PARALLEL SHORT COURSES

Parallel Short Course 1

INTRODUCTION TO CLINICAL AND ECONOMIC DECISION-ANALYTIC MODELLING
U. Siebert, Austria

Objectives:
By the end of this course, participants will
1)  understand the key concepts and goals of decision analysis,
2)  know the basic methods of decision tree analysis and Markov modeling and be able to choose the appropriate model type for a given research question
3)  understand why and when decision-analytic modeling should be used in clinical and economic evaluation, and
4)  be able to critically judge the conclusions derived from a model and know the strengths and limitations and of modeling

Course Description:
Decision making is an essential part of health care. It involves choosing an action after weighing the risks, benefits, and costs of the options available to the individual patient or the patient population. While all decisions in health care are made under conditions of uncertainty, the degree of uncertainty depends on the availability, validity, and generalizability of clinical and economic data. Decision-analytic modeling is a systematic approach to decision making under uncertainty that is used widely in clinical decision making, economic evaluation, and health technology assessment of preventive, diagnostic or therapeutic procedures. It involves combining evidence for different outcomes and from different sources. Outcome parameters may include disease progression, treatment efficacy/effectiveness, safety, quality of life, and costs. Sources may include epidemiological studies on the natural history of the disease, randomized clinical trials, observational studies, pharmacoepidemiologic studies, quality of life surveys, and resource utilization studies, and others.

This half day course provides an introduction into decision-analytic modeling as a tool for medical decision making and economic evaluation. The course consists of lectures and interactive group exercises and discussions. During the course, participants will develop a basic understanding of:
• Key concepts, definitions and goals of decision analysis
• Creating the structure of a model
• Measuring health effects and costs
• Application of modeling techniques such as decision trees and Markov models
• Perform sensitivity analysis
Based on practical examples, participants will be guided through the main modeling steps. Examples from the published literature will be discussed to understand the application of modeling techniques to specific research questions. Guidelines for good practice in decision modeling will be presented that help to assess the quality and validity of decision models. Ethical implications, strengths and limitations of decision analysis will be briefly discussed at the end of the course.

The intended audience includes researchers from all substance matter fields, as well as statisticians, epidemiologists, health economists, decision scientists, and others interested in decision-analytic modeling.
No previous knowledge of is required. No laptop is needed. Please bring a simple pocket calculator!

Parallel Short Course 2

FOCUSED OPERATIONS MANAGEMENT IN HEALTH CARE ORGANIZATIONS: DOING MORE WITH THE SAME RESOURCES
J. Pliskin, Israel and B. Ronen, Israel

How can a hospital successfully reduce the response time in the Emergency Department by 40% and at the same time increase the clinical quality, all this using existing resources? How can one increase the throughput of the Operating Room by 20% using the same resources? Why do performance measures sometimes undermine value creation? How can the removal of inexpensive bottlenecks easily increase throughput, reduce response time and increase quality? Why adding more personnel and making more capital investment are not usually the answer for the improvement of healthcare organizations?

These topics and more are the theme of this short course on managing healthcare organizations. The main theme of the course is that one can do much more with the same resources in terms of throughput, response time and quality by using simple practical tools and techniques. It provides a system view and touches upon issues of performance measures, operations management, quality, and above all, value creation and value enhancement.

The course includes the use of methods such as the Seven Focusing Steps of the Theory of Constraints (TOC) that yields fast improvement in systems such as operating rooms and emergency departments. The course demonstrates how simple tools like the Focusing Table, the Focusing Matrix, the Complete Kit concept, working in Small Batches, Specific Contribution and Pareto Analysis can increase throughput, reduce response time and create value in the healthcare industry.
 

Parallel Short Course 3
DISCRETE EVENT SIMULATION
B. Jahn, Austria

Goals:
By the end of this course, participants will
1) understand the key concepts of DES and know the element of a DES model,
2) understand basics of queue theory and its application, and
3) understand why and when DES should be applied and which are the strengths and limitations of DES.

Course Description:
This half day course provides an introduction into Discrete Event Simulation (DES) as a tool for clinical and economic decision analysis as well as for management optimization. The course consists of lectures and interactive hands-on activities.
Whereas DES has been successfully applied in industrial engineering since 1960s, it is today more and more used in the field of health care. Applications include health care management (e.g., management of ambulance services, optimization of emergency departments), health technology assessments (e.g., in cancer and other diseases requiring organ transplantations) as well as pharmacoeconomics.
In a DES, a system is modelled where state changes occur at a discrete set of points in time. These state changes are called events. Examples for events are admission to hospital, change in dose, adverse event, etc. The simulation allows modelling on the individual (e.g., patient) level. In addition, resources (e.g., facilities, staff) can be modelled explicitly and interdependencies between patients (e.g., competition for resources) can be incorporated in the model. Real-world scenarios of patient pathways or other real systems as well as conceptual systems can be analysed, compared for their effectiveness and cost-effectiveness, and optimized.
During the course, participants will develop a basic understanding of the key concepts of DES including Entities, Attributes, Events, Resources and Queues. It starts with a introduction to simulation models and modelling. Based on practical examples participants will be guided through the main modeling steps. The course will combine lectures and hands-on activities. Basic concepts of input modelling, verification and validation and output analysis will be covered. Models will be constructed using ARENATM or other software.
The intended audience includes researchers from all substance matter fields, as well as statisticians, epidemiologists, decision analysts, and others interested in decision modeling.
For the exercises, participants can bring their own laptop. No previous knowledge of is required.
 

Parallel Short Course 4
WHY AREN’T PHYSICIANS’ PRACTICES EVIDENCE-BASED? - COGNITIVE AND ENVIRONMENTAL CHALLENGES TO EVIDENCE-BASED PRACTICE
W.R. Smith, USA and R.M. Poses, USA

Evidence-based medicine (EBM) can be viewed as the integration of best research evidence with clinical expertise and patient values.  Physicians are frequently exhorted to practice in accord with the principles of EBM.  However, there are many examples of physicians failing to do so.  Furthermore, many attempts to change physician behavior, ostensibly to make it more evidence based, have failed to work

This workshop will discuss possible reasons physicians may fail to practice following EBM principles.  One set of explanations comes from cognitive psychology.  Human thinking strategies designed to cope with inherent cognitive limitations may lead to judgments and decisions that fail to conform with normative ideals.  We will review how such strategies may affect each stage of the evidence-based decision making process: identifying options, identifying outcomes of options, assessing the probability of outcomes, assessing the value of  options, and combining information to make a decision.  Of particular interest are various judgment and decision biases, and the use of heuristics.  Our discussion will be based on the best evidence about physicians’ cognition and how it affects their clinical practice.

Methods to address physicians’ human cognitive limitations may help physicians practice more in accord with EBM.  We will provide some examples of promising approaches based on findings in the cognitive psychology literature.   

On the other hand, increasingly dysfunctional health care environments may push even the most well-intentioned physicians away from the EBM approach.  Modern health care is increasingly dominated by large organizations.  Their leadership is increasingly ill-informed about the practice of health care on the ground, subject to conflicts of interest, and even corrupt.  (Transparency International’s 2006 Global Report, which was entirely devoted to global health care corruption, asserted, “the scale of corruption is vast in both rich and poor countries.”)

Multiple manifestations of such concentration and abuse of power may threaten evidence-based practice.  We will discuss how such manifestations as stealth marketing, special politically correct pleadings, suppression and manipulation of research, perverse bureaucratic and financial incentives, and intimidation and coercion may inhibit the stages of the EBM approach, and how the dysfunctional environment may dispute the fundamental assumptions of EBM.

Finally, we will summarize some general approaches to defend evidence-based decision practice from the threats caused by dysfunctional health care environments.