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

Simulating Influenza Epidemics with Waning Vaccine Immunity

In this study, we simulate an influenza epidemic that considers the effects of waning immunity by fitting epidemiological models to CDC secondary historical data aggregated on a weekly basis, and derive the transmission rates at which susceptible individuals become infected over the course of the influenza season. Using a system of differential equations, we define four groups of individuals in a population: susceptible, vaccinated, infected, and recovered. We show that a larger number of initially infected individuals might not only bring the influenza season to an end sooner but also reduce the epidemic size. Moreover, any influenza virus that entails a faster recovery rate does not necessarily lead to a smaller epidemic size

Six-Month Outcome of Transient Ischemic Attack and Its Mimics

Background and Objective: Although the risk of recurrent cerebral ischemia is higher after a transient ischemic attack (TIA), there is limited data on the outcome of TIA mimics. The goal of this study is to compare the 6-month outcome of patients with negative and positive diffusion-weighted imaging (DWI) TIAs (DWI-neg TIA vs. DWI-pos TIA) and also TIA mimics. Methods: We prospectively studied consecutive patients with an initial diagnosis of TIA in our tertiary stroke centers in a 2-year period. Every included patient had an initial magnetic resonance (MR) with DWI and one-, three-, and six-month follow-up visits. The primary outcome was defined as the composition of intracerebral hemorrhage, ischemic stroke, TIA, coronary artery disease, and death. Results: Out of 269 patients with the initial diagnosis of TIA, 259 patients (mean age 70.5 ± 15.0 [30–100] years old, 56.8% men) were included in the final analysis. Twenty-one (8.1%, 95% confidence interval [CI] 5.1-12.1%) patients had a composite outcome event within the six-month follow-up. Five (23.8%) and 13 (61.9%) composite outcome events occurred in the first 30 and 90 days, respectively. Among patients with DWI-neg TIA, the one- and six-month ischemic stroke rate was 1.5 and 4.6%, respectively. The incidence proportion of composite outcome event was significantly higher among patients who had the diagnosis of DWI-neg TIA compared with those who had the diagnosis of TIA mimics (12.2 vs. 2.1%—relative risk 5.9; 95% CI, 1.4–25.2). In our univariable analysis among patients with DWI-neg TIA and DWI-pos TIA, age (P = 0.017) was the only factor that was significantly associated with the occurrence of the composite outcome. Conclusion: Our study indicated that the overall six-month rate of the composite outcome among patients DWI-neg TIA, DWI-pos TIA, and TIA mimics were 12.2, 9.7, and 2.1%, respectively. Age was the only factor that was significantly associated with the occurrence of the composite outcome

A predictive analytics model for differentiating between transient ischemic attacks (TIA) and its mimics

Transient ischemic attack (TIA) is a brief episode of neurological dysfunction resulting from cerebral ischemia not associated with permanent cerebral infarction. TIA is associated with high diagnostic errors because of the subjective nature of findings and the lack of clinical and imaging biomarkers. The goal of this study was to design and evaluate a novel multinomial classification model, based on a combination of feature selection mechanisms coupled with logistic regression, to predict the likelihood of TIA, TIA mimics, and minor stroke

Six-Month Outcome of Transient Ischemic Attack and Its Mimics

Background and Objective: Although the risk of recurrent cerebral ischemia is higher after a transient ischemic attack (TIA), there is limited data on the outcome of TIA mimics. The goal of this study is to compare the 6-month outcome of patients with negative and positive diffusion-weighted imaging (DWI) TIAs (DWI-neg TIA vs. DWI-pos TIA) and also TIA mimics.Methods: We prospectively studied consecutive patients with an initial diagnosis of TIA in our tertiary stroke centers in a 2-year period. Every included patient had an initial magnetic resonance (MR) with DWI and one-, three-, and six-month follow-up visits. The primary outcome was defined as the composition of intracerebral hemorrhage, ischemic stroke, TIA, coronary artery disease, and death.Results: Out of 269 patients with the initial diagnosis of TIA, 259 patients (mean age 70.5 ± 15.0 [30–100] years old, 56.8% men) were included in the final analysis. Twenty-one (8.1%, 95% confidence interval [CI] 5.1-12.1%) patients had a composite outcome event within the six-month follow-up. Five (23.8%) and 13 (61.9%) composite outcome events occurred in the first 30 and 90 days, respectively. Among patients with DWI-neg TIA, the one- and six-month ischemic stroke rate was 1.5 and 4.6%, respectively. The incidence proportion of composite outcome event was significantly higher among patients who had the diagnosis of DWI-neg TIA compared with those who had the diagnosis of TIA mimics (12.2 vs. 2.1%—relative risk 5.9; 95% CI, 1.4–25.2). In our univariable analysis among patients with DWI-neg TIA and DWI-pos TIA, age (P = 0.017) was the only factor that was significantly associated with the occurrence of the composite outcome.Conclusion: Our study indicated that the overall six-month rate of the composite outcome among patients DWI-neg TIA, DWI-pos TIA, and TIA mimics were 12.2, 9.7, and 2.1%, respectively. Age was the only factor that was significantly associated with the occurrence of the composite outcome

Distribution-free Bounds for the Expected Marginal Seat Revenue Heuristic with Dependent Demands

This paper extends the fundamental static revenue management capacity control problem by incorporating statistical dependence. A single-resource is sold through multiple fare classes each with a corresponding stochastic, but not necessarily independent, demand. We explicitly account for any level of positive or negative dependence and focus on the traditional macro-level demand model in order to provide distribution-free bounds on the foundational expected marginal seat revenue heuristics, both without and with buy-up. We illustrate for the case with three fare classes and demand drawn from (i) normal distributions, and (ii) normal and exponential distributions

Price and revenue bounds for bundles of information goods

In this paper, we investigate the behavior of the expected revenue function generated from selling bundles with arbitrarily many components. A motivating example of such bundles includes the production and delivery of digital content, where variable costs are generally negligible. Specifically, we derive generic lower and upper bounds for the expected revenue function even when accounting for arbitrary, potentially complex, dependence structures among the bundle components. The expected revenue bounds in turn provide upper and lower bounds regarding the optimal pure bundle price. Our results reconcile the extant bundling literature involving expected revenue bounds, while sharpening some of these results even when relaxing the traditional assumption of independence among the valuations for the bundle components. We show how these bounds can be further tightened when the seller has additional information about the dependence relationship. Since these results effectively reduce the search space for the optimal bundle price, the pricing bounds provided by our framework have important managerial implications

A Revenue Management Approach for Managing Operating Room Capacity

The advanced scheduling of patients for elective surgeries is challenging when the operating room capacity usage by these procedures is uncertain. We study the application of some revenue management concepts and techniques to operating rooms for several surgical procedures performed in a multi-tier reimbursement system. Our approach focuses on booking requests for elective procedures, under the assumption that each request uses a random amount of time. We create and use a modified version of Belobaba\u27s well-known EMSRb algorithm (Belobaba 1989) to decide on near-optimal protection levels for various classes of patients. Under the random resource utilization assumption, we decide, for each planning horizon, how much time to reserve for satisfying the demand coming from each class of patients, based on the type of surgical procedure requested and the patient\u27s reimbursement level

Implications of the Stability Analysis of Preferences for Personalised Colorectal Cancer Screening

Patients are increasingly interested in becoming involved in the medical decision-making process. As a result, healthcare providers and researchers are concerned with finding new ways to integrate patients\u27 preferences, by understanding their commitment to and the stability of those preferences. Preventive medicine, such as colorectal cancer screening, presents an opportunity for personalising the decision-making trajectory based on patients\u27 preferences. In this paper, we propose a framework for a joint decision-making process, capable of integrating patients\u27 changing preferences, as described by a stability analysis of those preferences and design scenarios for implementing the process in clinical practice. In this study, a secondary data analysis, we present scenarios that demonstrate how the stability analysis of an Analytic Network Process (ANP) model supports personalising the process of agreeing on an appropriate colorectal cancer screening option. We illustrate the framework using two patients whose preferences have different stabilities and for whom the healthcare provider has different rankings for the recommended alternatives. The results show the differences in additional medical information the healthcare provider might need to provide as part of the joint decision-making process in order to reach an agreement between the patient and the provider. A stability analysis of the patient\u27s preferences provides the healthcare provider with a mapping of the preferred options. It also shows how the patient\u27s most preferred alternative might change as the patient obtains additional relevant medical information. Knowing how the patient\u27s priorities might change supports a personalisation of the medical decision-making process. We conclude that the healthcare provider can utilise the stability analysis of a patient\u27s preferences to identify possible dialogue paths that would enable reaching a consensus about an appropriate screening option

Rate and Associated Factors of Transient Ischemic Attack Misdiagnosis

Background and purpose The goal of this study was to investigate the rate and associated factors of Transient Ischemic Attack (TIA) misdiagnosis. Methods We retrospectively analyzed consecutive patients with an initial diagnosis of TIA in the emergency department (ED) in a 9-month period. All hospitalized TIA patients were evaluated by a neurologist within 24 h and had at least one hospital discharge follow-up visit within three months. Patients\u27 clinical data and neuroimaging were reviewed. The final diagnosis was independently verified by two stroke neurologists. Results Out of 276 patients with the initial diagnosis of TIA, 254 patients (mean age 68.7 ± 15.4 years, 40.9% male, 25.2% final diagnosis of TIA) were included in the analysis. Twenty-four patients (9.4%) were referred to our rapid-access TIA clinic. The rate of TIA misdiagnosis among TIA clinic referred patients was 45.8%. Among the 230 patients in inpatient setting, the rate of TIA misdiagnosis was 60.0%. A hospital discharge diagnosis of TIA was observed in 54.3% of hospitalized patients; however, only 24.8% had the final diagnosis of TIA. Among hospitalized patients, the univariate analysis suggests a significant difference (P \u3c .05) between the two groups (correctly versus misdiagnosed patients) in terms of hospital discharge diagnosis, final diagnosis, history of diabetes mellitus, and coronary artery disease. In regression model hospital discharge diagnosis (P \u3c .001), final diagnosis (P \u3c .001), and diabetes mellitus (P = .018) retained independent association with TIA misdiagnosis. Conclusion Our study indicates a high rate of TIA misdiagnosis in the emergency department, hospital, and outpatient clinics