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Data science 3.0:
Empowering End Users
Faisal Farooq, PhD
Principal Scientist, IBM Watson Health
Balaji Krishnapuram, PhD
...
© 2016 International Business Machines Corporation
Researchers: Biostatisticians,
PhD in ML
Tools: Matlab, R, SAS, SPSS
Dr...
Data Science 3.0 – Fully managed service and integrated solutions
3
Vertical Data Personas Workflow
Healthcare • Clinical
...
Current Workflow
4
CFO: Why did we lose money on the MSSP
contract last year? How do we break even
this year?
CMO: How do ...
3.0 Vision: Integrated Health & Life Sciences Platform
Develop
Predictive/Descript
ive Analytics
Pipeline
ConnectorstoSyst...
Illustrative Use Case: Transitioning from Volume to Value
Clinical
Billing
Claims Aggregation +
Analytics
Financial
Operat...
CPT
HCP-
HCPCS
Encounter
and Billing
Diagnosis
ICD-9 &
ICD-10
IMO
Diagnoses
NDC
Custom
Procedure
Problem List
& Medical
Hi...
Current Workflow
8
CFO: Why did we lose money on the MSSP
contract last year? How do we break even
this year?
CMO: How do ...
9
Systems of Engagement
Key Drivers for Utilization: Diabetics are hospitalized frequently
10
Prevalence of CKD among diabetics
11
Among diabetics CKD Patients account for majority of hospitalizations
12
Build Predictive Models
13
Deploy to Model to Care Managers
14
Care coordination and care
management via on-
demand registries
Track key metrics and
risk to prioritize
outreach
15
16
17
18
19
Facilitating the
Paradigm Shift
2.0: Professional Body for Data Science
Data science 2.0 needs a professional body similar to the American
Medical Associa...
3.0: Transition to User communities
• From NIPS, ICML, KDD (1.0) to AMIA, HIMSS, Strata (2.0) and further
to AHA, ASCO
– K...
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Strata2016_FF_BK

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Strata2016_FF_BK

  1. 1. Data science 3.0: Empowering End Users Faisal Farooq, PhD Principal Scientist, IBM Watson Health Balaji Krishnapuram, PhD Director and Distinguished Engineer, IBM Watson Health
  2. 2. © 2016 International Business Machines Corporation Researchers: Biostatisticians, PhD in ML Tools: Matlab, R, SAS, SPSS Drivers: New capabilities Deliverable: Point solution Characteristics: Custom math & algorithms for each problem Separate commercialization team Practitioners: SE, Data Scientist Tools: Python, Java, Spark, PMML, Azure Drivers: Scale, Production Deliverables: SaaS Tool Characteristics: Customized projects, Often Not repeatable Automated tools to commercialize/scale Domain specialist: Doctor, Nurse, Patient Tools: EMR, Care Mgmt/Pop Mgmt, mobile app Drivers: Reduce barrier to entry for end user Deliverables: Healthcare Cost, Quality, Satisfaction & Access Characteristics: Industrialized/Repeatable Professional Division of labor Automated / Tools Processes Standards Integrated Pipelines/Platforms 50s-90s 2004-2012 2013-now Analogy between Analytics & Industrial Revolution 2 1.0 2.0 3.0
  3. 3. Data Science 3.0 – Fully managed service and integrated solutions 3 Vertical Data Personas Workflow Healthcare • Clinical • Financial • Survey • Wearables • Clinicians, • Care managers, Analysts, • Directors, • CFO/VP finance • Patient Accountable Care Organization – Transitioning from Volume to Value • Focus on a vertical (e.g. healthcare) and its workflows (data, use cases, user personas). – Which 5% of users will incur 50% of costs? – How do we eliminate care gaps? – How do we empower patients to play a larger role in their own care? – How do we design more efficient systems and processes? • Facilitate division of labor and collaboration across all users in their workflow • Simplify deployment and consumption of insights by business users (from 2.0) • Tight integration with systems of record and engagement are critical to widespread business impact
  4. 4. Current Workflow 4 CFO: Why did we lose money on the MSSP contract last year? How do we break even this year? CMO: How do we reduce abnormally high rate of hospitalization among our diabetic population? ACO director: Define goals: cohorts & outcomes Develop process improvement programs Plan staffing & coverage Analyst: Identify key drivers of cost, utilization,… Predict which patients are at highest risk for hospitalization over next 3 mo Deploy results to care managers/ clinicians in their EMR/Care Mgmt s/w Care Manager: Who are the top 100 patients I need to focus on? Have all the evidence based best practice been completed for my patients? How do we best close care gaps? MD/ RN: What is the full set of tasks to be completed for patients scheduled today? Should I change the dose of Metformin for Mr Jones today? Is my muscle pain due to Metformin – should I stop taking the pill? Should we change my dose due to the high blood glucose reading last time? Can you help me stay healthy without needing to come in to the PCP all the time? Care Team Patient Data
  5. 5. 3.0 Vision: Integrated Health & Life Sciences Platform Develop Predictive/Descript ive Analytics Pipeline ConnectorstoSystemsofRecord Client EMR Repositories Deploy Long Tem Storage (HDFS, Hbase, Cloudant..) Instrument/Measur e & Experiment Analytics Dev-as-a-Service Data-as-a-Service Insights-as-a-Service Marketplace Client Patient data Analytics Runtime-as-a-Service Connectors to Systems of Engagement 5
  6. 6. Illustrative Use Case: Transitioning from Volume to Value Clinical Billing Claims Aggregation + Analytics Financial Operational Drivers of Cost & Utilization Personalized Risk Prediction Self Service Analytics Others 50 M Lives 6
  7. 7. CPT HCP- HCPCS Encounter and Billing Diagnosis ICD-9 & ICD-10 IMO Diagnoses NDC Custom Procedure Problem List & Medical History Brand Name Drug Vital Signs Laboratory Tests Race/Ethni city Surgical History History & Diagnoses Procedures Drugs & Therapeutics Observations / Lab Tests Other Non-Standard Code Standard Code Mapped Code Providers Social History Encounters SNOMED Diagnosis LOINC Observation SNOMED Procedure Pharma Class Ingredients Trade Name Dispensable Product ISO Patient Profile* Semantic Interoperability SNOMED/RxNorm Encounters NPPES Providers Implants Implantables DevicesVital Signs Intake RxNorm Generic Drug Systems of Record: Data Curation Prosthetics 7
  8. 8. Current Workflow 8 CFO: Why did we lose money on the MSSP contract last year? How do we break even this year? CMO: How do we reduce abnormally high rate of hospitalization among our diabetic population? ACO director: Define goals: cohorts & outcomes Develop process improvement programs Plan staffing & coverage Analyst: Identify key drivers of cost, utilization,… Predict which patients are at highest risk for hospitalization over next 3 mo Deploy results to care managers/ clinicians in their EMR/Care Mgmt s/w Care Manager: Who are the top 100 patients I need to focus on? Have all the evidence based best practice been completed for my patients? How do we best close care gaps? MD/ RN: What is the full set of tasks to be completed for patients scheduled today? Should I change the dose of Metformin for Mr Jones today? Is my muscle pain due to Metformin – should I stop taking the pill? Should we change my dose due to the high blood glucose reading last time? Can you help me stay healthy without needing to come in to the PCP all the time? Care Team Patient Data
  9. 9. 9 Systems of Engagement
  10. 10. Key Drivers for Utilization: Diabetics are hospitalized frequently 10
  11. 11. Prevalence of CKD among diabetics 11
  12. 12. Among diabetics CKD Patients account for majority of hospitalizations 12
  13. 13. Build Predictive Models 13
  14. 14. Deploy to Model to Care Managers 14
  15. 15. Care coordination and care management via on- demand registries Track key metrics and risk to prioritize outreach 15
  16. 16. 16
  17. 17. 17
  18. 18. 18
  19. 19. 19 Facilitating the Paradigm Shift
  20. 20. 2.0: Professional Body for Data Science Data science 2.0 needs a professional body similar to the American Medical Association • Standards: Beyond PMML we need standards for the entire pipeline from data to insight • Education & Certification: Expand the global pool of talent by certifying skills eg data wrangling vs statistical modeling • Guidance to legislative bodies eg HIPAA safe harbor vs statistical deidentification, implications of multi-tenancy 20
  21. 21. 3.0: Transition to User communities • From NIPS, ICML, KDD (1.0) to AMIA, HIMSS, Strata (2.0) and further to AHA, ASCO – KDD2016 is making several of these changes. Inputs and participation welcome! • Education: curriculum for end users at all levels including K12, College and Specialties (MD, RN) • Industry Standards for systems of record & engagement – see interoperability efforts underway in Healthcare (FHIR, etc). • Legislation for data governance: eg overcoming national boundaries for data 21

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