Presentation given to the BCS Data Management Specialist Group by Steve Higgins of CSC on healthcare data management A video of the presentation is available at http://youtu.be/Fqm4XDyA6fI

1. Healthcare Data Management :
Transformation through Migration
26th November 2013

2. Healthcare Data Management :
Transformation through Migration
CSC
HEALTHCARE
AND LIFE SCIENCES
Steve Higgins
Shiggins4@csc.com
November 2013
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3. Healthcare Data Management
Coverage for this evenings Presentation
• Case Study : Healthcare Data Migration – Challenges and Lessons Learned
• Case Study : Validation As A Service
• Reporting Services : A Practical Design ?
• On the Horizon
Healthcare Analytics & Big Data : The next technology step change
.... Lorenzo
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3

4. CSC’s Strategic Single Instance Healthcare Solution
LORENZO
• An integrated EPR System - originally developed in line with specifications of the
National Programme for IT (NPfIT).
• A Single Instance that can support Data Sharing across Local Health Communities
• Hosted across CSC Data Centres
• Designed for zero downtime (even during upgrades) & full disaster recovery
• Supports patient care for care settings such as
Acute, Community, Mental Health, and Primary Care Trusts
Data Aspects
• Microsoft Stack : SQL Server ; Schema is Additive
• The Data is Partitioned around the Patient for Performance
• Single Master Patient Index (MPI)
• Focused on Data Security via measures such as :
RBAC, Legitimate Relationships, Data Sealing and Locking, Consent to data sharing
Smart Card Access with single Role Logins & Complete security logging
• Integrated with other healthcare systems – Messaging, Desktop Integration .....
• SPINE connected for synchronisation of Patient Demographics
• National Data sets fully supported
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4

5. Healthcare
Data Migration
(Case Study)
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6. Healthcare Data Migration
Challenges & Lessons Learned
Selection of Coverage : Many Other areas for consideration .....
• Client Engagement : Understand the Requirements beforehand
• Data Transfer Mechanisms for Consideration
• Data Mapping through Analysis & The importance of Business Rules
• Reference Data Translations and the Management of Localised and National Datasets
• Error Identification and the Data Correction Process : Source or Meta-Data ?
• CSC BI/ETL Solution Overview ... to support Lorenzo multi-campus
• A Typical Data Migration Operating Model
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6

7. Client Engagement
Understand the Requirements beforehand
• The Scope of the Data to be Migrated ( Breadth & Depth )
• Reduced Data Scope Definition .... SOUNDS EASY
• Data Ownership, Sharing and Access Agreements .... Who & Where
• Availability of Source System SME’s
• Define the Process for Source Data Cleansing and Correction of Data Issues
• Localised Infrastructure, Tools and Configuration Requirements
• Report expectations – What are the expected report outputs :
• Data Quality Assessment
• Error Reports .... Identifying all data issues
• Reconciliation Reports .... Reconcile extracted data against loaded data
• Test Data Considerations – Real, Synthetic, Anonymised or Masked Data
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8. Data Transfer Mechanisms
For Consideration
Extract
(ET) 1
Data
Sets
Transform &
Load (TL)
Datasets
2
3
4
• Central to the Core solution
• PAS Messaging
and Clinical
• Approx 250 / 20 FA’s
Scripting
Source
Data
Target
Lorenzo
Direct Data
Entry
5
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8

9. Data Mapping Through Analysis &
The Importance of Business Rules
Specifications
• Well Defined Healthcare Specifications
• Target Schema Related
• Embedded Business Rules (Application)
• Embedded Transformations
• Embedded Target Schema mappings
• Low Level Data Mappings
• Gap Analysis
• Reference Data Translation (next slide)
• Internal Data Linkages
• Reference back to Legacy data records
Business
Rules
Not AutoGenerated as
Source Systems
Vary
Extraction
Coded :
Business
Rules
Validation
CONFIG
Auto-Generated
Data
Sets
PRELOAD
VALIDATE
TRANSFORM
LOAD
Business
Rules
Validation
• Validation & Error Identification
DATA ISSUES
( a following slide )
• Lesson learned : Auto-Generation
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10. Reference Data Translations &
The Management of Localised and National Datasets
• Source and Target Reference Data sets almost always differ
• Some similarities relating to medication codes and National data sets
• Localised reference data configuration within legacy systems
Many Localised configurations need remapping to National values
Local Code Mappings & Data Capture Sheets
Working closely with the Hospitals to provide suitable agreed translations
• Significant effort required to build and maintain Reference Data Translations
Typically used by the development tools for Lookup and translation
• MDM ( Master Data Management ) – Publish & Share translations across teams
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11. Error Identification and the Data Correction Process :
Source or Meta-Data ?
Error Identification
• All Issues should be identified per pass
• Ability to Warn/Report and Continue (Initial Data Quality Assessment)
• Orphanage & Cascade Issues
Extraction
• Target Validation for Duplicates (DTR)
• DWH structure to allow rollup ..etc
Data
Sets
•Error Report Publication Process
Coded :
Business
Rules
Validation
Business
Rules
Validation
Data Correction
• Uncorrected data is a real problem
• Source or Meta-Data Correction ?
Both require Health Organisation resourcing
• Ability to support defaults
Mandatory Target Fields
Invalid Reference Data Value
• Ability to Warn/Replace and Continue
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PRELOAD
VALIDATE
TRANSFORM
LOAD
DATA ISSUES
Health
Organisation
11

12. The CSC BI/ETL Solutions to support Lorenzo multi-campus
Specifications
Target
Lorenzo
Business
Rules
Configuration
(P/S/T)
CONFIG
Auto-Generated
CSC Data
Centre Hosted
Legacy
Systems
Extract
Tool
Preload
Data
Sets
Validate
Transform
Load
Migration Tool
Non-Hosted
Legacy
Systems
Error Reports
Health
Organisation
Silo 1
Health
Organisation
Silo 2
.
.
.
Transactional
Data
100 Million Records
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12

13. A Typical Data Migration Operating Model
DQ1
(VAAS)
Milestone
Migration
Acceptance
Hospital
Engagement
Initiated
DM
Organisation
al Readiness
Assessment
TL n
Readiness
Gate
Hospital
Data Cleanse
(DM-TDC)
DM PreTrial Load
Phase
TL1 Readiness
Gate
DQ2
Milestone
(80-90% DQ)
DM Environments
DM Trial
Load 1
Phase
DM Trial
Load 2
Phase
TL2 Readiness
Gate
..........
DM Trial
Load n
Phase
DRH
Readiness
Gate
Dress
Rehearsal
PROD
Readiness
Gate
PROD
DQ3
Milestone
(100% DQ)
Deployment E2E
Environments
DRH
Environment
PROD
Environment
DEPLOYMENT PHASES
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14. Validation As A
Service
(Case Study)
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15. Legacy Healthcare Source System Data Quality
Typical findings from several Legacy Healthcare systems show that the older, more
historic data is of a poor quality
There may be numerous reasons including :
• The Data does not conform to a rigorous set of constraints - For example :
• Data Types are not enforced – Character fields hold numerics (say)
• Check Constraints are not implemented or are ignored for historic data loads
• Data Usage and Content vary across systems
• No Standard for Reference Data
• Previous historic migrations were undertaken prior to applying constraints
• Initial releases of the Applications had issues, resolved via later upgrades
Hence, when entrusting health organisation users to construct IFF data sets, it is normal
that these data sets require significant rework and several iterations of validation.
However this is a costly activity ..... And so Validation As A Service was created to allow
the Health Organisations to create and validate their own data sets prior to release to the
CSC Deployment environments ( As per the DM Operating Model)
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16. Validation As A Service – The Objectives
•
•
•
•
•
•
•
•
Provide an easy to use application which required minimal training
Locate this application centrally (CSC data centre / Cloud) to allow multiple health
organisations to use the solution concurrently
Ensure full data security across health organisations
Enforce licensing constraints to prevent access to back-end systems ... A Pure
application only interface
Allow Health Organisation Users to create, transfer and then validate their own Data
files, transferred via Secure connections
Allow users to request the processing (Preload, Validation or Loading) of single or
multiple functional areas ... Incorporation of a queueing mechanism
Provide full, easy to understand error reports via secure connections
Provide a standard application where Lorenzo enhancements are managed via simple
configuration updates
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16

17. Validation As A Service
Specifications
Target
Lorenzo
Business
Rules
Configuration
(P/S/T)
CONFIG
Auto-Generated
Auto-Generated
Preload
Non-Hosted
Legacy
Systems
Transform
Data
Sets
Validate
Load
Migration Tool
Health
Organisation
Silo 1
Health
Organisation
Silo 2
.
.
.
App
Error Reports
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Preload
Validate &
Transform
Transactional
Data
Load
17

18. Reporting
Services
A Practical Design ?
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19. Reporting Services – A Practical Design?
Reports requested by our Clients :
OPERATIONAL REPORTING - “ Whats Happening Now “
• Reporting about operational events which support day-to-day activities within the organisation.
• Typically these reports will be generated directly from the OLTP system ( Real Time)
Did Not Attend Report, Appointment List, Outpatient
Clinic List, Ward Attendance List, Discharge List
DECISION MANAGEMENT & ANALYTICS REPORTING - “ What has happened “ .... TREND ANALYSIS
• Reporting to enable Business Managers to make informed decisions in the execution of the Business.
• Based upon the transformation of existing data into intelligent and high value information which can be used to
provide an Organisation with significant opportunities to improve their patient care plans and costs
• Typically Historic/Summary Data ; Snapshot Time ~ 24 hours ; Data Warehouse (say)
Operating Room/Theatres Efficiency
Management Performance Scorecards
PREDICTIVE ANALYTICS - “ What is going to happen “
Re-Admission Risk ( see later slide )
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19

20. Client
Side
Extraction
Extraction
Client
Side
Extraction
E
DATA FEEDS
&/or Messaging
Operational
Client
Reporting
Side
Near Real Time View
I
N
G
E
S
T
I
O
N
Result sets, Data Feeds, Structured
Data, Unstructured Data, Data
Quality Assessment,
Data Cleansing, Meta-Data Data
Correction
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Information Request
Self-Service
Reporting
FEDERATED
Translate
Transform
Aggregate
H
D
I
Validate
Translate
Transform
Time Variant View
H
D
O
NON-FEDERATED
(DWH,Mart,InMemory..)
Decision
Validation, Translation,
Transformation, Aggregation, Analytics
Management Quality,
Considerations, NLP, Data
Error Reporting, Deduplication.........
Reporting
T
Predictive
Analytics ?
ORGANISATION REPOTS ENGINE OF CHOICE
Client
Side
Extraction
RESULT SETS
Reporting Permutations
L
Generate a
consistent set of
relational and
multidimensional
objects
R
Published
components
for ORG
Access
20

21. MAIN CHALLENGES
Federated
HIM
OLTP
Development
1. Client Side Data Acquisition
2. Server Side Aggregation,
transformation, Translation and
Visualisation
Significant Challenges :
1. Data Feeds
2. HIM development
3. Visualisation
Reports developed and built
up over time
Data Import
Considerations
Resultset Aggregation, Transformation
&Translation
Management of several data
feeds to a common Data Input
Schema
N/A
Real Time Updates & CEP –
Data Latency
Current State on execution of client
side scripts
Typically 24hr Delay
Current View
Reference Data Alignment
Translation will typically occur after
receipt of the result set
Significant challenges
Minimal Impact per Single
Report
Data Security
1. Firewall restrictions
2. Client Side scripts should limit
resultset
Implement Security Model at HIM
associated with data access
Active Directory (say)
Data Residency
N/A
Significant challenges
N/A
Schema Alignment and
Upgrade
Client Side Result set Enhancements
& Upgrades
May Affect Schema and any
associated data feeds and
published output
Minimal Impact per Single
Report
Customer 360 matching
algorithms
Required if aggregating various
source system data
Required as part of the Ingestion
and transformation
N/A
(Assume resolved in OLTP)
........................... EVALUATED ON A CASE BY CASE BASIS ..................................
Data Quality
Data Growth & Retention
Policies
N/A
May provide significant
challenges, especially with
unstructured data
N/A
Performance
Considerations
1. Executing against Customer Prod
Instance
2. Network Bandwidth
Significant challenges
Monitored and Managed as
part of OLTP Performance
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21

22. On the Horizon
Healthcare
Analytics & Big
Data
The next technology step change
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23. Market Opportunity
Market Demand: Driven By The Triple Aim Of Healthcare Reform
Patient Experience: improved outcome and safety;
Population health status: reducing the burden of diseases
Healthcare cost and inflation.
Drivers
Opportunity
Care Coordination
• Enable effective collaboration across the care continuum to deliver joined-up healthcare
across often fragmented system
• To facilitate effective data sharing across all care settings
Financial Pressures
• To provide access to information that enables providers to deliver care in the most
appropriate care setting
Aligning Financial
Incentives
• To provide solutions that enables the shift from re-active, unplanned and episodic care
to planned, more coordinated and preventative care
Regulatory
• Provide products and solutions that facilitates qualification for incentives under Meaningful
Use Stages, which require more extensive use of HIE beginning in 2013
Population Health
Management
• Enable prospective identification, intervention, results monitoring platform focused on
chronic disease management; multi-specialty co –management of complex patients.
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23

24. Healthcare & Big Data
• Healthcare requires Big Data to
– Pull together and align structured and unstructured data from the wide
variety of sources to create longitudinal patient & population health records
– Drive insight from the data to support coordinated care, population care,
personalised and preventative healthcare, clinical trials – Correlation of the data
to find patterns
Volume
Variety
Velocity
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25. CSC in Healthcare
COORDINATED CARE
AMBULATORY CARE
ACUTE CARE
COMMUNITY CARE
RADIOLOGY
LABORATORY
BY THE
NUMBER
S
>100
million
PATIENT RECORDS
MEDICATION
1 million
PAYERS
HEALTHCARE
SOFTWARE
PRODUCT USERS
9,000
LIFE SCIENCES
Improving health
outcomes using
system wide
data.
BIG DATA /
ANALYTICS
Hosting
healthcare
applications and
processes ‘as-aservice’ in the
Cloud.
Achieving
Cyberconfidence
through
managed
security services.
CLOUD
CYBERSECURITY
Managing
enterprise-wide
application
portfolios.
APPLICATION
S
SERVICES
CLINICAL
INSTALLATIONS
Supporting
critical clinical
and business
processes with
innovative
software
products.
Creating client
value through
infrastructure
and business
processes.
Driving efficiency
through industry
knowledge and
technology
expertise.
HEALTHCARE
SOFTWARE
BPS &
OUTSOURCING
CONSULTING
8,000
PROFESSIONALS
SERVING OUR
CLIENTS
30
COUNTRIES
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25

26. Big Data - Data Services
CSC Target 100 Million Patient Records
Providers
Payer
Life
Sciences
Investigator
Selection/Patient
Care Coordination
Recruitment
• Use analytics to uncover hidden
patients with chronic disease.
Patient
• Identify patients who are not
following a standard care plan for
their chronic disease
Analytic Services
Drug Therapy Matching
CSC Data Workbench
Predictive Analytics identifying
patients most likely to benefit
from medication and/or
procedure
• Demographics
• Medication
• Diagnosis /Condition
• Genomics
CSC, Commercial, and Open Source Tools
BIG Data Aggregator
Public Sector
Primary Care
Licensed Claims
Licensed Patient
Clinical
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Deidentified Health
System
Licensed Clinical
and Genomic
Global Research
Genomic
100M
Patient
Records
CSC
Client Federated
Clinical, Administrative
Outcomes and
Accountable Care
Economics Metrics
•Assess Insurance Details
•Forecast health status.
•Identify and quantify financial and
clinical risk of this patient segment
•Forecast cost trajectory to get
new chronic disease patient into a
managed program
26

27. Drivers & Requirements
Industry
Drivers
Gain
Business
Agility
Healthcare Requirements
Business
Drivers
Multi-modal Channels of
delivery (Smart
devices….)
Improved Usability
Mitigate
Risk
Cross Organisation
Capability
Lower Cost
Application
Transformation from
Legacy to New
Accelerating
time to
market
Reduce
Complexity
Rapid creation of
new solutions
Increasing
speed of time
to value
High Availability,
Scalability & Perf
Increase
Competitiveness
Robust Security
throughout ECOSystem
Disruptive
Innovation
Improve End
User
Satisfaction
Customer 360 Centralised
View & Interoperability
(Displacing earlier
technology with
new innovative
solutions)
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Population Health
Information Creation
27

28. Coordinated Care offering
Healthcare
Requirements
Multi-modal
Usability
Cross Organisation
Actionable data
across the
extended timeline
What happened,
What’s happening
and What could
happen
Connecting all
stakeholders:
• Providers
• Patients
• Specialists
• State HIE
Standardized and
automated clinical
processes to
capture and
organize relevant
data
App Transformation
Rapid new solutions
Avail/Scale/Perf
Security
Interoperability
Population Health
Provide to a
variety of
consumers a
single view of
actionable data
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Effective
communication
and information
sharing between
all stakeholders
28

29. Conditional Alerting Model: Re-Admission Risk
• The CoordinatedCare engine combines
hospital data with community wide
information to assess readmission risk
and alerts all stakeholders
• Re-admission risk rules can be
configured to the specific requirements of
the organization
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Coordinated Care
Rules Engine
Automatic Calculation
of Re-admission Risk
Value
Automatic executes of
rules
Configurable
Readmission Criteria
Targeted Alerting:
Provider, Hospital or
Care Coordinator.
Dynamic list of Patient
at risk of re-admission
Re-Admission Risk Management
29

30. In Summary
A quick flavour for some of the Data Management touch points
Topics Covered :
• Healthcare Data Migration
• Validation As A Service
• Reporting Services – Several considerations
• On the Horizon - Healthcare Analytics & Big Data
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30

31. Questions
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