SILS 2015 - Transforming Data into Actionable Knowledge

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By: Karsten Russell-Wood, Philips Hospital to Home
At Sherbrooke International Life Sciences Summit - 2nd edition | September 28/29/30 2015
www.sils-sherbrooke.com

Publié dans : Santé & Médecine
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SILS 2015 - Transforming Data into Actionable Knowledge

  1. 1. 1 Russell-Wood Philips HealthTech September 30, 2015 Transforming Data into Actionable Knowledge: Methods and Applications to Support Population Health
  2. 2. 2 Objectives Session Plan 1. Drivers for change in healthcare today 2. Fueling big data 3. Harnessing big data for healthcare 4. Digital technologies transforming healthcare 5. Models of innovation
  3. 3. 3 Healthcare today A time of radical shifts… Growth in healthcare costs • Global health systems suffer from inefficiencies that increase costs and reduce the quality of care that patients receive. Change has become imperative. Prevalence of chronic conditions • As our population ages, providers will care for a rising numbers of patients with multiple chronic conditions An aging population • By 2030, there will be about 72.1 million older persons, more than twice their number in 2000 Changing payment models • The era of fee-for-service (US) medicine is coming to an end
  4. 4. 4 Driving Change in Healthcare Economic & Operational Realities Care Financing  Reduction in overall healthcare spend  Focus on clinical and economic outcomes  Deploying solutions that will lower total cost of care  Manage patients in the home Care Support Hospital  Technology to alleviate resource constraints  Combination equipment solutions instead of stand-alone units  Reduced budgets  Shortage of clinicians (RN, Pathologist)  High patient volume Care Support Home  Technology solutions to alleviate dependency on RNs and gain patient volume  Improve overall operational efficiency  Increased patient volume  Reduced availability of RNs  Services which allow for aging at home including personal support work, clinical support and safety at home Consumer  Aging population  Fiercely independent  Empowered to age at home Source: Booz & Company analysis
  5. 5. 5 Fueling Big Data Industry Applications - Scale Shipping Industry Source: http://www.nytimes.com/2014/10/05/business/international/aboard- a-cargo-colossus-maersks-new-container-ships.html?_r=0 • Represents $20 trillion of freight market annually • At any time, 17 million containers are moving by ship, by train, by truck Data answers the question of: • Where am I? • Am I moving? • What’s my temperature? • What’s inside me?
  6. 6. 6 Fueling Big Data Industry Applications - Access Banking Industry Source: Info: NYSE history • Average trading value (NYSE) of $170 billion in 2013 • Online access 24/7 from any device Anywhere access tells one: • Stock price • What’s my balance? • Any new news • Statistics
  7. 7. 7 Fueling Big Data Industry Applications - Harmonization Airline Industry Source: Info: http://sos.noaa.gov/Datasets/dataset.php?id=44 • On any given day, more than 87,000 flights are in the sky in the US • Passengers have access to the same information to book flights Consistent tools support: • Obtain the best price • Check availability • Share travel plans
  8. 8. 8 Why is Healthcare Different?
  9. 9. 9 Harnessing Big Data in Healthcare Convergence of Trends Source: McKinsey, “The Big Data Revolution in Healthcare” • Legacy of autonomous decision making • Under-investment in IT • Diverse, best in breed technologies • Lack of interoperability
  10. 10. 10 Variety • High volume of unstructured data • Unusable for traditional analytics Veracity • Being able to trust the data collected • User error / corruption affects value of data Volume • Capability of storage • Safety and security of data for access Velocity • Greater quantity of data sources • Streaming real time Source: IBM: Tapping Big Data for Healthcare Insights 4
  11. 11. 11 Translating Data into Knowledge Supporting Population Health Myriad patient data needs to be obtained to create actionable knowledge 1 2 3 Normalized data Data Storage Provide a simple way to collect data from all kinds of systems and devices Normalize data for consistency to be utilized for analytics, patient engagement and care coordination Apply advanced analytics capabilities to align the right care to the right patient at the right time
  12. 12. 12 Population Health Insights, Analytics and Workflow Development
  13. 13. 13 Improve the Experience of Care Supporting a Health Continuum
  14. 14. 14 Enterprise Analytics Hospital to Home Solutions Acute • ICU • General Ward Ambulatory • Readmissions • High cost management
  15. 15. 15 >3m ICU patient stays Acute Care Analytics eRI Database 800m lab values 100m med orders 2.8b vital measures
  16. 16. 16 Applications of Acute Data Discharge Readiness Score
  17. 17. 17 Moving Care Beyond the Hospital Connecting Data Solutions Leveraging Philips HealthSuite Digital Platform to connect data through disparate consumer health devices Normalizing data to be applied to individual or population health initiatives
  18. 18. 18 Philips CareSage Beyond Treatment; Prevention [1] M. Simons; D. Van de Craen; F. Wartena, CMS Patients’ Characteristics Analysis of Healthcare Expenditure: Who are the big spenders?, PR-TN 2013/00056, July 2013, M. Simons; D. Van de Craen; F. Wartena, R. Koymans, D. Bergmans, CMS Data Analysis of Healthcare Expenditure; Persistently High Cost Patients Flow Analysis, PR-TN 2014/00151, June 2014 Problem • 25% of elderly patients1 will get substantially more expensive due to clinical deterioration • HCOs need to identify at-risk patients in the “white space” The CareSage predictive analytics engine is designed to provide insight into HCO's at-risk patient population Solution
  19. 19. 19 Aligning Patient & Provider Solutions for Self-Supported Care • Improving access to patients in the home by care providers • Improving adherence and persistence in program through engagement • Integrating smart alerts to support population prioritization Patient solutions Provider solutions
  20. 20. 20 Models of Innovation
  21. 21. 21 Banner Health (Arizona) Targeting the ‘Superusers’ of Healthcare  27% reduction in cost of care  32% reduction in acute and long term care costs  45% reduction in hospitalizations Source: Forbes May 2015
  22. 22. 22 West Moreton Hospital (Australia) A Bold New Partnership Challenge: A small percentage of high-acuity patients are driving the majority of cost and resource expenditure Goal: Identify and map socioeconomic factors, mindsets, and values to improve outcomes “This program aims to transform the current, largely “reactive” model of care so that we prevent patients from becoming chronically ill” ~ S. McKee, CEO, West Moreton Hospital
  23. 23. 23 Key Takeaways • Leverage health data to move from treatment to prevention • Digital technologies improve cost, and access to patient populations • Population health approach enables resource prioritization and care optimization
  24. 24. 24 Thank You karsten.russell-wood@philips.com

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