a pragmatic computational perspective as well as from a theoretical perspective. As such, simple heuristics are desirable. We isolate two simple properties that, if satisfied by a problem within this class, guarantee that a myopic policy incurs a performance loss of at most 50% relative to the optimal adaptive control policy for that problem. We are able to verify that these two properties are satisfied for several interesting families of stochastic depletion problems and, as a consequence, we identify computationally efficient approximations to optimal control policies for a number of interesting dynamic stochastic optimization problems.

Association Among Icu Congestion, Icu Admission Decision, and Patient Outcomes.

In this paper we study communication routes in wireless networks supported by link arrays/grids. We consider canonical models and address key optimization issues associated with transmission schedules, links densities, etc. that are pertinent to performance of such structure in terms of throughput and the power/delay tradeoff. We find that in several interesting cases regular periodic link arrays/grids perform better than their topologically perturbed and/or randomized counterparts. We then consider wireless link routing structures called ‘bracelets’ and show that linear (straight) link arrays are optimal for constant exogenous interference fields. Finally, we address the problem of optimal large-scale routing in extraneous interference fields that vary in space. Keywords— Wireless Links, Power Feasibility, Signal-toNoise Ratio.

Wireless Video Broadcasting to Diverse Users

Mainstream queueing models are frequently employed in modeling healthcare delivery in a number of settings, and they further are used in making operational decisions for the same. The vast majority of these queueing models ignore the effects of delay experienced by a patient awaiting care. However, long delays may have adverse effects on patient outcomes and can potentially lead to a longer length of stay (LOS) when the patient ultimately does receive care. This work sets out to understand these delay issues from an operational perspective. Using data of more than 57,000 emergency department (ED) visits, we use an instrumental variable approach to empirically measure the impact of delays in intensive care unit (ICU) admission, i.e., ED boarding, on the patient’s ICU LOS for multiple patient types. Capturing these empirically observed effects in a queueing model is challenging because the effect introduces potentially long-range correlations in service and interarrival times. We propose a queueing model th...

Prioritizing Burn-Injured Patients During a Disaster

The growing popularity of multimedia streaming applications brings a growth in diversity of media clients (laptops, PDAs, cellphones). Effectively serving this heterogeneous group of users is highly desirable. Scalable media codecs such as H.264/MPEG-4 SVC help make this adaptation possible. To account for the various capabilities and requests of each user, such as varying spatial or temporal resolutions, multiple distortion measures (MDM) are considered. Rather than consider a homogeneity in users, the MDM framework considers multiple different distortion values for each media packet for each user type. We consider the scenario of simultaneously broadcasting a video stream to multiple users over wireless links. The objective is to design a scheduling algorithm which achieves the highest aggregate quality-of-service, measured by distortion and delay, over all different user types. We cast the problem as a stochastic shortest path problem and use dynamic programming to find the optimal policy. For statistically static channels, the optimal policy is shown to be of threshold type. For time-varying channels, a quasi-static policy is introduced. Experimental results show that our policy reduces distortion by up to a factor of 2 over conventional approaches which do not consider MDM.