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

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

An Operations management approach for radiology services

This paper focus on the application of Operations Management techniques in the context of radiological and diagnostic imaging services provision. More specifically, the outpatient appointment scheduling problem for MRI diagnostic imaging services in a radiology clinics is approached and solved taking into account set-up time minimization. This is pursued trough the design of an innovative system for the on-line assignment of appointments for specific diagnostic imaging scans. An appointment rule, a patient classification and an heuristic procedure for the booking process are defined in order to better manage uncertainty and improve system performance. The proposed approach was validated on the case of a diagnostic centre of Alliance Medical, a primary multinational company in the field of diagnostic imaging services

Essays in Appointment Management

Patients who no-show or who cancel their outpatient clinic appointments can be disruptive to clinic operations. Scheduling strategies, such as slot overbooking or servicing patients during overtime slots, may assist with mitigating such disruptions. In the majority of scheduling models, no-shows and cancellations are considered together, or cancellations are not considered at all. In this dissertation, I propose novel prediction models to forecast the probability of no-show and cancellation for patients. I present analyses to show that no-shows and cancellations are two different types of patient behavior, and should be treated separately when scheduling a patient. Additionally, I develop a multi-day, online, overbooking model that incorporates no-show and cancellation probabilities, and outlines how patients should be optimally overbooked in an outpatient clinic schedule to increase clinic service reward. I find that past history is an indicator of future no-show behavior for patients attending outpatient clinics, and that only a limited look-back window is needed in order to gain insight into patient’s future behavior. Advance appointment cancellations are more challenging to predict, and tend to occur at the beginning or at the end of an appointment’s lifecycle. The optimal overbooking strategy is a function of both the no-show and the cancellation probabilities, and affects both the day on which an overbooking may occur, and the appointment slot in which the patient is overbooked

Adaptive Appointment Systems with Patient Preferences

Patients\u27 satisfaction with an appointment system when they attempt to book a nonurgent appointment is affected by their ability to book with a doctor of choice and to book an appointment at a convenient time of day. For medical conditions requiring urgent attention, patients want quick access to a familiar physician. For such instances, it is important for clinics to have open slots that allow same-day (urgent) access. A major challenge when designing outpatient appointment systems is the difficulty of matching randomly arriving patients\u27 booking requests with physicians\u27 available slots in a manner that maximizes patients\u27 satisfaction as well as clinics\u27 revenues. What makes this problem difficult is that booking preferences are not tracked, may differ from one patient to another, and may change over time. This paper describes a framework for the design of the next generation of appointment systems that dynamically learn and update patients\u27 preferences and use this information to improve booking decisions. Analytical results leading to a partial characterization of an optimal booking policy are presented. Examples show that heuristic decision rules, based on this characterization, perform well and reveal insights about trade-offs among a variety of performance metrics important to clinic managers

Data Mining for Enhanced Operations Management Decision Making: Applications in Health Care

Data Mining involves the extraction of new knowledge from large data sets. Despite the growing research interest in data mining, however, integrating this extra knowledge into the subsequent decision making processes has received little attention. Within the context of operations management, this integration can occur in two different ways: by providing inputs for an optimization procedure and by analyzing the output of an optimization procedure. In this dissertation, I will begin by introducing a database exploration technique, which is used to improve the drug discovery process of a pharmaceutical company (Samorani et al., 2011). The same procedure is also applied to a mental health clinic\u27s database to predict whether patients will show up at their scheduled appointments. The knowledge obtained with this procedure is then used to improve patient scheduling procedures (Samorani and LaGanga, 2011). I will finally discuss how data mining can be used to learn useful information about the structure of a problem (Samorani and Laguna, 2012)

Probabilistic models for patient scheduling

In spite of the success of theoretical appointment scheduling methods, there have been significant failures in practice primarily due to the rapid increase in the number of no-shows and cancelations from the individuals in recent times. These disruptions not only cause inconvenience to the management but also has a significant impact on the revenue, cost and resource utilization. In this research, we develop a hybrid probabilistic model based on logistic regression and Bayesian inference to predict the probability of no-shows in real-time. We also develop two novel non-sequential and sequential optimization models which can effectively use no-show probabilities for scheduling patients. Our integrated prediction and optimization model can be used to enable a precise overbooking strategy to reduce the negative effect of no-shows and fill appointment slots while maintaining short wait times. Using both simulated and real-world data, we demonstrate the effectiveness of the proposed hybrid predictive model and scheduling strategy compared to some of the well-studied approaches available in the literature

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

Capacity Planning for Heterogeneous Patient Populations in Primary Care and Specialty Networks

Access to primary care has a direct impact on morbidity and mortality, and is strongly influenced by indirect waiting time: the delay between the requested and allotted appointment day. Our models describe the heterogeneous appointment seeking patterns of a primary care patient panel using stochastic processes parameterized to reflect the diversity of primary care visit rates in the US. For capacity planning, we estimate the distribution of daily appointments, and show that the distribution variability can be reduced by heuristics that use patient flexibility regarding the day of the appointment. For delays, we demonstrate that in a first-come, first-served system, patients who need the most frequent appointments suffer the greatest delays, motivating the need to reserve slots for high-visit patient classes. To further understand the inequity in delay, we model the primary care appointment system as a Discrete-Time Markov Chain. We derive an analytical expression for delay in terms of the patient’s probability of daily visit. We show that conditions for monotone mapping of the probability of visit to delay are intractable and give numerical results that support monotonicity. In our last chapter, we expand our scope to include specialty care networks. Using patient-level longitudinal data from the Medical Expenditure Panel Survey, we model the sequence of appointments with multiple specialty types and the time intervals between such appointments as a Markov Renewal Process (MRP). We use comorbidity count to model patient heterogeneity and extract the MRP parameters for each patient subgroup. Next, we adapt the steady state results to provide an analytical expression of the expected appointment fill-rate by specialty and patient subgroups. Our analytical results demonstrate that patients with higher comorbidity count typically have a lower fill-rate because of shorter lead time between appointments thereby necessitating either overtime or reserved slots to ensure timely access. We further simulate appointment seeking patterns of a nationally representative panel of patients in the specialty network and estimate the distribution of daily appointment requests for each specialty. Similar to the primary care case, we show that heuristics that leverage patient flexibility regarding the day of the appointment can reduce variability in appointment requests for each specialty