Alex F. Mills

Resource-Based Patient Prioritization in Mass-Casualty Incidents

The most widely used standard for mass-casualty triage, START, relies on a fixed-priority ordering among different classes of patients, and does not explicitly consider resource limitations or the changes in survival probabilities with respect to time. We construct a fluid model of patient triage in a mass-casualty incident that incorporates these factors and characterize its optimal policy. We use this characterization to obtain useful insights about the type of simple policies that have a good chance to perform well in practice, and we demonstrate how one could develop such a policy. Using a realistic simulation model and data from emergency medicine literature, we show that the policy we developed based on our fluid formulation outperforms START in all scenarios considered, sometimes substantially.

A simple yet effective decision support policy for mass-casualty triage

In the aftermath of a mass-casualty incident, effective policies for timely evaluation and prioritization of patients can mean the difference between life and death. While operations research methods have been used to study the patient prioritization problem, prior research has either proposed decision rules that only apply to very simple cases, or proposed formulating and solving a mathematical program in real time, which may be a barrier to implementation in an urgent situation. We connect these two regimes by proposing a general decision support rule that can handle survival probability functions and an arbitrary number of patient classifications. The proposed survival lookahead policy generalizes not only a myopic policy and a cμ type rule, but also the optimal solution to a version of the problem with two priority classes. This policy has other desirable properties, including index policy structure. Using simple heuristic parameterizations, the survival lookahead policy yields an expected number of survivors that is almost as large as published methods that require mathematical programming, while having the advantage of an intuitive structure and requiring minimal computational support.

Condition-dependent mate choice: A stochastic dynamic programming approach

We study how changing female condition during the mating season and condition-dependent search costs impact female mate choice, and what strategies a female could employ in choosing mates to maximize her own fitness. We address this problem via a stochastic dynamic programming model of mate choice. In the model, a female encounters males sequentially and must choose whether to mate or continue searching. As the female searches, her own condition changes stochastically, and she incurs condition-dependent search costs. The female attempts to maximize the quality of the offspring, which is a function of the females condition at mating and the quality of the male with whom she mates. The mating strategy that maximizes the females net expected reward is a quality threshold. We compare the optimal policy with other well-known mate choice strategies, and we use simulations to examine how well the optimal policy fares under imperfect information.

From Incident to Inpatient: How Healthcare Coalitions Can Improve Urban Incident Response

In recent years, many urban areas have established healthcare coalitions composed of autonomous (and often competing) hospitals, with the goal of improving emergency preparedness and response. We study the role of such coalitions in the specific context of response to multiple-casualty incidents in an urban setting, where on-scene responders must determine how to send casualties to medical facilities. A key function in incident response is multi-agency coordination. When this coordination is provided by a healthcare coalition, responders can use richer information about hospital capacities to decide where to send casualties. Using bed availability data from an urban area and a suburban area in the United States, we analyze the response capability of healthcare infrastructures under different levels of coordination, and we develop a stress test to identify areas of weakness. We find that improved coordination efforts should focus on decision support using information about inpatient resources, especially in urban areas with high inter-hospital variability in resource availability. We also find that coordination has the largest benefit in small incidents. This benefit is a new value proposition for healthcare coalitions, which were originally formed to improve preparedness for large disasters. This article is protected by copyright. All rights reserved.

Dynamic Distribution of Patients to Medical Facilities in the Aftermath of a Disaster

In the aftermath of a disaster, emergency responders must transport a large number of patients to medical facilities, using limited transportation resources (such as ambulances). Decisions about where to send the patients are typically made in an ad hoc manner by responders on the scene. Using a Markov decision process formulation, we develop two heuristic policies that use limited information such as mean travel times and congestion levels to determine (a) how to allocate ambulances to patient locations and (b) which medical facility should be the destination for those ambulances. In a simulation study, we incorporate patient survival rates and service times for different types of traumatic injuries, and show that the proposed heuristics can provide substantial improvement in the expected number of survivors compared to the common practice of transporting to the nearest facility, even when the decision maker has only limited up-to-date information about the system state. In particular, a myopic approach ...

Calling for Care? The Risky Proposition of Teletriage in Healthcare Demand Management

Problem definition: Many healthcare providers and payers offer teletriage, a service where concerned patients can get advice about their health condition. In theory, teletriage should help patients with an acute illness choose an appropriate provider, reducing unnecessary and duplicate provider visits, yet no study has assessed the overall costs and benefits of teletriage at a system level. Academic/Practical relevance: We model the use of teletriage in managing healthcare demand and determine in which cases teletriage is efficient and effective. Our model extends the academic literature on service operations and provides practical suggestions for healthcare payers. Methodology: We use a Markov decision process to model a patients choices during an acute illness episode, where the illness severity is partially observable to the patient and triage is subject to errors. We parameterize the model using data from the literature and provide both structural and numerical results. Results: Patients with high uncertainty about their state would use the teletriage service, which may improve their cost outcomes. However, when teletriage is added, the rate of arrivals to the emergency department (ED) may increase when the overtriage rate is above 5%, the lowest value observed in the literature. Patients choice as well as the copayment of other services affects the cost-effectiveness of teletriage. Managerial implications: TThere are several reasons why adding teletriage to the healthcare system could produce a negative cost outcome. Teletriage should not necessarily be free, which would encourage use by patients with low levels of uncertainty and actually increase the payers total cost. The overtriage rate is a key driver of performance and must be managed carefully.