Tahir Ekin

Augmented Markov Chain Monte Carlo Simulation for Two-Stage Stochastic Programs with Recourse

In this paper, we develop a simulation-based approach for two-stage stochastic programs with recourse. We construct an augmented probability model with stochastic shocks and decision variables. Simulating from the augmented probability model solves for the expected recourse function and the optimal first-stage decision. Markov chain Monte Carlo methods, together with ergodic averaging, provide a framework to compute the optimal solution. We illustrate our methodology via the two-stage newsvendor problem with unimodal and bimodal continuous uncertainty. Finally, we present performance comparisons of our algorithm and the sample average approximation method.

Resource allocation decision making in the military health system

The necessity to efficiently balance and re-allocate system resources among hospitals in a hospital network is paramount, especially as health systems experience increasing demand and costs for health services. In this paper, we proffer a resource allocation-based optimization model that adjusts resources (system inputs) automatically, which provides decision makers (such as health care managers and policy-makers) with a decision-support tool for re-allocating resources in large health systems that are centrally controlled and funded, such as the Military Health System. In these systems, inputs are fixed at certain levels and may only be adjusted within medical treatment facilities, while outputs must be maintained. We provide a mathematical formulation and example solutions from a case study using real-world data from sixteen U.S. Army hospitals. We also find utility in the use of multi-start evolutionary algorithms to store multiple optimal solutions for consideration by decision makers.

Overpayment models for medical audits: multiple scenarios

Comprehensive auditing in Medicare programs is infeasible due to the large number of claims, therefore, the use of statistical sampling and estimation methods is crucial. We introduce super-population models to understand the overpayment phenomena within the claims population. The zero- and one-inflated mixture-based models can capture various overpayment patterns including the fully legitimate or fraudulent cases. We compare them with the existing models for symmetric and mixed payment populations that have different overpayment patterns. The distributional fit between the actual and estimated overpayments is assessed. We also provide comparisons of models with respect to their conformance with Centers for Medicare and Medicaid Services (CMS) guidelines. In addition to estimating the dollar amount of recovery, the proposed models can help the investigators to detect overpayment patterns.

Medical overpayment estimation: A Bayesian approach

Abstract Overpayment estimation using a sample of audited medical claims is an often used method to determine recoupment amounts. The current practice based on central limit theorem may not be efficient for certain kinds of claims data, including skewed payment populations with partial overpayments. As an alternative, we propose a novel Bayesian inflated mixture model. We provide an analysis of the validity and efficiency of the model estimates for a number of payment populations and overpayment scenarios. In addition, learning about the parameters of the overpayment distribution with increasing sample size may provide insights for the medical investigators. We present a discussion of model selection and potential modelling extensions.

Analysis of Protective Behavior and Security Incidents for Home Computers

ABSTRACT This study analyzes the factors that affect security protective behavior and perceived security incidents. Protective behavior is found to have a positive impact on the perceived security incidents, especially for the more educated home computer user. Human factors such as “perceived barriers” (to use new security software tools), “self-efficacy” (confidence), and “cues to action” (awareness) are found to influence both the protective behavior and perceived security incidents.

Stochastic multi-objective auto-optimization for resource allocation decision-making in fixed-input health systems

The management of hospitals within fixed-input health systems such as the U.S. Military Health System (MHS) can be challenging due to the large number of hospitals, as well as the uncertainty in input resources and achievable outputs. This paper introduces a stochastic multi-objective auto-optimization model (SMAOM) for resource allocation decision-making in fixed-input health systems. The model can automatically identify where to re-allocate system input resources at the hospital level in order to optimize overall system performance, while considering uncertainty in the model parameters. The model is applied to 128 hospitals in the three services (Air Force, Army, and Navy) in the MHS using hospital-level data from 2009 – 2013. The results are compared to the traditional input-oriented variable returns-to-scale Data Envelopment Analysis (DEA) model. The application of SMAOM to the MHS increases the expected system-wide technical efficiency by 18 % over the DEA model while also accounting for uncertainty of health system inputs and outputs. The developed method is useful for decision-makers in the Defense Health Agency (DHA), who have a strategic level objective of integrating clinical and business processes through better sharing of resources across the MHS and through system-wide standardization across the services. It is also less sensitive to data outliers or sampling errors than traditional DEA methods.

Integrated maintenance and production planning with endogenous uncertain yield

The relationships among production planning, maintenance decisions and machine yield are crucial in a number of manufacturing environments such as the semi-conductor industry. This paper presents an integrated maintenance and production decision making framework with stochastically proportional endogenous yield rate and random demand. Finding the solution for this two-stage nonlinear stochastic program with endogenous uncertainty is not straightforward, and has not been considered previously. An augmented probability simulation based method is utilized to solve for the proposed decision model. We demonstrate the use of the proposed approach by conducting a numerical study and sensitivity analysis. We discuss the trade-offs involved among the yield, and simultaneous decisions of production planning and maintenance.

On the Use of the Concentration Function in Medical Fraud Assessment

ABSTRACT We propose a simple, but effective, tool to detect possible anomalies in the services prescribed by a health care provider (HP) compared to his/her colleagues in the same field and environment. Our method is based on the concentration function that is an extension of the Lorenz curve widely used in describing uneven distribution of wealth in a population. The proposed tool provides a graphical illustration of a possible anomalous behavior of the HPs and it can be used as a prescreening device for further investigations of potential medical fraud.