Resume

• 2011

  Ph.D.

  Operations

  Business Analytics and Information Systems

  University of Cincinnati

• 2003

  MBA

  Business

• 1995

  BA/BS

  Mathematics & Statistics

  Secondary Mathematics Education

  Kappa Delta

  \nMiami University Cheerleading

• Research

  Team Leadership

  Team Building

  Data Analysis

  Access

  Statistics

  Leadership Development

  Risk Management

  Market Research

  Healthcare

  Public Speaking

  Microsoft Excel

  Strategic Planning

  Project Management

  Analysis

  Qualitative Research

  SAS

  Understanding Emergency Care Delivery through Computer Simulation Modeling

  Robert L. Wears MD

  PhD

  Laura H. Barg-Walkow PhD

  Nathan R. Hoot MD

  MPH

  Daniel J. France PhD

  MPH

  In 2017

  Academic Emergency Medicine convened a consensus conference entitled

  “Catalyzing System Change through Health Care Simulation: Systems

  Competency

  and Outcomes.” This article

  a product of the breakout session on “understanding complex interactions through systems modeling

  ” explores the role that computer simulation modeling can and should play in research and development of emergency care delivery systems. This article discusses areas central to the use of computer simulation modeling in emergency care research. The four central approaches to computer simulation modeling are described (Monte Carlo simulation

  system dynamics modeling

  discrete-event simulation

  and agent-based simulation)

  along with problems amenable to their use and relevant examples to emergency care. Also discussed is an introduction to available software modeling platforms and how to explore their use for research

  along with a research agenda for computer simulation modeling. Through this article

  our goal is to enhance adoption of computer simulation

  a set of methods that hold great promise in addressing emergency care organization and design challenges.

  Understanding Emergency Care Delivery through Computer Simulation Modeling

  The healthcare industry has invested heavily in electronic health records and other clinical information systems in order to improve caregivers' access to information and ability to share information with other care providers. It has been shown that these systems can readily induce in their users a state of information overload

  where the volume and complexity of information overwhelms the user

  leading to lower decision speed and quality. This research introduces and tests a cognitive technique called “emphasis framing” as an operational tactic to help mitigate the effects of information overload

  thereby improving the quality and timeliness of clinical decision-making. Emphasis framing is the highlighting or stressing of some aspect or component of the information being exchanged in order to make it more easily processed

  or more likely to be processed

  by the recipient. We conducted a controlled laboratory experiment with emergency department physicians experiencing information overload to measure the effect of emphasis framing on two operational performance metrics: (1) the quality of the physician's clinical evaluation

  and (2) the efficiency (timeliness) of the physician's clinical decision-making. Our findings show that the emphasis frame helped mitigate the effects of information overload and increased the quality of clinical decision-making. Contrary to expectations

  however

  we found decision-making took longer with the emphasis frame

  reinforcing the need to consider the impacts of quality/speed trade-offs. Implications for theory and practice are discussed.

  Quality and Efficiency of the Clinical Decision-Making Process: Information Overload and Emphasis Framing

  ABSTRACT\n\nStudy objective\nEmergency departments (EDs) with both low- and high-acuity treatment areas often have fixed allocation of resources

  regardless of demand. We demonstrate the utility of discrete-event simulation to evaluate flexible partitioning between low- and high-acuity ED areas to identify the best operational strategy for subsequent implementation.\n\nMethods\nA discrete-event simulation was used to model patient flow through a 50-bed

  urban

  teaching ED that handles 85

  000 patient visits annually. The ED has historically allocated 10 beds to a fast track for low-acuity patients. We estimated the effect of a flex track policy

  which involved switching up to 5 of these fast track beds to serving both low- and high-acuity patients

  on patient waiting times. When the high-acuity beds were not at capacity

  low-acuity patients were given priority access to flexible beds. Otherwise

  high-acuity patients were given priority access to flexible beds. Wait times were estimated for patients by disposition and Emergency Severity Index score.\n\nResults\nA flex track policy using 3 flexible beds produced the lowest mean patient waiting time of 30.9 minutes (95% confidence interval [CI] 30.6 to 31.2 minutes). The typical fast track approach of rigidly separating high- and low-acuity beds produced a mean patient wait time of 40.6 minutes (95% CI 40.2 to 50.0 minutes)

  31% higher than that of the 3-bed flex track. A completely flexible ED

  in which all beds can accommodate any patient

  produced mean wait times of 35.1 minutes (95% CI 34.8 to 35.4 minutes). The results from the 3-bed flex track scenario were robust

  performing well across a range of scenarios involving higher and lower patient volumes and care durations.\n\nConclusion\nUsing discrete-event simulation

  we have shown that adding some flexibility into bed allocation between low and high acuity can provide substantial reductions in overall patient waiting and a more efficient ED.

  The Flex Track: Flexible Partitioning Between Low- and High-Acuity Areas of an Emergency Department

  The Nature and Necessity of Operational Flexibility in the Emergency Department

  Craig Froehle

  Hospital-based emergency departments (EDs)

  given their high cost and major role in allocating care resources

  are at the center of the debate about how to maximize value in delivering health care in the United States. To operate effectively and create value

  EDs must be flexible

  having the ability to rapidly adapt to the highly variable needs of patients. The concept of flexibility has not been well described in the ED literature. We introduce the concept

  outline its potential benefits

  and provide some illustrative examples to facilitate incorporating flexibility into ED management. We draw on operations research and organizational theory to identify and describe 5 forms of flexibility: physical

  human resource

  volume

  behavioral

  and conceptual. Each form of flexibility may be useful individually or in combination with other forms in improving ED performance and enhancing value. We also offer suggestions for measuring operational flexibility in the ED. A better understanding of operational flexibility and its application to the ED may help us move away from reactive approaches of managing variable demand to a more systematic approach. We also address the tension between cost and flexibility and outline how “partial flexibility” may help resolve some challenges. Applying concepts of flexibility from other disciplines may help clinicians and administrators think differently about their workflow and provide new insights into managing issues of cost

  flow

  and quality in the ED.

  The Nature and Necessity of Operational Flexibility in the Emergency Department

  Merck & Co. Inc (MSD)

  Xavier University

  University of Cincinnati

  Columbus Life Insurance Company

  Great American Insurance

  Xavier University

  Department of Emergency Medicine

  University of Cincinnati College of Medicine

  PhD Candidate

  Department of Operations

  Business Analytics

  and Information Systems\nCarl H. Lindner College of Business

  University of Cincinnati

  Assistant Professor

  Xavier University

  Adjunct Professor

  Adjunct Professor of Business Statistics

  Xavier University

  Sales Representative

  Merck & Co. Inc (MSD)

  Marketing & Sales System Consultant

  Columbus Life Insurance Company

  Senior Pricing Analyst

  Great American Insurance