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

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

Emerging Tech Trends

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

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%

OTHER MAJOR EMERGING TECH TRENDS

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 2017. That number is predicted to rise to 55% by 2022.

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

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

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

Artificial Intelligence A.I. Artificial Intelligence

Prediction mechanism for the ER recommendation process

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.

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

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

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

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

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

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

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

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