A series of 20+ modules covering healthcare applications of a range of common systems engineering methods, including process improvement, human factors, reliability, modeling, optimization and design. Can be viewed separately, with accompanying quizzes, or towards a certificate of completion of 12 core + elective topics.
To preview the HSyE lecture series, we have two modules publicly available for your viewing. For access to the rest of the lectures in edpuzzle, please join CHER or contact us by emailing email@example.com. For guidance on how to use edpuzzle, please watch the tutorial video (on right).
* core modules
Industrial Engineering and Healthcare
Quality Improvement Methods
This module gives an overview of basic process improvement methods used in healthcare and provides detailed examples. Some of the methods discussed include total quality management, continuous quality improvement, statistical process control, Plan Do Study Act cycles, IHI Model for Improvement, Six Sigma, Lean/Toyota Production System, and theory of constraints.
This module dives deeper into Six Sigma and Lean methodologies. You will gain a better understanding of the statistical meaning of Six Sigma and several Lean concepts and tools such as the 7 types of waste, 5 S, value stream maps, kaizen events, and more.
This module focuses on the growth and impact of the Institute for Healthcare Improvement (IHI) on process improvement methods with illustrating examples of PDSA learning cycles, rapid cycle testing, and run charts.
This module provides an overview of the collaborative improvement approach, terminology, and roles along with several examples. You will also gain a better understanding of how to put it into practice using breakthrough series, change packages, iterative testing, and improvement advisors.
Safety, Reliability, and Human Factors
This module provides an overview of safety and reliability science ideas and how to put them into practice. Includes concepts such as success analysis, harm reduction, resiliency and adaptation, and good variation “work-as-done”.
Statistical Quality Control
This module provides an overview of statistical process control charts covering a refresh of traditional statistical methods and probability distributions, basic concepts/terminology, examples and types of charts, why it’s important, and how to use or interpret these charts.
This module dives deeper into basic Shewhart charts, more advanced types of SPC charts, when certain types of charts should be used based on your data, and examples of common charts. This module also covers supplementary rules for detecting instability/inconsistency in the data or processes.
This module covers fundamental concepts in data mining and machine learning and provides in-depth examples. You will learn about different data mining and machine learning algorithms such as naïve bayes classifiers, decision trees, support vector machines, clustering, neural networks, and association rules.
This module provides an overview of design of experiments (DOE) concepts and its utility. You will learn the different types of basic and advanced designs and the process for conducting experiments. Using Box’s catapult exercise as an example, this module covers how to run various experiments and calculate/interpret the results.
Systems Engineering Models
This module provides an overview of queuing theory, applications in healthcare, and a guided exercise. You will gain a better understanding of common queuing problems, measures, characteristics and notation of queuing systems, Little’s Law, and queuing networks.
This module is an introduction to computer simulation in healthcare and discusses the utility of modelling process logic and variation to analyze process performance. You will gain a better understanding of the different types of models such as discrete event, Monte Carlo, agent-based, and systems dynamics models and the importance of model verification/validation. Examples of applications and software are also discussed in this module.
This module provides an introduction to differential equations and agent-based modeling including several examples and applications using SEIR models and network topologies.
This module provides an introduction to system dynamics and outlines the process for using this type of computer simulation model to understand complex systems. You will learn about system dynamics concepts such as casual loops (reinforcing and balancing), stock and flow diagrams, differential equations, and more.
This module provides an overview of operations research in medical decision-making covering typical applications, methods, objective functions, performance measures such as QALY, and examples.
Design and Innovation Methods
Deming’s System of Profound Knowledge
This module provides an overview of Deming’s system of profound knowledge and it’s importance when engaging in improvement efforts.
As one of the components of Deming’s system of profound knowledge, this module emphasizes the importance of measurement and understanding variation when testing improvements. This module also discusses the main takeaways from Deming’s Red Bead experiment and Dice Flow exercise and other important tools such as annotated run charts and statistical process control charts.