For clinical data science to be effective in healthcare—to achieve the outcomes desired—it must translate into decision support of some sort, either at the patient, clinician, or manager level. By the end of this course, students will be able to articulate the need for an intervention, to right size it, to choose the appropriate technology, to describe how knowledge should be obtained, and to design a monitoring plan.
Overview
Syllabus
- Knowing Where to Intervene
- In this module, you will be introduced to the course through a range of decision support interventions used in health care. We will examine the Five Rights of decision support and go on to discuss the basics for deciding whether to build an intervention and, having done so, how to evaluate it.
- Defining Decision Support
- In this module, we will focus on issues of design, both for decision support and as they apply more broadly across multiple environments.
- Using Transactional and Summative Data and Knowledge for Decision Support
- In this module, we return to decision support, focusing on rules-- the key structure in most decision support. We'll provide a key framework for making sure you have all the minimum components for ensuring a successful implementation. We'll also address issues of languages used by rules and how to keep rules consistent within and between institutions.
- Eliciting and Creating Knowledge for Decision Support
- In this module, we will go behind the scenes of decision support, examining where and how we get the knowledge that drives decision support --including data science-- for generating knowledge from data.
Taught by
Harold P. Lehmann
