Transforming Healthcare through Data & Analytics
- 1. 1 Transforming Healthcare through Data & Analytics Onur Torusoglu Chief Data & Analytics Officer, Ochsner Health System, New Orleans, LA
- 2. 2 We’ll talk about 4 things.. • Provider Industry Dynamics • Provider Business Priorities • Healthcare Data State of Union • Top Trends • Key Technologies • Trends in Acute Care, Aging Trends • Challenges • Opportunities • Implications for Acute Care, Aging What it Means • Some Thoughts Call to Action 1 2 3 4
- 3. 3 Healthcare Provider Industry Dynamics The healthcare industry continues to transform and innovate to deliver higher-value healthcare, reduce costs, and engage patients amidst a changing regulatory landscape and shifting ecosystem StateofUnion Consolidation and disruption across the HCLS ecosystem Changing policy landscape and healthcare reform Value based care and changing reimbursement models Increasing empowerment of consumers and patients Sources: Deloitte, 2017 Global Healthcare Outlook Frost & Sullivan, 2017 Global Healthcare Industry Outlook IBM Everest Group, Healthcare & Life Sciences Digital Adoption Trends, April 2016
- 4. 4 Healthcare Provider Business Priorities The idea of the “triple aim” in healthcare – improving health outcomes, lower costs, and improving the patient experience – continues to be the focus of healthcare organizations undergoing the business model transformation necessary to achieve it Business and operating model transformation Continued need to lower costs of healthcare StateofUnion Transition to value-based care Patient and consumer engagement Sources: Deloitte, 2017 Global Healthcare Outlook Frost & Sullivan, 2017 Global Healthcare Industry Outlook Everest Group, Healthcare & Life Sciences Digital Adoption Trends, April 2016 IBM
- 5. 5 Big Data Explosion in Healthcare StateofUnion Source: IBM
- 6. 6 Sources of Big Data in Healthcare Big Data comes from multiple sources StateofUnion
- 7. 7 Key Characteristics Volume Volume of health data estimated at over 300 exabytes in 2015, globally Velocity Volume of Health Data estimated to increase 40- 50% each year With increasing digitization, data velocity will increase exponentially Veracity Quality and accuracy of health data a critical precursor for improving health outcomes Variety Data is generated by EMRs, medical devices, sensors, wearables, mobile apps, genomic research, and even non health related sources Value McKinsey estimates value pathways developed using Healthcare Big Data could deliver annual savings worth $300-450 bn in the US alone Key Characteristics of Healthcare Big Data, Global, 2016 StateofUnion Sources: Frost & Sullivan, 2017 Report on Big Data in Healthcare Icons by : Flaticon:
- 8. 8 We’ll talk about 4 things.. • Provider Industry Dynamics • Provider Business Priorities • Healthcare Data State of Union • Top Trends • Key Technologies • Trends in Acute Care, Aging Trends • Challenges • Opportunities • Implications for Acute Care, Aging What it Means • Some Thoughts Call to Action 1 2 3 4
- 9. 9 Top Trends for Big Data in Healthcare Most of the trends for Big Data in Healthcare indicate progress towards meaningful use of data to improve outcomes and reduce the cost of care Healthcare Internet of Things (IoT) Value Based Patient Centric Care Sources: http://www.itproportal.com/features/five-big-data-trends-in-healthcare/ Frost & Sullivan, 2017 Global Healthcare Industry Outlook IBM Trends Cognitive Computing & Artificial Intelligence gaining acceptance Predictive Analytics to Improve Outcomes Reducing Fraud, Waste and Abuse
- 10. 10 Key Technologies Trends Big Data Technology Stack - 2017 Source: https://blogs.informatica.com/2017/04/05/big-data-moving-from-technology-to- business-value-delivery/#fbid=3pgsYm4p4fb
- 11. 11 A few case studies… (details in slide notes)
- 12. 12 We’ll talk about 4 things.. • Provider Industry Dynamics • Provider Business Priorities • Healthcare Data State of Union • Top Trends • Key Technologies • Trends in Acute Care, Aging Trends • Challenges • Opportunities • Implications for Acute Care, Aging What it Means • Some Thoughts Call to Action 1 2 3 4
- 13. 13 Current Challenges for Big Data Adoption In Healthcare Sources: IBM, McKinsey & Co. Frost & Sullivan, 2017 Report on Big Data in Healthcare Icons by : Flaticon: WhatitMeans Lack of integration and interoperability Around 80% of the data is unstructured In spite of the recognized benefits and potential of Big Data in Healthcare, industry action in this direction has lagged behind other verticals. The data exists, but the actionable insights derived are very limited Data security is an overarching concern Resistance to change – ‘clinical judgement’ vs. protocols Underinvested due to uncertain ROI – lack of infrastructure and talent
- 14. 14 Opportunities for Big Data in Healthcare The healthcare industry will continue to aggregate big data and leverage to gain insights that improve outcomes, both clinical as well as financial Sources: Frost & Sullivan, 2017 Report on Big Data in Healthcare Icons by : Flaticon: WhatitMeans Population Health Management Clinical Decision Support Systems Real World Data Integration Shift from reactive to proactive care through prescriptive analytics Cognitive Computing & Artificial Intelligence gaining acceptance
- 15. 15 Implications for Acute Care, Aging Sources: https://med.stanford.edu/content/dam/sm/sm-news/documents/StanfordMedicineHealthTrendsWhitePaper2017.pdf https://www.ncbi.nlm.nih.gov/pubmed/25787904 https://bismart.com/en/10-aging-population-statistics-big-data/ WhatitMeans Acute Care: • Mining Data: New ways to mine data analytics will enable new avenues of research, identifying new patients prior to acute episodes and improving efficiency • Artificial Intelligence : The advent of machine learning and artificial intelligence is allowing for the development of more detailed risk profiles, easier detection of emerging health concerns, and more personalized treatments for acute conditions. • Impact Areas: Top Areas for use of analytics in acute care will be triage, predicting disease events and also for population health based on acute disease incidence Aging: • Root Causes: By studying and analyzing trends and patterns that are happening in aged populations, social services and other care providers can pinpoint the real problems, and come up with solutions to address them before they spiral into crises. • Aging in Place: A system of "aging in place" has been implemented in some high-income countries in order to better provide coordinated and cost- effective health services for the elderly. This approach reduces institutional care while supporting home- or community-based care and other services • Transition: Use of machine learning to manage patients in transition from hospital and home and support “aging in place”
- 16. 16 We’ll talk about 4 things.. • Provider Industry Dynamics • Provider Business Priorities • Healthcare Data State of Union • Top Trends • Key Technologies • Trends in Acute Care, Aging Trends • Challenges • Opportunities • Implications for Acute Care, Aging What it Means • Some Thoughts Call to Action 1 2 3 4
- 17. 17 Some Thoughts Critical Success Factors for Big Data & Analytics in Provider Organizations Driving Adoption of Big Data and Analytics Culture change and enablement across levels in the provider organization will be the game changer in Big Data adoption Action : Maintain executive involvement, Communicate the vision at all levels The Journey from Data to Actionable Insights Data is necessary, but ROI will be derived only if actionable insights are obtained – possible only through predictive, prescriptive and cognitive analytics Action: Invest in specialist human talent, right tools and technology Every stakeholder in the ecosystem benefits from big data & analytics The true power of big data and analytics can be leveraged when we can provide these abilities to an end-user, backed by powerful tools Action: Raise the Data & Analytics IQ of the organization CalltoAction
- 18. Thank you!
- 19. Appendix
- 20. 20 Evolution of Data in Healthcare, Global, 1980 - 2020