Reducing Same Day Missed Appointments

Radiology Associates (RA) is a diagnostic imaging center that offers high-quality, digital medical imaging and interventional radiology services for patients, physicians and healthcare organizations across the Central Coast. They are an ongoing problem that involves a considerable portion of their patients not showing up for their appointments Our project aims to reduce same day missed appointments at Radiology Associates. Radiology Associates currently has a no-show rate of 13.48%. They lose approximately $240 for every same day missed appointment. Our goal was to find new ways to reduce their no-show rate to 8$39,285.35 by reducing the no-show percentage by 5.5%. We researched literature on causes of no-shows and alternative scheduling methods. We then mapped out the scheduling process and analyzed data on no-shows. After discovering some potential causes for the high no-show rate, we constructed solutions and created standard operating procedures

Improvement of surgery duration estimation using statistical methods and analysis of scheduling policies using discrete event simulation

The United States health care system currently faces many challenges, with the most notable one being rising costs. In an effort to decrease those costs, health providers are aiming to improve efficiency in their operations. A primary source of revenue for hospitals and some clinics is the surgery department, making it a key department for improvement in efficiency. Surgery schedules drive the department and affect the operations of many other departments. The most significant challenge to creating an efficient surgery schedule is estimating surgery durations and scheduling cases in a manner that will minimize the time a surgery is off schedule and maximize utilization of resources. To identify ways to better estimate surgery durations, an analysis of the surgery scheduling process at UnityPoint Health - Des Moines, in Des Moines, Iowa was completed. Estimated surgery durations were compared to actual durations using a t test. Multiple linear regression models were created for the most common surgeries including the input variables of age of the patient, anesthesiologist, operating room (OR), number of residents, and day of the week. To find optimal scheduling policies, simulation models were created, each representing a series of surgery cases in one operating room during one day. Four scheduling policies were investigated: shortest estimated time first, longest estimated time first, most common surgery first, and adding an extra twenty minutes to each case in the existing order. The performance of the policies was compared to those of the existing schedule. Using the historical data from a one-year period at UnityPoint Health - Des Moines, the estimated surgery durations for the top four surgeries by count and top surgeons were found to be statistically different in 75% of the data sets. After creating multiple linear regression models for each of the top four surgeries and surgeons performing those surgeries, the β values for each variable were compared across models. Age was found to have a minimal impact on surgery duration in all models. The binary variable indicating residents present, was found to have minimal impact as well. For the rest of the variables, consistencies were difficult to assess, making multiple linear regression an unideal method for identifying the impact of the variables investigated. On the other hand, the simulation model proved to be useful in identifying useful scheduling policies. Eight series based on real series were modeled individually. Each model was validated against reality, with 75% of durations simulated in the models not being statistically different than reality. Each of the four scheduling policies was modeled for each series and the average minutes off schedule and idle time between cases were compared across models. Adding an extra twenty minutes to each case in the existing order resulted in the lowest minutes off schedule, but significantly increased the idle time between cases. Most common surgery first did not have a consistent impact on the performance indicators. Longest estimated time first did not improve the performance indicators in the majority of the cases. Shortest estimated time first resulted in the best performance for minutes off schedule and idle time between cases in combination; therefore, we recommend this policy is employed when the scheduling process allows

Data-Driven Analytics to Support Scheduling of Multi-Priority Multi-Class Patients with Wait Targets

The aim of dynamic scheduling is to efficiently assign available resources to the most suitable patients. The dynamic assignment of multi-class, multi-priority patients over time has long been a challenge, especially for scheduling in advance and under non-deterministic capacity. In this paper, we first conduct descriptive analytics on MRI data of over 3.7 million patient records from 74 hospitals. The dataset captures patients of four different priority levels, with different wait time targets, seeking treatment for one of ten classes of procedures, which have been scheduled over a period of 3 years. The goal is to serve 90% of patients within their wait time targets; however, under current practice, 67% of patients exceed their target wait times. We characterize the main factors affecting the waiting times and conduct predictive analytics to forecast the distribution of the daily patient arrivals, as well as the service capacity or number of procedures performed daily at each hospital. We then prescribe two simple and practical dynamic scheduling policies based on a balance between the First-In First-Out (FIFO) and strict priority policies; namely, weight accumulation and priority promotion. Under the weight accumulation policy, patients from different priority levels start with varying initial weights, which then accumulates as a linear function of their waiting time. Patients of higher weights are prioritized for treatment in each period. Under the priority promotion policy, a strict priority policy is applied to priority levels where patients are promoted to a higher priority level after waiting for a predetermined threshold of time. To evaluate the proposed policies, we design a simulation model that applies the proposed scheduling policies and evaluates them against two performance measures: 1) total exceeding time: the total number of days by which patients exceed their wait time target, and 2) overflow proportion: the percentage of patients within each priority group that exceed the wait time target. Using historical data, we show that, compared to the current practice, the proposed policies achieve a significant improvement in both performance measures. To investigate the value of information about the future demand, we schedule patients at different points of time from their day of arrival. The results show that hospitals can considerably enhance their wait time management by delaying patient scheduling

Appointment planning and scheduling in primary care

The Affordable Care Act (ACA) puts greater emphasis on disease prevention and better quality of care; as a result, primary care is becoming a vital component in the health care system. However, long waits for the next available appointments and delays in doctors offices combined with no-shows and late cancellations have resulted in low efficiency and high costs. This dissertation develops an innovative stochastic model for patient planning and scheduling in order to reduce patients’ waiting time and optimize primary care providers’ utility. In order to facilitate access to patients who request a same-day appointment, a new appointment system is presented in which a proportion of capacity is reserved for urgent patients while the rest of the capacity is allocated to routine patients in advance. After the examination of the impact of no-shows on scheduling, a practical double-booking strategy is proposed to mitigate negative impacts of the no-show. Furthermore, proposed model demonstrates the specific circumstances under which each type of scheduling should be adopted by providers to reach higher utilization. Moreover, this dissertation extends the single physician’s model to a joint panel scheduling and investigates the efficiency of such systems on the urgent patients’ accessibility, the physicians’ utilization, and the patients’ waiting time. Incorporating the newsvendor approach and stochastic optimization, these models are robust and practical for planning and scheduling in primary care settings. All the analytical results are supported with numerical examples in order to provide better managerial insights for primary care providers

Queueing Approaches to Appointment System Design

We develop useful queueing models to analyze appointment-based service systems. There are many factors that make appointment scheduling in service systems extremely complex. For example, scheduled customers may not arrive on time or show up at all, customers with different priorities may have conflict of service access, service may last shorter or longer than expected, and so on. These kinds of uncertainties make stochastic modeling a perfect tool to be used to analyze and improve the performance of such systems. The objective of our research is to identify appointment scheduling policies that balance the utilization of expensive service resources and customer waiting. We specifically consider two problems that have been commonly observed in practice but received little attention from the past appointment-scheduling literature. The first problem is how to schedule appointments when scheduled services may be interrupted by service requests with higher priority. We generate the optimal scheduling policies under various scenarios: finite and infinite time horizon, equally spaced and non-equally spaced scheduling, constant and time-dependent interruption rate, and preemptive and non-preemptive service interruptions. In the second problem, we consider the appointment system as two queues in tandem: the appointment queue followed by the service queue. The customers join the appointment queue when they call for an appointment, stay there (not physically) until the appointment time comes, and then leave the appointment queue and physically join the service queue, and wait there until served. We explicitly capture the dependence between these two queues and derive important performance measures of interest, such as service utilization and customer long-run average waiting times in both queues.Doctor of Philosoph

STOCHASTIC MODELS FOR RESOURCE ALLOCATION, SERIES PATIENTS SCHEDULING, AND INVESTMENT DECISIONS

We develop stochastic models to devise optimal or near-optimal policies in three different areas: resource allocation in virtual compute labs (VCL), appointment scheduling in healthcare facilities with series patients, and capacity management for competitive investment. A VCL consists of a large number of computers (servers), users arrive and are given access to severs with user-specified applications loaded onto them. The main challenge is to decide how many servers to keep “on”, how many of them to preload with specific applications (so users needing these applications get immediate access), and how many to be left flexible so that they can be loaded with any application on demand, thus providing delayed access. We propose dynamic policies that minimize costs subject to service performance constraints and validate them using simulations with real data from the VCL at NC State. In the second application, we focus on healthcare facilities such as physical therapy (PT) clinics, where patients are scheduled for a series of appointments. We use Markov Decision Processes to develop the optimal policies that minimize staffing, overtime, overbooking and delay costs, and develop heuristic secluding policies using the policy improvement algorithm. We use the data from a local PT center to test the effectiveness of our proposed policies and compare their performance with other benchmark policies. In the third application, we study a strategic capacity investment problem in a duopoly model with an unknown market size. A leader chooses its capacity to enter a new market. In a continuous-time Bayesian setting, a competitive follower dynamically learns about the favorableness of the new market by observing the performance of the leader, and chooses its capacity and timing of investment. We show that an increase in the probability of a favorable market can strictly decrease the leaders expected discounted profit due to non-trivial interplay between leaders investment capacity and timing of the dynamically-learning follower.Doctor of Philosoph

Convex Nonlinear and Integer Programming Approaches for Distributionally Robust Optimization of Complex Systems

The primary focus of the dissertation is to develop distributionally robust optimization (DRO) models and related solution approaches for decision making in energy and healthcare service systems with uncertainties, which often involves nonlinear constraints and discrete decision variables. Without assuming specific distributions, DRO techniques solve for solutions against the worst-case distribution of system uncertainties. In the DRO framework, we consider both risk-neutral (e.g., expectation) and risk-averse (e.g., chance constraint and Conditional Value-at-Risk (CVaR)) measures. The aim is twofold: i) developing efficient solution algorithms for DRO models with integer and/or binary variables, sometimes nonlinear structures and ii) revealing managerial insights of DRO models for specific applications. We mainly focus on DRO models of power system operations, appointment scheduling, and resource allocation in healthcare. Specifically, we first study stochastic optimal power flow (OPF), where (uncertain) renewable integration and load control are implemented to balance supply and (uncertain) demand in power grids. We propose a chance-constrained OPF (CC-OPF) model and investigate its DRO variant which is reformulated as a semidefinite programming (SDP) problem. We compare the DRO model with two benchmark models, in the IEEE 9-bus, 39-bus, and 118-bus systems with different flow congestion levels. The DRO approach yields a higher probability of satisfying the chance constraints and shorter solution time. It also better utilizes reserves at both generators and loads when the system has congested flows. Then we consider appointment scheduling under random service durations with given (fixed) appointment arrival order. We propose a DRO formulation and derive a conservative SDP reformulation. Furthermore, we study a scheduling variant under random no-shows of appointments and derive tractable reformulations for certain beliefs of no-show patterns. One preceding problem of appointment scheduling in the healthcare service operations is the surgery block allocation problem that assigns surgeries to operating rooms. We derive an equivalent 0-1 SDP reformulation and a less conservative 0-1 second-order cone programming (SOCP) reformulation for its DRO model. Finally, we study distributionally robust chance-constrained binary programs (DCBP) for limiting the probability of undesirable events, under mean-covariance information. We reformulate DCBPs as equivalent 0-1 SOCP formulations under two moment-based ambiguity sets. We further exploit the submodularity of the 0-1 SOCP reformulations under diagonal and non-diagonal matrices. We derive extended polymatroid inequalities via submodularity and lifting, which are incorporated into a branch-and-cut algorithm incorporated for efficiently solving DCBPs. We demonstrate the computational efficacy and solution performance with diverse instances of a chance-constrained bin packing problem.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/149946/1/zyiling_1.pd

Towards facilitated optimisation

Optimisation modelling in healthcare has addressed a diverse range of challenges inherent to decision-making and supports decision-makers in determining the best solution under a variety of constraints. In contrast, optimisation models addressing planning and service delivery issues in mental healthcare have received limited attention. Mental healthcare services in England are routinely facing issues relative to scarcity of available resources, inequities in their distribution, and inefficiencies in their use. Optimisation modelling has the potential to support decision making and inform the efficient utilisation of scare resources. Mental healthcare services are a combination of several subsystems and partnerships comprising of numerous stakeholders with a diversity of interests. However, in optimisation literature, the lack of stakeholder involvement in the development process of optimisation models is increasingly identified as a missed opportunity impacting the practical applicability of the models and their results. This thesis argues that simulation modelling literature offers alternative modelling approaches that can be adapted to optimisation modelling to address the shortcoming highlighted. In this study, we adapt PartiSim, a multi-methodology framework to support facilitated simulation modelling in healthcare, towards facilitated optimisation modelling and test it using a real case study in mental healthcare. The case study is concerned with a Primary Care Mental Healthcare (PCMH) service that deploys clinicians with different skills to several General Practice (GP) clinics. The service wanted support to help satisfy increasing demand for appointments and explore the possibility of expanding their workforce. This research puts forward a novel multimethodology framework for participatory optimisation, called PartiOpt. It explores the adaptation and customisation of the and PartiSim framework at each stage of the optimisation modelling lifecycle. The research demonstrates the applicability and relevance of a 'conceptual model' to optimisation modelling, highlighting the potential of facilitated optimisation as a methodology. This thesis argues for the inclusion of conceptual modelling in optimisation when dealing with real world practice-based problems. The thesis proposes an analytics-driven optimisation approach that integrates descriptive, predictive, and prescriptive analytics stages. This approach is utilised to construct a novel multi-skill multi-location optimisation model. By applying the analytics-driven optimisation approach to the case study, previously untapped resource potential is uncovered, leading to the identification of various strategies to improving service efficiency. The successful conceptualisation of an optimisation model and the quantitative decision support requirements that emerged in the initial stages of the study drive the analytics-driven optimisation. Additionally, this research also presents a facilitative approach for stakeholder participation in the validation, experimentation, and implementation of a mathematical optimisation model. Reflecting on the adaptation and subsequent amendments to the modelling stages, the final PartiOpt framework is proposed. It is argued that this framework could reduce the gap between theory and practice for optimisation modelling and offers guidance to optimisation modellers on involving stakeholders in addressing real world problems