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Global big data bootcamp-april2016


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Slides that are presented at Big Data Bootcamp in Austin 2016.
This presentation covers
- Current challenges in the US healthcare
- How Accordion Health is helping the industry
- What machine learning techniques are useful to solve such problems

Published in: Data & Analytics
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Global big data bootcamp-april2016

  1. 1. Twitter : @bigdataconf
  2. 2. Data Analytics in Healthcare How Accordion has been helping the industry Yubin Park, PhD
  3. 3. Accordion Health • Founded in 2014 by – Sriram Vishwanath, PhD – Yubin Park, PhD – Joyce Ho, PhD • A team of data scientists and medical professionals • We help healthcare organizations smoothly transition from the old fee-for-service system to a new value-based reimbursement system • Located in Austin, TX • Website:
  4. 4. US Healthcare is Extremely Complex • Medicare • Medicaid • IPA • Hospitals • Nursing Homes • Pharmacies • Pharmaceutical companies • Device Manufacturers • Private Insurers • Employers • … • Source: le/63298/your-health-care- system-map
  5. 5. Wide Spectrum of Applications • Management/Operation Support – Automated Prior-Authorization for Prescription Drug and Surgery – Fraud, Waste, and Abuse Detection – Cost and Quality Optimization* • Clinical Decision Support – Medical Imaging Analysis – Diagnosis Prediction – ICU Early Warning System – Precision (or Personalized) Medicine using Genomic Data
  6. 6. Fee For Service (FFS) à Value-Based Reimbursement (VBR) Realigning incentives for providers and payers
  7. 7. FFS à VBR “This antiquated model (FFS) is the culprit behind exponential health-care cost growth.” - The Atlantic Under FFS, - Provider: More services, more reimbursements - Payer: Higher expenses this year, higher premiums next year - Patient: Constantly rising healthcare costs
  8. 8. Software Company Analogy I want to hire an iPhone app developer. I want to post a job description with compensation details. My options are: - Pay by hour? - Pay by the number lines of the code? - Pay when the app is completed - Pay a base when the app is completed, and a bonus when the app is downloaded 10,000 times - Pay a base when the app is completed, and a bonus when the app is downloaded 10,000 times, and a penalty if the rating is below 2 stars in the app store
  9. 9. Value? Data is the KEY - Risk-adjusted capitated rates for Medicare Advantage plans - Star Rating bonuses and penalties for Medicare Advantage plans - Shared savings for Accountable Care Organizations - Bundled payments for hospital systems - 30-day readmission rates - Other quality metrics Time Value is measured using longitudinal patient data (e.g. claims, Electronic Health Records, prescription data, etc.)
  10. 10. Steps for VBR Quantify Value Measure Value Predict Value Optimize Value US Healthcare is here
  11. 11. VBR: Bundled Payment • Before (current) – Hospitals do surgeries – Skilled Nursing Facilities take care of patients after the surgeries – Patients go through different facilities without proper coordination – Results: High readmissions rates, Over-utilization of services, Unhappy patients and payers • After (expected) – Hospitals do surgeries, but they are accountable for the episode of care – Skilled Nursing Facilities work together with hospitals for better care-paths for patients – Patients go through well- designed care routes – Results: Low readmission rates, optimal resource utilization
  12. 12. VBR-BP: Care-path Optimization
  13. 13. VBR-BP: How? Claims, EHR Collect Data Machine Learning Analyze Longitudinal Data Web Interface Care-path Design
  14. 14. Which Machine Learning Algorithms? • Time-Series Models are Useful – Care-path optimization is not a single snap-shot process • Tree-based Models are Useful – Lots of interaction effects • Deep-learning-based Models are Useful – Hierarchical feature representation
  15. 15. VBR: Population Health Management • Population-level View – Prevalence of Chronic Conditions – Patient Stratification – Intervention Strategies – To achieve better quality and lower cost
  16. 16. VBR-PHM: Risk Identification • Predictive Identification – Identifying patients with certain conditions • Sounds simple? – Identifying patients who ”may” have certain conditions in the future • Intervention – Figuring out the best intervention strategy for a specific population
  17. 17. VBR-PHM: How? Source:
  18. 18. Which Analytics Techniques? • Data Integration is the KEY • Proper data preprocessing is very very important • Identifying what’s actionable/predictable • Object/Data-driven machine learning models are desired – It is always case-by-case – Fresh fish à Sashimi, Good beef à Steak, etc.
  19. 19. Future of Healthcare Data Analytics ProviderPayer
  20. 20. Thanks • Contact: yubin [at] accordionhealth [dot] com