December 2026 CPHQ Prep Virtual Class-Module 6: Health Data Analytics

Module 6: Health Data Analytics | Slide Deck

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Module 6, <strong>Health Data Analytics</strong>, reviews how healthcare quality professionals use data to measure and improve performance. The module emphasizes building effective <strong>data management systems</strong> that can capture, store, retrieve, and securely report clinical and financial information; interface with other systems; support multiple users and operating environments; and enable tools such as alerts, triggers/thresholds, and rules-based processing for both concurrent and retrospective review. It also highlights the role of analytics in meeting <strong>accreditation and regulatory requirements</strong> and enabling data mining and statistical analysis.

A major focus is on <strong>data collection and sampling</strong>. Learners practice distinguishing <strong>probability vs. nonprobability sampling</strong> and identifying specific methods such as simple random, systematic, stratified random, convenience, purposive, quota, and snowball sampling. Examples include selecting names from a hat, sampling across age strata, observing hand hygiene during set time periods, and choosing survey approaches that provide the most reliable patient satisfaction information.

The module also covers <strong>benchmarking</strong>, reinforcing that the primary purpose of benchmarking is to <strong>improve performance</strong> by comparing results to external references.

Another core area is understanding <strong>data types</strong> (categorical vs. continuous) and applying <strong>data visualization</strong> appropriately. Through knowledge checks and exercises, learners choose the best charts/tools for different situations, including run charts and other displays for trends, variation, and relationships, as well as <strong>Ishikawa (fishbone) diagrams</strong> to analyze contributing factors such as staffing, methods, materials, measures, and equipment.

Finally, the module introduces using <strong>statistical process control (SPC)</strong> concepts and run-chart rules to detect <strong>non-random signals of change</strong>, supporting evaluation of process improvement and patient safety outcomes.

Keywords

health data analytics

data management systems

accreditation and regulatory requirements

data collection and sampling

probability sampling

nonprobability sampling

benchmarking

data visualization

Ishikawa diagram

statistical process control