Renata Konrad

Use of discrete event simulation to improve a mental health clinic

OBJECTIVE To improve clinic design, trial-and-error is commonly used to discover strategies that lead to improvement. Our goal was to predict the effects of various changes before undertaking them. METHOD Systems engineers collaborated with staff at an integrated primary care-mental health care clinic to create a computer simulation that mirrored how the clinic currently operates. We then simulated hypothetical changes to the staffing to understand their effects on percentage of patients seen outside scheduled clinic hours and service completion time. RESULTS We found that, out of the change options being considered by the clinic, extending daily clinic hours by two and including an additional psychiatrist are likely to result in the greatest incremental decreases in patients seen outside clinic hours and in service time. CONCLUSION Simulation in partnership with engineers can be an attractive tool for improving mental health clinics, particularly when changes are costly and thus trial-and-error is not desirable.

Overcoming Human Trafficking via Operations Research and Analytics: Opportunities for Methods, Models, and Applications

Human trafficking is a complex transnational problem for society and the global economy. While researchers have studied this topic in a variety of contexts, including the criminology, sociology, and clinical domains, there has been little coverage in the operations research (OR) and analytics community. This paper highlights how techniques from OR and analytics can address the growing issue of human trafficking. We describe some of the unique concerns, problems, and challenges of human trafficking in relation to analytical techniques; subsequently, we demonstrate a variety of ways that OR and analytics can be applied in the human trafficking domain.

Input modeling for hospital simulation models using electronic messages

Health care organizations function in a complex, non-integrated setting, yet the coordination of information, tasks, and equipment across multiple units is essential for productive operations. A variety of simulation models of hospitals exist; however, few reflect resource sharing across multiple departments. Furthermore few models capture the inherent heterogeneity of a hospitals patient mix which plays a crucial role in determining how care is delivered and resources allocated. Patient flow paths can be used as input data to provide systematic insight into resource allocation processes and medical care within a hospital. To date, flow path approaches to studying hospital operations have been hindered by lack of a comprehensive data source. This tutorial describes how electronic communication exchanges between hospital departments are used to create an input model for hospital simulations.

Finding diverse optima and near-optima to binary integer programs

Typical output from an optimization solver is a single optimal solution. There are contexts, however, where a set of high-quality and diverse solutions may be beneficial; for example, problems involving imperfect information or those for which the structure of high-quality solution vectors can reveal meaningful insights. In view of this, we discuss a novel method to obtain multiple diverse optima / near optima to pure binary (0–1) integer programs, employing fractional programming techniques to manage these typically competing goals. Specifically, we develop a general approach that makes use of Dinkelbach’s algorithm to sequentially generate solutions that evaluate well with respect to both (i) individual performance and as a whole and (ii) mutual variety. We assess the performance of our approach on a number of MIPLIB test instances from the literature. Using two diversity metrics, computational results show that our method provides an efficient way to optimize the fractional objective while sequentially generating multiple high-quality and diverse solutions.

Effectively Engaging Industry Partners Within the Classroom

Student projects with an industry partner provide a meaningful way for students to translate abstract knowledge into practice, and to develop the data management and communication skills desired in industry. This paper provides suggestions for engaging an industry partner in a classroom and improving the classroom project experience. Set up as a competition, a single industry partner works with a multitude of student teams on the same problem. The project design aims to develop students’ project framing, data management, and communication skills. The paper covers general considerations for field-based course projects, and provides suggestions on how to address these issues. In general, students have given exceptionally good feedback ratings for the project. Learnings from the student, faculty, and industry partner perspectives are discussed. Although based primarily on experiences in a simulation class, instructions and practitioners can apply many of these observations more widely in an operations resear...

Are Our Students Prepared? The Impact of Capstone Design Pedagogical Approaches on Student Skill Development During Industry-Sponsored Fieldwork

Capstone design courses are field-based courses in which students work on real-world industry-sponsored projects. The structure of engineering capstone design courses varies between institutions as well as within an institution in the context of faculty engagement, industry involvement, and course learning objectives. We present a summary of an ongoing study focused on assessing engineering skills prefieldwork and postfieldwork experience at two institutions with different course structures in their respective industrial engineering programs. Our research goals are twofold: (1) to develop a framework for measuring the changes in students’ engineering skills during their capstone course and determine how these skills align with industry expectations and (2) to explore how differences in capstone course delivery impact the capstone experience. We developed two assessment instruments, one that involves student self-assessment across a set of engineering skills, and one with which the industry partners involv...

Accelerating the adoption of bundled payment reimbursement systems: A data-driven approach utilizing claims data

ABSTRACT Bundled payments as a reimbursement mechanism have the potential to reduce health care expenditures and improve the quality of care by aligning the incentives of payers, providers and, most importantly, patients. The Centers for Medicare and Medicaid Services (CMS) launched the Bundled Payments for Care Improvement (BPCI) program in April 2013 and has set ambitious goals for adopting alternative payment models on a large scale. One of the crucial components for successful implementation of a bundled payment system is the identification of procedural homogeneous groups within an episode of care (a set of services needed to treat a medical condition), to which a flat reimbursement rate can be applied. In this study, we propose a data-driven clustering approach to automatically detect and explicitly represent homogeneous sub-groups of services for a given condition. Manual detection is slow and relies on consensus decisions, but automatic detection can serve as an important foundational input for bundle building. We explore the results from analyzing two conditions, one with a low and the other with a high degree of treatment complexity. Resulting clusters characterize episodes of care by specifying included services. The automatically extracted clusters of services have different cost patterns and highlight the payers expenditure and providers financial risk under bundled payments. Such a data-driven approach could be used by payers (e.g., CMS) to facilitate the adoption of bundled payments by different providers. To demonstrate, we use the clusters identified to model a payment scheme that minimizes providers’ financial risk.

A new data source to support hospital operations modeling, message-exchange protocols as illustrated through simulation

ABSTRACT Studies pertaining to hospital operations typically face significant data collection challenges, particularly when defining patient flow patterns. The majority of such studies determine patient flows through observations, stakeholder interviews, and historical patient data analysis. Such methods are time-consuming and typically omit important interactions between resources and patients. This leads to incomplete descriptions of current practices, which can hinder the development and practical application of quantitative models. Furthermore, such processes are expensive and, possibly, subjective. This article presents a methodology for collecting large volumes of very detailed patient flow information. This information is obtained from message-exchange protocols used by hospital information systems to communicate among themselves. The methodology outlines a procedure for extracting detailed information related to (1) individual patient paths, (2) interaction among shared resources, and (3) task duration. The granularity of this information is flexible but can cover various actions in great detail, such as time, location, and person conducting a particular lab test. In this article, we present the general framework of the proposed method, steps for extracting patient flow information, and an illustrative example of a well-known problem from hospital operations management.

Workforce design in primary care-mental health integration: a case study at one veterans affairs medical center

Many Veterans screen positive for mental health disorders in primary care, yet it appears that only a fraction of those who could benefit receive treatment. One potential way to ensure that a larger proportion of these Veterans receive appropriate care would be to increase access to mental health services through primary care-mental health integration (PC-MHI) clinics. Yet a systematic method to evaluate the impact of projected increases in patient volumes on PC-MHI clinics is lacking. As a first step, we develop and validate a discrete-event simulation model to understand how the clinic could respond to a projected increase in PC-MHI utilization at one Veterans Affairs Medical Center. Numerical results illustrate the impact of increased patient volume and the availability of providers on patient wait times and patients seen by mental health providers outside of clinic hours. We also note that although discrete-event simulation has a long history in health care, it is rarely used in the assessment of the resource allocation decisions in mental health.