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Big Data in Healthcare

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  1. Big Data in Healthcare

  2. Big Trends in Healthcare • Healthcare service model is transitioning into Patient Centeredcare model driven by thehealthcare reforms and the needto cut costs while improveoutcomes. • Payment methods based on “Payfor performance” are drivingcollaborative care models like ACO(Accountable Care Organizations)and PCMH (Patient CenteredMedical Homes)

  3. Big Data in Healthcare Today • A number of use cases inhealthcare are well suited for a big data solution. • Some academic- or research- focused healthcare institutions areeither experimenting with big dataor using it in advanced researchprojects. • This presentation will examinewhat are some of the big trendsin healthcare industry and how Big Data solutions can enable the transformations.

  4. A Brief History of Big Data in Healthcare • In 2001, Doug Laney, now at Gartner, coined the term “the 3 V’s” to define big data: • Volume • Velocity • Variety • Other analysts argued that this is too simplistic but for this purpose let’s start here.

  5. A Brief History of Big Data in Healthcare EMRs alone collect huge amounts of data, but not all of them are relevant to the current practice of medicine and its corresponding analytics use cases. Lots of very useful data sets relevant for analytics use cases may come from outside the organizations, like socio-economic data, behavioral data, environmental data etc.

  6. Health Systems Without Big Data Most healthcare institutions are swamped with some very pedestrian problems such as regulatory reporting and operational dashboards. As basic needs are met and some of the initial advanced applications are in place, new use cases will arrive (e.g. wearable medical devices and sensors) driving the need for big-data-style solutions.

  7. Big Data and Care Management • ACOs focus on managed care and want to keep people at home and out of the hospital. • Sensors and wearables will collect health data on patients in their homes and push all of that data into the cloud. • Healthcare institutions and care managers, using sophisticated tools, will monitor this massive data stream and the IoT to keep their patients healthy.

  8. Big Data and the Internet of Things For healthcare, any device that generates data about a person’s health and sends that data into the cloud will be part of this IoT. Wearables are perhaps the most familiar example of such a device.  Many people now can wear a fitness device that tracks their heartrate, their weight, how it’s all trending, and then their smartphone sends that data to a cloud service.

  9. Predictive and Prescriptive Analytics • Real-time alerting is just one important future use of big data. Another is predictive analytics. • The use cases for predictive analytics in healthcare have been limited up to the present because we simply haven’t had enough data to work with. • Big data can help fill that gap.

  10. Predictive and Prescriptive Analytics One example of data that can play a role in predictive analytics is socioeconomic data. Socioeconomic data might show that people in a certain zip code are unlikely to have a car. There is a good chance, therefore, that a patient in that zip code who has just been discharged from the hospital will have difficulty making it to a follow-up appointment at a distant physician’s office.

  11. Predictive and Prescriptive Analytics This and similar data can help organizations predict missed appointments, noncompliance with medications, and more. That is just a small example of how big data can fuel predictive analytics. The possibilities are endless.

  12. Predictive and Prescriptive Analytics Another use for predictive analytics is predicting the “flight path” of a patient. Leveraging historical data from other patients with similar conditions, predictive algorithms can be created using programming languages such as R and big data machine learning libraries to faithfully predict the trajectory of a patient over time.

  13. Predictive and Prescriptive Analytics • Once we can accurately predict patient trajectories, we can shift to the Holy Grail–Prescriptive Analytics. • Intervening to interrupt the patient’s trajectory and set him on the proper course will become reality. • Real life use-cases • Major Payor uses member segmentation analytics to drive Clinical programs that focus on prevention and proactive management of chronic diseases among its members • Big data is well suited for these futuristic use cases.

  14. Big Data in Healthcare In conclusion, Big Data solutions are increasing enabling traditional healthcare service providers transforming into patient centric, collaborative care providers using analytics to drive decision making at the point of care

  15. Barriers Exist for using Big Data - Expertise • Hospital IT experts familiar with SQL programming languages and traditional relational databases aren’t prepared for the steep learning curve and other complexities surrounding big data. • These experts are hard to come by and expensive, and only research institutions usually have access to them.

  16. Big Data Differs from Current Systems – Big Data has Minimal Structure Big data differs from a typical relational database. The biggest difference between big data and relational databases is that big data doesn’t have the traditional table-and-column structure found in relational databases. In contrast, big data has hardly any structure at all. Data is extracted from source systems in its raw form stored in a massive, distributed file system.

  17. Big Data Differs from Current Systems – Big Data is Less Expensive Due to its unstructured nature and open source roots, big data is much less expensive to own and operate than a traditional relational database. A Hadoop cluster is built from inexpensive, commodity hardware, and it typically runs on traditional disk drives in a direct-attached (DAS) configuration rather than an expensive storage area network (SAN).

  18. Q&A Thank You • References • www.healthcatalyst.com • LifeMasters • sanders d protii, D, Electronic Healthcare 11(2) 2012: e5-e6