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The Role of Big Data Analytics in
Predicting Patients' Outcome
Prof Marcus Ong Eng Hock
Senior Consultant and Clinician Scientist
Dept of Emergency Medicine, Singapore General Hospital
Director, Health Services and Systems Research (HSSR), Duke-NUS Medical School
Vice Chair (Research), Emergency Medicine Academic Clinical Program
Director, Health Services Research Center, Health Services Research Institute
Director, Unit for Prehospital Emergency Care (UPEC), Senior Consultant, Ministry of Health, Hospital Services Division
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Data is the New Oil of Healthcare and Biomedicine
Harnessing and Using the Data
Disease and Biological Insights Improve Hospital
Efficiencies and Processes
Improve Patient Outcomes and
Experiences
New Tools for Healthcare
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Lower Healthcare
Costs
Data Liquidity!
Data Rich with INformation and Knowledge (DRINK!)
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MEASURED REALITY
Intermountain’s
4-steps to a LHS
Data is Key to a Learning Healthcare System
Experience of CarePopulation Health
Cost Per Capita Work life of Health
Care Providers
The Quadruple Aim
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Undergirding EnablersCase Management | Operations | HR | Finance | IT | Education and Research Infrastructure
Regional Health System (RHS)
Changi
General
Hospital
Our Patients
Community / Primary Acute / Secondary Tertiary / Quaternary Intermediate / Long Term
Chinatown
FMC, Tiong
Bahru
CHC
NHGP
GP
SHPPrimary 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-
scienceCampus DEM
Campus Inpatient
Campus ASC
KKH
Campus
Bright
Vision
Hospital
Outram Community HospitalPost-Acute & Continuing Care
SengKang
Community
Hospital
Nursing Homes Palliative
Care
HomecareCommunity
Partners
Seamless Transfer of Patients Across Care
Continuum
* Collaboration with Pearl’s Hill Care Home
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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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Roles of HSRI and HSRC
Long term goal- Learning, Integrated Health System
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* Modified from Best Care at Lower Cost, Institute of Medicine 2012
TECHNOLOGY
TALENT
GOVERNANCE
TALENTData Literarcy, Manpower Training
TECHNOLOGYAI/ Data Science Supporting Infrastructure
GOVERNANCEData QualityAI/ Data GovernancePolicies and Processes
USER EMPOWERMENT
1010
Data Governance Framework
Master Data Metadata Data Quality Data Security
Create and Maintain
Definitions
Review and Approve Changes/ Additions
RemediateResolve Conflicts
Train and Communicate
Technology Enablers (e.g. workflow engines, corporate portals and etc.)
Key ProcessesKey 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
DATA GOVERNANCE ENABLES DATA SHARING AND RESEARCH
Data Sharing
•Data Quality
•Data Governance
•Data Security
•Open-Access
•Inter-institutional
•Common Platform
•DEDUCES
•TriNetX
•Deidentification
•Data Warehouse
•Database/ Data Science Infrastructure (RSD)
Data Sharing
DataSharingGAPGAP
GAP
HSRC Data Science works closely with SingHealth RICE and OIA
GAP
Access Restricted Cluster (External Collaborations)
Cluster Deidentification Process and Training
Within GOVERNANCE:Training – Systems – Policies and Processes
A comprehensive tender should be called to provide a rigorous evaluation
of privacy-preserving software and technologies
DATA GOVERNANCE INFRASTRUCTURE
Enhanced Privacy Preserving System (For IT Evaluation)
• Privacy Preserving System technology is available and rapidly evolving
• UK National Health Service called for a tender to develop a Data Services Platform (DSP) which includes various components, including De-ID services
• Key objectives:– Enhance safety and security
– Improve timeliness and utility
– Remove duplication and drive efficiency
– Etc …
• Major contenders: IBM, Privitar, A*STAR etc
N-CRiPT: https://ncript.comp.nus.edu.sg/
TALENT DEVELOPMENT
Data Producers
Data Users
Data Analysts / Scientists
REDCap
Data Visualisation; Design Thinking, R/ Python for Data Preprocessing, Predictive Modellin, Optimization and Simulation
Practical Citizen Data Science Program for Health Services
Beginners / Advanced
ResearchersData Analytics ProfessionalsSenior Residents/Clinicians
User Group Course Target AudienceConducted by
HSRC
OIA/HSRC
HSRC/OIA
Data QualityManagement TBD After FY19
Intro Health Services Research
HSRC
HBRA Deidentification HSRCInstitutional Deidentification 3rd Party
Everyone
Everyone
Clinical Data Warehouse (DW) at SingHealth
Research Standing Database
eHINTS - SingHealth Data Warehouse• Sample Data Sources ingested:
• LAB, OAS, OPEC; SAPISH; MAXCARE; SCM-ED; OTMS; RIS; REDCap
• Structured DW that facilitates logical data consumption from disparate data sources
HBRA/ PDPA Compliant
Multi-layer Privacy and Security Policies
Shared Roles and Responsibilities
RSD Value Proposition and Design Parameters
1. Purpose-built for Research and Innovation: Structured and Cleaned Data
Pipelines (to ensure provenance)
2. HBRA Compliant Deidentified Database. Transparent Data Governance
3. Robust IT security
4. Linkage to other data sets approved for research (OMICS, Images, Hospice,
external data sources)
5. Decoupled from clinical/operations needs No impact on
clinical/operational effectiveness in using the data-warehouse
6. Rapid Response for Research and Innovation with no bottleneck
7. User-centric design. Adaptable to different analysis needs and
requirements (from basic statistics to advanced data science and AI)
8. Facilitate research collaborations across disciplines and institutions
DATA SHARING INFRASTRUCTURE for RESEARCH
• SingHealth joined the TriNetX Consortium in 2017
• HSRC currently hosts the internal TriNetX hardware servers and software
• Pending feasibility studies and IT Security clearance for full pilot
• Only aggregate results can be obtained by Pharma / CRO
• Collaborative research is also possible between hospitals
• Hospitals’ data always remains within the internal system
TriNetX - 46.2M patients with 10.4B clinical facts in TriNetX network
DEDUCES – Collaboration between SingHealth – Duke NUS – Duke Health
Proposal for a SingHealth-A*STAR Partnership on “High-Profile Use
Cases and Registries”
Institution Pilot Use Case
SGH a) Peri-operative databaseb) Surgical databasec) ED Database
NHCS a) Nuclear databaseb) Echo database
NCCS a) Pathology databaseb) Radiology database
PRISM PRISM Cohort
Multiple Institutions
a) Diabetes Registry b) Respiratory Medicinec) Population Health
Name Instn Title
1 Ecosse Lamoureux SERI/SNEC The Clinical And Economic Effectiveness Of Extending
Diabetic Eye Screening In Singapore.
2 Aung Tin SERI/SNEC Singapore angle closure glaucoma program:
characterization, prevention and management
3 Pierce Chow Kah
Hoe
NCCS A patient-specific diagnostic and predictive platform for
precision medicine in HCC
4 John Chia Whay
Kuang
NCCS Aspirin for dukes c and high risk dukes b colorectal
cancers - an international, multi-centre, double blind,
randomised placebo controlled phase III trial (ASCOLT)
5 Kenneth Kwek /
Sng Ban Leong
KKH Improving the Outcomes of Women and Children Health In
Singapore - An Intergative Translational Research
Programme (II) [CG Main Pot]
6 Kenneth Kwek /
Sng Ban Leong
KKH Integrated Platform for Research in Advancing Metabolic
Health Outcomes in Women and Children (I-PRAMHO)
(Collaborative Pot)
7 Wong Tien Yin SERI/SNEC DYNAMO: Diabetes studY in Nephropathy And other
Microvascular cOmplications
8 Poon Choy Yoke NDCS Transforming the Future of Oral Healthcare - A Clinical,
Translational and Health Services Research Approach
9 Leopold
Schmetterer
SERI/SNEC Singapore Imaging Eye Network
(SIENA)
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SELENA for DR Screening
Total retinal images : 493,667• 76,370 and 112,648 for DR • 125,189 and 71,896 for Glaucoma• 71,616 and 35,948 for AMDfor training and validation
Precision Diagnostics by Multi-Modal Data Collection
Scanadu
Scout
Autographer
LifeLogger
Fitbit
Charge
iHealth
Pulse Ox
Athos
Smart Shorts
Withings
Smart Scale
DexCon
Glucose Sensor
Genetic
Sequence
Body
Imaging
Metabolite
Profiling
Clinical
Records
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Integrating Multi-Modal Data with EHRs
• Collaboration with Health Services Research Centre (HSRI)
Integrate PRISM data with
longitudinal electronic
medical records (EMR)
Supported by ARTS (Analytics and Research Technologies for SingHealth) program
Development and Implementation of Nationwide Predictive
Model for Admission Prevention
HOSPITAL CARE COMMUNITY CARE
Patient admitted to
hospital
Risk prediction generated
H2H Care team assesses these
high risk patients
Provide suitable intervention to
patients
Patient discharged timely and remains
in community
Any patient with an unscheduled admission into
the hospital
Utilizes routine data found in the NEHR to build a nationwide FA
prediction tool.The tool flags out the risk of having a subsequent or
multiple readmissions
Clinical team enrolls into 3 tiers for
timely/safe transition care from Hospital to
Home (H2H)
Arranges for post-discharge medical,
nursing & social community supports
H2H Community teams review, track, monitor and report outcomes. This will
enhance the predictive model
The predictive model was Launched in all Public Hospitals in Singapore since April 2017To date more than 12,000 patients have benefitted
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Singapore Cardiac Longitudinal OUtcomes Database
(SingCLOUD)
Reporting and Analytics Services
Manage Calls and Dispatch Manage EMS Transition and Return
Emergency Call Dispatch Monitoring ConveyanceLocate/Treat/Deliver
Handover to ED Return to Service
Operations Centre Mobile DevicesSmart 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
ePCRNEHR
Dial 995 and send your geo-location at the same time
Know where the nearest AED is located
Sign up as a volunteer responder
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2
3
myResponder app
A novel model for predicting inpatient mortality after emergency admission to hospital in Singapore
XIE Feng
Health Services & Systems Research (HSSR)
Duke-NUS Medical School
2019.4.12
Predictive variables
Demographics ED administrative data Clinical data
• Age• Gender• Nationality• Race
• Triage class (PACS score)• Consultation waiting time • ED boarding time • Day of week • Shift time
• Blood gas • Pulse • Respiration rate • FiO2 • SPO2 • Diastolic BP • Systolic BP • Bicarbonate • Creatinine • Potassium • Sodium
Novel Triage Model For Predicting Inpatient Mortality
CART (Baseline model) Our model
AUC 0.71 0.83
Sensitivity 0.730 0.770
Specificity 0.561 0.733
Score threshold 9 0.035
M I N D T O M A R K E T
AiTriage™ enabledTriage Pathway for Risk Stratification
of Low Risk MACE Patients in the
Emergency Department
Confidential
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PRODUCT: AI ALGORITHM
InputECG, BP, SPO2
5 min
OuputRisk score
> CO score: High Risk< CO score: Low Risk
CO: cut off
aiTriage™ ML Algorithm trained with 39 variables
ML Algorithm based on 39 variables from HRV parameters, ECG 12-Leads and Vital signsAll parameters are objective and measured or derived from acquired signals.
Patents Granted
1. Method of predicting acute cardiopulmonary events and survivability of a patient | US 8668644
2. System and Method of Determining A Risk Score For Triage | US 13/791,764
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To Improve Patient Care and Clinical Outcomes for Asthma and
COPD Patients through Data Linkages and Analytics
© 2019 AI Singapore. Confidential & Proprietary.
JARVISDHL: Transforming Chronic Care for
Diabetes, Hypertension and hyperLipidemia with AI
NUS – Wynne Hsu, See-Kiong Ng, Mong Li Lee, Chee Yong Chan
SingHealth – Marcus Ong, Ngiap Chuan Tan, Tien-Yin Wong, Ming-Ming Teh, Khung Keong Yeo
JARVISDHL (“Just” A Rather Very Intelligent System)
Physical activity
DietJ.A.R.V.I.S.
(personalisedintelligent
management)
Blood glucose
Clinical history
Current state
Previous experience
Patient updates
Adherence to Rx
Drug responses
Current diseases
Family history
Biomarkers
Genetics
Adherence to appts
Admission/ED visit
App inputs
Chatbot
Anthropometrics
Appointment reminders
Medication reminders
Physical activity reminders
Dietary recommendation
Voice recognition
Face recognition
Insulin dose recommendation
Treatment suggestions
Glucose prediction
Complications detection
Complications prediction
Health services planning
AI System
AI/ Data Science models need to go beyond validation to IMPLEMENTATION
1. Moons et al. Heart. 2012;98(9):691-6982. Moons et al. Heart. 2012;98(9):683-6903. Amarasingham et al. Health affairs 2014;33(7):1148-54
Scale up
• Implement
• Sustain
Test-bedding
• Explore context
• Adoption
Impact assessment
• Quantify impact on behaviour and decision making, health outcomes & cost-effectiveness
• Comparative designs
Model updating
• To adjust/improve performance for other settings or populations
• Need to undergo further external validation
External validation
• Temporal
• Geographical
• Domain (different population)
Assessing incrementa
l value of new
(bio)marker
• C-statistic
• Net reclassification improvement
Internal validation
• From same sample
• Random split
•Bootstrapping
Development
• Data source
• Quality
• Missing data
• Variable selection
Research Implementation
Our Goal is Implementation!
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