Data-Driven Learning and Resource Allocation in Healthcare Operations Management

The tremendous advances in machine learning and optimization over the past decade have immensely increased the opportunity to personalize and improve decisions for a plethora of problems in healthcare. This brings forward several challenges and opportunities that have been the primary motivation behind this dissertation and its contributions in both practical and theoretical aspects. This dissertation is broadly about sequential decision-making and statistical learning under limited resources. In this area, we treat sequentially arriving individuals, each of which should be assigned to the most appropriate resource. Per each arrival, the decision-maker receives some contextual information, chooses an action, and gains noisy feedback corresponding to the action. The aim is to minimize the regret of choosing sub-optimal actions over a time horizon. We provide data-driven and personalized methodologies for this class of problems. Our data-driven methods adaptively learn from data over time to make efficient and effective real-time decisions for each individual, when resources are limited. With a particular focus on high-impact problems in healthcare, we develop new online algorithms to solve healthcare operations problems. The theoretical contributions lie in the design and analysis of a new class of online learning algorithms for sequential decision-making and proving theoretical performance guarantees for them. The practical contributions are to apply our methodology to solve and provide managerial and practical insights for problems in healthcare, service operations, and operations management in general.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/174261/1/mzhale_1.pd

Hub-and-spoke network design under operational and disruption risks

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Augmenting Kalman Filter Machine Learning Models with Data from OCT to Predict Future Visual Field Loss: An Analysis Using Data from the African Descent and Glaucoma Evaluation Study and the Diagnostic Innovation in Glaucoma Study.

PurposeTo assess whether the predictive accuracy of machine learning algorithms using Kalman filtering for forecasting future values of global indices on perimetry can be enhanced by adding global retinal nerve fiber layer (RNFL) data and whether model performance is influenced by the racial composition of the training and testing sets.DesignRetrospective, longitudinal cohort study.ParticipantsPatients with open-angle glaucoma (OAG) or glaucoma suspects enrolled in the African Descent and Glaucoma Evaluation Study or Diagnostic Innovation in Glaucoma Study.MethodsWe developed a Kalman filter (KF) with tonometry and perimetry data (KF-TP) and another KF with tonometry, perimetry, and global RNFL data (KF-TPO), comparing these models with one another and with 2 linear regression (LR) models for predicting mean deviation (MD) and pattern standard deviation values 36 months into the future for patients with OAG and glaucoma suspects. We also compared KF model performance when trained on individuals of European and African descent and tested on patients of the same versus the other race.Main outcome measuresPredictive accuracy (percentage of MD values forecasted within the 95% repeatability interval) differences among the models.ResultsAmong 362 eligible patients, the mean ± standard deviation age at baseline was 71.3 ± 10.4 years; 196 patients (54.1%) were women; 202 patients (55.8%) were of European descent, and 139 (38.4%) were of African descent. Among patients with OAG (n = 296), the predictive accuracy for 36 months in the future was higher for the KF models (73.5% for KF-TP, 71.2% for KF-TPO) than for the LR models (57.5%, 58.0%). Predictive accuracy did not differ significantly between KF-TP and KF-TPO (P = 0.20). If the races of the training and testing set patients were aligned (versus nonaligned), the mean absolute prediction error of future MD improved 0.39 dB for KF-TP and 0.48 dB for KF-TPO.ConclusionsAdding global RNFL data to existing KFs minimally improved their predictive accuracy. Although KFs attained better predictive accuracy when the races of the training and testing sets were aligned, these improvements were modest. These findings will help to guide implementation of KFs in clinical practice