Intra-day Dynamic Rescheduling under Patient No-shows

Existing work on appointment scheduling assumes that appointment times cannot be updated once they have been assigned. However, advances in communication technology and the adoption of online (as opposed to in-person) appointments make it possible for appointments to be flexible. In this paper, we describe an intra-day dynamic rescheduling model that adjusts upcoming appointments based on observed no-shows. We formulate the problem as a Markov Decision Process in order to compute the optimal pre-day schedule and the optimal policy to update the schedule for every scenario of no-shows. We also propose an alternative formulation based on the idea of 'atomic' actions that can solve for the optimal policy more efficiently. Based on a numerical study, we estimate that using intra-day dynamic rescheduling can lead to a 5-7% decrease in expected cost when compared to static scheduling

Modeling And Optimization Of Non-Profit Hospital Call Centers With Service Blending

This dissertation focuses on the operations problems in non-profit hospital call centers with inbound and outbound calls service blending. First, the routing policy for inbound and outbound calls is considered. The objective is to improve the system utilization under constraints of service quality and operators\u27 quantity. A collection of practical staffing assignment methods, separating and mixing staffing policy are evaluated. Erlang C queuing model is used to decide the minimum number of operators required by inbound calls. Theoretical analysis and numerical experiments illustrate that through dynamically assigning the inbound and outbound calls to operators under optimal threshold policy, mixing staffing policy is efficient to balance the system utilization and service quality. Numerical experiments based on real-life data demonstrate how this method can be applied in practice. Second, we study the staffing shift planning problem based on the inbound and outbound calls routing policies. A mathematical programming model is developed, based on a hospital call center with one kind of inbound calls and multiple kinds of outbound calls. The objective is to minimize the staffing numbers, by deciding the shift setting and workload allocation. The inbound calls service level and staffing utilization are taken into consideration in the constraints. Numerical experiments based on actual operational data are included. Results show that the model is effective to optimize the shift planning and hence reduce the call centers\u27 cost. Third, we model the staffing shift planning problem for a hospital call center with two kinds of service lines. Each kind of service is delivered through both inbound calls and outbound calls. The inbound calls can be transferred between these two service lines. A mathematical programming model is developed. The objective is to minimize the staffing cost, by deciding the shift setting and workload allocation. The inbound calls service level and staffing utilization are taken into consideration in the constraints. Numerical experiments are carried out based on actual operational data. Results show that the model is effective to reduce the call centers\u27 labor cost

ACHIEVING UNIVERSAL LIAISONS AND HEALTHCARE CONTACT CENTER CENTRALIZATION THROUGH THE USE OF DECISION SUPPORT TOOLS

Healthcare contact centers often experience a large volume of calls and traditional standardized guidelines can be difficult to follow during an active call. While more common workflows can be memorized, they change often because Healthcare is a dynamic field. Constant updates to workflows, an abundance of different processes and provider preferences, and a fast paced environment can lead Customer Service Liaisons (CSLs) to handle patient inquiries incorrectly. Active decision support tools enable a CSL to follow an updated workflow without needing to navigate through complex guidelines and emails. This research shows that contact center centralization through the use of decision support tools can reduce Average Speed to Answer by 70 seconds even with an increase to Average Handle Time by 30 seconds. This research also identifies key features the tool may need to facilitate widespread adoption by clinicians and CSL alike

Use of network analysis, and fluid and diffusion approximations for stochastic queueing networks to understand flows of referrals and outcomes in community health care

Community services are fundamental in the delivery of health care, providing local care close to or in patient homes. However, planning, managing and evaluating these services can be difficult. One stand out challenge is how these services may be organised to provide coordinated care given their physical distribution, patients using multiple services, and the increasing use of these services by patients with differing needs. This is complicated by a lack of comparable measures for evaluating quality across differing community services. Presented in this thesis is work that I conducted, alongside the North East London Foundation Trust, to understand referrals and the use of outcome data within community services through data visualisation and mathematical modelling. Firstly, I applied several data visualisations, building from a network analysis, to aid the design of a single point of access for referrals into community services - helping to understand patterns of referrals and patient use. Of interest were concurrent uses of services, whether common patterns existed and how multiple referrals occurred over time. This highlighted important dynamics to consider in modelling these services. Secondly, I developed a patient flow model, extending fluid and diffusion approximations of stochastic queueing systems to include complex flow dynamics such as re-entrant patients and the use of multiple services in sequence. Patient health is also incorporated into the model by using states that patients may move between throughout their care, which are used to model the differential impact of care. I also produced novel methods for allocating servers across parallel queues and patient groups. Finally, I developed the concept of ``the flow of outcomes'' - a measure of how individual services contribute to the output of patients in certain health states over time - to provide operational and clinical insight into the performance of a network of services

EUROPEAN CONFERENCE ON QUEUEING THEORY 2016

International audienceThis booklet contains the proceedings of the second European Conference in Queueing Theory (ECQT) that was held from the 18th to the 20th of July 2016 at the engineering school ENSEEIHT, Toulouse, France. ECQT is a biannual event where scientists and technicians in queueing theory and related areas get together to promote research, encourage interaction and exchange ideas. The spirit of the conference is to be a queueing event organized from within Europe, but open to participants from all over the world. The technical program of the 2016 edition consisted of 112 presentations organized in 29 sessions covering all trends in queueing theory, including the development of the theory, methodology advances, computational aspects and applications. Another exciting feature of ECQT2016 was the institution of the Takács Award for outstanding PhD thesis on "Queueing Theory and its Applications"

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

Stochastic Models of Patient Access Management in Healthcare

abstract: This dissertation addresses access management problems that occur in both emergency and outpatient clinics with the objective of allocating the available resources to improve performance measures by considering the trade-offs. Two main settings are considered for estimating patient willingness-to-wait (WtW) behavior for outpatient appointments with statistical analyses of data: allocation of the limited booking horizon to patients of different priorities by using time windows in an outpatient setting considering patient behavior, and allocation of hospital beds to admitted Emergency Department (ED) patients. For each chapter, a different approach based on the problem context is developed and the performance is analyzed by implementing analytical and simulation models. Real hospital data is used in the analyses to provide evidence that the methodologies introduced are beneficial in addressing real life problems, and real improvements can be achievable by using the policies that are suggested. This dissertation starts with studying an outpatient clinic context to develop an effective resource allocation mechanism that can improve patient access to clinic appointments. I first start with identifying patient behavior in terms of willingness-to-wait to an outpatient appointment. Two statistical models are developed to estimate patient WtW distribution by using data on booked appointments and appointment requests. Several analyses are conducted on simulated data to observe effectiveness and accuracy of the estimations. Then, this dissertation introduces a time windows based policy that utilizes patient behavior to improve access by using appointment delay as a lever. The policy improves patient access by allocating the available capacity to the patients from different priorities by dividing the booking horizon into time intervals that can be used by each priority group which strategically delay lower priority patients. Finally, the patient routing between ED and inpatient units to improve the patient access to hospital beds is studied. The strategy that captures the trade-off between patient safety and quality of care is characterized as a threshold type. Through the simulation experiments developed by real data collected from a hospital, the achievable improvement of implementing such a strategy that considers the safety-quality of care trade-off is illustrated.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201