bcs dmsg healthcare data management : transformation through migration 26-11-13

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’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


Healthcare Data Migration(Case Study)


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


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


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


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


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 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


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 • 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


12

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


13

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 ..........


Validation As A Service

(Case Study)


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)


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


17

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


Reporting Services

A Practical Design ?


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

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 “


20

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 ?


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 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


On the Horizon

Healthcare Analytics & Big Data The next technology step change


23

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.


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


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


26

• 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


27

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


28

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


29

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


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


Questions

bcs dmsg healthcare data management : transformation through migration 26-11-13

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