Big data in the real world opportunities and challenges facing healthcare - v04b - slide share
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The Healthcare system will be target of major disruption more than any other industry in the next 10 years. The Digital economics and increasing demand by consumers for more real time information in order to make better decisions on who they want to "hire" to perform services for them or in their behalf will be the driver of this disruption. Analytics, Big Data and Machine Learning will lay the foundation for the next generation of healthcare yet there are still many challenges to truly revolutionize the healthcare system end to end (Providers, Pharma, Payers)
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Big data in the real world opportunities and challenges facing healthcare - v04b - slide share
- 2. 2 "From the dawn of civilization until 2003, humankind generated five exabytes of data. Now we produce five exabytes every two days...and the pace is accelerating." Eric Schmidt Executive Chairman, Google 2
- 4. Big Data Categories 4 Web & Social Media Data Machine-to- Machine Data Big Transaction Data Biometric Data Human- Generated Data
- 5. 5 The 3 Vs of Big Data Volume 90% of the data in the world today was created within the last two years Variety People to people (e.g. social media) People to machine (e.g. computers, mobile, medical devices) Machine to machine (e.g. sensors, GPS, barcode scanner) Velocity 2.9 emails sent every second 20 hours of video uploaded every minute 50 million tweets per day
- 6. 6 Industry Shifts in Data Data is becoming the world’s new natural resource The emergence of cloud is transforming IT and business processes into digital services Social, mobile and access to data are changing how individuals are understood and engaged 500 million DVDs worth of data is generated daily 1 trillion connected objects and devices by 2015 80% of the world’s data is unstructured 85% of new software is being built for cloud 25% of the world's applications will be available in the cloud by 2016 72% of developers say cloud-based services are central to the applications they are designing 80% of individuals are willing to trade their information for a personalized offering 84% of millennials say social and user-generated content has an influence on what they buy 5 minutes: response time users expect once they have contacted a company via social media
- 7. IT Evolution Compared Healthcare
- 9. 9
- 10. 10 Implications in Healthcare Source: http://www.alphasixcorp.com/images/big-data-infograph.jpg
- 11. Megatrends Impacting Entire Spectrum of Care 11 A Modern Health Care System is on the Horizon, Demanding a Paradigm Shift FROM TO One Size Fits All Fragmented, One Way Provider Centric Centralized, Hospital-based Fragmented, Specialized Procedure-based Treating Sickness Personalized Medicine Integrated, Two Way Patient Centric Decentralized, Community-based Collaborative, Share Information Outcomes-based Preventing Sickness (Wellness)
- 12. 1122
- 13. Connected Health Ecosystem 13 Remote Monitoring Telemedicine mHealth General Healthcare IT (CIS and Non- CIS) • Video Diagnostic Consultation • Remote Doctor/Specialist Services • Distance Learning/Simulation • Retail Telehealth • Teleimaging • Electronic Health Records (EHR) • Health Information Exchange (HIE) • Patient Portals • Hosted Cloud Infrastructure • Home and Disease Management Monitoring • Activity Monitoring • Diabetes Management • Wellness Programs • Remote Cardiac ECG • PERS • Medication Management • Professional Apps • Wellness Apps • Fitness Apps • Texting Informational Services
- 14. Moving to the Left Benefits of Proactive Mitigation of Disease Risk Health Status 20 % of Population Generates 80% of the Cost Healthy/ Low Risk At Risk High Risk Chronic Disease Early Stage Chronic Disease Progression End of Life Care VALUE COST
- 15. Exponential Technologies 15 EMPOWERING THE PATIENT ENABLING THE PHYSICIAN ENHANCING WELLNESS CURING THE WELL…BEFORE THEY GET SICK
- 16. What Prevents Insurers from Effectively Using Data? Inability to get to accurate, integrated data that can provide actionable insights. Lack of a clear strategy and roadmap Budget and resources Data fragmentation System fragmentation Poor data quality Data silos across departments Inadequate analytic tools and skill sets
- 17. Overcoming the Gaps Leadership commitment to data as a strategic asset Long term commitment to drive health care value Alignment with enterprise priorities Dedicated resources to infrastructure and quality Continuous improvement mindset Strategic decisions consider data requirements Operational decisions include data implications
- 18. Strategies • Implement a data governance framework • Engage providers • Foster competition and transparency • Bake analytics into training • Provide for flexibility in information transference • When possible, choose in-house solutions over vendor-generated solutions • Create simple, understandable tools such as dashboards for clinicians on the front lines to visualize incoming data. • Don’t scale up, scale out • Close the quality loop 18
- 19. 19 leo.barella@excellus.com Leo Barella
Editor's Notes
- Health Care reform redefined how individuals can obtain health insurance. Providers will receive incentives on positive outcomes which will lead to their increased interest in improving the health not only of the patients they visit in their offices but the patients they seldom see. The information available about their patients is growing rapidly and can be harvested from sources that are not typically linked to medical records. In this session you will learn about emerging sources of data and the use of advanced analytics that can lead to the proactive improvement of population health and wellness. - See more at: http://theinnovationenterprise.com/summits/bdhealth-philadelphia-2014/schedule#sthash.TVRsSoX9.dpuf
- In order to win in the new market we must be able to harvest actionable information from all the data that is being generated by the internet of things
- Web and social media data: Clickstream and interaction data from social media such as Facebook, Twitter, Linkedin, and blogs. It can also include health plan websites, smartphone apps, etc. 2. Machine-to-machine data: Readings from sensors, meters, and other devices. 3. Big transaction data: Health care claims and other billing records increasingly available in semi-structured and unstructured formats. 4. Biometric data: Fingerprints, genetics, handwriting, retinal scans, and similar types of data. This would also include X-rays and other medical images, blood pressure, pulse and pulse-oximetry readings, and other similar types of data. 5. Human-generated data: Unstructured and semi-structured data such as electronic medical records (EMRs), physicians’ notes, email, and paper documents.9
- Implement a data governance framework. A carefully structured framework for enterprise-wide data governance is arguably the first and most critical priority to ensure the success of any effort to leverage big data for health care delivery. The Data Governance Institute, a provider of in-depth, vendor-neutral information relating to tools, techniques, models, and best practices for the governance of data and information, defines such a framework as a “logical structure for classifying, organizing, and communicating complex activities involved in making decisions about and taking action on enterprise data.” Engage providers. Engaging providers is critical to changing the culture of resistance to new approaches to data collection and analysis. Health care organizations are highlighting the importance of big data initiatives by rolling them out at department- wide meetings and rewarding their physicians when they meet standards for data collection and improvement of quality metrics. Foster competition and transparency. Similarly, health care organizations are attaching monetary incentives to measuring and looking at data; displaying peer and colleague data with respect to patient satisfaction and quality metrics; and using dashboards, all in an effort to leverage competition and improve performance among clinicians. Bake analytics into training. More institutions are recognizing that physicians and nurses both need training in analytics to understand how big data tools add value to overall health care performance. Even medical schools, like those at the University of North Carolina at Chapel Hill and the University of Washington-Seattle, are revising their curricula to encourage critical thinking and the use of information. Provide for flexibility in information transference. There is a growing recognition that work and learning styles vary among clinicians; facilities are demonstrating a growing willingness to deliver data in multiple ways based on clinician preference and style. When possible, choose in-house solutions over vendor-generated solutions. At times the inflexibility of some vendor-generated solutions can be a major obstacle to leveraging big data technology in a given organization. Organizations are increasingly recognizing that some of the most successful solutions to their challenges can sometimes be developed with “in-house” input and expertise. In most cases, only large organizations currently have the resources to build in-house solutions. However, in the future, even smaller provider groups and companies will need to tap into one or more big data streams. For these groups, vendor-generated solutions are the only options. When looking at commercially available solutions, ensure that they are sufficiently flexible, scalable and configurable to meet the users’ present and future needs. Create simple, understandable tools such as dashboards for clinicians on the front lines to visualize incoming data. Organizations should strive to update processes and develop capabilities to enable tool use, and focus on real- or near-real time clinical decision support. Traditional analytics use Extract, Transform and Load (ETL) processes that upload data nightly or weekly to a data warehouse, from which it is then extracted for processing elsewhere. Increasingly big data is moving toward real- or near-real time processing, often at the point of care, to derive value from the data far more quickly for clinical decision support. Don’t scale up, scale out. Some organizations may be prone to lean toward replacing their older servers with bigger and more powerful servers. Today’s trend is to “scale out;” ie, to improve performance and scalability of a system by adding nodes for processing and data storage. This approach may be worth considering because it can make systems easier to manage and to expand to accommodate big data solutions. Close the quality loop. Achieving health care transformation requires dramatic and sustainable changes to the structure and processes of health care. Data analytics teams must work in lockstep with quality improvement teams so that analytics tools and techniques can be integrated into the various quality-improvement methodologies which, together, can provide a framework that drives the front-line and administrative changes necessary for achieving desired improvements to health care outcomes and efficiency.