Research DWHToetsen van ideeën

Radboudumc Technology Centers

• Proteins• Metabolites

• Preclinical• Clinical

• Behavioural

• Animal facility• Pathology

• Cell analysis• Sorting

• Pediatric• Adult• Phase 1, 2, 3, 4

• Vaccines• Pharmaceutics• Radio-isopes• Malaria parasites

• Management• MDM• Disclosure• Analysis• Logging• Audit• Sharing• …

• DNA• RNA

• Internal• External

• HTA• Field lab

• Statistics• Biological• Structural

• Preclinical• Clinical

Information principles

1. Data shall be used consistently across all processes; the definitions should be understandable and available for users and adhere to standards where possible.

2. Data is a vital asset.

3. All data has an identifiable source and owner

4. Data is shared across processes: enter once, reuse many times

5. Data is available to authorized users, managed and protected

Data stewardship Handbook (Data4lifesciences, HANDS)As a data steward, you:• are accountable and responsible for your research data;• are 'in control' of the complete research data flow;• reuse existing data when possible;• collaborate with patient organisations throughout your research;• protect the privacy and safety of study subjects;• apply the FAIR principle;• protect the quality of your research and ensure its reproducibility;• use available expertise and UMC-advised infrastructure;• think ahead about intellectual property rights;• share your data responsibly.

FAIR principle• Findable: the data should be uniquely and persistently identifiable and

other researchers should be able to find your data.• Accessible: the conditions under which the data can be used should be

clear to machines and humans.• Interoperable: data should be machine-readable and use terminologies,

vocabularies, or ontologies that are commonly used in the field;• Reusable: compliant with the above and sufficiently well described with

metadata and provenance information so that the data sources can be linked or integrated with other data sources and enable proper citation.

Clinical DWH Functional requirements • Historical traceability• Source Tracing• Flexible Design, integration without remake• Scalability• Data Integrity• Metadata

• Master Data

• Findable data structures• User Friendly searching• Standardization• Data Cleansing

Old (?) style data collection style

Klik op het pictogram als u een afbeelding wilt toevoegen

Using EDC tool (like Castor)

Research projectScores (GCS, CAM-ICU, DOS, RASS, NRS slaap, AVPU)

Practitioner Glasgow Coma Scale

Research Coworker MUST Score

Patient SNAQ score

Diagnosis Term Pain assessment score

Lab Component Ventilator settings

Enrollment Allergy Intolerance

Episode Family History

Hospital Admission Social History

Visit Intoxications

ED visit Tobacco use

ProblemList Alcohol intake

Complication Procedure

Lab results Surgical Case

Vital Signs Imaging Study Report

Height EKG

Weight Long Function

BSA Pathology

Temperature QuestionnaireResponse

Heart rate

Respirations Metadata

Blood pressure CodeSystem

Oxygen saturation NamingSystem

Adverse Event reporting ValueSet

Blood transfusion Concept

Home medication use Concept Map

Medication Order Questionnaire

Observations Question

Door onderzoekers

aangedragen

objecten zijn

vindbaar en

modelleerbaar

(n=56)

Academisch Medisch Centrum Algemene Dienst ICT - www.amc.nl/ict

SourceIntergratie /

Transformatie

Source

ZorgZorg

Zorg

Research

External

/ other

ResearchAMC/

Overig

External

/ otherExternal

/ other

Stag

ing

DWH

(*) Workspace

/ Portal

Data Space

Compute Space

Workspace

/ Portal

Data Space

Compute Space

Workspace

/ Portal

Data Space

Compute Space

AD-HOC

Data Metadata

Metadata

Archive

Publish

Research datasets can be sources for future studies

Common components:

• Identity and Access

management

• Traceability

• Pseudonimisation

• Sharing & Collaboration

Workflow

Architectuurschets ACRIS

Pseu

doni

misa

tie /

TTP

Datamining

Research spec. Data

Pseu

doni

misa

tie /

TTP….

onderzoeks

dataset

Catalogus

Catalogus

Academisch Medisch Centrum Algemene Dienst ICT - www.amc.nl/ict 11

STG HSTG DV Universe Dataset

Enterprise Data warehouse

Masterdata

Matrix, DBC, etc.

Sources systems

Palga, Glims

SAP

Target systems (e.g. DRE, Objects)

Epic

Chronicals

Clarity

Star

Enterprise Data warehouse model

Palga, Glims,

etc.

Matrix, HR, AD,

etc.

Clarity

Matrix, HR, AD,

etc.

Specific Data mart

Master data Datamart

Delivery

Direct Delivery

*Links to linked data like images, genetics or audio recordings will be preserved as-is throughout all steps. Views (can be materialized for performance reasons

Delivery

Staging >Data Inte-

gration>

Data Transfor-mation

and Quality

>Data

Pressen-tation

Masterdata

Integrated model

Enterprise Data

warehouse

Palga, Glims,

etc.

Palga, Glims,

etc.

Proposal• One ready to use integration and staging layer• Clinical data for Research in same environment• Staging with Data vault is an option, made easier by tools like CDT• General Data Mart with Dimensional modeling • Model compliant with Epic Star and ZIB and FHIR• Demand driven development• Common tool like MS SSDT

Requirements and proposed techniquesStaging Data integration

Data transformation(Enterprise data

warehouse)Presentation (Data mart)

As- Is Data Vault Star model or 3NF-like Star model (Kimbal)

•View on data sources•Can be materialized for performance reasons

•No (in and output) business logic•Decoupled from staging and Datawarehouse•Change should not invalidate Data warehouse or Data marts•Traceable to the source•Time dimension•Completeness•Can be virtualized

•View on Integration layer•Can be materialized for performance reasons•Limited business logic (only for optimizing output usage)•For healthcare data based on FHIR•Adds terminology translation•Data Quality Check

•View on Transformation layer•Can be materialized for performance reasons•Multi propose data delivery•Focus on usabillity•Add business logic for output optimization

Microsoft Data ToolsMicrosoft Data Tools or

specific tool for Data Vault (if chosen) e.g. CDT

Microsoft Data Tools

Different environments, different Requirements

Operational

Data Capture

Departmental

Transaction Processing

Business Function

Accuracy

Speed

Record for Use Case

Business Operations

Capture and Log

Stable and firm

Data Warehouse

Data Integration

Enterprise Wide

Historization

Core Business Concept

Completeness

Auditability

All data all time

Enterprise Knowledge

Time Slice

Agile

Data Marts

Data Delivery

Demand Driven

Online Analytics

Fact based analysis

Flexibility

Usability

Right data right time

Specific analytics

Prepare and deliver

Responsive

Chronicles Clarity Star-like DWHJob Job Dataset???

Academisch Medisch Centrum Algemene Dienst ICT - www.amc.nl/ict 16

Stellingen uit overleg 27-5-2016

• Historical staging zeer goed automatiseerbaar• DV creatie kan ook virtueel• Behoefte eindgebruiker is leidend• Waar mogelijk ZIB conceptueel volgen, maar alleen als

bronsysteem compliant is• ZIB onvoldoende rijk• Logisch model is kan op FHIR gebaseerd worden• Object lijst is beschikbaar • Naamgeving is irrelevant• Uitgifte via views

Door onderzoekers

aangedragen

objecten zijn

vindbaar en

modelleerbaar

Het grotere geheel• Digital Research Environment

• Data beschikbaar maken voor onderzoekers• Behoeften en prioriteiten zijn geinventariseerd• Uitleveren datasets• Castor koppeling

• Architecture and Data Governance (EVAMEG)• Master Data Management (niet centraal belegd)

• Provisioning Epic, GLIMS, Helix• Gebruik LOINC, SNOMED, G-standaard, Vektis, zorgmail, postcode tabel

• Integratie vraagstukken• Web site en services interne klanten (KCC FOS)• Web site en services externe klanten (RadboudOnline)• Behoefte aan centrale regie over data kwaliteit en uitwisseling• Projecten rond track en trace over platforms heen