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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
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Healthcare Data Management : Transformation through Migration
26th November 2013
CSC Proprietary and Confidential
CSCHEALTHCAREAND LIFE SCIENCES
Steve [email protected]
November 2013
Healthcare Data Management : Transformation through Migration
CSC Proprietary and Confidential 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
CSC Proprietary and Confidential 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 sharingSmart 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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Healthcare Data Migration(Case Study)
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Healthcare Data MigrationChallenges & 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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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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Data Transfer Mechanisms For Consideration
Source Data
Target Lorenzo
Data Sets
Extract (ET)
Transform & Load (TL)
Scripting
Messaging
Direct Data Entry
1
2
3
4
5
Datasets
• Central to the Core solution• PAS and Clinical • Approx 250 / 20 FA’s
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Data Mapping Through Analysis & The Importance of Business Rules
• Well Defined Healthcare Specifications • Target Schema Related • Embedded Business Rules
(Application)• Embedded Transformations • Embedded Target Schema mappings
Data Sets
Extraction PRELOADVALIDATETRANSFORMLOAD
CONFIG
Specifications
Auto-Generated
BusinessRules
Coded : Business
RulesValidatio
n
BusinessRules
Validation
DATA ISSUES
Not Auto-Generated as
Source Systems Vary • Low Level Data Mappings
• Gap Analysis • Reference Data Translation (next slide)• Internal Data Linkages • Reference back to Legacy data records
• Validation & Error Identification ( a following slide )
• Lesson learned : Auto-Generation
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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 valuesLocal Code Mappings & Data Capture Sheets
Working closely with the Hospitals to provide suitable agreed translations
• Significant effort required to build and maintain Reference Data TranslationsTypically used by the development tools for Lookup and translation
• MDM ( Master Data Management ) – Publish & Share translations across teams
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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 • Target Validation for Duplicates (DTR)• DWH structure to allow rollup ..etc • Error Report Publication Process
Data Sets
ExtractionPRELOADVALIDATETRANSFORMLOAD
BusinessRules
Validation
DATA ISSUES
HealthOrganisation
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
Coded : Business
RulesValidatio
n
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The CSC BI/ETL Solutions to support Lorenzo multi-campus
CSC Data Centre Hosted
Legacy Systems
Target Lorenzo
Data Sets
Extract Tool
Migration Tool
Validate
Transform Load
Preload
Non-Hosted Legacy
Systems
Health Organisation
Silo 1
Transactional Data
CONFIG
Auto-Generated
SpecificationsBusiness
Rules
Health Organisation
Silo 2
.
.
.
Configuration (P/S/T)
100 Million Records
Error Reports
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DEPLOYMENT PHASES
DM Organisational Readiness Assessment
DM Trial Load 1 Phase
DM Trial Load 2 Phase
DM Trial Load n Phase
Hospital EngagementInitiated
DQ1(VAAS)
Milestone
DQ2 Milestone
(80-90% DQ)
TL1 ReadinessGate
TL2 ReadinessGate
TL n ReadinessGate
DM Environments Deployment E2E Environments
Hospital Data Cleanse
(DM-TDC)
Dress Rehearsal
PROD
DRH ReadinessGate
PROD Readiness
Gate
DRH Environment
PROD Environment
Migration Acceptance
DQ3Milestone (100% DQ)
A Typical Data Migration Operating Model
DM Pre-Trial Load Phase ..........
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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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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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Validation As A Service
Target Lorenzo
Data Sets
Migration Tool
Validate
Transform Load
Preload
Non-Hosted Legacy
Systems
Health Organisation
Silo 1
Transactional Data
CONFIG
Auto-Generated
SpecificationsBusiness
Rules
Health Organisation
Silo 2
.
.
.
Configuration (P/S/T)
App
Validate & Transform
LoadPreload
Auto-Generated
Error Reports
CSC Proprietary and Confidential
Reporting Services
A Practical Design ?
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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
Operating Room/Theatres Efficiency Management Performance Scorecards
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)
Re-Admission Risk ( see later slide )
PREDICTIVE ANALYTICS - “ What is going to happen “
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INGESTION
Client Side
Extraction
Client Side
Extraction
Client Side
Extraction
Client Side
Extraction RE
SU
LT S
ET
SD
ATA
FE
ED
S
&/o
r M
essa
gin
g HDI
ValidateTranslateTransform
HDO
TranslateTransformAggregate
O
RG
AN
ISA
TIO
N R
EP
OT
S E
NG
INE
OF
CH
OIC
E
FEDERATED
NON-FEDERATED(DWH,Mart,InMemory..)
E LTResult sets, Data Feeds, Structured Data, Unstructured Data, Data Quality Assessment,Data Cleansing, Meta-Data Data Correction
Validation, Translation, Transformation, Aggregation, Analytics Considerations, NLP, Data Quality, Error Reporting, Deduplication.........
Generate a consistent set of relational and multidimensional objects
RPublished components for ORG Access
Near Real Time View
Time Variant View
Information Request Self-Service Reporting
Operational Reporting
Decision Management
Reporting
Reporting Permutations
Predictive Analytics ?
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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 development3. 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)
Data Quality ........................... EVALUATED ON A CASE BY CASE BASIS ..................................
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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On the Horizon
Healthcare Analytics & Big Data The next technology step change
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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.
Market Demand: Driven By The Triple Aim Of Healthcare Reform
Market Opportunity
Patient Experience: improved outcome and safety;Population health status: reducing the burden of diseases Healthcare cost and inflation.
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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
CO
OR
DIN
AT
ED
CA
RE
Driving efficiency through industry knowledge and technology expertise.
CONSULTING
CSC in Healthcare
BY THENUMBER
S
>100 million
PATIENT RECORDS
1 millionHEALTHCARE SOFTWARE
PRODUCT USERS
9,000CLINICAL
INSTALLATIONS
8,000PROFESSIONALS
SERVING OUR CLIENTS
30COUNTRIES
Improving health outcomes using system wide data.
BIG DATA /ANALYTICS
Hosting healthcare applications and processes ‘as-a-service’ in the Cloud.
CLOUD
Achieving Cyberconfidence through managed security services.
CYBER-SECURITY
Supporting critical clinical and business processes with innovative software products.
HEALTHCARESOFTWARE
Creating client value through infrastructure and business processes.
BPS &OUTSOURCING
Managing enterprise-wide application portfolios.
APPLICATIONS
SERVICES
LIFE SCIENCES
PAYERS
COMMUNITY CARE
ACUTE CARE
AMBULATORY CARE
RADIOLOGY
LABORATORY
MEDICATION
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• Use analytics to uncover hidden patients with chronic disease.
• Identify patients who are not following a standard care plan for their chronic disease
Big Data - Data ServicesCSC Target 100 Million Patient Records
Licensed Patient Clinical
Licensed Claims
Global Research Genomic
BIG Data Aggregator
CSC Data WorkbenchCSC, Commercial, and Open Source Tools
Analytic Services
Providers PatientPayer Life Sciences
Public Sector Primary Care
Licensed Clinical and Genomic
Deidentified Health System
CSC Client Federated
Clinical, Administrative
Investigator Selection/Patient Recruitment
Predictive Analytics identifying patients most likely to benefit from medication and/or procedure • Demographics • Medication • Diagnosis /Condition • Genomics
Drug Therapy Matching
Outcomes and Economics Metrics 100M
Patient Records
Care Coordination
Accountable Care
• 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
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Drivers & Requirements
Gain Business
Agility
Mitigate RiskLower
CostReduce Complexity
Increase
Competitiveness
Improve
End User Satisfactio
n
Industry Drivers
Healthcare RequirementsMulti-modal
Channels of
delivery (Smart devices
….)
Improved
Usability
Cross Organisation Capabi
lity
Application
Transformati
on from
Legacy to New
Rapid creatio
n of new
solutions
High Availability,
Scalability & Perf
Robust
Security
throughout
ECOSystem
Customer 360
Centralised
View & Interoperabil
ity
Population
Health Information Creati
on
Acceleratin
g time
to mark
etIncreasing speed of time
to value
Disruptive Innovation(Displa
cing earlier technol
ogy with new
innovative
solutions)
Business Drivers
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Coordinated Care offering
Connecting all stakeholders:• Providers• Patients • Specialists• State HIE
Standardized and automated clinical processes to capture and organize relevant data
Effective communication and information sharing between all stakeholders
Multi-modal
Usability
Rapid new solutions
Avail/Scale/Perf
App Transformation
Cross Organisation
Security
Interoperability
Population Health
Healthcare Requirements
Actionable data across the
extended timeline What happened,
What’s happening and What could
happen
Provide to a variety of
consumers a single view of
actionable data
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Conditional Alerting Model: Re-Admission Risk
Re-Admission Risk Management
Co
ord
inat
ed C
are
Ru
les
En
gin
e
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
• 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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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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Questions