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Seattle code camp 2016 - Role of Data Science in Healthcare

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Everyone loves to shake a stick at the healthcare industry for being backward. Fact is there is no lack of technology or data in healthcare.
Biggest challenge for healthcare providers is to identify what questions to ask the data. My team has implemented over 75 enterprise data warehouse projects in US healthcare industry. At the annual Seattle Code Camp, we discussed some of the examples of how data is used in the healthcare industry for compliance reporting (BI) and predictive analytics.
These slides are from Seattle Code Camp 2016, shares technologies, concepts and ideas for data science in the US healthcare industry.

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Seattle code camp 2016 - Role of Data Science in Healthcare

  1. 1. DATA SCIENCE IN HEALTHCARE Seattle Code Camp 2016 1
  2. 2. ABOUT THE SPEAKER 2 Gaurav Garg “GG” is a business savvy IT leader playing the role of trusted advisor and product development manager. • Leads a Healthcare consulting practice for a national IT consulting firm (www.calance.com). • Participated in product strategy team - identified High Value Questions resulting in 150+ new products and features in $16B GE Healthcare product portfolio. • Played the role of IT Director at UCLA Health System, acting as an interface between the Clinical Directors and the IT Department. • Led the delivery of 8 products and 70+ Enterprise Data Warehouse projects in the Healthcare industry. • P&L responsibility managing application development up to $45MM. • Presented and won Phase I, Phase II grants from the NIH under SBIR funding to build a product in partnership with UCLA Health System. • As a thought leader, GG publishes white papers, appears in speaking engagements and guides healthcare startups at Cambia Grove. Gaurav Garg (GG) https://www.linkedin.com/in/gauravkinatus@gaurav_kinatus
  3. 3. PAST PROJECTS 3 USC UNIVERSITY HOSPITAL
  4. 4. HEALTHCARE BASICS Technologies & Flow of Information 4
  5. 5. WHY IS HEALTHCARE DATA MESSY? 5 Integrated Clinical Information System Financial System Lab Anatomic Pathology (6 vendors) EMPI (5 vendors) Dictation (4 vendors) Interface Engines (7 vendors) Ambulatory Specialty (4 vendors) Hospice (5 vendors) Advanced Visualization (5 vendors) Budgeting (3 vendors) Labor and Delivery (7 vendors) Time and Attendance (3 vendors) Ambulatory EMR (Small practice) (18 vendors) Anesthesia (1 vendor) PACS (31 vendors) Patient Monitoring System (1 vendor) Laboratory Blood Bank (9 vendors) CT Ultrasound Other medical Devices Ventilators Fetal monitors Core Measures Rapid Response PHR integration Ambulatory Inpatient Throughput Tracking Infection Tracking Quality Scorecard Care Quality Analysis Charge & Payment Analysis IHI Triggers
  6. 6. BI & DATA WAREHOUSE EXAMPLES 6 Clinical and Syndrome Surveillance MEWS; HAC Risk Scoring (DVT, DU, CLBSI, CA-UTI, CTI, PU, Wound); Sepsis; school absenteeism; bio-surveillance 1 Patient 1 Chart Optimizing Resource Utilization Imaging Tracking; Room Utilization; Capital Investment Optimization; Payer Compliance; Leakage; LOS; Re-Admissions Manager (RAM) Improving Physician Satisfaction Protocol Compliance Tracking Pharmacy Protocol, Core Measures (AMI, HF, PN, SCIP, MU), Nurse Admission Tracking Improving Patient Satisfaction Data explosion from EMR adaption has created lots of data. Calance has implemented over data warehouse projects in over 70 hospitals and HIEs. • EMPI Integration – we have experience integrating with popular EMPI solutions and troubleshooting performance issues. • Enterprise Interface Engine – implementation, upgrade, migrate, configure interfaces. Caradigm Intelligence Platform (aka Amalga), Orion Rhapsody, eGate, Intersystem Ensemble, BizTalk, SSIS. • Data Warehouse/Data Lake – we have implemented and managed some of the largest data warehouse/data lake infrastructure in the healthcare industry (largest @ 3 petabyte data). • Scorecard/Reports – data visualization for scorecards, dashboards and reports on SharePoint, Roambi, tableau. • Predictive Analytics – build risk stratification and other predictive analytics algorithms using hadoop ecosystem and RStudio.
  7. 7. 7 DATA PIPELINE EMPI Staging Enterprise Service Bus Data Warehouse BI Analytics, Dashboards, Visualization & Report Coordination Browser Mobile Device Redshift, NoSQL, SQL Server Authorization and DevOpsData Governance Products and Logos acknowledged to respective owners. Logos used to for illus
  8. 8. 8 ESB VS. INTERFACE ENGINE Courtesy of
  9. 9. 9 ESB VS. INTERFACE ENGINE Courtesy of
  10. 10. 10 FINANCIAL FLOW https://www.youtube.com/watch?v=RJKIjxl1KeE Who is paying for your healthcare? Watch the video on the following slide
  11. 11. 11 CASE STUDIES Retrospective & Predictive
  12. 12. 12 PREDICTIVE ANALYTICS EXAMPLE –AUTOBED https://www.youtube.com/watch?v=eI1l_s4zo_s Predictive Analytics Example –AutoBed Watch the video on the following Slide
  13. 13. 13 CHANGING BUSINESS MODEL
  14. 14. Know Your Patients Clinical ProtocolsBusiness Intelligence & Predictive Analytics Cohort Identification Standardized Care PathPatient Demographics Lifestyle Medical History Risk Stratification Adverse Event Prediction Family History Proactive Appointments Treatment Effectiveness Patient Generated Data Variance Management Financial Review Patterns Treatment Adjustment Actionable Intelligence POPULATION HEALTH EXPLAINED
  15. 15. PROTOCOL COMPLIANCE Clinical desktop integration using “Knowledge Hub” Protocol Definition and Matching Clinician view, compliance officer view, reporting moduleLanguage & Terminologies Data Aggregation TouchPoint360 Data existing in hospital systems Calance Protocol Compliance Framework
  16. 16. IDEAS High Value Questions 19
  17. 17. 20 AMBULATORY • Is my practice setup to be successful as a Pay-for-Performance contract or should I stay as pay per service? • Should I go an form an Accountable Care Organization (ACO) with 75 other physicians or join an Integrated Delivery Network (IDN)? • What are my compliance requirements with different contracts? • How do I rapidly understand the business implications of (program de jour) for my specific practice so I can rapidly make informed decisions to participate (or not)?
  18. 18. 21 HIGH ACUITY CARE • What is the probability of patient developing an infection? • Do I have everything for tomorrow? • How are we doing on infection management (SEPSIS)? Is there anything we can do to use the devices to proactive alerts? • What is the correlation between cost index and length of stay?
  19. 19. 22 DIGITAL PATHOLOGY • Find similar cases • What other information do you need for diagnosis? Additional images, molecular image, additional test results, additional symptoms • How to close the loop between Radiologist and Pathologist to reduce discordance?
  20. 20. 23 RADIOLOGY • Automatically detect abnormalities in the image • Which Radiologist results in good/bad follow-up actions? • What are my compliance requirements with different contracts? • Is this the right procedure, modality and protocol?
  21. 21. 24 REVENUE CYCLE • Recommend an immediate step to avoid hospitalization. e.g. cab for picking up Rx • Who is the best provider to treat this patient? • What is the best site for this patient? • Coding accuracy dashboard

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