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CPIA Certification Program
Module 24: Advanced Analytics & Predictive Modeling
From Hindsight to Foresight: Using Data to Predict the Future.
The Pharmacist’s Clinical Intuition, Amplified
As a clinical pharmacist, you are a natural predictive modeler. When you review a patient’s profile and see advanced age, poor renal function, and a new prescription for an aminoglycoside, you don’t just process the order—you intuitively predict a high probability of nephrotoxicity. When you counsel a patient on a complex regimen of anticoagulants and antiplatelets, you predict a heightened risk of bleeding and advise them accordingly. This ability to synthesize diverse data points and forecast a future clinical outcome is the essence of your professional judgment.
This module is about taking that innate clinical intuition and amplifying it with the power of data science. We will move beyond the traditional informatics role of reporting on what has happened (hindsight) and step into the advanced role of predicting what will happen (foresight). The vast datasets within our clinical systems are filled with the patterns and signals that precede adverse events, operational bottlenecks, and readmissions.
Here, you will learn to speak the language of data science and machine learning. We will demystify the concepts of predictive modeling and provide you with a framework to build, validate, and deploy models that can identify high-risk patients before they decline, forecast operational demands for the pharmacy, and ultimately allow the health system to act proactively, not reactively. This is the cutting edge of health informatics, where you can translate your clinical expertise into scalable, predictive tools that prevent harm before it ever occurs.
Your Guide to Data-Driven Foresight
This module provides the foundational skills to leverage data science, machine learning, and advanced visualization to transform raw data into predictive insights.
Data Science Basics for Pharmacists
A foundational introduction to the core concepts of data science and machine learning, translated into a clinical context. We’ll demystify terms like regression, classification, and clustering with practical, pharmacy-centric examples.
Model Development and Validation Process
Learn the rigorous, scientific process of building a predictive model. We’ll cover everything from data cleaning and feature engineering to training, testing, and validating a model to ensure it is accurate, fair, and reliable for clinical use.
Visualization Techniques with BI Tools (Tableau, Power BI)
A model’s insights are useless if they can’t be understood. This section is a hands-on introduction to modern business intelligence (BI) tools, focusing on how to create compelling, interactive dashboards that tell a clear story with your data.
Operational Forecasting and Staffing Models
Apply predictive analytics to the business of pharmacy. Learn how to build models that can forecast future medication dispensing volumes, allowing you to optimize inventory and create data-driven staffing models for the pharmacy.
Real-World Project – Adverse Event Prediction
A capstone project where we will walk through the process of building and validating a machine learning model designed to predict which patients are at the highest risk for an adverse drug event, turning theory into a tangible, life-saving tool.
