Currently Empty: USD$0.00
Home » SQL Courses » SQL Healthcare Analytics
⭐ Popular
SQL Healthcare
Use SQL to analyze patient populations, clinical encounters, care gaps, hospital utilization, scheduling and claims.
3 course parts · 3 capstone projects · realistic healthcare database · AI Tutor · Certificate issued by CiAI.
Original price was: USD$50.00.USD$15.99Current price is: USD$15.99. /month
Self-paced online · Designed for learners who have completed SQL Basics and SQL Beginner, or have equivalent foundational SQL skills.
Self-paced access
Hands-on SQL practice
Analyze Real Healthcare Data
Work with patients, encounters, diagnoses, procedures, claims and appointments rather than generic business tables.
Measure Clinical & Population Health
Build cohorts, registries, care-gap queries, readmission logic and patient-risk views.
Improve Healthcare Operations
Analyze no-shows, provider productivity, visit volumes, length of stay and reimbursement patterns.
3 Applied Capstone Projects
Complete one healthcare analytics capstone after each course part.
Use SQL to Turn Healthcare Records into Actionable Insight
Healthcare data is spread across patient records, encounters, diagnoses, procedures, appointments and claims. This course teaches learners how to connect those records with SQL and answer practical questions about patient populations, care delivery, utilization, scheduling and financial performance.
Why take SQL Healthcare after SQL Basics and SQL Beginner?
SQL Basics and SQL Beginner teach you how to retrieve, filter, group and combine data. SQL Healthcare applies those skills to real healthcare questions: Which patients belong to a clinical cohort? Where are preventive care gaps? Which providers have the most no-shows? How long are inpatient stays? Which claims are denied or underpaid?
patientsdemographics · insurance · PCP
encountersinpatient · outpatient · ED
diagnoses & proceduresICD-10 · CPT/HCPCS
claims & appointmentspayments · status · no-shows
What you will learn
- Analyze patient demographics by sex, race and insurance type
- Measure appointment no-shows and scheduling patterns
- Filter clinical records using ICD-10 diagnosis codes
- Join encounters, diagnoses and procedures
- Build clinical cohorts and simple risk-stratification views
- Calculate length of stay and analyze encounter timelines
- Apply 7-day and 30-day readmission logic
- Build population-health summaries and chronic-disease registries
- Identify preventive-care gaps
- Measure provider productivity and visit volumes
- Analyze claims, allowed amounts, paid amounts and healthcare costs
Who this course is for
- Healthcare administrators and analysts
- Population health and quality-improvement professionals
- Clinical operations and scheduling teams
- Claims, reimbursement and healthcare finance professionals
- Data analysts who want healthcare-domain SQL experience
- Non-technical professionals moving into healthcare analytics
SQL Healthcare Curriculum: 3 Applied Parts
The course moves from patient and clinical query foundations, to time and utilization analytics, and then to operational and financial healthcare analysis.
Learn how to retrieve, filter, summarize and connect core healthcare records using familiar SQL patterns.
- Patient demographics analysis
- Appointment no-show analysis
- Filtering by ICD-10 codes
- Joining encounters → diagnoses → procedures
- Extracting clinical cohorts
- Risk stratification
- Capstone 1: Patient & Clinical Cohort Report
Use dates, encounter history and population-level SQL to analyze utilization and identify care-management opportunities.
- Length of stay calculations
- Encounter timelines
- Readmission logic: 30-day, 7-day and rolling windows
- Population health aggregates
- Chronic disease registries
- Preventive care gaps
- Capstone 2: Population Health & Utilization Audit
Apply SQL to healthcare operations, scheduling and reimbursement questions used in administrative and management reporting.
- Appointment no-show patterns
- Provider productivity
- Visit volumes and forecasting
- Claims grouping
- Allowed vs. paid amount analysis
- Cost per patient and cost per encounter
- Capstone 3: Healthcare Operations & Claims Dashboard
See the Healthcare Analytics Workflow End to End
1. Understand the PatientDemographics, insurance, diagnoses, appointments and clinical cohorts.
→
2. Measure Care & UtilizationEncounters, length of stay, readmissions, registries and preventive-care gaps.
→
3. Improve OperationsProvider productivity, no-shows, visit volumes, claims and reimbursement.
Part 2 · Length of Stay
Example: Calculate average inpatient length of stay
Length of stay is a common utilization metric used in hospital operations and capacity planning. SQL can calculate it directly from admission and discharge dates.
SELECT
AVG(DATEDIFF(discharge_date, admit_date)) AS avg_length_of_stay
FROM encounters
WHERE encounter_type = 'Inpatient';
-- Measures the average number of inpatient days.
The course then expands from one utilization metric into readmission logic, population-health registries, scheduling patterns and financial analysis.
3 Hands-On SQL Healthcare Capstone Projects
The course includes exactly 3 capstone projects — one after each part — so learners apply SQL to patient, population-health and healthcare-operations problems.
Capstone 1
Patient & Clinical Cohort Report
Build a patient-level clinical report using demographics, ICD-10 filters, encounters, diagnoses and procedures to define and summarize a healthcare cohort.
Skills: filtering, grouping, joins, ICD-10 logic, cohort extraction.
Capstone 2
Population Health & Utilization Audit
Analyze length of stay, encounter timelines, readmission patterns, chronic-disease registries and preventive-care gaps.
Skills: date functions, utilization metrics, registries, care-gap logic.
Capstone 3
Healthcare Operations & Claims Dashboard
Create an operational SQL summary covering no-shows, provider productivity, visit volumes, claims, reimbursement differences and healthcare costs.
Skills: scheduling analytics, grouping, claims analysis, cost metrics.
Recommended SQL Learning Path After This Course
Assuming you have completed SQL Basics and SQL Beginner, the following courses are recommended after SQL Healthcare to deepen your SQL skills and broaden your analytics capability.
Next 1
SQL Intermediate
Build stronger query logic with subqueries, CTEs, advanced filtering and data cleaning.
Explore SQL Intermediate →Next 2
SQL Advance
Move into performance, advanced set logic, window functions, recursive CTEs and BI reporting.
Explore SQL Advance →Next 3
SQL AI-Assisted
Learn to generate, inspect, debug and verify SQL with AI while keeping human judgment in control.
Explore SQL AI-Assisted →Next 4
SQL Data Analytics
Broaden into BI, data engineering and machine-learning preparation using applied analytical SQL.
Explore SQL Data Analytics →Course Details
Format100% self-paced, online
LevelPost-beginner / applied healthcare SQL
Structure3 course parts
Healthcare tablespatients, encounters, diagnoses, procedures, claims, appointments
PracticeHands-on SQL using realistic healthcare data
AI TutorIncluded — healthcare-specific SQL guidance
Capstone Projects3 — one after each course part
CertificateIssued by the Canadian Institute of Artificial Intelligence (CiAI)
Recommended preparationSQL Basics + SQL Beginner
Frequently Asked Questions
The course is designed for healthcare administrators, population-health professionals, clinical operations teams, claims and reimbursement professionals, analysts, and learners who want practical healthcare-domain SQL experience.
The SQL Healthcare course includes exactly 3 capstone projects: 1) Patient & Clinical Cohort Report, 2) Population Health & Utilization Audit, and 3) Healthcare Operations & Claims Dashboard.
The certificate of completion is issued by the Canadian Institute of Artificial Intelligence (CiAI).
The course uses patients, encounters, diagnoses, procedures, claims and appointments. These tables let learners connect clinical, operational and financial healthcare data.
Part 1 covers patient demographics, appointment no-shows, ICD-10 filtering, healthcare joins, clinical cohorts and risk stratification.
Part 2 covers length of stay, encounter timelines, readmission logic, population-health aggregates, chronic-disease registries and preventive-care gaps.
Part 3 covers appointment no-show patterns, provider productivity, visit volumes, claims grouping, allowed-versus-paid amounts and cost analysis.
Yes. Learners use diagnosis_code filtering and LIKE patterns to identify diagnoses and create clinical cohorts and registries.
Yes. The course includes DATEDIFF-based length-of-stay calculations and readmission logic using time windows such as 7 days and 30 days.
Yes. Learners analyze claim counts, claim status, allowed amounts, paid amounts, reimbursement differences and healthcare costs.
Assuming you completed SQL Basics and SQL Beginner, recommended next courses are SQL Intermediate, SQL Advance, SQL AI-Assisted and SQL Data Analytics.
For current subscription, cancellation, and refund information, visit the DataSoSi FAQ page or Contact Us.
Meet the Creator of the Course
Learn from an educator and machine learning engineer working across analytics, AI, data science and applied business problems.
Dr. Mark Lokanan
Professor · Senior Machine Learning Engineer · Data Scientist · AI Specialist
Dr. Mark Lokanan is a professor at Royal Roads University and a Senior Machine Learning Engineer at Vedia. His work spans data science, machine learning, artificial intelligence, analytics, financial crime, and applied research. Drawing on both academic and industry experience, he designed SQL Healthcare Analytics to help learners apply SQL to patient data, population health, clinical utilization, scheduling, claims and operational decision-making.
SQL Healthcare Course Assistant
Ask me about the 3 course parts, 3 capstone projects, patient demographics, ICD-10, joins, clinical cohorts, risk stratification, length of stay, readmissions, registries, preventive-care gaps, provider productivity, no-shows, claims, reimbursement, the CiAI certificate, or recommended next courses.