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

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

Sequential Appointment Scheduling Considering Walk-In Patients

This paper develops a sequential appointment algorithm considering walk-in patients. In practice, the scheduler assigns an appointment time for each call-in patient before the call ends, and the appointment time cannot be changed once it is set. Each patient has a certain probability of being a no-show patient on the day of appointment. The objective is to determine the optimal booking number of patients and the optimal scheduling time for each patient to maximize the revenue of all the arriving patients minus the expenses of waiting time and overtime. Based on the assumption that the service time is exponentially distributed, this paper proves that the objective function is convex. A sufficient condition under which the profit function is unimodal is provided. The numerical results indicate that the proposed algorithm outperforms all the commonly used heuristics, lowering the instances of no-shows, and walk-in patients can improve the service efficiency and bring more profits to the clinic. It is also noted that the potential appointment is an effective alternative to mitigate no-show phenomenon

A probabilistic patient scheduling model for reducing the number of no-shows

No-shows in medical centres cause under-utilisation of resources and increase waiting times in specialty health care services. Although this problem has been addressed in literature, behavioural issues associated with the patient's socio-demographic characteristics and diagnosis have not been widely studied. In this article, we propose a model that includes such behavioural issues in order to reduce impact of no-shows in medical services. The objective is maximising the health centre's expected revenue by using show-up probabilities estimated for each combination of patient and appointment slot. Additionally, the model considers the requirements imposed by both the health centre's management and the health authorities. An extension of the model allows overbooking in some appointment slots. Experimental results show that the proposed model can reduce the waiting list length by 13%, and to attain an increase of about 5% in revenue, when comparing to a model that assigns patients to the first available slot

Effective Management of Virtual and Office Appointments in Chronic Care

Patients suffering from a chronic disease often require regular appointments and treatments. Due to the constraints on the availability of office appointments and the capacity of physicians, access to chronic care can be limited; consequently, patients may fail to receive the recommended care suggested by clinical guidelines. Virtual appointments can provide a cost-effective alternative to traditional office appointments for managing chronic conditions. Advances in information technology infrastructure, communication, and connected medical devices are enabling providers to evaluate, diagnose, and treat patients remotely. In this study, we first build a capacity allocation model to study the use of virtual appointments in a chronic care setting. We consider a cohort of patients receiving chronic care and model the flow of the patients between office and virtual appointments using an open migration network. We formulate the planning of capacity needed for office and virtual appointments with a news vendor model to maximize long-run average earnings. Moreover, we develop two optimization models to determine the optimal follow-up rate for patients and a two-stage stochastic programming model to investigate the capacity allocation decisions along with the patients’ scheduling decisions under uncertainty. We consider differences in treatment and diagnosis effectiveness for office and virtual appointments. We derive optimal policies and perform numerical experiments. With the model developed, capacity allocation, follow-up rate determination and patient scheduling decisions for office and virtual appointments can be made more systematically with the consideration of patients’ disease progressions.Master of Science in EngineeringIndustrial and Systems Engineering, College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/154744/1/Xiao Yu Final Thesis.pdfDescription of Xiao Yu Final Thesis.pdf : Thesi

APPLICATIONS OF REVENUE MANAGEMENT IN HEALTHCARE

Most profit oriented organizations are constantly striving to improve their revenues while keeping costs under control, in a continuous effort to meet customers‟ demand. After its proven success in the airline industry, the revenue management approach is implemented today in many industries and organizations that face the challenge of satisfying customers‟ uncertain demand with a relatively fixed amount of resources (Talluri and Van Ryzin 2004). Revenue management has the potential to complement existing scheduling and pricing policies, and help organizations reach important improvements in profitability through a better management of capacity and demand. The work presented in this thesis investigates the use of revenue management techniques in the service sector, when demand for service arrives from several competing customer classes and the amount of resource required to provide service for each customer is stochastic. We look into efficiently allocating a limited resource (i.e., time) among requests for service when facing variable resource usage per request, by deciding on the amount of resource to be protected for each customer and surgery class. The capacity allocation policies we develop lead to maximizing the organization‟s expected revenue over the planning horizon, while making no assumption about the order of customers‟ arrival. After the development of the theory in Chapter 3, we show how the mathematical model works by implementing it in the healthcare industry, more specifically in the operating room area, towards protecting time for elective procedures and patient classes. By doing this, we develop advance patient scheduling and capacity allocation policies and apply them to scheduling situations faced by operating rooms to determine optimal time allocations for various types of surgical procedures. The main contribution is the development of the methodology to handle random resource utilization in the context of revenue management, with focus in healthcare. We also develop a heuristics which could be used for larger size problems. We show how the optimal and heuristic-based solutions apply to real-life situations. Both the model and the heuristic find applications in healthcare where demand for service arrives randomly over time from various customer segments, and requires uncertain resource usage per request

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

Sequential Appointment Scheduling Considering Walk-In Patients

This paper develops a sequential appointment algorithm considering walk-in patients. In practice, the scheduler assigns an appointment time for each call-in patient before the call ends, and the appointment time cannot be changed once it is set. Each patient has a certain probability of being a no-show patient on the day of appointment. The objective is to determine the optimal booking number of patients and the optimal scheduling time for each patient to maximize the revenue of all the arriving patients minus the expenses of waiting time and overtime. Based on the assumption that the service time is exponentially distributed, this paper proves that the objective function is convex. A sufficient condition under which the profit function is unimodal is provided. The numerical results indicate that the proposed algorithm outperforms all the commonly used heuristics, lowering the instances of no-shows, and walk-in patients can improve the service efficiency and bring more profits to the clinic. It is also noted that the potential appointment is an effective alternative to mitigate no-show phenomenon