Decide with Data: Boost Patient Outcomes

Overview

Did you know that healthcare organizations using data-driven decision making see 15-20% improvements in patient outcomes? Yet many healthcare professionals struggle to translate data into actionable insights that truly impact care delivery. This Short Course was created to help healthcare data analysts accomplish evidence-based decision making that directly improves patient outcomes. By completing this course, you'll be able to confidently explain analytical approaches to clinical teams, systematically evaluate care options using structured frameworks, and distinguish between statistically interesting and practically meaningful findings that warrant action. By the end of this course, you will be able to: - Explain the difference between descriptive and prescriptive analytics - Apply a decision matrix to evaluate options and recommend a course of action - Understand the concept of practical significance in data analysis This course is unique because it connects foundational analytics concepts directly to bedside scenarios, helping you bridge the gap between data science and patient care in ways that resonate with clinical teams. To be successful in this project, you should have a background in basic healthcare operations and familiarity with quality improvement processes.

Syllabus

Module 1: Descriptive and Prescriptive Analytics

Learners will distinguish between analytical approaches and communicate their applications effectively to clinical teams in healthcare settings.

Module 2: Decision Matrix to Evaluate Options

Learners will systematically evaluate healthcare options using structured decision-making frameworks and provide evidence-based recommendations.

Module 3: Practical Significance in Data Analysis

Learners will distinguish between statistical significance and practical importance, applying appropriate thresholds to determine when findings warrant operational changes.