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®© 2016 MapR Technologies 1
®
© 2016 MapR Technologies
®© 2016 MapR Technologies 2
Today’s Presenters
George DemarestDirector of Industry Solutions
@g_demarestEmail: [email protected]
Alicia d’EmpaireAVP, BI and Decision SupportEmail: [email protected]
®© 2016 MapR Technologies 3
Agenda
• Big Data Trends in Healthcare• Introduction of Baptist Health South Florida • Healthcare Changes and Challenges• Role of Technology and Analytics• BHSF Big Data Analytics Strategy• Big Data Analytics Challenges• Questions
®© 2016 MapR Technologies 4
A Once-in-30-Year Re-Platforming of the Enterprise
Critical infrastructure for next-gen applicationsData platform enabler for required Speed, Scale, Flexibility
New Applications Existing Applications
Open Source Analytic Innovations Legacy
Disruptive Data Platform
On Premise Private Cloud Public Cloud
Heterogeneous Hardware
Next Gen Data Platform
®© 2016 MapR Technologies 5
(100,000)
(80,000)
(60,000)
(40,000)
(20,000)
-
20,000
40,000
60,000
80,000
100,000
120,000
2013 2014 2015 2016 2017 2018 2019 2020
N.G. Architecture Growth vs. Legacy Shrinkage ($M)
Total $ Growth of IT Mkt N.G. $ Growth Legacy Mkt Growth/Shrink in $
IT Spending at an Inflection Point: Next-Gen is Now
Data source: IDC, Gartner; Analysis & Estimates: MapR
®© 2016 MapR Technologies 6
5 Examples of Big Data in Healthcare That Can Save People’s Lives
http://www.datapine.com/blog/big-data-examples-in-healthcare/
Electronic Health Records (EHRs)
Real-time Alerting
Predictive Analytics in Healthcare
Using Health Data For Informed Strategic Planning
Telemedicine
®© 2016 MapR Technologies 7
Big Data Trends in Healthcare and Life SciencesHealthcare Analytics• Clinical decision support• Predictive modeling across conditions• Disease management• Population health management
Data Management• Electronic Medical Records (EMR)• Medical imaging• Genomics• Insurance claims data
IoT and Networked Medical Devices• Consumer devices• Wearables• Internally embedded devices• Stationary devices (drug dispensers, et al)
Fraud, Waste and Abuse• False claims, identity theft• Kickbacks and beneficiary fraud• Waste from Duplication and unbundling• Insurance Claims Data
®© 2016 MapR Technologies 8
Building a Healthcare Data Lake on MapR
DataLake
Claims
Clinical
Pharmacy
EMRLogs and Notes
3rd Party
Additional Data
CB Header data, Social, ...
Historical procedures, co-morbidities (prof & inst.)
Lab results, vital signs, ...
Dr. Notes, Customer call logs, emails
Licensing, death master, …
Electronic Medical Records, images & text
Prescriptions, adherence
SolvingHealthcareProblemswithBigData
Aliciad’Empaire
Agenda
Ø Introduction of Baptist Health South Florida
Ø Healthcare Changes and Challenges
Ø Role of Technology and Analytics
Ø BHSF Big Data Analytics Strategy
Ø Big Data Analytics Challenges
Ø Questions
BaptistHealthSouthFlorida
BaptistHospitalofMiami
DoctorsHospital HomesteadHospital MarinersHospital SouthMiamiHospital WestKendallBaptistHospital
BaptistCardiacandVascularInstitute
eICU MedicalArtsandSurgeryCenteratBaptistHospital
MedicalArtsandSurgeryCenteratSouth
MiamiHospital
UrgentCareCenters Imaging/DiagnosticCenters
MiamiCancer Institute SleepCenters
EndoscopyCenters HomeCare InternationalCenter EmployedPhysiciansBHMG- 163physicians
(41practices)
BaptistHealthQualityNetwork (BHQN)- 850communityphysicians
EmployeeHealth&Wellness
BaptistHealthStatistics
Ø Admissions………………………………………………..…71,681Ø PatientDays…………………………………………..… 342,942Ø Births……………………………………………………….… 10,977Ø EDVisits……………………………………………….…. 313,116Ø UrgentCareVisits…………………………….………242,177Ø TotalSurgicalCases……………………………….……64,662Ø InternationalPatients…………………………………… 7,710Ø LicensedBeds………………………………………………1,742Ø BHMGvisits……………………………………………….. 203,059
BaptistHealthStatistics
Ø MedicalStaff…………………………………………...…...… 2,211Ø Employees……………………………………….……...……... 16,300Ø CharityCareanduncompensatedservices
(atcost)………………………………..………………..… $292,190,000
HealthcareChallenges
• AffordableCareActo Accessto&AffordabilityofCare andQuality&CostofCare
üMedicare1. ReduceAvoidableUtilization2. ImproveCoordinationofCare3. Quality,Service,&CostTransparency
üMedicarepopulation+1. Percentageincreaseofages65+from2014– 2022:27%increase(Counties:MiamiDadeincrease
of27%,Browardincreaseof23%,PalmBeachincreaseof32%)2. Medicareistobecomemajoritypatientvolumeby2022
üMedicaidExpansion
WhatisConsumer-CentricHealthcare
Source: IBM
Consumers
KeydriversandfactorsforConsumersinselectinghealthcareservices
1. OutofPocketExpenses
2. Access3. Convenience4. Transparency
HCAHPS
HCAHPS:asurveyinstrumentanddatacollectionmethodologyformeasuringpatients'perceptionsoftheirhospitalexperience.
RoleofTechnologyandAnalytics
Ø Use latest technology to track patient data throughout the continuum of care – Home Health Devices, Wearables etc.
Ø Integrate data from multiple data sources real time
Ø Generate actionable insight using performance monitoring analytics and providing automated alerts
Ø Optimize Outcome using Predictive Analytics
Ø Improve Patient Experience by personalizing care
Ø Reduce cost by analyzing data to identify cost saving opportunities
RoleofTechnologyandAnalytics
•Medical•NonMedical•Wearables•SocialMedia•Structured•Unstructured
ConsumerData
•HL7•ETL(Extraction, TransformationandLoading)•Analytics tools – BigDataHadoop, Predictive Analytics,MachineLearning,NLP, Tableauetc.
IntegratedRealTimeData •Mobile devices
•Email•TextMessaging•MarketingCampaign viamail•SocialMedia
MultiChannelstoConsumer
HealthcareBigDataUseCases
Ø Admission/Readmission prediction
Ø Telemedicine (Diabetes care & patient home monitoring)
Ø Sepsis early detection (real time vital signs streaming)
Ø Patient Engagement (Social Media)
Ø Genomics Study
DataAnalyticsMaturityModel
Reporting
EnterpriseDataWarehouse
BusinessPerformanceManagement•Dashboards•Scorecards
BigData•Hadoop
AdvancedAnalytics•PredictiveAnalytics•MachineLearning•NaturalLanguageProcessing(NLP)
DAT
A SY
STEM
SAN
ALYT
ICS
APPL
ICAT
IONS
DAT
A SO
UR
CES
BHSFDataAnalyticsFutureState
Reporting and Analytics• Dimensional Insight Diver• SAP BOE• IBM Cognos• IBM Watson Content Analytics• MS SQL Reporting Services
Data Visualization & Dashboards
• Tableau• Xcelsius
• SPSS• Stata• MCSS-
PASS
• SAS• Treeage• R
Research & Statistical Analysis
Existing Sources(EMR, Ancillary Systems, Devices)
Emerging Sources(Sensor Streaming,Social Media,
Telemedicine, Unstructured)
Advanced Analytics• Predictive Analytics• Machine Learning• NLP (Natural Language
Processing)
Traditional Data WarehouseIndependent Data Marts Big Data:
Ø Implementing Hadoop (Big Data)
Ø Achieve cost savings via offloading storage
Ø As we migrate to Cerner, Big Data is a great platform for us to store historical data from our existing clinical and financial applications
BHSFBigDataStrategyNextSteps
Ø Pilot projects to begin:
1. Set up Hadoop environment and offload storage
2. Sepsis prediction by loading real time streaming vital signs, labs, orders, census data, etc.
BHSFBigDataStrategyNextSteps
Ø Continue to expand our tools for Advanced Analytics:
§ Predictive Analytics§ Machine Learning§ Natural Language Processing
BHSFAdvancedAnalyticsNextSteps
SuccessfulStrategy:4KeyPillars
Data• InformationManagementFoundation•DataGovernance
•DataStandardization
Technology• AppropriateTechnologyPlatform
People• Organization•Organizationalstructure&Roledefinitions
•CentersofExcellence
Process• InformationasanEnterpriseAsset• Standardizationofworkflows
•Adoption
Big Data Analytics Challenges
ØNew Stack of Technology and Data
ØLack of resources
ØInteroperability challenges
ØHIPAA restrictions
ØLack of Data Governance
KeyTake-Aways
Ø Use the BI Maturity Model to ensure the value of your current Analytics investments while developing the capabilities for the Advanced Analytics and Big Data phases.
Ø Although important, BI is not just about having the right tools. Address your biggest challenges of the BI Maturity Model, such as those related to data governance, cultural transformation, and BI-related skills.
Ø Plan for the future by developing plans that consider patient-reported data, events-driven architecture, social media, streaming data and machine learning.
Ø Balance principles with pragmatism. Health care BI is an immature and rapidly evolving area so progress may be made by taking “two steps forward and then one step back.”
SOURCE: The Advisory Board Company
®© 2016 MapR Technologies 30
MapR Converged Data Platform
Typically 1/3 less hardware needed
Multi-tenant
Self-service data exploration with Drill
Industry’s only mirroring, point-in-time consistent snapshots
Trillions of files vs. 100M limit
POSIX, NFS
Typically2x-7x faster
Built-in real time NoSQL DBMS and Streaming
Most complete Spark stack Multiple
versions of community software supported
Big data foundation for files, enterprise apps
®© 2016 MapR Technologies 31
MapR Healthcare Architecture
®© 2016 MapR Technologies 32
MapR Life Sciences and Healthcare Customers
Delivers clinical intelligence to healthcare providers
Next generation data platform for healthcare
and life sciences
Research grant analysis
80+ use cases; Fraud, Waste and Abuse
Clinical integration, population health, and value-based care
solutions and services
Diagnostics and solutions for animal health
UnitedHealthcare A UHG Company
Drug discovery and biomedical Research
®© 2016 MapR Technologies 33
MapR Healthcare Blog
© 2016 MapR Technologies
Q & A1. Coming Soon:
MapR Guide to Big Data in Healthcare
2. eBook:Implementing a Digital Transformationhttps://www.mapr.com/architect-guide-to-digital-transformation