A User's Guide to Results from Descriptive,
Comparative and Predictive Analyses

It seems that everywhere we turn, someone is talking about harnessing the power of analytics and ‘Big Data’ to help us make decisions about healthcare finances and delivery or how to personalize care for individual patients. Consultants are marketing data platforms and analytic services that leverage electronic health records and administrative databases to find answers for healthcare decision makers. But how can you tell if the answers are true? These sessions look at the background and principles for sensible interpretation of results from routine healthcare data. This is NOT a technical guide on how to perform analyses.

Format: Live, interactive, online using WebEx.

Sessions: 2 sessions. Each 1.5 hours.

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Session 1: Healthcare Analytics - Fundamental Concepts

This session will review fundamental statistical concepts relevant to interpreting the results of analyses done using healthcare data. Topics include: descriptive versus inferential statistics, telling signal from noise, how much data is needed, what is bias, experiments versus observational analyses, and bias in terms of incomplete/missing data.

Session 2: Healthcare Analytics - Data, Datasets and Decision Making

This session will focus on how healthcare datasets are created through routine clinical practice and how this affects the validity of analytics. It will cover how clinicians collect data, what medical records contain, what administrative databases contain, how datasets can be linked, the quality of data and validation studies, different uses of this data to answer questions, and why the data is better for some questions than for others (and which ones). Several key considerations are included to help you make your data-driven decisions defensible and to avoid getting fooled by grand unfounded claims.

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