1. Emerging AI & Tech Trends ABU BAKAR
CIO/CDO CityMD, NY

2. Emerging Tech Trends
Are you smarter than a 5-year-old?

3. Emerging Tech Trends

4. Did You Know? How many smart devices will be connected by 2050?
50 billion
US has the fastest computer that exists today, capable of processing ____ calculations per second.
200,000 trillion calculations
How many new computer viruses are created every month?
Roughly 6,000

5. Did You Know? Everyday, Google Translate processes how many words?
140B
The world’s human population is 7 billion people. From that number, how many mobile devices exist?
7.5 Billion
By 2020, what percentage of online searches will be voice-based?
50%

6. OTHER MAJOR EMERGING TECH TRENDS

7. Voice Recognition
The ability to recognize spoken words only, and not the individual voice
On The Rise
60% of people using voice search have started  in the last year
The prediction is more than 50 percent of searches will be voice-based by 2020.
56% of online grocery shoppers use or plan to use voice controlled smart assistant/speaker
The Echo’s installed base in the U.S. grew from 20 million to 30 million in the fourth quarter ONLY.
13% of all households in the United States owned a smart speaker in That number is predicted to rise to 55% by 2022.

8. Fifth Generation Wireless
What is 5G?
Fifth-generation wireless, or 5G, is the latest iteration of cellular technology, engineered to greatly increase the speed and responsiveness of wireless networks
Data Boost up to 100 times faster than 4G LTE

9. Fifth Generation Wireless
What is 5G?
IoT Support – Without the network support and capability of 5G, Internet of Things would never be able to meet it’s full potential
Rollout date is starting in 2019 with 2021 it will go mainstream
A half-billion 5G mobile subscriptions are expected worldwide by 2022

10. Definitions of AI What is the PARADIGM SHIFT here Wikipedia
Artificial intelligence(AI) is the ability of a computer program or a machine to think and learn. It is also a field of study which tries to make computers "smart".
What is the PARADIGM SHIFT here

11. Artificial Intelligence
A.I.
Artificial Intelligence

12. Prediction mechanism for the ER recommendation process

13. Challenges on the Horizon
Every major advancement brings new set of challenges
Consequences of using incorrect data in this hyper connected and highly automated economies could be catastrophic
Major Coordinated Global Cyber Attacks is real and will get intensified as we make major advances in the global connected economy. Bad actors can start the first global cyber world war in a matter of minutes
Impact of over ambitious AI can lead to global labor crisis
To realize AI’s potential in health care, the regulatory, legal, data, and adoption challenges that are slowing safe and effective innovation need to be addressed
The volume of data generated every day about disease, treatments, prevention, and wellness exceeds the ability of clinicians to absorb and process it all.

14. NoSQL for data normalization
Proposed Solution - ER recommandation - high level design 6.
ML model trained on existing clinical data (portion of it for ER Use Case) 1. 3.
Patients come into the CityMD clinic
Patient information stored into eCW 5.
NoSQL for data normalization
MD/PA
Scribe 4.
MSSQL
After Care 2.
Doctor notes key information regarding the patient’s health
7. Review
Alerts
Reporting
Automated
Scalable
Integrated with the current eCW Logship process
Secure
Non-ER patient ER
patient
8. AfterCare follow up with patients via Contact Center
AfterCare

15. Import data into NO-SQL Continuous cycle for multiple use cases
Scalable for other future use-cases and validated with
ER Use case
Step 6
Machine learning model training
Step 7
Apply machine learning model to presented dataset
Import data into NO-SQL
Feature engineering
Continuous cycle for multiple use cases
Step 1
Step 2
Step 3
Step 4
Step 5
Machine learning model design
Step 8
Analyze results
Decide on MVP dataset
Normalize data
Step 9
Changes and improvements
Reuse this “ingestion pipeline” for other use cases that require ML applications, not just for ER Use Case

16. compiled SQL queries to determine the required data for retrieval
Step 2 - Import data into NOSQL
SQL data is retrieved with a python script and sent through a pipeline for transformation
The pipeline transforms the SQL data into NOSQL format and forwards the data into the Elastic Stack
compiled SQL queries to determine the required data for retrieval

17. Step 2 - Import data into NOSQL
Data extraction troubleshooting requires checking only a query not the entire ingestion pipeline
Faster normalization process by cleaning the data at the source
Process efficiency by extracting only the data we need
Easily scalable by adding additional queries as needed
Avoid SQL limitations

18. Step 3 - Data normalization
Data is taken out of tables and into a field : value format
Field : Value
Information grouped on a per encounter basis
Granularity when analyzing the data
Easy to apply machine learning

19. Data is kept unchanged to have it available for any use case
Step 4 - Feature engineering
The normalized data was analyzed for further required cleaning or enrichment
Data is kept unchanged to have it available for any use case

20. Next steps 6 5 7
Prepare training data set (known facts, labeled dataset) and feed it to the implemented model to generate the weight matrix that will be used for evaluating dataset records
Convert dataset to a specialized subset to be used for use case; Select appropriate mathematical model, functions and parameters as optimal as possible taking into account the expected results ; Initial implementation for model
Evaluate the model by selecting a subset from the labeled data and using it as input for the trained model 9 8
Based on the improvement plan implement changes, readjust dataset, parameters and machine learning model in order to achieve better results
Analyze outcome, accuracy of the classification / prediction, determine overfitting issues and a plan to improve the model for a specific use case

21. AI Pilot to improve quality of care and reduce total cost of care for patients
Prediction mechanism for the ER recommendation process
Input 8+ million patient visits
Process structured and unstructured datasets
Learn the key signals contributing to the ER recommendation
Train the model to look for signals
Evaluate the effectiveness of the recommendation