Data Sharing and Caring In HealthCare - MedYear's experience building Big Data Health Apps

download Data Sharing and Caring In HealthCare - MedYear's experience building Big Data Health Apps

of 26

  • date post

    23-Aug-2014
  • Category

    Healthcare

  • view

    160
  • download

    0

Embed Size (px)

description

MedYear is the world's first Personal Health Network. This deck is from a joint session with Sqrrl that addresses building Protected Health Information Applications using a Big Data platform. Check out Medyear.com as a platform that puts members in control of what health information they share with whom they share it and for how long they share it using granular, easy controls built on a rich, secure platform.

Transcript of Data Sharing and Caring In HealthCare - MedYear's experience building Big Data Health Apps

  • DATA SHARING AND CARING IN HEALTHCARE Presented By: Mark Scrimshire, Advisor +1 703 623 2789 mark@personiform.com Oybek Jumaniyozov Senior .NET Developer Personiform.com
  • DEALING WITH A DATA EXPLOSION New Data Sources: From the Human Genome to Wearable technology Data demands are growing exponentially
  • DATA STEWARDSHIP HIPAA demands Accountability 1. Data Security 2. Active Monitoring 3. Process Accountability But who should be in control
  • DATA MUST BE SECURELY SHARED A growing need to share data uBetween Providers uWith Payers uWith Regulatory Bodies And uTo and From Patients
  • CLINICAL RECORDS Obtaining clinical data in digital form is now very simple and efficient. 1. Give your Medyear address (user@medyear.com) to provider 2. Data is securely transmitted from provider EHR to Medyear (as XML file) 3. Clinical data is parsed into nine categories (see right) 4. Individual entries, or entire sections, can now be easily shared
  • HOW CAN NOSQL HELP?
  • HORIZONTAL SCALABILITY Predictable Performance uLinear performance in line with growth uCommodity building blocks
  • MORE DATA. MORE INSIGHTS Medyear links clinical and Non-Clinical data uPreserving data source integrity uPreviously disparate data creates new insights
  • SQRRL-OUR DATA PLATFORM Analytics (via Dell Kitenga) uFast and easy to manipulate data using drag & drop uActionable intelligence from massive amounts of unstructured and structured data uAnalytics and visualization on unstructured and structured data Data-Centric security at the cell-level Scalable to multiple petabytes Complex search and analytics
  • DATA-CENTRIC SECURITY Data Encryption at Rest Encryption in Motion Fine-grained Access Controls Extensive Auditing
  • FOUR BIG DATA LESSONS FOR HEALTHCARE 1. Data-centric Security 2. Start small but design for scale 3. Iterative refinement 4. Discovery Analytics as critical building blocks
  • BUILDING THE FIRST PERSONAL HEALTH NETWORK Simple but powerful controls put the Member in charge of: uWho they share with uWhat they share uHow long they share Security Made Simple. NOT Simple Security
  • INTUITIVE SHARING Privacy is flexible and established on the fly. 1. Sharing takes place on the secure Medyear social network 2. Users dictate which data is shared, with whom it is shared, and for how long it is shared. @ = certain groups or people # = private chronicles ## = public chronicles (as anonymous) + = time limit on visibility
  • MEDYEAR PLATFORM
  • SQRRL BRINGS RAPID DEVELOPMENT BENEFITS Sqrrl enables fast, iterative development: uIntegrated Lucene Search capability uREST API and JSON Support uGraphSearch
  • SAMPLE DATA{ "Id":"u1", "ElementType":"User", "User_UserName":"Isis", "User_DateRegistered":635317426614205340, "User_FullName":"Oybek Jumaniyozov" } { "Id":"u2", "ElementType":"User", "User_UserName":"jdoe", "User_DateRegistered":635321746614215345, "User_FullName":"John Doe" } { "Id":"u3", "ElementType":"User", "User_UserName":"GeekGuy", "User_DateRegistered":635326066614215345, "User_FullName":"Michael Pitt" } { "Id":"p1", "ElementType":"Post", "Post_PostContent":"Hello John", "Post_PostDate":635326930614215345 } { "Id":"p2", "ElementType":"Post", "Post_PostContent":"Hello Isis. Happy birthday.", "Post_PostDate":635326939254225345 } { "Id":"p3", "ElementType":"Post", "Post_PostContent":"Hello Everyone. No birthdays.", "Post_PostDate":635326947894225345 } { "Id": "p4", "ElementType": "Post", "Post_PostContent": "Hey guys what about a party?", "Post_PostDate": 635326956534225345 } { "Id": "p5", "ElementType": "Post", "Post_PostContent": "What party?", "Post_PostDate": 635326965174225410 } { "Id": "p6", "ElementType": "Post", "Post_PostContent": "I guess he is talking about a birthday party. No?", "Post_PostDate": 635326982454225345 }
  • EDGES { "Id":"u1", "ElementType":"User", "User_UserName":"Isis", "User_DateRegistered":635317426614205340, "User_FullName":"Oybek Jumaniyozov" } { "Id":"u2", "ElementType":"User", "User_UserName":"jdoe", "User_DateRegistered":635321746614215345, "User_FullName":"John Doe" } { "Id":"u3", "ElementType":"User", "User_UserName":"GeekGuy", "User_DateRegistered":635326066614215345, "User_FullName":"Michael Pitt" } { "Id":"p1", "ElementType":"Post", "Post_PostContent":"Hello John", "Post_PostDate":635326930614215345 } { "Id":"p2", "ElementType":"Post", "Post_PostContent":"Hello Isis. Happy birthday.", "Post_PostDate":635326939254225345 } { "Id":"p3", "ElementType":"Post", "Post_PostContent":"Hello Everyone. No birthdays.", "Post_PostDate":635326947894225345 } { "Id": "p4", "ElementType": "Post", "Post_PostContent": "Hey guys what about a party?", "Post_PostDate": 635326956534225345 } { "Id": "p5", "ElementType": "Post", "Post_PostContent": "What party?", "Post_PostDate": 635326965174225410 } { "Id": "p6", "ElementType": "Post", "Post_PostContent": "I guess he is talking about a birthday party. No?", "Post_PostDate": 635326982454225345 } u1 => p1 UserPost u2 => p2 UserPost u3 => p3 UserPost u1 => p4 UserPost u2 => p5 UserPost u3 => p6 UserPost Edges with label UserPost logically means User (VertexIn) owns a post (VertexOut).
  • SQL FAMILIARITY WITH ADDED POWER +--------------------------------------------------+ |uuid() json() | +------+-------------------------------------------+ |u1 | +- ElementType: "User" | | | +- Id: "u1" | | | +- User_DateRegistered: 635317426614205310| | | +- User_FullName: "Oybek Jumaniyozov" | | | +- User_UserName: "Isis" | +------+-------------------------------------------+ |u2 | +- ElementType: "User" | | | +- Id: "u2" | | | +- User_DateRegistered: 635321746614215300| | | +- User_FullName: "John Doe" | | | +- User_UserName: "jdoe" | +------+-------------------------------------------+ |u3 | +- ElementType: "User" | | | +- Id: "u3" | | | +- User_DateRegistered: 635326066614215300| | | +- User_FullName: "Michael Pitt" | | | +- User_UserName: "GeekGuy" | +------+-------------------------------------------+ select uuid(), json() from testdataset where ElementType='User' +------------------------------------------------------------------------+ |uuid() Id ElementType User_DateRegistered User_FullName User_UserName| +------+--+-----------+-------------------+--------------+---------------+ |u1 |u1|User |635317426614205310 |Oybek Jumaniyozov|Isis | +------+--+-----------+-------------------+---------------+--------------+ |u2 |u2|User |635321746614215300 |John Doe |jdoe | +------+--+-----------+-------------------+---------------+--------------+ |u3 |u3|User |635326066614215300 |Michael Pitt |GeekGuy | +------+--+-----------+-------------------+---------------+--------------+ select uuid(), Id, ElementType, User_DateRegistered, User_FullName, User_UserName from testdataset where lucene('ElementType:User') Integrated Lucene search: