Examining The Influence Of Dependent Demand Arrivals On Patient Scheduling

This research examines the influence of batch appointments on patient scheduling systems. Batch appointments are characterized by multiple patients within a family desiring appointments within the same time frame

Reducing No-Show Rates at an Urban Community Health Center.

D.N.P. Thesis. University of Hawaiʻi at Mānoa 2018

Managerial Intervention Strategies to Reduce Patient No-Show Rates

High patient no-show rates increase health care costs, decrease healthcare access, and reduce the clinical efficiency and productivity of health care facilities. The purpose of this exploratory qualitative single case study was to explore and analyze the managerial intervention strategies healthcare administrators use to reduce patient no-show rates. The targeted research population was active American College of Healthcare Executives (ACHE), Hawaii-Pacific Chapter healthcare administrative members with operational and supervisory experience addressing administrative patient no-show interventions. The conceptual framework was the theory of planned behavior. Semistructured interviews were conducted with 4 healthcare administrators, and appointment cancellation policy documents were reviewed. Interpretations of the data were subjected to member checking to ensure the trustworthiness of the findings. Based on the methodological triangulation of the data collected, 5 common themes emerged after the data analysis: reform appointment cancellation policies, use text message appointment reminders, improve patient accessibility, fill patient no-show slots immediately, and create organizational and administrative efficiencies. Sharing the findings of this study may help healthcare administrators to improve patient health care accessibility, organizational performance and the social well-being of their communities

Taxonomic classification of planning decisions in health care: a review of the state of the art in OR/MS

We provide a structured overview of the typical decisions to be made in resource capacity planning and control in health care, and a review of relevant OR/MS articles for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making

Simulation modeling and analysis of a multi-resource medical clinic.

Healthy for Life is a relatively new University of Louisville medical clinic which attempts to stem the epidemic of childhood obesity. This program offers a range of face-to-face services for overweight children. The main problem addressed by this research is the no show rate (nearly 50%) of the clinic. There are two goals of this thesis. One is to increase the staff utilization; the other is to decrease the waiting time. In this thesis, we study two potential methods to solve this problem. One involves using multiple resources for every visit; the other involves overbooking the patients. Two simulation models were developed for studying the system. One is an overbooking model in which the interarrival times are controlled for each type of patients. By increasing arrival rate of patients I the waiting time I the total number of served patients and the utilization of staff are increased. We need to trade off in order to choose the best arrival rate for the clinic. The second model involves using multiple resources for every visit. Each time a returning patient can visit one or two staff personnel depending on their willingness. We also change the interarrival time for patients in order to estimate the best values for these inputs

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

Predicting Patient No-Shows in Community Health Clinics: A Case Study in Designing a Data Analytic Product

The data science revolution has highlighted the varying roles that data analytic products can play in a different industries and applications. There has been particular interest in using analytic products coupled with algorithmic prediction models to aid in human decision-making. However, detailed descriptions of the decision-making process that leads to the design and development of analytic products are lacking in the statistical literature, making it difficult to accumulate a body of knowledge where students interested in the field of data science may look to learn about this process. In this paper, we present a case study describing the development of an analytic product for predicting whether patients will show up for scheduled appointments at a community health clinic. We consider the stakeholders involved and their interests, along with the real-world analytical and technical trade-offs involved in developing and deploying the product. Our goal here is to highlight the decisions made and evaluate them in the context of possible alternatives. We find that although this case study has some unique characteristics, there are lessons to be learned that could translate to other settings and applications

Implementation of Oncology Standardized Scheduling Bundle Impacting Staff Productivity and Patient Satisfaction

BACKGROUND: Patients having timelyaccess to healthcare services is the entryway to quality of care and patient safety. Timely access is essential in cancer treatment and often requires complex scheduling requiring multiple, highly coordinated, time sensitive appointments. To facilitate optimal clinical care, timely appointment scheduling must be patient-centered. With the heavy scheduling workload growing at the the Cancer Network at Froedtert & the Medical College of Wisconsin and recognition of timely scheduling as a quality indicator, the need to examine the workflow and improve the process became apparent. METHODS: The goal of the quality improvement project was to provide patients with individualized and timely scheduling of appointments. The development and implementation of a standardized oncology scheduling bundle began with a SWOT analysis and the use of Donebedian Model to organize the improvement process. A scheduling bundle that included consistent scheduling practices and staff education to drive patient satisfaction scares and scheduling staff productivity metrics was developed. As well, a productivity dashboard was developed. RESULTS: The implementation of a standardized oncology scheduling bundle showed positive results with both quantative and qualitative metrics. There was positive shift in average scheduling staff productivity and staff turnover decreased pre and post implementation. Additionally, there was positive changes in feedback from patients, staff, and physicians. CONCLUSION: With the implementation of the standardized oncology scheduling bundle, patients are receiving timely, optimal, individualized clinical care. Additionally, there is now a standardized process for scheduling staff creating efficient and effective workflows

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