The changing face of healthcare is shown through a series of images and guesses what the new face of healthcare would look like in not-too-distant-future

1. Smart Care – The New Avatar
of Healthcare
Dr. SB Bhattacharyya
MBBS, MBA, FCGP
Member, EMR Standards Committee, MoH&FW, GoI
Member, Health Informatics Sectional Committee, MHD
17, BIS
President (2010 – 2011), IAMI
Hony. State Secretary (2015), IMA Haryana

2. The times they are a’Changin…
Bob Dylan (1964)
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14. Healthcare Internet of Things (HIoT)
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16. “Smart” Care, Not “Sick” Care
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17. Big Data in Healthcare
• High Volume
• Data from all sorts of sources in electronic format
• High Velocity
• Data from devices, monitors and variety of systems continuously streaming in
24x7
• High Variety
• Data is in almost all types
• High Veracity
• Data sources are dependable as they are mostly known
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18. Big Data in Healthcare
• Sources of data
• Wi-Fi/Bluetooth/NCF-enabled personal healthcare monitoring devices,
wearables
• Smartphones/smart devices (iPod, iPad, etc.)
• Radio-diagnostic imaging devices
• Electronic medical records/health records
• Social media
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19. Big Data in Healthcare
• Data Types
• Textual: EHR and clinical and nursing informatics systems
• Numeric: lab systems and devices
• Coded: EHR and devices
• Audio: EHR and lab systems
• Image: EHR and radio-diagnostic systems
• Video: EHR and radio-diagnostic systems
• Waveforms: monitors and devices
• Streaming binary data: wearables, bio-sensors, monitors
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20. Types of Data Analysis
• Prediction
• Classification
• Regression
• Latent Knowledge Estimation
• Structure Discovery
• Clustering
• Factor Analysis
• Domain Structure Discovery
• Network Analysis
• Relationship mining
• Association rule mining
• Correlation mining
• Sequential pattern mining
• Causal data mining
• Distillation of data for human
judgment
• Discovery with models
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22. Machine Learning Techniques Used
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Algorithm Application Areas
Linear Regression Cost predictions
Logistic Regression Likely outcomes (treatment/intervention)
Neural Networks Likely outcomes (treatment/intervention)
Support Vector Machines In place of linear / logistic regression
Classification (Decision Tree, OneR) and
Clustering (K-Means, Cobweb)
Finding groups (clusters) of similar observations like
clinical outcomes
Principal Component Analysis Data and image compression
Anomaly Detection (Signal Detection)
Any significant observation (signal) amongst a ton of
observations (noise)
Recommender Systems (Collaborative Filtering
& Market Basket Analysis)
Drug & treatment suggestions based on care
provider/patient/peer preferences - personalised
medicine

23. Predictive Analytics
• Data pre-processing and visualisation
• Attribute selection
• Classification (OneR, Decision trees)
• Prediction (Nearest neighbour)
• Clustering (K-means, Cobweb)
• Association rules
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24. Application Areas
• Operational
• Administrative
• Clinical
• Nursing
• Predictive
• Clinical decision support
• Outcomes (prognostics)
assessment
• Readmission prevention
• Adverse event avoidance
• Disease management
• Patient matching
• Personalised medicine
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25. “Intelligent” Healthcare
• Application uses advance algorithms, logic and/or artificial
intelligence to simulate and/or replicate the decision-making process
and guidance of expert clinicians
• Gathering data passive and/or actively from sensors, devices and
human input to both understand the current health status of the user
and provide confirmation or corrective guidance

26. New Face of Health Care…
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34. All images sourced from the Internet (Google Search) and used
in good faith to help illustrate the point being made…
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