Big Data Analytics for Healthcare Decision Support- Operational and Clinical
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Transcript of Big Data Analytics for Healthcare Decision Support- Operational and Clinical
Copyright © 2015 Splunk Inc.
Big Data Analy<cs and Decision Support
Adrish Sannyasi, Healthcare Solu<ons Architect
Agenda
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Aim of Big Data Analy<cs Opera<onal Decision Support Clinical Decision Support Data Analy<cs Infrastructure and Methods
Aim of Big Data Analy<cs
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Help Make Op<mal Decisions
Pa<ents Administrators and Policy Makers Providers
Proac,ve Precise Predic,ve
Current Vs. Desired Decision Support
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Current Approach Desired Approach Rule based system High rate of false alarms Missed opportuni<es
Precise and Context Sensi<ve
Workflow Interrup<on Automated Data Collec<ons
Mainly structured EMR or Claims data Structured and Unstructured data from EMR, sensors, wearable, behavior, and environmental data, and condi<on focused social network data.
One <me measurements of physiological sta<s<cs
Con<nuous measurements and pathway oriented measurements
Low transparency and accountability Transparent and Accountable to pa<ents and care team
Building a “Learning and Improvement Engine”
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Sympathy-‐Man, We could do be`er
Empathy – I feel your pain
Compassion-‐ Let me help you
Source: HCA
Big Data Analy<cs must be:
Valid: hold on new data with some certainty Useful: Should be ac<onable Unexpected: non-‐obvious to consumers Understandable: humans should be able to interpret Measurement is useful if it facilitates ac,on. Measure what is important to customer.
Examples: Opera<onal Decision Support
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• Scheduling and Staffing Assistance • Predic<ng and alloca<ng service area and unit capacity • Predic<ng bed/room requests, LOS targets, transport services • Op<mizing Asset/Inventory U<liza<on • Reducing claims processing cost, error, and <me • Reducing Fraud, Waste, and Abuse
Opera<ons Decision Support Center: Air Traffic Control System for Opera<onal Decisions
• Modeled afer your security opera<ons center and IT opera<ons center • Track pa<ent movements and oversee opera<ons and throughput • Proac<vely an<cipate needs for services • Coordinate staffing and scheduling • Coordinate admissions, transfers, discharge planning and execu<on • Reduce cross departmental hand-‐off issues
Examples: Clinical Decision Support
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• Be`er decisions using con<nuous physiological streaming data • Op<mize alarm and alert sehngs in devices and applica<ons • Care Coordina<on for complex co-‐morbid condi<ons • Hyper-‐personalized engagement • Crea<ng checklists based on predic<on of cri<cal events, early warning signs.
Clinical Decision Support Center: Air Traffic Control System for Clinical Decisions
• Think of this is like a department like Radiology • Helps with near real <me evidence findings, implementa<ons, and valida<ons • Provide data driven opinions when no established guidelines exists • Help validate output of analy<cs with exis<ng guidelines and evidence from clinical trials.
Prac<ce Based Evidences (source: greenbu`on.stanford.edu)
Prac<ce Research
Applying Evidence
Genera<ng Evidence
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Example App: Care Coordina<on Assistance-‐ Find gaps, redundancies, conflicts, and interac<ons and predict adverse events
Virtual
Physical
Cloud
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Healthcare Data Is Time Oriented and Diverse
EHR Systems
Web Services
Developers
App Support Telecoms
Networking
Desktops
Servers
Security
Devices
Storage
Messaging
Pa<ent Surveys
Clickstream
HIE
Pa<ent Networks
Healthcare Apps IT Systems and Med Devices Pa,ent-‐Generated Data
Medical Devices
CDR
Mobile
PHI Access Audit Logs
HL7 Messaging
Sensors Departmental
and Homegrown Applica<ons
Disrup,ve Approach to Diverse Data What Happened? What's Happening?
Structured RDBMS
SQL/Cube
Schema at Write
ETL
Search
Schema at Read
Universal Indexing
Unstructured
Volume | Velocity | Variety
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What Might Happen?
Predict/Prescribe
Opera,onalize
Machine Learning
Data Analy<cs Infrastructure
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DATA SOURCES
IOT DATA
IT DATA
Acquiring Enriching (real <me)
In Mo<on Data Acquisi<on, Analysis, and Engagement (security and privacy monitoring and audit)
Searching Analyzing (real <me)
Delivering Engaging (real <me)
At-‐Rest Data Acquisi<on, Analysis, Compose, and Deploy (security and privacy monitoring and audit)
APPS DATA
Data At-‐Rest
Historical Data Storage
Data Discovery, Explora<on, Modeling, Evalua<on (At Rest)
Compose and Deploy (DevOps)
Streaming Data Storage
Data In Mo<on
80% of healthcare data in unstructured text
High velocity <me series data from devices-‐ different <me zones, different <me intervals
Variety of structured formats for the same object
Unit of Measures do not match
Data Integra<on and Normaliza<on
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Probabilis<c Methods to validate exis<ng data or fill in missing data
Data Analy<cs Knowledgebase
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• Computable Care Plans
• Guidelines/Rules
• Health System Workflow
• Data Models
• Ontologies
• Treatment-‐Outcome data
Data Analy<cs Methods
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• What you feed into the algorithm differen<ates winners from averages.
• Sophis<cated techniques are generally worse than simple methods.
Visualiza<on Search/Explora<on Sta<s<cs and Machine Learning
Sofware Engineering
Data Analy<cs Driven User Engagement
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Task
Bo`lenecks, Issues
Knowledge Integra<on
User
Incen<ves, Habits
Impacts of new knowledge,
Trust
Detail or summary or
Both
Responsive
Adap<ve
Managed
People have priori,es beyond just geSng treated.
Courtesy: DJ Pa,l
Lastly, do not forget Sofware Engineering prac<ces
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• Tes<ng • Privacy and Security • Design and Refactoring • Version Control and Provenances • Logs and Documenta<ons • Produc<on Deployment Review
Summary
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Aim of Big Data Analy<cs is to help make op<mal decisions-‐ opera<onal or clinical.
Success in analy<cs requires mul<-‐disciplinary skills.
Personalize the analy<cs output to alter current behavior/habits.