research information systems and data science core at … · 2018-03-15 · 5 singhealth duke-nus...
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Research Information Systems and Data Science Core at Singhealth-Duke Health
Services Research Institute SingHealth Duke-NUS
Academic Medical Centre
A/Prof Marcus Ong Eng Hock Senior Consultant and Clinician Scientist
Dept of Emergency Medicine, Singapore General Hospital Vice Chair (Research), Emergency Medicine Academic Clinical Program
Head Data Science, Health Services Research Center Associate Director, Health Services and Systems Research (HSSR)
Duke-NUS Medical School Director, Unit for Prehospital Emergency Care (UPEC)
Senior Consultant, Ministry of Health, Hospital Services Division
Why Do Research?
It’s all about the patient!
Key Challenges
1. Rapid Ageing of the Population
2. Increasing Burden of Chronic Diseases
3. Rising Cost of Healthcare
4. Limited Health Workforce and Competing
Demands
Stresses on Our Resources
People
Space
Money
Singapore Healthcare Landscape
Experience of Care Population Health
Cost Per Capita Work life of Health
Care Providers
Population Health Evaluation and Outcomes
• Adopting the Quadruple AIM
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SingHealth Duke-NUS AMC Health Services Research Institute
Shared Vision & Mission
VISION
To improve the health of Singaporean and the region
through health services research driving excellence in
population health Note: Measurable improvements in patient care at a systems and individual level would
be the primary focus of the vision, with academic excellence synergising this process.
MISSION
To integrate cutting-edge health services research into
SingHealth Duke-NUS AMC to optimise health
at individual, systems and population levels now
and in the future
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Undergirding Enablers
Case Management | Operations | HR | Finance | IT | Education and Research Infrastructure
Regional Health System (RHS)
Regional
Hospitals
Our Patients
Community / Primary Acute / Secondary Tertiary / Quaternary Intermediate / Long Term
Chinatown
FMC, Tiong
Bahru
CHC
NHGP
GP
SHP Primary Care
Integrated
Community
Primary Care
Lung
Diabetes and
Metabolism
Breast
Future
SDDCs
Blood
Cancer
Head &
Neck
Liver
Transplant
Sengkang
General
Hospital
SGH
Campus
MSK
Digestive
Disease
Transplant
Dental Heart
Eye
Neuro-
science Campus DEM
Campus Inpatient
Campus ASC
KKH
Campus
Bright
Vision
Hospital
Outram Community Hospital Post-Acute & Continuing Care
SengKang
Community
Hospital
Nursing Homes Palliative
Care
Homecare Community
Partners
Seamless Transfer of Patients Across Care Continuum
* Collaboration with Pearl’s Hill Care Home
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Data and Data Science in Health
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PATIENTS PROVIDERS
RESEARCHERS
DATA ANALYTICS
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Health Services Research Center -Data Science Core
Translating Data into Actionable Insights:
Data Science (not just) Data Analytics
• Data Analytics can produce operational insights, but
• Data Analytics Data Science!
• Data Science requires rigorous scientific thought processes
• “Data-driven Science”
• “Evidence-based”
• “Statistical Science”
• “Statistics” – Jeff Wu of Michigan U
• Etc…
Max {f:f (IS, SM, AM)>1} [(Health Services Research X Data Science)f]
Scientific Thinking (ST)
Analytical Methods (AM)
Implementation Science (IS)
Translational Impact =
Data Assets + Scientific Thinking
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Supporting Analytics Infrastructure in SingHealth –
Duke NUS AMC
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Integration Services
Data Sources
L T
SAP ISH
Max Care
RMS OTM C&S Emerge
LAB OAS RIS SAP HR
SAP MM
Other Systems
Data Management Services
Info
rmat
ion
Sec
uri
ty
Dat
a Q
ual
ity
Met
adat
a M
anag
emen
t D
ata
Go
vern
ance
SCM Analytics
Reporting and Analytics Services
SWI PCS
Legacy
Dashboard / Scorecards
Adhoc Reports
OAD E XTRACT RANSFORM
eHIntS(Data Repository)
Future Phase
Location Analytics
Text Mining
Future Phase
Predictive Analytics
REDCap
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What is eHIntS?
Electronic Health Intelligence System (eHIntS) is SingHealth’s
“Enterprise Analytics Platform” that includes:
Enterprise Data Warehouse - eHIntS?
Dat
a M
anag
em
en
t an
d D
esc
rip
tive
An
alyt
ics
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What is REDCap? Research Electronic Data Capture
A collaborative software developed by Vanderbilt University
•free of charge but requiring a license agreement
Used by more than 1309 international institutional partners
• including Cornell, Duke, Mayo Clinic, Harvard, Kaiser, etc.
Recommended by SCRI
Architecture and Security approved by IHiS Technical Review Board
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Research Data Collection (CRFs,
Surveys, Questionnaires etc)
eHints
•SingHealth integrated enterprise data warehouse
•Consolidates patient’s and operation/clinical data of SingHealth
•Enabled self-service query/request & extraction needs
• Web-based software for data collection, management and analysis
• Secured eCRFs
• Centralized disease registries
• Automatic removal of data identifiers and seamless access control
• Pulls prospective data from eHINS and integrates with collected research data
REDCapR; PyCapR API
• Facilitates the process of
accessing data with options to
prepare an analysis-ready
data set.
• Allows users to access data
and project meta data (such as
the data dictionary) from the
web programmatically.
Registry of Disease Databases
•>110 disease registries reported
•Facilitate collaborations
•Research data governance
egistry f atabases
Integrated suite of advanced
analytics capabilities for data
manipulation, simulation, analytics
and graphical display.
• Allows users to observe patterns, identify trends and discover
visual insights easily through simple visual illustrations
• Customized reports/ dashboards
Data flow
Rserve
OracleR
Dat
a G
ove
rnan
ce;
Dat
a Q
ual
ity
Man
age
me
nt;
Dat
a Se
curi
ty
Enterprise
System
STRATEGY 3 – Advanced Analytics
STRATEGY 4 : Enterprise Integration
STRATEGY 3 – Rapid Deployment
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SingHealth Analytics Infrastructure
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Hearts 2017 Projects
Project Title PI TYPE (PRELIM EVAL) Institution
Response of the Myocardium to Hypertrophic Conditions in the Adult Population (REMODEL)
Dr Calvin Chin Woon Loong
Economic Analysis and REDCap
NHCS
Leveraging Big Data to Assess Trends in Medication Utilisation, Glycaemic Control and Rates of Severe Hypoglycaemia and Complications in Adult with Type-2 Diabetes
Mr McVin Cheen Hua Heng
Biostatistics and Descriptive SGH Pharmacy
Evaluation of Compliance of MRI Brain Scans to American College of Radiology (ACR) Ordering Guidelines in a Multi-centre Study
Dr Tang Phua Hwee Text Mining RadSci
Development of a Tool for Predicting Drug Response Based on Ethnicity and Genomics Data
Assoc Prof Caroline Lee Guat Lay
Predictive Model NCCS
Optimization of Surgical Theatre Scheduling – Development of a Pilot Decision Support System
Dr Sean Lam Shao Wei
Simulation/Optimization Model
SingHealth
Using Electronic Health Records and Multi-dimensional Patient Similarity Analytics to Select Optimal Therapy for Patients with Rheumatoid Arthritis
Dr Ng Chin Teck Patient Similarity Analysis SGH RHI
ITICO Study: Does Integrated Team-based Care Improve Clinical Outcomes? Dr Hu Pei Lin Health Economics and Descriptive
SHP
Implementing Enhanced Recovery Protocols in Children with Intussusception Dr Shireen Anne Nah
Data Extraction and Retrieval KKH Paeds
Using Electronic Medical Records to Evaluate the Safety of Sodium–Glucose Cotransporter 2 (SGLT2) Inhibitors Among Patients With Type 2 Diabetes: Focus on Diabetic Ketoacidosis, Urinary Tract Infection and Genital Tract Infection
Ms Quek Chung Ling
Data Extraction and Decriptive Analysis
SHHQ Gp Medical
Urological Cancer Registry at Singapore General Hospital Assoc Prof Weber Lau Kam On
Development of Registry SGH Uro
No
PDPA/HBRA self-evaluation start
Continue with approval process
Yes
No
CIRB approval/
exemption is not required
* Excludes insects, C.elegans
Research Not
Allowed
Yes
Yes
Does my Project/study involve humans?
Follow PDPA process
No
Yes
Ops processes including QA/SI ,audit etc.
No
Does my Study
involve Animals? *
Apply to IACUC
Challenges: Research Proposal Approval Process & Data (HBRA)
Azendian Solutions Copyright 2017 19
What is Data Governance?
Data governance can be defined as an organisational approach to data and information management that is formalised as a set of policies and procedures that encompass the full life cycle of data, from acquisition to use to disposal
A Data Governance program includes Master Data Management, Metadata Management, Data Quality Management and Data Security Management
Is Is Not
Cooperative effort between the Business and IT Activity “thrown over the wall” to IT, or an activity performed by IT and then “presented” to the Business
Combination of people, process and technology Technology problem
Ownership and approval Endless loop of “you need to ask…”
Continuous process Something that can be ignored once a project is delivered
Enterprise initiative Functional, departmental, or project effort
Comprehensive structure to ensure data quality Data cleansing effort
Enterprise program that stands on its own Business Intelligence (BI) activity relegated to the Enterprise Data Warehouse (EDW) team
Making decisions about data (e.g. Aggregate, Master/ Reference, etc)
Making decisions about Enterprise Information Management (EIM) Capabilities (e.g. Master Data, Data Security, etc)
Azendian Solutions Copyright 2017 20
Data Governance Framework
Master Data Metadata Data Quality Data Security
Create and Maintain
Definitions
Review and Approve Changes/ Additions
Remediate Resolve Conflicts
Train and Communicate
Technology Enablers (e.g. workflow engines, corporate portals and etc.)
Key Processes
Key Processes
Data Governance Pillars
Technology enablers can assist the management of the governance but these may only be feasible when the rest of the components are already established
Sound policies and standards must be in place for the governance framework to be effective
are required for the maintenance/ enhancement of the standards and policies of the Data Governance Pillars
are required for the maintenance/ enhancement of the standards and policies of the Data Governance Pillars
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• As per HBRA guidelines, research data involving any individually
identifiable human biological material or any individually identifiable
health information has to be de-identified
• De-identification is the process rendering information/ material non-
identifiable
– Simply removing direct and strong indirect identifiers is rarely
sufficient to render data non-identifiable.
– Researchers should make sure that no individuals can be
identified by using additional information from other sources
Rendering Information/Materials Non-identifiable
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• In the process of de-identification, some
original information in the dataset may be
lost.
• The organization need to decide on the
acceptable trade-off in the dataset utility
• The de-identification techniques only reduce
the likelihood of re-identification of dataset.
• Residual risk of re-identification will still
remain in large datasets
Purpose, Utility and Risk
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Dealing with the 15 DI’s and Acknowledgement
of Risk by PIs
The list of 15 data elements considered direct identifiers by SingHealth :
Release Data to PI Acknowledgement for the transfer of data without 15 DI’s by PI
Data Masking Technique
Action
Deletion Personal identifiers like names
will be completed removed.
Substitution Identifiers like NRIC will be
substituted
Example: NRIC : S1234567Z
De-Identified ID :
S1F9E4C227Z5FE5
Masking Information such as postal
codes will be masked out. Only
the first four digits of postal code
will be shown.
Removal, Masking or Substitution by 3rd party:
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DEDUCE Collaboration with Duke Durham
• Flagship Analytics Collaboration with Duke-Durham
• Envisioned as cluster-wide system for Research,
Clinical, QI, Cohort Identification
Geospatial analysis
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TriNetX Model and Implementation
Network Overview
• Hospitals have their own internal TriNetX hardware servers and
software
• Only aggregate results can be obtained by Pharma / CRO
• Collaborative research is also possible between hospitals
• Hospitals’ data always remains within the internal system
• De-identification and data harmonization are done locally
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Challenges: Structured vs Unstructured Data
Working with Industry: Access Restricted Cluster
Academia Level 6, Room 017
Aca
dem
ia L
evel
6,
Ro
om
00
3
Singapore Cardiac Longitudinal
OUtcomes Database (SingCLOUD)
SLIDE | 29
Descriptive and Predictive Analytics: Personalized Patient Treatment in the RHI Dept
Descriptive and Predictive Models
De
scri
pti
ve a
nd
Pre
dic
tive
An
alyt
ics
SLIDE | 30
Descriptive and Predictive Analytics: Personalized Patient Treatment in the RHI Dept
Overall Framework for Modelling and Implementation
De
scri
pti
ve a
nd
Pre
dic
tive
An
alyt
ics
Reporting and Analytics Services
Manage Calls and Dispatch Manage EMS Transition and Return
Emergency Call Dispatch Monitoring Conveyance Locate/Treat/Deliver
Handover to ED Return to Service
Operations Centre Mobile Devices Smart Ambulance Device
Participating EDs (SGH, KTPH and TTSH)
995 call/ situational
data
Electronic Case Record
Incident Details on
IBCR
Clinical Data
Capture
My Responder
Heartsave forms
IBCR
Dispatch Tape Review
HDG (MOHH)
MHA Firewall
Data Warehouse
Extract, Transform, Load
Hospital Firewall
PEC IT Blueprint and Analytics
Potential Area of Analytics
• Research on OHCA, Trauma, Stroke and STEMI
Comms between Paramedics and
ED
ePCR NEHR
Dial 995 and send your geo-location at the same time
Know where the nearest AED is located
Sign up as a volunteer responder
1
2
3
myResponder app
Cardiac Arrest Survival Rates in Singapore Increased 10 fold
• Witnessed cardiac arrest survival rates have doubled from 11.6 to 21.3%
• Overall survival rates have gone up from 3.5 to 5.3%
• Younger patients (<65) are 2.6 times more likely to survive than older patients (>65)
Data-Driven EMS Interventions
• Total survivors increased from 48 to 125.
• Bystander CPR rates increase from 22% to 54%
• AED use 1.8% to 4.1%
• EMS response time gradually increasing 8.3mins9.3mins
Roadmap for SGH Research Database Infrastructure
SingHealth Expansion
SGH REDCap Development started
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• Research is crucial for us to improve outcomes for
patients
• Great opportunities for us in Data Science
• Many barriers and challenges to overcome
• We need the support of Research Institutes, IRBs,
Regulators and Authorities to enable and not disable
research
Conclusion
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SingHealth HSRC Data Science Core
General Enquiries
Email: [email protected]
HSRC Data Science Core:
A/Prof Marcus Ong
Email: [email protected]
Sean Lam
Email: [email protected]
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