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

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

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

Nonattendance in pediatric pulmonary clinics: an ambulatory survey

<p>Abstract</p> <p>Background</p> <p>Nonattendance for scheduled appointments disturbs the effective management of pediatric pulmonary clinics. We hypothesized that the reasons for non-attendance and the necessary solutions might be different in pediatric pulmonary medicine than in other pediatric fields. We therefore investigated the factors associated with nonattendance this field in order to devise a corrective strategy.</p> <p>Methods</p> <p>The effect of age, gender, ethnic origin, waiting time for an appointment and the timing of appointments during the day on nonattendance proportion were assessed. Chi-square tests were used to analyze statistically significant differences of categorical variables. Logistic regression models were used for multivariate analysis.</p> <p>Results</p> <p>A total of 1190 pediatric pulmonology clinic visits in a 21 month period were included in the study. The overall proportion of nonattendance was 30.6%. Nonattendance was 23.8% when there was a short waiting time for an appointment (1–7 days) and 36.3% when there was a long waiting time (8 days and above) (p-value < 0.001). Nonattendance was 28.7% between 8 a.m. to 3 p.m. and 37.5% after 3 p.m. (p = 0.007). Jewish rural patients had 15.4% nonattendance, Jewish urban patients had 31.2% nonattendance and Bedouin patients had 32.9% nonattendance (p < 0.004). Age and gender were not significantly associated with nonattendance proportions. A multivariate logistic regression model demonstrated that the waiting time for an appointment, time of the day, and the patients' origin was significantly associated with nonattendance.</p> <p>Conclusion</p> <p>The factors associated with nonattendance in pediatric pulmonary clinics include the length of waiting time for an appointment, the hour of the appointment within the day and the origin of the patient.</p

Managing Operational Efficiency And Health Outcomes At Outpatient Clinics Through Effective Scheduling

A variety of studies have documented the substantial deficiencies in the quality of health care delivered across the United States. Attempts to reform the United States health care system in the 1980s and 1990s were inspired by the system\u27s inability to adequately provide access, ensure quality, and restrain costs, but these efforts had limited success. In the era of managed care, access, quality, and costs are still challenges, and medical professionals are increasingly dissatisfied. In recent years, appointment scheduling in outpatient clinics has attracted much attention in health care delivery systems. Increase in demand for health care services as well as health care costs are the most important reasons and motivations for health care decision makers to improve health care systems. The goals of health care systems include patient satisfaction as well as system utilization. Historically, less attention was given to patient satisfaction compared to system utilization and conveniences of care providers. Recently, health care systems have started setting goals regarding patient satisfaction and improving the performance of the health system by providing timely and appropriate health care delivery. In this study we discuss methods for improving patient flow through outpatient clinics considering effective appointment scheduling policies by applying two-stage Stochastic Mixed-Integer Linear Program Model (two-stage SMILP) approaches. Goal is to improve the following patient flow metrics: direct wait time (clinic wait time) and indirect wait time considering patient’s no-show behavior, stochastic server, follow-up surgery appointments, and overbooking. The research seeks to develop two models: 1) a method to optimize the (weekly) scheduling pattern for individual providers that would be updated at regular intervals (e.g., quarterly or annually) based on the type and mix of services rendered and 2) a method for dynamically scheduling patients using the weekly scheduling pattern. Scheduling templates will entertain the possibility of arranging multiple appointments at once. The aim is to increase throughput per session while providing timely care, continuity of care, and overall patient satisfaction as well as equity of resource utilization. First, we use risk-neutral two-stage stochastic programming model where the objective function considers the expected value as a performance criterion in the selection of random variables like total waiting times and next, we expand the model formulation to mean-risk two-stage stochastic programming in which we investigate the effect of considering a risk measure in the model. We apply Conditional-Value-at-Risk (CVaR) as a risk measure for the two-stage stochastic programming model. Results from testing our models using data inspired by real-world OBGYN clinics suggest that the proposed formulations can improve patient satisfaction through reduced direct and indirect waiting times without compromising provider utilization

Simulation and Modeling for Improving Access to Care for Underserved Populations

Indiana University-Purdue University Indianapolis (IUPUI)This research, through partnership with seven Community Health Centers (CHCs) in Indiana, constructed effective outpatient appointment scheduling systems by determining care needs of CHC patients, designing an infrastructure for meaningful use of patient health records and clinic operational data, and developing prediction and simulation models for improving access to care for underserved populations. The aims of this study are 1) redesigning appointment scheduling templates based on patient characteristics, diagnoses, and clinic capacities in underserved populations; 2) utilizing predictive modeling to improve understanding the complexity of appointment adherence in underserved populations; and 3) developing simulation models with complex data to guide operational decision-making in community health centers. This research addresses its aims by applying a multi-method approach from different disciplines, such as statistics, industrial engineering, computer science, health informatics, and social sciences. First, a novel method was developed to use Electronic Health Record (EHR) data for better understanding appointment needs of the target populations based on their characteristics and reasons for seeking health, which helped simplify, improve, and redesign current appointment type and duration models. Second, comprehensive and informative predictive models were developed to better understand appointment non-adherence in community health centers. Logistic Regression, Naïve Bayes Classifier, and Artificial Neural Network found factors contributing to patient no-show. Predictors of appointment non-adherence might be used by outpatient clinics to design interventions reducing overall clinic no-show rates. Third, a simulation model was developed to assess and simulate scheduling systems in CHCs, and necessary steps to extract information for simulation modeling of scheduling systems in CHCs are described. Agent-Based Models were built in AnyLogic to test different scenarios of scheduling methods, and to identify how these scenarios could impact clinic access performance. This research potentially improves well-being of and care quality and timeliness for uninsured, underinsured, and underserved patients, and it helps clinics predict appointment no-shows and ensures scheduling systems are capable of properly meeting the populations’ care needs.2021-12-2

Essays in Healthcare Operations

This dissertation includes three essays, which address significant issues that healthcare practitioners throughout the world face today. The fundamental research that I first address is a research agenda for reimbursement impacts upon healthcare operations management. The purpose of the first essay is to offer conceptual frameworks that portray the fundamental architecture of the U.S. healthcare system and its connections to healthcare reimbursement systems. The research method involves inductive theory development. I contend such frameworks are useful for healthcare operations management research. Using the frameworks, this essay suggests promising research opportunities that should stimulate emerging research themes in the healthcare industry and in academic healthcare operations research. These findings furnish a research agenda with timely insights for practitioners and academia. One conclusion of the essay is the lack of prior research relevant to healthcare reimbursement processes and their impacts on healthcare operations. The essay also concludes that key research opportunities relate to reimbursement boundaries, reimbursement strategy, reimbursement resources, reimbursement impacts, and reimbursement technology. In the second essay, I examine how scheduling policies can improve healthcare quality and doctor efficiency in outpatient healthcare facilities. The purpose is to develop an outpatient appointment scheduling approach under situations of patient no-shows and patient heterogeneity. Based on detailed analytical and simulation methods, the essay evaluates and compares the performance of my approach against several outpatient scheduling policies under various scenarios, and provides advice regarding optimal policies for outpatient clinics. The findings show that my proposed scheduling algorithms show efficient scheduling performance relative to prior proposed policies. In short, the findings of the second essay provide new applicable scheduling polices for outpatient scheduling. The findings also derive qualitative implications for clinic schedulers for improving the most effective way of scheduling outpatient operations. The conclusion is that the proposed scheduling approach can be potentially useful for outpatient facilities. Finally, the third essay empirically examines how managerial operational responses of hospitals vary in response to external pressures imposed upon them by government policies. The purpose is to examine whether hospitals respond to such policies by improving operating processes and quality outcomes, or by gaming their response by adjusting patient case mixes and other metrics associated with financial benefits for the hospital, instead of operational improvement. To validate whether hospitals respond suitably to an ongoing U.S. government quality improvement program, called the Value Based Purchasing (VBP) program, I explore how the program influences subsequent behaviors of U.S. hospitals. Using observational data from the Center for Medicare & Medicaid Services (CMS) and several other sources, I use regression analysis methods to provide empirical evidence of the effects of this government policy. The essay findings show that financially penalized hospitals use tactics consistent with symbolic practices, which may be an unintended outcome from the VBP project. The conclusion is that theoretically motivated contextual differences exist in the behaviors of hospitals when facing these external government pressures