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The US Healthcare Industry

Dataiku
Dataiku
DataikuSocial Media Manager at Dataiku

Why does the US Healthcare industry suck? And how can predictive analytics cure it?

The US Healthcare Industry

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The US Healthcare Industry
One dataset a day keeps the doctor away
What is wrong with the US Healthcare
Industry
Fromthe CommonwealthFund« Mirror, Mirror on the Wall »
http://www.commonwealthfund.org/publications/fund-reports/2014/jun/mirror-mirror
An American Paradox
The best hospitals in the world, the worst service in the world
An American Paradox
Destroying Miami each year
An American Paradox
Making people fatter and fatter
A Paradigm Shift

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The US Healthcare Industry

  • 1. The US Healthcare Industry One dataset a day keeps the doctor away
  • 2. What is wrong with the US Healthcare Industry
  • 3. Fromthe CommonwealthFund« Mirror, Mirror on the Wall » http://www.commonwealthfund.org/publications/fund-reports/2014/jun/mirror-mirror An American Paradox The best hospitals in the world, the worst service in the world
  • 5. An American Paradox Making people fatter and fatter
  • 7. Towards a Value-Based Care System From FFS
  • 8. Towards a Value-Based Care System To VBC « Value is the only goal that unites the interests of all the parties in the healthcare system »
  • 9. Towards a Value-Based Care System From Volume to Value to improve the patientcare and reduce costs Fee For Service Value-Based Care Rewards Rewarding Volumes Rewarding patient final Care Incentives Keeping patients at the hospital as long as possible Keeping the patients away fromhospitals KPI Occupancy Rate 30days readmissionRate
  • 10. Towards a Value-Based Care System Analytics to create an unified data flowsystem Social media/Web data Machine2machine data Human-generated Data BiometricData Transactional Data 2,314 exabytes by 2020 ! - Administrative (HR, scheduling, performance management, etc.) - Clinical systems (data from patients getting treated, actions taken by providers) - Subscriber info (if an HMO or health plan) - DNA sequencing - Health Data - « Social Feelings » data - Claim processing - …
  • 11. What could possibly go wrong?
  • 12. What could possibly go wrong? High IT fragmentation
  • 13. What could possibly go wrong? Interoperability out of reach?
  • 14. What could possibly go wrong? To sum it up 37% 17% 15% Having  unstandardized  data  in  silos Lack  of  technology  infrastructure Data  &  Analytics  Skills  gap Main  barriersto  implement data  and  analytics tools in  Healthcare http://www.kpmg.com/us/en/issuesandinsights/articlespublications/press-releases/pages/only-10-percent-of-healthcare- organizations-using-data-analytics-to-fullest-potential-kpmg-survey-finds.aspx
  • 15. DSS: just what the doctor ordered
  • 16. What we can do DSS: Just what the doctor ordered ETL & Claims Processing CombiningmultipleEHR, Database Cleaning& CollaborativeTool DeliveringBetterCare Enhancingandenriching health outcomes MaximizingService Value Enablingforcost containment DevelopingHealth KnowledgeandAwareness from the patientlevelto the population level
  • 17. What we can do ETL & Claim Processing • Human Capacities Upgrade. DSS offers a smooth learningcurve for beginnersandensures increased productivity. Don’thirenewresources, they are already in your teams andDSS is goingto empowerthem • ETL Operations. DSS is a collaborativetoolthatallows for interoperabilityfromanysources. It also aggegates data from disparate internalandexternalsystems.
  • 18. What we can do Delivering Better Care • Disease Management. DSS enablesdata agregation frommultiplesources to have an unifiedviewof the patient. • PredictingAdverse Events. DSS enablesto correlatedisease analysis to preventpatientharms orineffective treatments • ExpandingPrecision Medicine. DSScan linkmedication datato genomicinformation,to fine-tunedelivered treatments.
  • 19. What we can do Maximizing Service Value • Fraud Detection. DSScan detectfraudulent activity • Physician profiling. DSSenablesto isolatehazardous patterns • Staffing Optimization. DSS forecasts patientvolumes and allowfor staffing needsprediction. • No Show Rates. DSS can highlight potentialno-showersandtargetthem moreefficiently. • Risk Forecasting. DSSlets payers forecast who is about to churn • ProviderBenchmarking . DSS developsbenchmarks to lowerpractice variation andimproveclinicaloutcomes
  • 20. What we can do Developing Health Knowledge and Awareness • StrenghteningConsumerEngagement. DSS can highlight high risk patientsto support moreproa active management. Italso helps engagedpatient. • Population Management. DSScan givea 360°population viewin real-time.
  • 23. Wrap-Up What you should keep in mind • The healthcare market mainlyconsists of healthcare payers andproviders that have various interests. • A low-efficiencyhealthcaresystem obligedthe US administration to improveit • GoingfromFSS to VBC: a paradigmshift in the healthcare industry • This reform needsdata and data interoperabilitymorethan ever • Alas, littleinteroperabilityandlowITskills don’thelp • Dataiku’s DSS is then a kick-ass toolto do the basic work: clean, gather, process • But it can also help healthcare providersandpayers to reduce costs and improvepatientfinalcare