BCS DMSG Healthcare Data Management : Transformation through Migration   26-11-13
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BCS DMSG Healthcare Data Management : Transformation through Migration 26-11-13



Presentation given to the BCS Data Management Specialist Group by Steve Higgins of CSC on healthcare data management

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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BCS DMSG Healthcare Data Management : Transformation through Migration   26-11-13 BCS DMSG Healthcare Data Management : Transformation through Migration 26-11-13 Presentation Transcript

  • Healthcare Data Management : Transformation through Migration 26th November 2013
  • Healthcare Data Management : Transformation through Migration CSC HEALTHCARE AND LIFE SCIENCES Steve Higgins Shiggins4@csc.com November 2013 CSC Proprietary and Confidential
  • 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 3 View slide
  • 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 CSC Proprietary and Confidential 4 View slide
  • Healthcare Data Migration (Case Study) CSC Proprietary and Confidential
  • 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 CSC Proprietary and Confidential 6
  • 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 CSC Proprietary and Confidential 7
  • 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 CSC Proprietary and Confidential 8
  • 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 CSC Proprietary and Confidential 9
  • 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 CSC Proprietary and Confidential 10
  • 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 CSC Proprietary and Confidential PRELOAD VALIDATE TRANSFORM LOAD DATA ISSUES Health Organisation 11
  • 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 CSC Proprietary and Confidential 12
  • 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 CSC Proprietary and Confidential 13
  • Validation As A Service (Case Study) CSC Proprietary and Confidential
  • 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) CSC Proprietary and Confidential 15
  • 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 CSC Proprietary and Confidential 16
  • 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 CSC Proprietary and Confidential Preload Validate & Transform Transactional Data Load 17
  • Reporting Services A Practical Design ? CSC Proprietary and Confidential
  • 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 ) CSC Proprietary and Confidential 19
  • 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 CSC Proprietary and Confidential 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
  • 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 CSC Proprietary and Confidential 21
  • On the Horizon Healthcare Analytics & Big Data The next technology step change CSC Proprietary and Confidential
  • 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. CSC Proprietary and Confidential 23
  • 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 CSC Proprietary and Confidential 24
  • 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 CSC Proprietary and Confidential 25
  • 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 CSC Proprietary and Confidential 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
  • 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) CSC Proprietary and Confidential Population Health Information Creation 27
  • 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 CSC Proprietary and Confidential Effective communication and information sharing between all stakeholders 28
  • 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 CSC Proprietary and Confidential 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
  • 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 CSC Proprietary and Confidential 30
  • Questions CSC Proprietary and Confidential