Visit Allocation Problems in Multi-Service Settings: Policies and Worst-Case Bounds

Problem definition: We consider a resource allocation problem faced by health and humanitarian organizations deploying mobile outreach teams to serve marginalized communities. These teams can provide a single service or an assortment of services during each visit. Combining services is likely to increase operational efficiency but decrease the relative benefit per service per visit, as operations are no longer tailored to a single service. The aim of this study is to analyze this benefit-efficiency trade-off. Academic/practical relevance: Increased operational efficiency will enable organizations to serve more people using fewer resources. This is important given the increasing funding gap organizations are facing. Our work adds to the literature on resource allocation problems and visit allocation problems specifically, where the focus has been primarily on single services. Methodology: We analyze a general visit allocation problem incorporating demand distribution (where to go) and return time (how frequently to go). We derive analytical bounds for the benefit-efficiency trade-off, and propose visit allocation policies with worst-case optimality guarantees. Results: Our results show the benefit-efficiency trade-off can be assessed based on high level parameters. We show demand alignment is a key driver of this trade-off. We apply our results to Praesens Care, a social enterprise start-up developing mobile diagnostic laboratories, and verify our insights using real-world data. Managerial Implications: Our research contributes to the discussion on innovation and increased efficiency in health and humanitarian aid delivery by quantifying operational trade-offs in offering assortments of services. Specifically, our results help assess the potential of integrated models for health and humanitarian aid delivery and provide organizations with easy-to-implement methods to determine close-to-optimal visiting policies. Importantly, our methods remain applicable in case of limited data, making them suitable for strategic decision-making

A systematic literature review of operational research methods for modelling patient flow and outcomes within community healthcare and other settings

An ambition of healthcare policy has been to move more acute services into community settings. This systematic literature review presents analysis of published operational research methods for modelling patient flow within community healthcare, and for modelling the combination of patient flow and outcomes in all settings. Assessed for inclusion at three levels – with the references from included papers also assessed – 25 “Patient flow within community care”, 23 “Patient flow and outcomes” papers and 5 papers within the intersection are included for review. Comparisons are made between each paper’s setting, definition of states, factors considered to influence flow, output measures and implementation of results. Common complexities and characteristics of community service models are discussed with directions for future work suggested. We found that in developing patient flow models for community services that use outcomes, transplant waiting list may have transferable benefits

Healthcare Operations Management: A Snapshot of Emerging Research

A new generation of healthcare operations management (HOM) scholars is studying timely healthcare topics (e.g., organization design, design of delivery, and organ transplantation) using contemporary methodolog- ical tools (e.g., econometrics, information economics, and queuing games). A distinguishing feature of this stream of work is that it explicitly incorporates behavior, incentive, and policy considerations arising from the entanglements across multiple entities that make up the complex healthcare ecosystem. This focus is a departure from an earlier generation of research that primarily centered on optimizing given operations of a single entity. This paper provides an introduction to this burgeoning field and maps out research opportunities. We start with identifying key entities of healthcare delivery, financing, innovation, and policymaking, illustrating them on a healthcare ecosystem map (HEM). Next, we explore the HOM literature examining the interactions among various entities in the HEM. We then develop a taxonomy for the recent HOM literature (published in Manufacturing & Service Operations Management, Management Science, and Operations Research between 2013 and 2017), provide a tool-thrust graph mapping methodological tools with research thrusts, and situate the HOM literature in context by connecting it with perspectives from medical journals and mass media. We close with a reference to technological innovations that have the potential to transform the healthcare ecosystem in future decades

Toward Elimination of Infectious Diseases with Mobile Screening Teams

I n pursuit of Sustainable Development Goal 3 “Ensure healthy lives and promote well-being for all at all ages,” considerable global effort is directed toward elimination of infectious diseases in general and Neglected Tropical Diseases in particular. For various such diseases, the deployment of mobile screening teams forms an important instrument to reduce prevalence toward elimination targets. There is considerable variety in planning methods for the deployment of these mobile teams in practice, but little understanding of their effectiveness. Moreover, there appears to be little understanding of the relationship between the number of mobile teams and progress toward the goals. This research considers capacity planning and deployment of mobile screening teams for one such neglected tropical disease: Human African trypanosomiasis (HAT, or sleeping sickness). We prove that the deployment problem is strongly NP-Hard and propose three approaches to find (near) optimal screening plans. For the purpose of practical implementation in remote rural areas, we also develop four simple policies. The performance of these methods and their robustness is benchmarked for a HAT region in the Democratic Republic of Congo (DRC). Two of the four simple practical policies yield near optimal solutions, one of which also appears robust against parameter impreciseness. We also present a simple approximation of prevalence as a function of screening capacity, which appears rather accurate for the case study. While the results may serve to more effectively allocate funding and deploy mobile screening capacity, they also indicate that mobile screening may not suffice to achieve HAT eliminatio

Planning for HIV screening, testing, and care at the veterans health administration

We analyzed the planning problem for HIV screening, testing, and care. This problem consists of determining the optimal fraction of patients to be screened in every period as well as the optimum staffing level at each part of the healthcare system to maximize the total health benefits to the patients measured by quality-adjusted life-years (QALYs) gained. We modeled this problem as a nonlinear mixed integer programming program comprising disease progression (the transition of the patients across health states), system dynamics (the flow of patients in different health states across various parts of the healthcare delivery system), and budgetary and capacity constraints. We applied the model to the Greater Los Angeles (GLA) station in the Veterans Health Administration system. We found that a Centers for Disease Control and Prevention recommended routine screening policy in which all patients visiting the system are screened for HIV irrespective of risk factors may not be feasible because of budgetary constraints. Consequently, we used the model to develop and evaluate managerially relevant policies within existent capacity and budgetary constraints to improve upon the current risk based screening policy of screening only high risk patients. Our computational analysis showed that the GLA station can achieve substantial increase (20% to 300%) in the QALYs gained by using these policies over risk based screening. The GLA station has already adapted two of these policies that could yield better patient health outcomes over the next few years. In addition, our model insights have influenced the decision making process at this station

Planning for HIV Screening, Testing, and Care at the Veterans Health Administration

We analyzed the planning problem for HIV screening, testing, and care. This problem consists of determining the optimal fraction of patients to be screened in every period as well as the optimum staffing level at each part of the healthcare system to maximize the total health benefits to the patients measured by quality-adjusted life-years (QALYs) gained. We modeled this problem as a nonlinear mixed integer programming program comprising disease progression (the transition of the patients across health states), system dynamics (the flow of patients in different health states across various parts of the healthcare delivery system), and budgetary and capacity constraints. We applied the model to the Greater Los Angeles (GLA) station in the Veterans Health Administration system. We found that a Centers for Disease Control and Prevention recommended routine screening policy in which all patients visiting the system are screened for HIV irrespective of risk factors may not be feasible because of budgetary constraints. Consequently, we used the model to develop and evaluate managerially relevant policies within existent capacity and budgetary constraints to improve upon the current risk based screening policy of screening only high risk patients. Our computational analysis showed that the GLA station can achieve substantial increase (20% to 300%) in the QALYs gained by using these policies over risk based screening. The GLA station has already adapted two of these policies that could yield better patient health outcomes over the next few years. In addition, our model insights have influenced the decision making process at this station

Planning for HIV screening, testing, and care at the veterans health administration

We analyzed the planning problem for HIV screening, testing, and care. This problem consists of determining the optimal fraction of patients to be screened in every period as well as the optimum staffing level at each part of the healthcare system to maximize the total health benefits to the patients measured by quality-adjusted life-years (QALYs) gained. We modeled this problem as a nonlinear mixed integer programming program comprising disease progression (the transition of the patients across health states), system dynamics (the flow of patients in different health states across various parts of the healthcare delivery system), and budgetary and capacity constraints. We applied the model to the Greater Los Angeles (GLA) station in the Veterans Health Administration system. We found that a Centers for Disease Control and Prevention recommended routine screening policy in which all patients visiting the system are screened for HIV irrespective of risk factors may not be feasible because of budgetary constraints. Consequently, we used the model to develop and evaluate managerially relevant policies within existent capacity and budgetary constraints to improve upon the current risk based screening policy of screening only high risk patients. Our computational analysis showed that the GLA station can achieve substantial increase (20% to 300%) in the QALYs gained by using these policies over risk based screening. The GLA station has already adapted two of these policies that could yield better patient health outcomes over the next few years. In addition, our model insights have influenced the decision making process at this station

Evidence-Based Optimization in Humanitarian Logistics

Humanitarian crises like the Syrian war, Ebola, the earthquake in Haiti, the Indian Ocean tsunami, and the ongoing HIV epidemic prompt substantial demands for humanitarian aid. Logistics plays a key role in aid delivery and represents a major cost category for humanitarian organizations. Optimizing logistics has long been at the core of operations research: the discipline that explores the use of advanced analytical methods to improve decision making. The commercial sector has substantially benefited from such methods. This thesis discusses whether and how such methods can also guide policy and decision making in the humanitarian sector. This is done through in-depth analyses of three case studies. The first investigates suitability of advanced planning and routing tools. Next, we investigate decision support methods for designing networks of roadside HIV clinics. The third case study concerns the deployment of mobile teams that screen for infectious disease outbreaks. Optimization tools come with assumptions about objectives to be reached and about their link with the decisions to be optimized. In humanitarian logistics, safeguarding adequacy of these assumptions is challenging but crucial. Throughout our case-studies, we explore how “best available evidence” can be used to link decisions to objectives, so as to enable evidence-based optimization in humanitarian logistics