Big Data in Healthcare.

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Big Data in Healthcare
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Big Trends in Healthcare

• Healthcare service model is transitioning
  into Patient Centered care model driven by
  the healthcare reforms and the need to cut
  costs while improve outcomes.
• Payment methods based on Pay for performance
  are driving collaborative care models like
  ACO (Accountable Care Organizations) and
  PCMH (Patient Centered Medical Homes)

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Big Data in Healthcare Today

• A number of use cases in healthcare are well
  suited for a big data solution.
• Some academic- or research- focused
  healthcare institutions are either
  experimenting with big data or using it in
  advanced research projects.
• This presentation will examine what are
  some of the big trends in healthcare industry
  and how Big Data solutions can enable the
  transformations.

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A Brief History of Big Data in Healthcare

• In 2001, Doug Laney, now at Gartner, coined the
  term the 3 Vs to define big data
• Volume
• Velocity
• Variety
• Other analysts argued that this is too simplistic
  but for this purpose lets start here.

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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.

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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.

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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.

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Big Data and the Internet of Things

• For healthcare, any device that generates data
  about a persons 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 its
  all trending, and then their smartphone sends
  that data to a cloud service.

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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 havent had enough data to work
  with.
• Big data can help fill that gap.

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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
  physicians office.

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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.

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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.

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Predictive and Prescriptive Analytics

• Once we can accurately predict patient
  trajectories, we can shift to the Holy
  GrailPrescriptive Analytics.
• Intervening to interrupt the patients 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.

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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

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Barriers Exist for using Big Data - Expertise

• Hospital IT experts familiar with SQL programming
  languages and traditional relational databases
  arent 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.

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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 doesnt
  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.

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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).

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QA Thank You

• References
• www.healthcatalyst.com
• LifeMasters
• sanders d protii, D, Electronic Healthcare 11(2)
  2012 e5-e6