Community health pathways modeling and scheduling under uncertainty.

Scheduling and coordinating constrained resources in community healthcare settings at a centralized Pathways Community HUB is challenging due to limited resources and the inherent dynamics of the processes and the organizational structures. In this work, we introduce a stochastic programming (SP) approach for connected community health for optimally scheduling community health pathways (CHPs) under uncertainty in resource availability. A CHP is a standardized tool that details multiple steps of a healthcare-related service and the required resources for each step. The SP methodology was implemented and applied to data for a real Pathways Community HUB for a U.S. county involving several CHPs, community health workers, physicians, and other resources. The computational results are promising and they show that client access times depend on the HUB resources uncertain future availability and the level of client demand, with high client demand resulting in relatively longer access time. The study reveals that schedules provided by a deterministic approach where resource availability is assumed to be known can be too optimistic. Several managerial insights are learned from this study, including the observation that the SP model provides client schedules that are equitable across the same type of community health workers.

A data-driven simulation approach to quantify the effect of group counseling on system performance of college counseling centers.

Objective: To investigate the effectiveness, from a system's perspective, of offering group counseling options in college counseling centers. Methods: We achieve this through a data-driven simulation-based approach with the aim of providing administrators with a quantitative tool that informs their decision-making process. Results: Our simulation experiments reveal that offering group counseling options without resource reallocation does not have the desired positive impact on the system's performance. However, with resource reallocation, our results demonstrate that the introduction of group counseling options can significantly improve the performance of the system by as much as 40%. Conclusions: Group counseling options, coupled with proper resource reallocation strategies, are effective in reducing access time of first-time patients by as much as 40%. The effect of group counseling is highly dependent on both the number of offered groups as well as their scheduling policy. Scheduling policies have to be scrutinized in light of their resulting group waiting time and resource-utilization efficiency.

An integrated chance constraints approach for optimal vaccination strategies under uncertainty for COVID-19.

Despite concerted efforts by health authorities worldwide to contain COVID-19, the SARS-CoV-2 virus has continued to spread and mutate into new variants with uncertain transmission characteristics. Therefore, there is a need for new data-driven models for determining optimal vaccination strategies that adapt to the new variants with their uncertain transmission characteristics. Motivated by this challenge, we derive an integrated chance constraints stochastic programming (ICC-SP) approach for finding vaccination strategies for epidemics that incorporates population demographics for any region of the world, uncertain disease transmission and vaccine efficacy. An optimal vaccination strategy specifies the proportion of individuals in a given household-type to vaccinate to bring the reproduction number to below one. The ICC-SP approach provides a quantitative method that allows to bound the expected excess of the reproduction number above one by an acceptable amount according to the decision-maker's level of risk. This new methodology involves a multi-community household based epidemiology model that uses census demographics data, vaccination status, age-related heterogeneity in disease susceptibility and infectivity, virus variants, and vaccine efficacy. The new methodology was tested on real data for seven neighboring counties in the United States state of Texas. The results are promising and show, among other findings, that vaccination strategies for controlling an outbreak should prioritize vaccinating certain household sizes as well as age groups with relatively high combined susceptibility and infectivity.

COVID-19 vaccination policies under uncertain transmission characteristics using stochastic programming.

We develop a new stochastic programming methodology for determining optimal vaccination policies for a multi-community heterogeneous population. An optimal policy provides the minimum number of vaccinations required to drive post-vaccination reproduction number to below one at a desired reliability level. To generate a vaccination policy, the new method considers the uncertainty in COVID-19 related parameters such as efficacy of vaccines, age-related variation in susceptibility and infectivity to SARS-CoV-2, distribution of household composition in a community, and variation in human interactions. We report on a computational study of the new methodology on a set of neighboring U.S. counties to generate vaccination policies based on vaccine availability. The results show that to control outbreaks at least a certain percentage of the population should be vaccinated in each community based on pre-determined reliability levels. The study also reveals the vaccine sharing capability of the proposed approach among counties under limited vaccine availability. This work contributes a decision-making tool to aid public health agencies worldwide in the allocation of limited vaccines under uncertainty towards controlling epidemics through vaccinations.

Chemotherapy appointment scheduling under uncertainty using mean-risk stochastic integer programming.

Oncology clinics are often burdened with scheduling large volumes of cancer patients for chemotherapy treatments under limited resources such as the number of nurses and chairs. These cancer patients require a series of appointments over several weeks or months and the timing of these appointments is critical to the treatment's effectiveness. Additionally, the appointment duration, the acuity levels of each appointment, and the availability of clinic nurses are uncertain. The timing constraints, stochastic parameters, rising treatment costs, and increased demand of outpatient oncology clinic services motivate the need for efficient appointment schedules and clinic operations. In this paper, we develop three mean-risk stochastic integer programming (SIP) models, referred to as SIP-CHEMO, for the problem of scheduling individual chemotherapy patient appointments and resources. These mean-risk models are presented and an algorithm is devised to improve computational speed. Computational results were conducted using a simulation model and results indicate that the risk-averse SIP-CHEMO model with the expected excess mean-risk measure can decrease patient waiting times and nurse overtime when compared to deterministic scheduling algorithms by 42 % and 27 %, respectively.

Stochastic online appointment scheduling of multi-step sequential procedures in nuclear medicine.

The increased demand for medical diagnosis procedures has been recognized as one of the contributors to the rise of health care costs in the U.S. in the last few years. Nuclear medicine is a subspecialty of radiology that uses advanced technology and radiopharmaceuticals for the diagnosis and treatment of medical conditions. Procedures in nuclear medicine require the use of radiopharmaceuticals, are multi-step, and have to be performed under strict time window constraints. These characteristics make the scheduling of patients and resources in nuclear medicine challenging. In this work, we derive a stochastic online scheduling algorithm for patient and resource scheduling in nuclear medicine departments which take into account the time constraints imposed by the decay of the radiopharmaceuticals and the stochastic nature of the system when scheduling patients. We report on a computational study of the new methodology applied to a real clinic. We use both patient and clinic performance measures in our study. The results show that the new method schedules about 600 more patients per year on average than a scheduling policy that was used in practice by improving the way limited resources are managed at the clinic. The new methodology finds the best start time and resources to be used for each appointment. Furthermore, the new method decreases patient waiting time for an appointment by about two days on average.

Finding optimal vaccination strategies under parameter uncertainty using stochastic programming.

We present a stochastic programming framework for finding the optimal vaccination policy for controlling infectious disease epidemics under parameter uncertainty. Stochastic programming is a popular framework for including the effects of parameter uncertainty in a mathematical optimization model. The problem is initially formulated to find the minimum cost vaccination policy under a chance-constraint. The chance-constraint requires that the probability that R(*)