Taxonomic classification of planning decisions in health care: a review of the state of the art in OR/MS

We provide a structured overview of the typical decisions to be made in resource capacity planning and control in health care, and a review of relevant OR/MS articles for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making

New Statistical Learning Methods for Evaluating Dynamic Treatment Regimes and Optimal Dosing

Dynamic treatment regimes (DTRs) have gained increasing interest in the field of personalized health care in the last two decades, as they provide a sequence of individualized decision rules for treating patients over time. In a DTR, treatment is adapted in response to the changes in an individual's disease progression and health care history. However, specific challenges emerge when applying the current methods of DTR in practice. For example, a treatment decision often happens after a medical test, and is thus nested within the decision of whether a test is needed or not. Such nested test-and-treat strategies are attractive to improve cost-effectiveness. In the first project of this dissertation, we develop a Step-adjusted Tree-based Learning (SAT-Learning) method to estimate the optimal DTR within such a step-nested multiple-stage multiple-treatment dynamic decision framework using test-and-treat observational data. At each step within each stage, we combine a doubly robust semiparametric estimator via Augmented Inverse Probability Weighting with a tree-based reinforcement learning procedure to achieve the counterfactual optimization. SAT-Learning is robust and easy to interpret for the strategies of disease screening and subsequent treatments when necessary. We applied our method to a Johns Hopkins University prostate cancer active surveillance dataset to evaluate the necessity of prostate biopsy and identify the optimal test-and-treatment regimes for prostate cancer patients. Our second project is motivated by scenarios in medical practice where one need to decide on patients radiation or drug doses over time. Due to the complexity of continuous dose scales, few existing studies have extended their methods of multi-treatment decision making to a method to estimate the optimal DTR with continuous doses. We develop a new method, Kernel-Involved-Dosage-Decision learning (KIDD-Learning), which combines a kernel estimation of the dose-response function with a tree-based dose-search algorithm, in a multiple-stage setting. At each stage, KIDD-Learning recursively estimates a personalized dose-response function using kernel regression and then identifies the interpretable optimal dosage regime by growing an interpretable decision tree. The application of KIDD-Learning is illustrated by evaluating the dynamic dosage regimes of the adaptive radiation therapy using a Michigan Medicine liver cancer dataset. In KIDD-Learning, our algorithm splits each node of a tree-based decision rule from the root node to terminal nodes. This heuristic algorithm may fail to identify the optimal decision rule when there are critical tailoring variables hidden from an imperceptible parent node. Therefore, in the third project, we propose an important modification of KIDD-Learning, Stochastic Spline-Involved Tree Search (SSITS), to estimate a more robust optimal dosage regime. This new method uses a simulated annealing algorithm to stochastically search the space of tree-based decision rules. In each visited decision rule, a non-parametric smooth coefficient model is applied to estimate the dose-response function. We further implement backward induction to estimate the optimal regime from the final stage in a reverse sequential order to previous treatment stages. We apply SSITS to determine the optimal dosing strategy for patients treated with Warfarin using data from the International Warfarin Pharmacogenetics Consortium.PHDBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163090/1/mingtang_1.pd

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table

A hybrid, auto-adaptive, and rule-based multi-agent approach using evolutionary algorithms for improved searching

Selecting the most appropriate heuristic for solving a specific problem is not easy, for many reasons. This article focuses on one of these reasons: traditionally, the solution search process has operated in a given manner regardless of the specific problem being solved, and the process has been the same regardless of the size, complexity and domain of the problem. To cope with this situation, search processes should mould the search into areas of the search space that are meaningful for the problem. This article builds on previous work in the development of a multi-agent paradigm using techniques derived from knowledge discovery (data-mining techniques) on databases of so-far visited solutions. The aim is to improve the search mechanisms, increase computational efficiency and use rules to enrich the formulation of optimization problems, while reducing the search space and catering to realistic problems.Izquierdo Sebastián, J.; Montalvo Arango, I.; Campbell, E.; Pérez García, R. 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Access and Resource Management for Clinical Care and Clinical Research in Multi-class Stochastic Queueing Networks.

In healthcare delivery systems, proper coordination between patient visits and the health care resources they rely upon is an area in which important new planning capabilities are very valuable to provide greater value to all stakeholders. Managing supply and demand, while providing an appropriate service level for various types of care and patients of differing levels of urgency is a difficult task to achieve. This task becomes even more complex when planning for (i) stochastic demand, (ii) multi-class customers (i.e., patients with different urgency levels), and (iii) multiple services/visit types (which includes multi-visit itineraries of clinical care and/or clinical research visits that are delivered according to research protocols). These complications in the demand stream require service waiting times and itineraries of visits that may span multiple days/weeks and may utilize many different resources in the organization (each resource with at least one specific service being provided). The key objective of this dissertation is to develop planning models for the optimization of capacity allocation while considering the coordination between resources and patient demand in these multi-class stochastic queueing networks in order to meet the service/access levels required for each patient class. This control can be managed by allocating resources to specific patient types/visits over a planning horizon. In this dissertation, we control key performance metrics that relate to patient access management and resource capacity planning in various healthcare settings with chapters devoted to outpatient services, and clinical research units. The methods developed forecast and optimize (1) the access to care (in a medical specialty) for each patient class, (2) the Time to First Available Visit for clinical research participants enrolling in clinical trials, and (3) the access to downstream resources in an itinerary of care, which we call the itinerary flow time. We also model and control how resources are managed, by incorporating (4) workload/utilization metrics, as well as (5) blocking/overtime probabilities of those resources. We control how to allocate resource capacity along the various multi-visit resource requirements of the patient itineraries, and by doing so, we capture the key correlations between patient access, and resource allocation, coordination, and utilization.PhDIndustrial and Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116770/1/jivan_1.pd