1. Health Information - Retrieval, Analysis and Archival on cloud
Dr. K. Sudheer, Sandeep Kunkunuru, Pradeep Kandru, Balaji Chopparapu

2. Agenda Data collection - Techniques and tools
Data cleansing, linking, transforming, enriching - Techniques and tools
Access and collaboration - Patterns
Data analytics and Machine Learning
Standards
Data lifecycle
Cloud native architecture
Q & A
References

3. Data Collection - Techniques and Tools
Public/Open/Government data sources ex: clinicaltrials.gov, PubMed
Bulk download from source sites
Web scraping individual records using html parsers
API - collect data on need/demand
Private/Hospital/Point-of-Care data sources ex: ultrasound devices, MRI, PET/CT
Scheduled direct data sync to cloud storage services
Event driven processing - push da to API
API - collect data on demand
Initial bulk data collection - Database dumps, file storage, Scan/OCR, Data entry operations etc.
Direct upload from patient through mobile and/or
Upload - case sheets, Diagnostic test results, Family history reports, Questionnaire responses
Passive data collection
Wearables

4. Data Linking, Preparation - Techniques and Tools
Strong ids - -id, phone number, aadhar/government-id number, patient registration number,
Weak ids - full name.
Additional ids - trial id, referring physician’s .
NLP techniques to identify ontology terms and corresponding codes.
Parse metadata to extract ids ex: from a DICOM image
Image/Video/Audio segmentation

5. Access, Reporting and Collaboration - Patterns
Direct web access to the cloud storage services
Time bound, permission based access sharing ex:
Read-only
Read-only with permissions to annotate
Bookmark content, generate permalinks, share them, gather comments/annotations
Notify all stakeholders as changes are collected.

6. Data Analytics - Techniques and Tools
SQL based interactive queries
Descriptive statistics using tools like R
Predictive analytics using Machine Learning services

7. Standards Storage - DICOM Access & Interoperability - FHIR
Exchange - HL7 v2(EDI/X12), HL7 v3, LOINC, CDISC-ODM
Stacks - SMART on FHIR
Ontologies - ICD10, Snomed CT, UMLS, MeSH

8. Data Lifecycle - Needs and Considerations
Data Collection→ Data Cleansing → Data linking/preparation/enrichment → Data Processing/Analysis → Access, report, collaborate → Data Archival
Data collection - get the data from external data sources
Data cleansing - audit and filter the data. Report outliers.
Data preparation - integrate the various data sets, enrich and transform them into a core data model.
Data processing - run interactive query, generate summary statistics, machine learning algorithms based on statistical models
Access, report, collaborate - generate off-the-shelf and custom data analysis reports based on processed data and/or core data model
Archive/Purge data - based on data retention policy archive the data and/or based on contractual agreements purge the data.

9. Architecture

10. Q & A

11. References

12. Papers
SMART - The Substitutable Medical Applications, Reusable Technologies Platform.
Paper 1 : The SMART Platform: early experience enabling substitutable applications for electronic health records
Paper 2 : SMART on FHIR: a standards-based, interoperable apps platform for electronic health records
FHIR - An illustrative usage in Radiology
AWS - Reference Architecture for Healthcare Data
AWS - Architecting for HIPAA in the cloud