Developing a multi-methodological approach to hospital operating theatre scheduling

Operating theatres and surgeons are among the most expensive resources in any hospital, so it is vital that they are used efficiently. Due to the complexity of the challenges involved in theatre scheduling we split the problem into levels and address the tactical and day-to-day scheduling problems.Cognitive mapping is used to identify the important factors to consider in theatre scheduling and their interactions. This allows development and testing of our understanding with hospital staff, ensuring that the aspects of theatre scheduling they consider important are included in the quantitative modelling.At the tactical level, our model assists hospitals in creating new theatre timetables, which take account of reducing the maximum number of beds required, surgeons’ preferences, surgeons’ availability, variations in types of theatre and their suitability for different types of surgery, limited equipment availability and varying the length of the cycle over which the timetable is repeated. The weightings given to each of these factors can be varied allowing exploration of possible timetables.At the day-to-day scheduling level we focus on the advanced booking of individual patients for surgery. Using simulation a range of algorithms for booking patients are explored, with the algorithms derived from a mixture of scheduling literature and ideas from hospital staff. The most significant result is that more efficient schedules can be achieved by delaying scheduling as close to the time of surgery as possible, however, this must be balanced with the need to give patients adequate warning to make arrangements to attend hospital for their surgery.The different stages of this project present different challenges and constraints, therefore requiring different methodologies. As a whole this thesis demonstrates that a range of methodologies can be applied to different stages of a problem to develop better solutions

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

Guidelines for Scheduling in Primary Care: An Empirically Driven Mathematical Programming Approach

Primary care practices play a vital role in healthcare delivery since they are the first point of contact for most patients, and provide health prevention, counseling, education, diagnosis and treatment. Practices, however, face a complex appointment scheduling problem because of the variety of patient conditions, the mix of appointment types, the uncertain service times with providers and non-provider staff (nurses/medical assistants), and no-show rates which all compound into a highly variable and unpredictable flow of patients. The end result is an imbalance between provider idle time and patient waiting time. To understand the realities of the scheduling problem we analyze empirical data collected from a family medicine practice in Massachusetts. We study the complete chronology of patient flow on nine different workdays and identify the main patient types and sources of inefficiency. Our findings include an easy-to-identify patient classification, and the need to focus on the effective coordination between nurse and provider steps. We incorporate these findings in an empirically driven stochastic integer programming model that optimizes appointment times and patient sequences given three well-differentiated appointment types. The model considers a session of consecutive appointments for a single-provider primary care practice where one nurse and one provider see the patients. We then extend the integer programming model to account for multiple resources, two nurses and two providers, since we have observed that such team primary care practices are common in the course of our data collection study. In these practices, nurses prepare patients for the providers’ appointments as a team, while providers are dedicated to their own patients to ensure continuity of care. Our analysis focuses on finding the value of nurse flexibility and understanding the interaction between the schedules of the two providers. The team practice leads us to a challenging and novel multi step multi-resource mixed integer stochastic scheduling formulation, as well as methods to tackle the ensuing computational challenge. We also develop an Excel scheduling tool for both single provider and team practices to explore the performance of different schedules in real time. Overall, the main objective of the dissertation is to provide easy-to-implement scheduling guidelines for primary care practices using both an empirically driven stochastic optimization model and a simulation tool

Acuity-based Performance Evaluation and Tactical Capacity Planning in Primary Care

Effective primary care requires timely and equitable access to care for patients as well as efficient and balanced utilization of physician time. Motivated by a family health clinic in Ontario, Canada, this research proposes ways to improve both of these aspects of primary care through tactical capacity planning based on acuity-based performance targets. First, we propose a new metric based on acuity levels to evaluate timely access to primary care. In Canada, as well as other participant countries in the Organization for Economic Co-operation and Development (OECD), the main metric currently used to evaluate access is the proportion of patients who are able to obtain a same- or next-day appointment. However, not all patients in primary care are urgent and require a same- or next-day appointment. Therefore, accurate evaluation of timely access to primary care should consider the urgency of the patient request. To address this need, we define multiple acuity levels and relative access targets in primary care, akin to the CTAS system in emergency care. Furthermore, current access time evaluation in the province is mostly survey-based, while our evaluation is based on appointment data and hence more objective. Thus, we propose a novel, acuity-based, data-driven approach for evaluation of timely access to primary care. Second, we develop a deterministic tactical capacity planning (TCP) model to balance workload between weeks for each family physician in the specific primary care clinic in this study. Unbalanced workload among weeks may lead to provider overtime for the weeks with high workload and provider idle time for weeks with low workload. In the proposed TCP model, we incorporate the results from access time evaluation in the first study as constraints for access time. The proposed TCP model considers 11 appointment types with multiple access targets for each appointment type. The TCP model takes as input a forecast of demand coming from an ARIMA model. We compare the results of the TCP model based on current access time targets as well as targets resulting from our acuity-based metrics. The use of our proposed acuity-based targets leads to allocation of time slots which is more equitable for patients and also improves physician workload balance. Third, we also propose a robust TCP model based on the cardinality-constrained method to minimize the highest potential physician peak load between weeks. Therefore, the developed robust TCP model enables protection against uncertainty through providing a feasible allocation of capacity for all realizations of demand. The proposed robust TCP model considers two interdependent appointment types (e.g., new patients and follow ups), multiple access time targets for each appointment type and uncertainty in demand for appointments. We conduct a set of experiments to determine how to set the level of robustness based on extra cost and infeasibility probability of a robust solution. In summary, this dissertation advocates for the definition and subsequent use of acuity-based access time targets for both performance evaluation and capacity allocation in primary care. The resulting performance metrics provide a more detailed view of primary care and lead to not only more equitable access policies but also have the potential to improve physician workload balance when used as input to capacity planning models

Novel approaches to radiotherapy treatment scheduling

Radiotherapy represents an important phase of treatment for a large number of cancer patients. It is essential that resources used to deliver this treatment are used efficiently. This thesis approaches the problem of scheduling treatments in a radiotherapy centre. Data about the daily intake of patients are collected and analysed. Several approaches are presented to create a schedule every day. The first presented are constructive approaches, developed due to their simplicity and low computational requirements. The approaches vary the preferred treatment start, machine utilisation reservation levels, and the frequency and number of days in advance with which schedules are created. An Integer Linear Programming (ILP) model is also presented for the problem and used in combination with approaches similar to the ones above. A generalisation of the constructive utilisation threshold approach is developed in order to vary the threshold level for each day according to how far it is from the current day. In addition, the model is evaluated for different sizes of the problem by increasing the rate of patient arrivals per day and the number of machines available. Different machine allocation policies are also evaluated. An exact method is introduced for finding a set of solutions representing the whole Pareto frontier for integer programming problems. It is combined with two robust approaches: the first considers known patients before they are ready to be scheduled, while the second considers sets of predicted patients who might arrive in the near future. A rescheduling approach is also suggested and implemented. A comparison is made amongst the best results from each group of approaches to identify the advantages and disadvantages of each. The robust approaches are found to be the best alternative of the set

Essays On Stochastic Programming In Service Operations Management

Deterministic mathematical modeling is a branch of optimization that deals with decision making in real-world problems. While deterministic models assume that data and parameters are known, these numbers are often unknown in the real world applications.The presence of uncertainty in decision making can make the optimal solution of a deterministic model infeasible or sub-optimal. On the other hand, stochastic programming approach assumes that parameters and coefficients are unknown and only their probability distribution can be estimated. Although stochastic programming could include uncertainties in objective function and/or constraints, we only study problems that the goal of stochastic programming is to maximize (minimize) the expectation of the objective function of random variables. Stochastic programming has a wide range of application in manufacturing production planning, machine scheduling, dairy farm expansion planning, asset liability management, traffic management, and automobile dealership inventory management that involve uncertainty in decision making. One limitation of stochastic programming is that considering uncertainty in mathematical modeling often leads to a large-scale programming problem. The most widely used stochastic programming model is two-stage stochastic programming. In this model, first-stage decision variables are determined before observing the realization of uncertainties and second-stage decision variables are selected after exposing first-stage variables into the uncertainties. The goal is to determine the value of first-stage decisions in a way to maximize (minimize) the expected value of second-stage objective function. 1.1 Motivation for Designing Community-Aware Charging Network for Electric Vehicles Electric vehicles (EVs) are attracting more and more attentions these days due to increase concern about global warming and future shortage of fossil fuels. These vehicles have potential to reduce greenhouse gas emissions, improve public health condition by reducing air pollution and improving sustainability, and addressing diversication of transportation energy feedstock. Governments and policy makers have proposed two types of policy incentives in order to encourage consumers to buy an EV: direct incentives and indirect incentives. Direct incentives are those that have direct monetary value to consumers and include purchase subsidies, license tax/fee reductions, Electric Vehicle Supply Equipment (EVSE) financing, free electricity, free parking and emission test exemptions. On the other hand, indirect incentives are the ones that do not have direct monetary value and consist of high-occupancy vehicle access, emissions testing exemption time savings, and public charger availability. Lack of access to public charging network is considered to be a major barrier in adoption of EVs [38]. Access to public charging infrastructure will provide confidence for EV owners to drive longer distances without going out of charge and encourage EV ownership in the community. The current challenge for policy makers and city planners in installing public charging infrastructure is determining the location of these charging service stations, number of required stations and level of charging since the technology is still in its infancy and the installation cost is high. Since recharging of EV battery takes more time than refueling conventional vehicles, parking lots and garages are considered as potential locations for installing charging stations. The aim of this research is to develop a mathematical programming model to find the optimal locations with potentially high utilization rate for installing community-aware public EV charging infrastructure in order to improve accessibility to charging service and community livability metrics. In designing such charging network, uncertainties such as EV market share, state of battery charge at the time of arrival, driver’s willingness to charge EV away from home, arrival time to final destination, driver’s activity duration (parking duration), and driver’s walking distance preference play major role. Incorporating these uncertainties in the model, we propose a two-stage stochastic programming approach to determine the location and capacity of public EV charging network in a community. 1.2 Motivation for Managing Access to Care at Primary Care Clinics Patient access to care along with healthcare efficiency and quality of service are dimensions of health system performance measurement [1]. Improving access to primary care is a major step of having a high-performing health care system. However, many patients are struggling to get an in-time appointment with their own primary care provider (PCP). Even two years aer health insurance coverage was expanded, new patients have to wait 82% longer to get an internal-medicine appointment. A national survey shows that percentage of patients that need urgent care and could not get an appointment increased from 53% to 57% between 2006 and 2011 [30]. This delay may negatively impact health status of patients and may even lead to death. Patients that cannot get an appointment with their PCP may seek care with other providers or in emergency departments which will decrease continuity of care and increase total cost of health system. The main issue behind access problem is the imbalance between provider capacity and patient demand. While provider panel size is already large, the shortage in primary care providers and increasing number of patients mean that providers have to increase their panel size and serve more patients which will potentially lead to lower access to primary care. The ratio of adult primary care providers to population is expected to drop by 9% between 2005 and 2020 [12]. Moreover, patient flow analysis can increase efficiency of healthcare system and quality of health service by increasing patient and provider satisfaction through better resource allocation and utilization [39]. Effective resource allocation will smooth patient ow and reduce waste which will in turn results in better access to care. One way to control patient flow in clinic is managing appointment supply through appointment scheduling system. A well-designed appointment scheduling system can decrease appointment delay and waiting time in clinic for patients and idle time and/or overtime for physicians at the same time and increase their satisfaction. Appointment scheduling requires to make a balance between patient needs and facility resources [13]. The purpose of this study is to gain a better understanding for managing access to care in primary care outpatient clinics through operations management research. As a result of this under standing, we develop appointment scheduling models using two-stage stochastic programming to improve access while maintaining high levels of provider capacity utilization and improving patient flow in clinic by leveraging uncertainties in patient demand, patient no-show and provider service time variability

Online Clinic Appointment Scheduling

Health care is a fast growing industry in the United States. Appointment scheduling is one of the key processes in this industry. This thesis focused on on-line appointment system for clinics. The objective of this thesis is to maximize patients\u27 preferences and the number of patients seen during normal business hours. This is a multi-objective problem to balance the trade-off between overtime and patients\u27 preferences.To achieve the objective, a simulation model was built to compare four policies proposed. Based on simulation results, it was found that most of non-dominated solutions were close both minimum objective values, so policies proposed were helpful for the clinics to balance overtime and patients\u27 preferences