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Big Data in Healthcare.

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The volume of Big data in healthcare is anticipated to grow over the coming years and the healthcare industry is anticipated to grow with changing healthcare reimbursement models thus posing critical challenges to the healthcare environment. – PowerPoint PPT presentation

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Title: Big Data in Healthcare.


1
Big Data in Healthcare
2
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3
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)

4
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.

5
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.

6
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.

7
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8
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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10
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.

11
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.

12
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.

13
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.

14
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.

15
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.

16
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.

17
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

18
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.

19
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.

20
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).

21
QA Thank You
  • References
  • www.healthcatalyst.com
  • LifeMasters
  • sanders d protii, D, Electronic Healthcare 11(2)
    2012 e5-e6
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