Durga Analytics • Healthcare FHIR Analytics • 500 Chapters
Healthcare FHIR Analytics — 500-Chapter Master Course
A practical, in-depth curriculum covering FHIR data modelling, ingestion, governance, analytics, ML, interoperability and deployment. Designed for healthcare data engineers, analysts, informaticians and architects.
Course Snapshot
- • 20 modules × 25 chapters = 500 chapters
- • Hands-on labs, capstones, templates and reference datasets
- • Compliance-first design (HIPAA/GDPR patterns)
- • Audience: Data engineers, MDM/Integration leads, clinicians & researchers
Full Curriculum — 20 Modules, 500 Chapters
Expand any module below to view all 25 chapters. Each chapter maps to a 10–20 minute lesson, downloadable notes and a lab or capstone where applicable.
MODULE 1 — Foundations: Healthcare Data & FHIR Basics (Ch 1–25)
1. Introduction to Healthcare Data — Types and Sources
2. Challenges in Healthcare Data Management
3. What is FHIR — Origins, Purpose, Evolution
4. FHIR Resource Types: Overview (Patient, Observation, Encounter, etc.)
5. FHIR Data Formats: JSON, XML, Turtle, RDF
6. REST vs Messaging vs Graph in FHIR Interactions
7. FHIR API Fundamentals
8. FHIR Versioning — DSTU2, STU3, R4, R5
9. FHIR Extensions & Profiles — Need and Use Cases
10. Terminology Standards: SNOMED-CT, LOINC, ICD, RxNorm
11. Code Systems & Value Sets in Healthcare Analytics
12. Reference Data vs Master Data vs Transaction Data in Healthcare
13. Privacy, Compliance & Regulations in Healthcare Data (HIPAA / GDPR)
14. Data Governance in Healthcare Analytics Context
15. Data Quality: Common Issues in Healthcare Data
16. Data Security & Consent Management
17. Integration Patterns — EHR, EMR, Clinical Systems, Wearables, IoT devices
18. Data Ingestion vs Real-time Streaming vs Batch for Healthcare Data
19. Metadata & Provenance in Medical Data
20. Data Lifecycle in Healthcare — From Patient Record to Analytics
21. Concepts of Data Warehousing vs Data Lake vs Data Lakehouse for Healthcare
22. Observability & Auditing in Healthcare Data Systems
23. Interoperability Challenges & Semantic Interoperability
24. Use-cases & Scenarios where FHIR-based Analytics adds value
25. Module 1 Capstone: Map out a hypothetical FHIR data architecture for a small hospital
MODULE 2 — FHIR Data Modeling & Resources (Ch 26–50)
26. Deep dive into core FHIR Resources — Patient, Practitioner, Organization
27. Encounter, Observation, Condition, Procedure, MedicationRequest resources
28. Managing Relationships between Resources (references, bundles)
29. Handling Identifiers, IDs, and Unique Constraints in FHIR
30. Use of FHIR Bundles for Transactions & Batch Operations
31. FHIR Profiles for Domain-specific Extensions
32. FHIR ValueSets: Creation, Management, Versioning
33. Terminology binding: Enforcing code systems & value sets
34. Handling Complex Data Types (e.g. Observations with multiple components)
35. Representing Temporal Data: Encounters, History, Visits
36. Representing Hierarchies: Organizations, Departments, Patients → Visits → Observations
37. FHIR AuditEvent & Provenance for Tracking Data Changes
38. Consent, Data Sharing and Access Control via FHIR Resources
39. Handling Missing/Incomplete Data in FHIR Context
40. Data Validation & Schema Validation for FHIR Resources
41. Data Standardization & Normalization in FHIR-based Systems
42. Mapping Legacy Schema to FHIR Resource Model
43. Data Enrichment & Reference Data Linking (e.g. ICD mapping)
44. Versioning Strategies for FHIR Data over Time
45. Backward Compatibility & Migration across FHIR Versions
46. Handling Data Merges, Duplicates, Identity Resolution in Healthcare Data
47. Audit Trail & History Maintenance for Patient/Encounter Data
48. GDPR/HIPAA Compliant Data Modeling Patterns
49. Data Archival & Retention Policies for Healthcare Data
50. Module 2 Lab: Design a FHIR-based Data Model for a Clinical Use-case
MODULE 3 — Data Ingestion, Integration & ETL for Healthcare (Ch 51–75)
51. Data Source Onboarding — EHR, EMR, Lab Systems, Wearables, Imaging, Devices
52. Methods of Data Extraction: API-based, File dumps, HL7, DICOM, CSV, Streams
53. Bulk ingest vs incremental ingest vs streaming for FHIR data
54. ETL/ELT pipelines for FHIR Data: Extract → Transform → Load
55. Normalization & Standardization during ingestion (codes, formats, units)
56. Handling Heterogeneous Data Sources (legacy, IoT, external partners)
57. Data Cleansing & Validation — Schema & Business Rules
58. Mapping Source Data to FHIR Resources & Profiles
59. Handling Data Privacy & Consent during ingestion & integration
60. Data Versioning & Change Data Capture for patient/clinical data
61. De-duplication & Identity Resolution for Patients / Entities
62. Merging Duplicate Records while Preserving History
63. Audit Logging & Provenance Capture in Ingestion Pipelines
64. Data Enrichment — Adding Reference Data (standard code sets, demographic data)
65. Handling Real-time Streaming Data (e.g. vital signs, sensors) into FHIR pipelines
66. Storage Patterns: Transactional Store vs Data Lake vs Hybrid for healthcare data
67. Partitioning and Archival Strategies for Large Datasets
68. Backup & Disaster Recovery Planning for Healthcare Data Store
69. Data Quality Checks & Alerts during Ingestion
70. Data Lineage Tracking — From Ingestion to Analytics
71. Monitoring and Observability of ETL / Ingestion Pipelines
72. Scalable Ingestion Architecture for Healthcare Organizations
73. Secure Data Transfer — Encryption, TLS, VPN, Data Masking at Ingest
74. Compliance Logging & Audit Trails for Regulatory Compliance
75. Module 3 Capstone: Build a sample ingestion + ETL pipeline for lab + EMR data into FHIR store
MODULE 4 — Data Storage, Lakehouse & Warehousing for FHIR Analytics (Ch 76–100)
76. Choosing Storage Backend: Document DB vs Relational DB vs Data Lake vs Lakehouse
77. Hybrid Storage Models for Clinical & Analytical Workloads
78. Columnar Storage & Parquet/ORC for Analytical Queries
79. Time-series & Longitudinal Data Storage Strategies (for Observations, Vitals, History)
80. Handling JSON / Nested Data (FHIR resources) in Analytical Stores
81. Schema-on-read vs Schema-on-write for Healthcare Data Analytics
82. Partitioning Strategies — By date, patient, organization, data type
83. Data Deduplication & Compaction Strategies
84. Data Lakehouse Architecture for Healthcare Data
85. Delta / Versioned Tables for Patient Histories & Audits
86. Data Warehouse Design for Analytics (star schema, fact-dimension based on FHIR data)
87. Data Mart Patterns for Clinical Analytics, Reporting, BI
88. Dimensional Modelling for Healthcare Analytics (patient-visit-observation model)
89. Historical Data Archiving & Retention Compliance
90. Data Snapshotting & Slowly Changing Dimensions (SCD) for Patient / Visit Data
91. Ensuring Data Consistency & ACID for Patient Records
92. Query Performance Optimization for Large Healthcare Datasets
93. Cost Management & Storage Optimization Strategies
94. Security & Encryption at Rest & In-Transit for Stored Data
95. Access Control, Row/Column-level ACLs for PHI data
96. Logging & Audit for Data Access & Queries
97. Data Masking & Tokenization for Sensitive Fields
98. Data Governance & Lineage for Stored Data
99. Data Backup, Archival & Disaster Recovery Procedures
100. Module 4 Lab: Design a Lakehouse + Warehouse architecture for FHIR data
MODULE 5 — Data Governance, Privacy, Compliance & Ethics in Healthcare Analytics (Ch 101–125)
101. Understanding Regulatory Requirements (HIPAA, GDPR, Local Regulations)
102. Data Privacy Principles for Healthcare Data
103. Patient Consent Management & Audit Trails
104. Role-based Access Control (RBAC) for PHI / Sensitive Data
105. Attribute-based Access Control (ABAC) & Policy Management
106. Data Anonymization & Pseudonymization Strategies
107. Data Masking & Tokenization for Sensitive Fields
108. Consent Lifecycle Management (Revocation, Update, Logging)
109. Data Retention & Deletion Policies
110. Data Minimization & Purpose Limitation Principles
111. Data Ethics — Usage, Sharing, Aggregation
112. Data Sharing Agreements with External Partners / Institutions
113. Data Use Monitoring & Auditing
114. Data Breach Handling & Reporting Process
115. Compliance Documentation & Reporting Templates
116. Governance Committee Structure for Healthcare Data Programs
117. Metadata & Data Cataloguing for Patient Data
118. Data Lineage & Provenance for Clinical Data
119. Versioning & Change Management for Data Models & Resources
120. Quality Assurance & Data Stewardship Processes
121. Incident Management & Logging for Data Issues
122. User Training & Awareness for Data Privacy & Security
123. Monitoring & Alerting for Governance Violations
124. Periodic Compliance Audits & Reviews
125. Module 5 Capstone: Draft a Governance, Privacy & Compliance Plan for FHIR Analytics
MODULE 6 — Analytical Use-cases: Clinical Analytics & Reporting (Ch 126–150)
126. Clinical Reporting — Patient Summaries, Visit Reports, Lab Reports
127. Population Health Analytics (Chronic disease trends, demographic analysis)
128. Quality Metrics & KPIs (Readmissions, Mortality, Lab abnormality rates)
129. Longitudinal Patient Analytics (History, Outcomes, Trajectories)
130. Ad-hoc Queries on FHIR Data for Clinical Research
131. Cohort Discovery & Selection using FHIR Data
132. Aggregation & Roll-ups — By Region, Hospital, Time, Disease Type
133. Temporal Analytics — Trends over time, seasonal patterns
134. Data Visualization Patterns for Clinical Data (dashboards, reports)
135. Data Productization of Clinical Analytics — Sharing with Stakeholders
136. Self-service Reporting for Physicians & Admins
137. Data Validation & QA of Analytical Reports
138. Handling Missing Data, Outliers, Data Imbalance in Clinical Data
139. Statistical Analysis for Healthcare Data (distributions, hypothesis testing)
140. Predictive Analytics Pre-requisites & Data Readiness Checks
141. Data Lineage & Traceability from Reports to Raw Data
142. Access Control for Reports & Aggregated Data
143. Anonymization & Aggregation to Protect PHI in Reports
144. Role-based Views (Clinician, Admin, Researcher)
145. Compliance in Reporting (Audit, Export, Sharing)
146. Versioning of Reports & Snapshots
147. Data Refresh Strategies for Reports
148. Report Automation & Scheduling
149. Reporting Platform Integration (BI tools, dashboards)
150. Module 6 Lab: Build a Clinical Analytics Dashboard using FHIR-derived Data
MODULE 7 — Advanced Analytics & Machine Learning on FHIR Data (Ch 151–175)
151. Data Readiness Assessment for ML on FHIR Data
152. Feature Engineering from Clinical Data (demographics, vitals, lab results, time-series)
153. Handling Temporal Sequences & Longitudinal Data for ML
154. Handling Missing Values, Sparse Data, Data Imbalance
155. Data Labeling & Outcome Definition (e.g. disease onset, readmission)
156. Cohort Creation & ML Dataset Preparation
157. Privacy-preserving ML & De-identification Techniques
158. Model Training & Validation on Healthcare Data
159. Cross-validation Techniques for Clinical Data
160. Explainable AI & Interpretability for Medical Models
161. Fairness, Bias & Ethical Concerns in Healthcare ML
162. Model Versioning, Monitoring & Drift Detection
163. Deployment Patterns for Clinical ML Models (Batch vs Real-time)
164. Data Access & Governance for ML Pipelines
165. Model Serving & Integration with Clinical Systems
166. Logging, Auditing & Monitoring Predictions
167. Feedback Loops & Model Retraining Strategies
168. Integration of ML Results with EHR/Clinical Systems via FHIR
169. Edge / Device Data Integration (Wearables, IoT) for Analytics / ML
170. ML Model Security & Compliance
171. Clinical Decision Support Systems (CDSS) Design Principles
172. Risk Management & Validation of ML Systems in Healthcare
173. Regulatory Compliance for ML Models (Medical Device Regulations, GDPR, HIPAA)
174. Explainability Reports & Documentation for Regulatory Audits
175. Module 7 Capstone: Build a Predictive Analytics Model on FHIR Data
MODULE 8 — Data Productization & Data-as-a-Service for Healthcare (Ch 176–200)
176. What is a Healthcare Data Product?
177. Data Products vs Reports vs Raw Data
178. Designing Data APIs for Clinical & Analytical Use-cases
179. API Security, Authentication & Authorization in Healthcare Context
180. Versioning & Compatibility of Data APIs
181. Data Product Governance & SLA Definition
182. Metadata & Documentation for Data Products
183. Data Subscription & Access Patterns (Internal, External, Partners)
184. Data Sharing & Consent Management for Data Products
185. Data Licensing, Agreements & Audit Trails
186. Audit Logging & Monitoring of Data Product Usage
187. Rate-limiting, Quotas & Throttling for Data Access
188. Data Masking & Anonymization for Shared Data Products
189. Dynamic Data Products (Streaming, Real-time updates)
190. Snapshot Data Products (point-in-time cohorts)
191. Data Contracts for Data Products
192. Data Product Testing & Validation Frameworks
193. Data Product Packaging & Distribution (FHIR bundles, JSON, CSV, Parquet)
194. Data Product Onboarding for Consumers (BI tools, ML, Research)
195. Data Product Version Management & Deprecation Strategy
196. Data Product Metrics & Usage Analytics
197. Monetization Strategy for Data Products (optional)
198. Compliance & Privacy for Shared Data Products
199. Data Product Marketplace / Catalog for Healthcare Data
200. Module 8 Lab: Design and Build a Data-as-a-Service API for FHIR Data
MODULE 9 — Real-time & Streaming Analytics for Healthcare (Ch 201–225)
201. Real-time Data Sources: IoT, Wearables, Monitoring Devices, Telemetry
202. Streaming Data Ingestion Patterns for Healthcare Data
203. Event-driven Architecture for Clinical Events & Alerts
204. Handling High-frequency Data (Vitals, Sensor Streams)
205. Streaming Storage: Time-series Databases, Event Stores
206. Low-latency Analytics vs Batch Analytics — Tradeoffs
207. Data Aggregation, Windowing & Time-series Analysis
208. Real-time Alerting & Monitoring (e.g. patient vitals breach)
209. Streaming Data Quality Checks & Validation
210. Streaming Data Lineage & Auditability
211. Real-time ML Inference & Alert Pipelines
212. Data Privacy & Secure Stream Handling
213. Consent & Access Controls for Streaming Data
214. Data Anonymization / Aggregation in Real-time Streams
215. Fault-tolerance & Disaster Recovery for Streaming Pipelines
216. Scalability & Throughput Management in Streaming Systems
217. Data Retention & Archival of Streamed Data
218. Streaming + Historical Data Integration for Analytics
219. Temporal Analytics Combining Real-time & Historical Data
220. Monitoring & Observability for Stream Systems
221. Latency, Throughput & SLA Management
222. Data Subscription Models for Stream Consumers
223. Real-time Dashboards & Medical Monitoring Tools
224. Security & Compliance in Streaming Healthcare Systems
225. Capstone: Build a Real-time Monitoring Pipeline for Patient Vitals
MODULE 10 — Metadata, Data Catalog & Lineage for Healthcare Data (Ch 226–250)
226. Importance of Metadata in Healthcare Analytics
227. Building Data Dictionary for Clinical & FHIR Data
228. Data Catalog Concepts & Requirements
229. Cataloging FHIR Resources & Data Products
230. Metadata Standards for Healthcare Data
231. Value Sets, Terminology Registries & Code Systems
232. Versioning & Maintenance of Metadata
233. Data Lineage — From Source Systems to Analytics Output
234. Provenance Tracking for Clinical Data Changes
235. Audit Trail for Data Transformations & ETL/ELT Jobs
236. Data Catalog APIs & Access Patterns
237. Tagging & Classification of Data (PHI, Sensitive, Public)
238. Metadata-driven Data Quality Rules & Validation
239. Metadata Governance & Stewardship Roles
240. Search & Discovery Mechanisms for Data Products
241. Access Control for Metadata & Catalog
242. Documentation & Data Glossary for Clinical Stakeholders
243. Impact Analysis for Schema or Code Set Changes
244. Change Notification for Metadata Consumers
245. Metadata-driven Data Governance & Compliance
246. Metadata Synchronization across Systems
247. Automated Metadata Extraction & Updates
248. Data Catalog User Interface & UX Considerations
249. Metadata Audit & Version History
250. Module 10 Lab: Build a Data Catalog & Lineage System for FHIR Data
MODULE 11 — Performance, Scalability & Cost Optimization (Ch 251–275)
251. Performance Challenges in Healthcare Data Analytics
252. Query Optimization for FHIR-based Stores
253. Indexing Strategies for Large Clinical Datasets
254. Partitioning & Sharding for Scalability
255. Compression & Storage Efficiency for Historical Data
256. Caching & In-memory Techniques for Fast Analytics
257. Batch vs Real-time Cost Tradeoffs
258. Cost Management for Cloud Storage & Compute
259. Autoscaling & Resource Optimization
260. Monitoring Resource Usage & Cost Metrics
261. Data Tiering Strategies (hot, warm, cold)
262. Archival & Cold Storage for Historical Medical Records
263. Cost-aware Data Retention Policies
264. Storage Optimization for Long-term Data
265. Query Cost Estimation & Monitoring
266. Spot Instances / Preemptible Instances for Cost Savings
267. Data Storage Budgeting & Forecasting
268. Performance Testing & Load Testing for Analytics Workloads
269. Failover & Disaster Recovery Cost Planning
270. Scalability Planning for High Throughput / Big Data Volumes
271. Data Compression & Deduplication Techniques for Healthcare Data
272. Secure & Cost-optimized Data Backup / Archive Strategy
273. Audit & Compliance Costs vs Storage Performance Balance
274. Monitoring SLAs & SLOs for Performance & Cost
275. Module 11 Capstone: Optimize Performance & Cost for a Large Hospital Dataset
MODULE 12 — Data Sharing, Interoperability & Partner Integrations (Ch 276–300)
276. Interoperability Standards: FHIR, HL7 v2/v3, DICOM, CDA, etc.
277. Data Sharing Models: Internal, External Partners, Research, Public Health
278. Cross-institution Data Exchange via FHIR APIs
279. Consent & Privacy Considerations in Sharing
280. Data Transformation & Mapping for Inter-org Sharing
281. Data De-identification / Anonymization for Shared Datasets
282. Shared Data Contracts & SLAs with Partners
283. Multi-tenant Data Product Distribution
284. Data Licensing & Use Agreements
285. Audit & Logging for Shared Data Access & Usage
286. Governance for Data Sharing — Policies & Reviews
287. Data Quality Standards for Shared Data
288. API Rate Limiting & Access Controls for Shared Data
289. Monitoring Usage & Access Patterns by Consumers
290. Data Sharing via Streaming vs Batch vs API
291. Federated Queries Across Institutions
292. Research & Analytics Data Exports — Compliance & Security
293. Cross-organization Metadata & Data Catalog Integration
294. Interoperability Testing & Validation
295. Data Versioning & Change Notifications for Shared Data
296. Documentation & Consumer Onboarding for Shared Data Products
297. Data Harmonization Across Institutions (code sets, standards)
298. Disaster Recovery & Redundancy for Shared Data Systems
299. Compliance & Legal Review for Data Sharing
300. Capstone: Design a Healthcare Data Sharing Platform using FHIR
MODULE 13 — Monitoring, Auditing, Logging & Observability (Ch 301–325)
301. Importance of Monitoring in Healthcare Data Systems
302. Logging Standards for Clinical Systems
303. Audit Logging for PHI Access & Changes
304. Access Logging & Role-based Audits
305. Data Change History & Versioning Logs
306. ETL Pipeline Monitoring & Alerting
307. Data Quality Monitoring Dashboards
308. Monitoring Data Freshness, Completeness, Integrity
309. Data Lineage-based Alerts for Downstream Breakages
310. Usage Monitoring for Data Products & APIs
311. Performance Monitoring Metrics & SLAs
312. Infrastructure Monitoring (Compute, Storage, Network)
313. Security & Compliance Monitoring (Unauthorized Access, Breaches)
314. Incident Response & Logging in Healthcare Systems
315. Anomaly Detection for Data Streams & Loads
316. Retention of Logs & Audit Trails for Compliance
317. Secure Storage of Logs & Audit Data
318. Log Anonymization & Masking for Sensitive Entries
319. Monitoring Cost & Resource Usage Over Time
320. Governance for Observability Data & Who Can Access Logs
321. Alerts & Notification Systems for Data Issues
322. Dashboard & Report Versioning & Archival
323. Recovery Playbooks for Data Incidents
324. Periodic Audit & Review Processes
325. Module 13 Capstone: Build a Full Observability & Audit System for Healthcare Data Platform
MODULE 14 — Research, Population Health & Public Health Analytics (Ch 326–350)
326. Role of Analytics in Public Health & Research
327. De-identified Cohort Building & Anonymization Techniques
328. Population-level Analytics: Epidemiology, Trends, Patterns
329. Longitudinal Studies & Patient Journey Analytics
330. Cohort Comparison & Outcome Analysis
331. Statistical Methods for Medical Research (survival analysis, incidence/prevalence)
332. Data Aggregation & Harmonization Across Sources
333. Data Quality & Integrity for Research Analytics
334. Data Consent & Ethics for Research Use
335. Regulatory Approvals & Compliance for Research Data
336. Data Sharing for Research & Public Health Use-cases
337. Data Governance & Oversight for Research Analytics
338. Reporting to Public Health Agencies (anonymized data)
339. Real-time Surveillance Systems (e.g. outbreaks, public health monitoring)
340. Integration with National / Regional Health Systems & Registries
341. Metadata & Provenance for Research Data
342. Audit Trails for Research Data Usage
343. Versioning of Research Data & Results
344. Collaboration Tools & Data Sharing Frameworks for Research Teams
345. Visualizations & Dashboarding for Public Health Insights
346. Risk Stratification & Predictive Analytics for Public Health
347. ML / AI Models for Population Health Predictions
348. Ethical AI & Bias Mitigation in Public Health Models
349. Reporting & Publication Standards for Research Analytics
350. Module 14 Capstone: Build a Population Health Analytics Pipeline using FHIR Data
MODULE 15 — Data Migration, Versioning & Historical Data Handling (Ch 351–375)
351. Migrating Legacy Healthcare Data to FHIR-based Platform
352. Data Mapping & Transformation Strategy for Legacy EMR/EHR
353. Handling Historical Patient Data & Records
354. Backfilling Data into FHIR Store
355. Data Validation & Cleansing for Legacy Data
356. De-duplication & Identity Resolution during Migration
357. Versioning & Audit for Migrated Data
358. Data Archival Strategies for Historical Records
359. Data Retention & Compliance for Historical Records
360. Snapshotting Old vs New Records
361. Dual-system Coexistence Strategy (Legacy + New)
362. Gradual Cutover & Rollout Planning
363. Data Lineage & Provenance for Migrated Data
364. Testing & Validation Framework for Migration
365. User Acceptance Tests & Clinical Validation
366. Rollback & Recovery Strategies
367. Consent & Privacy Handling during Migration
368. Documentation & Sign-off Procedures
369. Training & Handover to Clinical Staff
370. Performance & Scalability Considerations for Historical Data
371. Archival Storage Design & Policies
372. Metadata Maintenance for Migrated Data
373. Reporting Continuity during Migration
374. Data Governance & Approval Workflows for Migration
375. Module 15 Capstone: Plan & Execute a Full Migration from Legacy EMR to FHIR Analytics Platform
MODULE 16 — Operationalizing Analytics: Deployment, CI/CD, Platforms & Automation (Ch 376–400)
376. Analytics Platform Setup for Healthcare (On-prem / Cloud / Hybrid)
377. Infrastructure as Code (IaC) for Healthcare Data Pipelines
378. CI/CD Pipelines for ETL, Data Models, Analytics, ML
379. Version Control for Data Models & Schemas
380. Automated Testing Frameworks for Clinical Data Pipelines
381. Data Contract Testing & Validation Automation
382. Data Quality Regression Tests
383. Monitoring & Alerting Integration in CI/CD
384. Deployment Automation for Data Products & APIs
385. Scheduled Jobs vs Real-time Jobs
386. Disaster Recovery & Failover Automation
387. Backup & Snapshot Automation
388. Data Masking & Anonymization Automation During Deployments
389. Access Provisioning Automation for New Users / Systems
390. Compliance Checks & Audits Embedded in Deployment Pipelines
391. Logging & Monitoring Setup for New Deployments
392. Versioned Release & Rollback Strategies
393. Multi-environment Deployment (Dev / QA / Production)
394. Data Migration Automation Tools
395. Deployment Governance & Approval Workflows
396. Documentation & Change Logs for Deployments
397. Cost Monitoring & Optimization Automation
398. Platform Scalability & Autoscaling Configurations
399. User Training & Onboarding Automation
400. Module 16 Capstone: Build a Full CI/CD Pipeline for FHIR Analytics Platform
MODULE 17 — Visualization, Dashboards & Reporting Tools Integration (Ch 401–425)
401. BI Tools Overview (Tableau, Power BI, Looker, Superset) for Healthcare Analytics
402. Connecting BI Tools to FHIR-based Data Stores
403. Building Clinical Dashboards (Patient Summary, KPI Dashboards)
404. Dashboard Security & Access Control (Role-based, PHI masking)
405. Real-time Dashboards for Monitoring Vitals / Streaming Data
406. Historical Reports & Trend Dashboards
407. Data Aggregation & Roll-ups for Dashboards
408. Interactive Reporting vs Static Reports
409. Data Export Options (CSV, Excel, JSON, Parquet) for Researchers / BI Consumers
410. Automated Report Scheduling & Delivery
411. Versioning & Snapshotting of Reports & Dashboards
412. Annotation & Collaboration Features on Dashboards
413. Data Lineage & Traceability in Dashboards
414. Data Governance for Dashboard Metadata & Definitions
415. Metadata-driven Dashboard Template Library
416. Compliance-ready Reporting & Export Formats (Anonymized, Aggregated)
417. Audit Logging of Dashboard Access & Exports
418. Data Sharing Features from Dashboards (Partners, Vendors)
419. User Training & Documentation for Dashboard Consumers
420. Performance Optimization for Large Dataset Dashboards
421. Monitoring Dashboard Usage & Performance Metrics
422. Cost Management for BI Tool Integration
423. Role-based Dashboards for Different Stakeholders (Clinicians, Admins, Researchers)
424. Data Productization of Dashboards
425. Module 17 Capstone: Build a Complete Analytics Dashboard Suite for a Hospital
MODULE 18 — Scaling, Multi-center & Multi-institution Analytics (Ch 426–450)
426. Multi-center Data Aggregation Challenges
427. Data Standardization & Harmonization Across Institutions
428. Cross-institution Data Sharing & Consent Management
429. Federated Analytics vs Centralized Analytics Models
430. Data Privacy & Compliance Challenges across Jurisdictions
431. Federated Query & Analytics Engines
432. Data Mesh Patterns for Healthcare Data Sharing
433. Governance Framework for Multi-institution Collaboration
434. Data Contracts Across Institutions
435. Version Control & Change Propagation in Federated Environments
436. Data Quality & Validation Across Centers
437. Unified Metadata / Catalog across Institutions
438. Access Control & Role Management across Organizations
439. Shared Dashboards & Reporting Platforms
440. Disaster Recovery & Data Redundancy across Centers
441. Load Balancing & Scalability for High-volume Multi-center Data
442. Performance Monitoring & Cost Sharing Models
443. Data Sharing Agreements, SLAs & Legal Frameworks
444. Research & Public Health Data Aggregation Use-cases
445. Consent & Privacy Management for Multi-center Data
446. Auditing & Logging across Institutions
447. Data Lineage & Provenance across Shared Datasets
448. Federation Governance Bodies & Roles
449. Change Management & Versioning in Federated Systems
450. Module 18 Capstone: Design a Multi-institution Data Analytics Platform
MODULE 19 — Case Studies, Industry Patterns & Best Practices (Ch 451–475)
451. Hospital Analytics Platform — Clinical, Operational & Financial KPIs
452. Public Health & Epidemiology Analytics Platform
453. Telemedicine & Remote Monitoring Data Analytics
454. Wearables & IoT Device Data Integration for Chronic Care Analytics
455. Research & Clinical Trials Data Analytics Platform
456. Population Health Management Analytics Use-case
457. Healthcare Fraud Detection & Risk Analytics
458. Personalized Medicine Analytics & Predictive Models
459. Outcome-based Analytics & Value-based Care Reporting
460. Patient Journey Analytics across Systems
461. Multi-institution Analytics for Regional Health Networks
462. Multi-lingual / Multi-region Data Analytics Challenges & Solutions
463. Compliance-driven Analytics Workflows (Audit, Reporting)
464. Hybrid On-prem + Cloud Deployments in Healthcare Analytics
465. Data Monetization & Data-as-a-Service for Healthcare Data (Research, Aggregators)
466. Ethical Data Sharing & Privacy-preserving Analytics Case Studies
467. Disaster Response & Emergency Analytics Platform (Pandemic, Outbreaks)
468. Real-time Monitoring & Alert Systems for Hospitals
469. Longitudinal Study & Cohort Analytics Platform for Chronic Diseases
470. Cross-specialty Analytics — Lab + Radiology + Clinical + Administrative Data
471. ML-driven Predictive Analytics for Patient Risk, Readmission, Resource Optimization
472. Hybrid Analytics + ML + BI Platforms for Healthcare
473. Best Practices for Healthcare Data Governance & Compliance at Scale
474. Cost vs Benefit Analysis for Healthcare Analytics Projects
475. Module 19 Capstone: Write a Comprehensive Case Study for a Healthcare Analytics Implementation
MODULE 20 — Graduation, Certification, Templates & Program Wrap-up (Ch 476–500)
476. Capstone Program Overview & Success Criteria
477. Template Pack: FHIR Data Models & Profiles
478. Template Pack: ETL / Ingestion / Pipeline Templates
479. Template Pack: Governance, Compliance & Consent Forms
480. Template Pack: Analytics, Reporting & Dashboard Templates
481. Template Pack: ML Pipeline & Feature Store Templates
482. Template Pack: Data Product & API Specification Templates
483. Template Pack: Storage & Lakehouse / Warehouse Architecture Templates
484. Template Pack: Metadata, Catalog & Lineage Templates
485. Template Pack: Data Sharing & Federation Templates
486. Template Pack: Multi-center Data Sharing Agreements / SLAs
487. Final Project: Build Full Healthcare Analytics Platform (FHIR-based)
488. Final Project: Perform Data Migration from Legacy EMR to FHIR Analytics Platform
489. Final Project: Build Clinical & Public Health Dashboards + ML Models
490. Final Project: Implement Governance, Compliance & Access Controls
491. Final Project: Establish Data Sharing Workflows & Metadata Catalog
492. Final Project: Deploy Platform with CI/CD & Automation
493. Final Project: Testing, Validation & Performance Optimization
494. Final Project: Documentation & Training Materials for Stakeholders
495. Final Project: Cost & ROI Analysis Template
496. Certification Exam Blueprint & Preparation Guide
497. Sample Exam Questions & Mock Assessments
498. Alumni Resources & Continuous Learning Roadmap
499. Graduation, Certification & Badge Issuance
500. Future Trends: Gen-AI, Real-time Healthcare Analytics, IoT & Precision Health
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Instructor-led + Templates
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Includes instructor sessions, corporate licensing and tailored templates.
Enterprise Cohorts
Customized cohorts
Contact Sales
Custom delivery, private labs, on-prem/cloud setup and integration support.
Instructors & Credibility
Course Authors
Practitioners with strong skills in FHIR data modelling, clinical data integration, interoperability standards, analytics engineering, governance, and production-grade healthcare deployments.
Includes: Labs, runbooks, realistic datasets, and enterprise playbooks.
Get Started
Enroll or request a cohort. We'll provide access to curriculum, lab datasets and project briefs.
Email: contact@durgaanalytics.com • For enterprise: contact@durgaanalytics.com