Healthcare Data Analytics

Welcome to Healthcare Data Analytics

What healthcare data analysts do, the questions they answer, where they work, the tools they use, and the path in this course from zero to job-ready.

Healthcare Data Sources

EHR clinical data, claims, pharmacy, lab, registries, surveys, public data sets, devices and social determinants — what each contains, strengths and limitations.

Healthcare Codes Every Analyst Uses

ICD-10-CM, ICD-10-PCS, CPT/HCPCS, DRGs, revenue codes, place of service, NDC, LOINC and SNOMED — and how to group codes into analysis categories.

Healthcare Metrics 101

Rates, proportions and per-1,000 measures, length of stay, readmissions, utilization (visits per 1,000), cost (PMPM), mortality, wait times and patient experience.

Privacy, De-identification & Data Governance

HIPAA for analysts — PHI, minimum necessary, de-identification (Safe Harbor's 18 identifiers, Expert Determination), limited data sets, data use agreements, small cells and governance.

Cleaning Healthcare Data in Excel

Spot and fix the messy data analysts meet daily — inconsistent text, codes stored as numbers, duplicates, blanks, impossible values — with TRIM, UPPER, PROPER, LEFT/RIGHT and IF.

Excel Formulas for Healthcare Questions

COUNTIF, SUMIF and AVERAGEIF for counts by group, IF for categories, absolute references, percentages and building a small measure table.

Lookups: Joining Healthcare Tables in Excel

Use XLOOKUP, VLOOKUP and INDEX/MATCH to bring in code descriptions, fee schedules, provider details and groupers — and avoid the classic lookup mistakes.

PivotTables & Summary Reports

Summarize thousands of rows fast with PivotTables — rows, columns, values, filters, slicers, percent of total, grouping dates — and build monthly healthcare summaries.

Charts & Simple Reports in Excel

Choose the right chart for healthcare data, build trend and comparison charts, add targets, avoid misleading visuals, and assemble a one-page monthly report.

Your First SQL Queries

Relational tables, SELECT, WHERE, ORDER BY and LIMIT on a patient table — and how analysts read a data dictionary before querying.

Counting & Summarizing with GROUP BY

COUNT, SUM, AVG, MIN, MAX, COUNT(DISTINCT), GROUP BY and HAVING — answering 'how many and how much by group' questions on encounters.

Joining Patients, Encounters & Diagnoses

INNER and LEFT joins, join keys, table aliases, one-to-many relationships, and joining three tables to answer clinical questions without duplicating rows.

Dates & Time in Healthcare SQL

Length of stay, age at encounter, filtering date ranges, grouping by month, and the pitfalls of datetimes, time zones and inclusive ranges.

CASE, NULLs & Cleaning in SQL

Create categories with CASE, handle NULLs with COALESCE and IS NULL, standardize text, search codes with LIKE, and write conditional counts in one query.

Window Functions & 30-Day Readmissions

ROW_NUMBER, RANK, LAG and LEAD with PARTITION BY — and using them to build a 30-day readmission analysis, first/last events and running totals.

CTEs & Building Quality Measure Logic

Break complex logic into readable steps with common table expressions — denominator, exclusions and numerator — the way real quality measures are built and validated.

Analyzing Claims Data

Claim headers and lines, billed vs allowed vs paid, claim status and adjustments, de-duplicating versions, and denial and payment analysis with SQL.

Utilization & Cost: Member Months and PMPM

Build member months from enrollment, compute utilization per 1,000 and PMPM cost by service category, compare populations and spot high-cost members.

Data Quality & Validation

The dimensions of data quality, automated checks in SQL (completeness, validity, uniqueness, timeliness, consistency), reconciliation to source systems and documenting data issues.

Descriptive Statistics for Healthcare Data

Mean, median, percentiles, standard deviation, distributions and skew — summarizing length of stay, costs, wait times and lab values correctly.

Comparing Groups: Rates, Confidence & Significance

Compare rates between clinics, time periods and populations — confidence intervals for proportions, chance variation, statistical vs practical significance, and common traps.

Risk Adjustment & Observed/Expected Ratios

Why raw comparisons are unfair, how expected rates are built from patient risk, observed-to-expected (O/E) ratios, indirect standardization, and how public ratings use them.

Trends, Run Charts & Control Charts

Track performance over time — run charts, statistical process control (SPC), common vs special cause variation, control limits and rules for detecting real change.

Correlation & Regression Basics

Scatter plots, correlation coefficients, simple and multiple regression, logistic regression for yes/no outcomes, and why correlation is not causation in healthcare data.

Data Visualization Principles for Healthcare

How people read charts, pre-attentive attributes, decluttering, color and accessibility, honest scales, small multiples and designing for clinical audiences.

Power BI Fundamentals

Get data, transform with Power Query, model with fact and dimension tables (star schema), relationships, visuals, filters and slicers, and publishing securely.

DAX Measures for Healthcare KPIs

Measures vs calculated columns, CALCULATE and filter context, DIVIDE, DISTINCTCOUNT, time intelligence, and writing readmission, LOS and PMPM measures.

Designing Healthcare Dashboards

Operational vs strategic dashboards, choosing KPIs, layout, drill-downs, refresh and data latency, user testing, adoption and maintaining dashboards over time.

Data Storytelling & Presenting to Clinicians

Turn analysis into decisions — know your audience, structure the story (context, conflict, resolution), executive summaries, presenting uncertainty and handling tough questions.

Clinical Quality Analytics

Quality frameworks (structure, process, outcome), HEDIS, CMS Star Ratings, eCQMs, hospital quality programs, gap lists and supporting improvement teams.

Population Health & Risk Stratification

Registries, attribution, risk stratification tiers, rising-risk patients, social risk, care management targeting and measuring program impact.

Revenue Cycle Analytics

Key revenue cycle KPIs, A/R aging, denial analytics, payer performance, charge lag and DNFB, net collection rate, and turning insights into prevention.

Operations & Capacity Analytics

Patient flow, bed occupancy and census by hour, ED and OR throughput, staffing to demand, forecasting volumes, and queueing intuition.

Health Equity Analytics

Collect and use race, ethnicity, language and social data responsibly; stratify measures, find disparities, avoid misleading comparisons and support equity improvement.

Python & pandas for Healthcare Data

Why analysts add Python, loading and inspecting data with pandas, cleaning, groupby summaries, merging tables, and reproducible notebooks.

Predictive Analytics in Healthcare

Prediction vs explanation, building a readmission or no-show model, train/test splits, evaluation (confusion matrix, sensitivity, PPV, AUC), calibration, fairness and deployment.

The Analytics Project Lifecycle

Intake and requirements, scoping, data access requests, building and validating, documentation, peer review, delivery, and maintaining analytics products.

Capstone: An End-to-End Healthcare Analysis

Complete a portfolio project — question, data, SQL and Excel/Python analysis, statistics, a Power BI dashboard, and an executive presentation — using synthetic data.

Job-Ready: Becoming a Healthcare Data Analyst

Roles and titles, certifications (CHDA, CPHQ, Microsoft PL-300), resumes and portfolios, technical interviews (SQL, metrics, case questions), and your first 90 days.