EHR Challanges in bigdata

1. Nesma Almoazamy
Bigdata analytics – EHR – Hadoop components – Healthcare
EHR CHALLENGES [Bigdata]

2.  Hadoop components [tools]
 Conclusion
 Introduction
 Challenges
 Goal
 Architecture
 Data sources
Overview

3. Motivation

4.  A collection of large and
complex data sets which are
difficult to process using
common database
management tools or
traditional data processing
applications.
 Big data refers to the tools,
processes and procedures
allowing an organization to
create, manipulate, and manage
very large data sets and storage
facilities” – according to
zdnet.com
Definition of Big Data

5. EHR 3 Vs…

6.  Standard medical practice is moving from relatively ad-hoc and
subjective decision making to evidence-based healthcare.
 More incentives to professionals/hospitals to use EHR
technology
Reasons for Growing Complexity/Abundance of
Healthcare Data

7. A schematic illustrating the big healthcare data
analytic pipeline

8.  Inferring knowledge from complex heterogeneous patient sources.
Leveraging the patient/data correlations in longitudinal records.
 Understanding unstructured clinical notes in the right context.
 Efficiently handling large volumes of medical imaging data and
extracting potentially useful information and biomarkers.
 Analyzing genomic data is a computationally intensive task and
combining with standard clinical data adds additional layers of
complexity.
 Capturing the patient’s behavioral data through several sensors; their
various social interactions and communications.
Big Data Challenges in Healthcare

9. Overall Goals of Big Data Analytics in Healthcare
Take advantage of the
massive amounts of data and
provide right intervention to
the right patient at the right
time.
Personalized care to the
patient.
Potentially benefit all the
components of a healthcare
system i.e., provider, payer,
patient, and management.

10. Architecture

11. An applied conceptual architecture of big data
analytics Detailed -- Approach I

12. An applied conceptual architecture of big data analytics
Detailed -- Approach II

13. An applied conceptual architecture of big data analytics
Detailed -- Approach III

14. Hadoop Components
[ Tools ]

15. ELECTRONIC HEALTH
RECORDS (EHR) DATA
Data sources

16.  Lab results
Challenges for lab
– Many lab systems still use local
dictionaries to encode labs
– Diverse numeric scales on different
labs
• Often need to map to normal, low or
high ranges in order to be useful for
analytics
– Missing data
• not all patients have all labs
• The order of a lab test can be
predictive, for example, BNP indicates
high likelihood of heart failure
Medication
 Medication
Data sources

17.  Clinic notes
Challenges for handling
clinical notes
– Ungrammatical, short
phrases – Abbreviations
– Misspellings
– Semi-structured information
Copy-paste from other
structure source
 ICD
ICD stands for International
Classification of Diseases
– ICD is a hierarchical
terminology of diseases, signs,
symptoms, and procedure
codes maintained by the World
Health Organization (WHO)
Data sources

18. Summary of common EHR data

19. Analytics Ecosystem Vision
19
IT&DataSystems
Search & Federation
(SOA; Interoperability; Security)
Adapters
Meta Data
Services Web Services
Mobile &
Social MediaViews/Marts
APIsIntranet
Software Systems
Hadoop
WikiSharepoint
.Net/Java
SAS/R Visualization
ETL OptimizationWorkflow
Integration & Monitoring
(MDM, ETL, BPM, BAM)
Unstructured DataEDW
Source System DBs
Diagnostics
& Quality
Exec DashboardsPortals &
Kiosks
Data Models
/Cubes
Reports
BI & Analyses
Scenarios & Strategy
Simulations
Statistical Models Optimization
Models
Tests
Biz Apps
Decision Support & Strategy
Analytics Designs
+ Decision Sciences 
Strategy & Decisions
Computer Technology
+ Info/Data Sciences 
Crude Information & Reports

20. 20
Analytics Cloud & Portals
Enterprise
Data Model
Analytic
Models Layer
Data Sources Instruments Clickstream
Social
Media
Mobile Call
Center
Hospital
Data Experian Claims
Member
Eligibility
Disease &
Wellness
Data Movement
Data
Access
Layer
Encounter
s
Medical
Claims
Call
Center
Lists
Rx Claims
Marketing
Lists
Lab
Claims
Campaign
s
Patients
Eligibility
Prospects
Insights
Layer
Hospital
Encounters
Episodic
Variance &
Trends
Rx Regimen
& Gaps
Patients
Behavior &
Propensity
Advanced
Personalization
Regional
Trends
Influence
Strategy
Responses
Intelligent Version of Influence
NavigateEmpowerConvertPredict

21. 21
Marts/Cubes
Reports
Portal/Kiosks
Biz Apps
Dashboards
EDW
Unstructured
Big Data
Models
Tests
Evaluation
Simulation
&
Optimization
Insights
& Strategy
Integrated
Marketing
Analytics Road map

22. Digital & Data
Shifting
Financial
Risk
Healthcare
R&D
In Conclusion
22
• Three Converging Market
Drivers will cause Massive
Disruption
• Healthcare will go beyond
managing patients within
four walls to Influencing
Health Continuum
• Big Data Analytics will
drive Advanced
PersonalizationDigital
Influence

23. Health care use cases for Bigdata and analytics

24. Thanks…
Email: Nesma.almoazamy93@gmail.com
Questions & Discussion