1. Big Data BEGIN WITH THE END IN MIND Personalized medicine Disease prediction Uber-esque city services Smart homes, cars, appliances Revolutionize education Predictive legal processes Measuring behavior with the intention of altering it or mimicking it?

2. Use Cases for Big Data  Sentiment analysis  A/B Testing  Log analytics  Genomics research  Recommender Systems  Behavioral analytics

3. In-Stream Big Data  Data Streams, transactions, logs, CEP  IoT sensors 7.2B connected devices today, 25B by 2020  Machine learning  NLP  Event Processing 500M tweets per day, 200B per year.

4. Recent Big Data Use Cases in Healthcare  IBM acquires Merge Healthcare  Using Watson to “see” medical images  Apple to feed Watson cloud with real-time and biometric data from the millions of Apple phone and Watch users.  Methodist Health Systems analyzing Medicare claims data to identify individuals who will need expensive care in the future.  Ginger.io app-based data collection allows providers to reach patients in distress 5-10X faster.

5. Emerging Technologies will Accelerate Big Data Analytics  RTLS  Lets keep track of our most important assets…like patients in a hospital  Let’s make sure we have the right asset before we operate  Sensor Networks  Parking, traffic, mass transit  Infrastructure monitoring  Land-based human behaviors, i.e. shopping, driving, waiting, sleeping, etc.  Evidence-adaptive clinical decision support – Markov chain model  Continuously updated state info informs the probability of the next state  Requires information retrieval (IR) systems that integrate real-time data streams of clinical data with existing research data

6. Step 1-4. Challenges to Achieving These Ends  Step 1. Data collection and warehousing  Streams and lakes, warehouses and purpose-based data repositories  Step 2. Talent acquisition  Degreed Data Scientists?  Math or Stats?  Step 3. Choosing the right tools  Servers or clouds, make or buy?  Hadoop, MongoDB, NoSQL, MapReduce, Big Query, Redshift, Kinesis  Step 4. Capturing the insights correctly  Dashboards, infographics, and graph theory

7. Challenges of CDS

8. Who’s the Customer?? Patient Insurance i.e. BCBS, Aetna, Cigna, UHC, etc. Referring Physician Infusion Express Bills ins. For pt service Pt pays premium Reports back to Dr. Ins. pays IVX Gets Rx from Dr. Dr. suggests IV center Insurance may suggest IV center

9. Step 5. Who is my customer and would I know one if I saw one? People who buy my stuff People who influence who buys my stuff What are their key characteristi cs? How do they decide to buy my stuff? What would I need to know in order to get more customers or to get existing customers to buy more? Historical Competiti ve analysis

10. Who the heck is Don Peterson?  Don Peterson began his career in technology with AMD in the 1980s, and is an outspoken advocate that innovations should actually advance the state of the art. In 1990, Don started DeskStation Technology, a maker of high-speed graphics workstations and rendering engines, which he sold to Samsung in 1999. In the mid-2000s, Don developed a system of medical imaging analyses for clinical decision support systems. In 2011, Don founded the Kansas City Big Data Meetup group, which for the past three years has produced the annual Big Data Summit. Today, Don is CEO of Infusion Express. He holds a patent in biometric technology using steganography and is a past Entrepreneur of the Year in High Technology.