M03-07 · AI + Healthcare Operations

Clinical Data Analysis and Quality Improvement

Teaches students to analyze clinical data for quality improvement, population health management, and regulatory reporting. Covers clinical outcome metrics, quality improvement methodologies (Plan-Do-Study-Act, Lean, Six Sigma basics), population health analytics using de-identified datasets, clinical decision support evaluation, and mandatory regulatory reporting (Meaningful Use/Promoting Interoperability measures).

35 Hours

8 Learning objectives

Create Bloom's ceiling (?)

5 Competencies

What You'll Be Able To Do

Learning Objectives

Objectives

Depth

Analyze clinical outcome metrics (readmission rates, infection rates, documentation compliance, medication error rates) using Clarity/Caboodle data and identify statistically meaningful trends Analyze
Apply quality improvement methodologies (PDSA cycles) to design and measure clinical workflow interventions Apply
Create population health dashboards using de-identified data that segment patient populations by risk factors, chronic conditions, and utilization patterns Create
Evaluate clinical decision support tools by comparing AI-generated recommendations against clinical expectations and measuring impact on workflow Evaluate
Apply Meaningful Use/Promoting Interoperability reporting requirements to extract, validate, and submit required quality measures Apply
Design a regulatory compliance report combining clinical quality data, patient safety indicators, and operational metrics for Joint Commission or CMS review Create
Analyze the impact of EHR configuration changes on clinical documentation quality, billing rejection rates, and physician satisfaction using before/after metrics Analyze
Understand the ethical considerations of population health analytics including algorithmic bias, data representativeness, and equitable resource allocation Understand

Levels: Remember · Understand · Apply · Analyze · Evaluate · Create — highest demands most original thinking.

Core Competencies

What You'll Master

Clinical Data Analysis

Clarity/Caboodle querying for clinical outcomes, statistical trend identification, de-identification for analysis, cohort definition, clinical metric interpretation.

Quality Improvement Methods

PDSA cycles, Lean process mapping, root cause analysis, intervention measurement, control charts, sustainability planning.

Population Health Analytics

Risk stratification, chronic disease cohort analysis, utilization pattern identification, geographic and demographic segmentation, health equity analysis.

Clinical Decision Support Evaluation

AI tool accuracy measurement, clinical workflow impact assessment, false positive/negative analysis, provider trust and adoption metrics.

Regulatory Reporting

Meaningful Use/Promoting Interoperability measures, Joint Commission survey data, CMS Conditions of Participation metrics, automated report generation, data validation.

Portfolio Artifact

What You'll Build

Clinical Quality Improvement Analysis — Student conducts a complete quality improvement project using simulated clinical data: a population health dashboard showing risk-stratified patient segments, a PDSA cycle design for reducing a target clinical metric (e.g., 30-day readmission rate), a before/after analysis of an EHR workflow change with statistical validation, a Meaningful Use/Promoting Interoperability reporting package, and a presentation of findings to a simulated clinical leadership audience.

Tools You'll Use

Industry Tools, Not Toy Projects

SQL

Querying Clarity/Caboodle data warehouses for clinical outcomes data and quality metric extraction.

Power BI / Tableau

Business intelligence platforms for building clinical dashboards and population health visualizations.

Epic Reporting Workbench

Native Epic reporting tool for operational and clinical performance reporting.

Google Sheets / Excel

Spreadsheet tools for statistical analysis, PDSA cycle tracking, and quality metric documentation.

Claude

AI assistant for data pattern analysis with de-identified data only, and quality improvement methodology guidance.

Python / R (Optional)

Programming languages for advanced statistical analysis and clinical data modeling.