1) Industry point of view 2) Functional domain - Reference Architecture 3) Modern data platform - Solution Architecture Tenets 4) Proposed solution architecture 5) Implementation roadmap & execution methodology 6) Team structure 7) Commercials

1. Healthcare Data Analytics Platform
BlueCard Modernization (BCM)
Architecture & Platform
29th June 2021

2. Industry point of view
Functional domain – reference architecture
Modern data platform – solution architecture tenets
Proposed solution architecture
04
03
02
01
Table of Contents
Implementation roadmap & execution methodology
05
Team structure
06
Commercials
07

3. Increasing demand
(amongst other
reasons due to
ageing population)
Increasing effects
of market forces
Patient
empowerment
Healthcare
Survey
Patient orientation
Besides cure,
increasing focus on
prevention and
care
Creating greater
insight within
healthcare supply
Pressure on labor
market
Current situation in healthcare
Availability of the right information in the right place can facilitate this considerably. This
requires clear insight into (necessary) process information and therefore also a holistic
vision of the infrastructure.
Industry point of view

4. Healthcare Global Market
14
50.5
2019 - e 2024 -p
CAGR
28.3% Favourable government
initiatives to increase EHR
adoption, growing
pressure to curb
healthcare costs,
availability of big data in
healthcare, and increase
in VC investments are
major factors driving
market growth.
North America, especially
the US, offers significant
growth opportunities for
market players.
The market is dominated by North
America, followed by Europe, Asia,
and RoW.
Factors for the high growth of the North
American market
• Federal healthcare mandates to curb
rising healthcare costs
• Increasing regulatory requirements
• Growing EHR adoption
• Rising government initiatives focusing
on personalized medicine, population
health management, and value-based
reimbursements
The global healthcare analytics
market is projected to reach
USD $50.5 billion by 2024
from $14.0 billion in 2019

5. Technology Trends in Healthcare to watch in 2021
TREND #1: TELEMEDICINE
TREND #2: ARTIFICIAL INTELLIGENCE (AI)
TREND #3: CHATBOTS
TREND #4: DATA SCIENCE AND PREDICTIVE
ANALYTICS

6. TREND #1: TELEMEDICINE
• Evolution in telemedicine is one the
biggest sources of rapid change in the
US healthcare system.
• Telemedicine is improving diagnosing
and treatment by making it easier for
patients to get access to specialists.
• Availability of electronic records has
also made it simpler to forward
documents to specialists.
• Difference between having or not
having expert input into a case.
Technology Trends in Healthcare to watch in 2021

7. TREND #2: ARTIFICIAL INTELLIGENCE (AI)
Technology Trends in Healthcare to watch in 2021

8. TREND #3: CHATBOTS
• Chatbots are already revolutionizing the
business world.
• Would continue playing big part of the
digital transformation in healthcare.
• Address easily diagnosed problems allows
professionals to focus on matters that might
require the full attention of a physician.
• Specially more beneficial for practices
dealing with older patients
• Any chatbot system will be subject to the
same rules that govern the rest of the
industry, so compliance with HIPAA in the US
and the GDPR in the EU will feature highly
TREND #4: DATA SCIENCE AND PREDICTIVE
ANALYTICS
• Data scanned and analysed to improve efficiency
within a healthcare organization.
• Doctors to identify and address problems that are
endemic to regions, families, trades and other
population clusters.
• Patients who are at heightened risk for re-
admission may, for example, be treated for longer
periods during their initial admissions in order to
improve long-term care.
• Information derived from studies of patients can
also be employed to predict which individuals
might be at higher risk of negative outcomes.
• Analytics is another field why synergies can be
achieved. With greater inflows of data from IoMT
devices, Data science consultants can construct
more detailed models.
Technology Trends in Healthcare to watch in 2021

9. Overview – reference architecture
Functional domain – Reference architecture

10. Current situation in healthcare
 Increasing demand (amongst other reasons due to ageing population)
 Increasing effects of market forces
 Patient empowerment
 Patient orientation
 Besides cure, increasing focus on prevention and care
 Need for measurable results
 Creating greater insight within healthcare supply
 Pressure on labour market
IT Reference Architecture for Healthcare
Availability of the right information in the right place can facilitate this
considerably. This requires clear insight into (necessary) process information
and therefore also a holistic vision of the infrastructure.
Functional domain – Reference architecture

11. Modern data platform – solution architecture tenets
A modern data architecture exhibits the following ten characteristics:
Customer-
centric
Adaptable
Elastic
Secure Automated
Smart
Flexible
Collaborative Governed
Simple

12. The new data environment is a living, breathing organism that detects and responds to changes,
continuously learns and adapts, and provides governed, tailored access to every individual.
Modern data platform – solution architecture

13. Expected Key Business Benefits
• Enabled additional use cases and innovative
services.
• Reduced operational cost through automation
• Integrated data in unified platform for multiple
workloads
• Defined an operational model to support new
platform.
• Improved business agility, reduced TCO and
improved margins
• On-prem ODS
migration to Azure
• Configurable workflows
for data ingestion
• Integrated definition of
data
• Rearchitected for cloud
native and MPP
architecture
• Removed source
dependencies
• Reuse existing business
logic
Enterprise Data
Platform
• Cross functional
analysis and
integrated data
entities.
• Established full life
cycle tracking of
enterprise data.
• Rationalization of
reporting environment
• Decommissioning of
legacy reporting
platforms
Self Service
Analysis
• Deployment flexibility,
Automated monitoring
through Azure Monitor
• Reusable pipelines,
bring more dynamism
in meta data driven
ingestion.
• Dynamic resource
provisioning scale up
and down using
performance metrics.
Automated
Maintenance
Business Challenge
Approach
Platform Modernization Reduced Cost of Operations &
Ownership
Self Service Exploration/Analysis
Tools
What we are trying to achieve … Transition from legacy data landscape to Enterprise data platform, driven by consumer driven
mindset, follow an open approach to technologies and design with support and maintenance in mind to reduce TCO.
Blob , ADF, Azure SQL
DWH, SQL DB, Logic
App, Tableau &
Analysis Services
• Organizational Silos, Point Solutions – No business focused view
• No integrated Data & unified platform, No thought on long term
architecture
• Enforced requirements & models upfront, limiting the changes and scale
• Multiple reporting systems lack of single source of truth.
Proposed solution architecture – Azure Cloud

14. Storage Considerations
• Azure Blob storage/ADLS Gen 2 provides cost
effective, scalable, Data sharing capabilities.
• Object life cycle Management to archive the data
from hot tier to cold tier.
• Data redundancy in multiple zones to provide
High Availability
• SQL DWH as an aggregated layer for Dash
boarding & reporting
• Polybase for ad-hoc analysis, SQL on blob storage
through federated queries..
• Unified Storage Supports multiple access patterns
Data Processing & Compute
• ADF v2 – Azure Data Factory for workflow
management and on demand runtime to reduce
cost
• Logic Apps – Real Time integration through event
base/recursive triggers. Wide range of
connectors.
• Cloud Functions – for serverless light weight
asynchronous minor ETL operations.
• Spark/SSIS – Take advantage of cloud’s auto
scaling features parallel processing to handle the
complex scenario’s and huge volume.
• Hadoop based utility to process semi-structured
data
Data Model & ETL considerations
• Through ADF v2 leveraging the existing SSIS
packages and stored procedures.
• Leveraging the MPP architecture and ELT approach
for faster ingestion
• Reuse existing SP’s Business logic to minimize
migration cost & business disruption
Volumes
• Daily data processing – 6 GB of compressed Data
(20 GB of uncompressed)
• Data Lake – 3TB , Archived – 8TB
• 3000+ On-premise objects includes (ODS, DWH
& Reports)
Technical Architecture

15. START
RELEASE PLANNING MEETING
Finalizing Product Backlog with
Product owners, Business Leads and
stakeholders
Estimation of the
Product Backlog
Committing the User
Stories
Ussr Acceptance Testing
with all Users and Product
Owners and stakeholders
Agreeing to the
schedules and show
and tell sessions
Analysis and
Design
SHOW and TELL with
Product owners and
stakeholders and
capturing their feedback
System Integration
Deployment on
PRODUCTION
Build, Peer
Review
and UT
END
Implementation roadmap & execution methodology

16. Implementation roadmap & execution methodology

17. Blue Cross Management
Cloud
Architect
Developer
Data
Modeler
Informatica
Developer
Data Test
Analyst
TechOnDesk Program
Manager
Technical Manager
(Singapore)
• The Rampup and ramp down of team members is subjected to support and continuous improvement opportunities
• Onsite (Singapore) and offshore (Malaysia) model is considered, with 20/80 spilt between onsite/offshore.
• Robust training and upskilling proposed to ensure each member of team is cross skilled to minimize dependency on one
resource
TechOnDesk
Enablers
Virtualization
Quality.
Center of Excellence
Cloud Iinformatica: DI,DQ,DG and MM
BI & DA Security: ISG
BigData
Blue Cross
Technology Stakeholders
Blue Cross
Business Stakeholders
Business
Analyst
ADF
Developer
Blue Cross
TechOnDesk
Power BI
Lead
Databricks
Developer
Team structure

18. Cost Proposal
Terms & Conditions
 All commercials are in US Dollar and excludes any applicable taxes. Taxes will be charged
 On-site location is assumed to be Blue Cross’s office in NY, US. Any travel required to other locations for this project will be charged extra
 Engagement will be executed in continuous span without any breaks in between
 TechOnDesk’s estimation and price does not account for Blue Cross or any 3rd party resources involved in this project
 All software licenses cost will be borne by Blue Cross
 Any change in scope, assumptions or timeline resulting in an impact to effort, cost or schedule, will be subject to the change management process
 TechOnDesk associates will work as per general shift timing from 9AM to 6 PM IST and if any extended support is required that will be at extra cost
 Post development completion we have considered 2 weeks of UAT support and 2 weeks of production roll out support. Any additional efforts required
beyond 4 weeks will be charged as per agreed MSA rates
 If there is any change in scope beyond ~10% , the revised cost will be submitted to Blue Cross management for approval
Proposed the Data platform setup to be delivered on a Fixed Price (FP) basis for duration of XX weeks
Project Price in $
• Data platform on Azure
• License Cost
• Implementation/Development Cost
• AMC Cost
US $ XXXX
Commercials