Cloud Analytics and the Future of Health HIMSS Innovation Community Webinar

Transcription

1 Cloud Analytics and the Future of Health HIMSS Innovation Community Webinar March 14, 2014

2 1 Agenda The Healthcare Landscape and Drivers Toward the Cloud Lynda Rowe Cloud Analytics Technology and Applications and What it Means for Healthcare James Owen Case Studies: Where We are Today and Opportunities for the Future Kaajal Singh

3 2 The Healthcare Landscape and Drivers Toward the Cloud Lynda Rowe

4 3 Electronic Health Records, Health IT, and Data Availability HITECH investments promoted strong health IT adoption According to the HHS National Center for Health Statistics (NCHS): in 2013, 78% of officebased physicians used any type of electronic health record (EHR) system, up from 18% in And 48% of office-based physicians reported having a system that met the criteria for a basic system, up from 11% in Capacity for health information exchange and interoperability is increasing New data sources coming online Nontraditional care settings (LTPAC and behavioral health) Mobile health sector (telemonitoring) Patients themselves (PHRs) Today there is greater volume and variety of data, with a growing ability for real-time transmission and expedient analysis

5 QUANTITY 4 The Growing Data Analysis Gap SUSTAINABLE CHALLENGING MISSED OPPORTUNITIES 44 times Data Analysis Gap TIME By 2020, the amount of information in our economy will grow 44 times. Very few organizations are prepared for this wave of data.

6 5 Transforming Clinical Landscape Expanded patient access The Patient Protection and Affordable Care Act (ACA) will expand health insurance coverage and provide access to the formerly uninsured Persistently high costs In 2010, National Health Expenditures were $8,402 per capita, or 17.9% of GDP Aging population leading to a more complex patient mix According to a 2012 American Hospital Association study, chronic disease rates are rising among Medicare patients, while Medicare patients are also living longer, requiring more, higher-intensity care Payment reform leading to providers taking on more risk for delivering high-quality, high-value care A 2013 Medscape report said that nearly one in four doctors, or 24 percent, were either in an ACO or planned to be in an ACO within a year.

7 6 Health Care Today Combined pressures and opportunities have created a greater need to share information, coordinate care, and track patients across the continuum Need to tame available data--extracting, aggregating, and analyzing data to inform patient care and identify opportunities to improve quality and value Given the evolving landscape, trends are moving away from large infrastructure investments toward more lightweight data management platforms New platforms, combined with cloud-based analytic tools, are flexible and adaptable for today s health care environment

8 7 As the field focuses on quality, a diverse set of players is shaping the future direction of the industry Major Player Categories and Roles in the Interoperability Ecosystem Research and Technologies Clinical Services Providers Funding Mechanisms Life Sciences Firms Bringing new data to market EHR Vendors Bringing new IT platforms and tools to market Academic Medical Centers Leading edge of analytics capabilities Commercial Labs Generating increasing amounts of clinical data via new tests Specialized Testing Exclusive licenses for specific new types of tests Consumer Engagement Offering consumer-facing products Integrated Health Systems Evidence-based delivery with visibility into total cost of care Payors Influencing care by setting the payment for quality agenda Grant-makers Supporting R&D for new analytic technologies Industry Oversight Office of the National Coordinator Establish requirements and programs Professional Associations Providing recommendations Regulatory Bodies Standardizing reporting requirements and oversight

9 8 Cloud Analytics Technology and Applications and What it Means for Healthcare James Owen

10 Analytic Capabilities 9 Analysis in the Cloud transforms information into insights and then into mission value Small amounts of data or samples (megabytes to gigabytes) Large (or all) Amounts of data (gigabytes to petabytes) ADVANCED ANALYTICS Deals with smaller datasets but uses advanced techniques to analyze the impact of future scenarios. CLOUD ANALYTICS Can fuse different data types on a massive scale resulting in predictive and real-time analysis capabilities. Predictive and real-time analytics capabilities BASIC ANALYTICS Relies on historical observations to help avoid past mistakes and duplicate past success. CLOUD COMPUTING From a systems perspective, data becomes more consolidated while analytic workflows are more streamlined and automated. Accurate historical observations Volume of Data

11 10 Cloud Analytics Reference Architecture Booz Allen s Cloud Analytics Reference Architecture provides a holistic approach to people, processes, and technology in four tightly integrated layers. Human Insights and Actions Analytics and Services Data Management Human Insights and Actions Building on results and outputs from various analytical methods, multiple data visualizations can be created in your new cloud analytics solution. These are used to compose the interactive, real-time dashboard interfaces your decision- makers and analysts need to make sense of your data. Analytics and Services Both traditional and Big Data tools and software can operate on the information stored in your Data Lake, producing advanced specific analysis, modeling, testing, and simulations you need for decision making. Data Management Your Data Lake is a secure, distributed repository of a wide variety of data sources. Security, metadata, and indexing of Big Data are enabled by distributed key value systems (NoSQL), but the Architecture allows for traditional relational databases as well. Infrastructure This foundational layer allows for quick, streamlined, low-risk deployment of the cloud implementation. Infrastructure

12 Cloud Analytics Reference Architecture 11

13 12 The Health Data Lake Workbench integrates a variety of data sets in the cloud and present results via web-based user interface Human Insight & Action Analytics & Services Data Management Data Sources (Current) Data extraction platform query ingest Master Region Servers 0 Task Trackers Job Tracker 1000 Genomes User Interface query Zookeeper Data Sources (Future) query Javascript D3 Cloud Index Index Index Data Nodes Name Node Health Information Exchange Data Staging Server Provider Electronic Health Record Infrastructure

14 Evolution of a Data Science Project 13

15 14 Case Studies: Where We are Today and Opportunities for the Future Kaajal Singh

16 Case Study #1: Booz Allen Health Surveillance Investment 15

17 16 Health surveillance was chosen due to the complex challenges associated with the problem set and the potential value of information in the big data Surveillance Challenge Velocity Recent advances in data collection make it possible to analyze massive amounts of real time data from newly emerging and disparate sources, providing essential support to time-sensitive, key decisions Variety For decades, past digital disease detection efforts have relied upon stand alone, stove-piped surveillance systems with multiple data types and formats, an approach which the GAO cites as ineffective and in need of central coordination Our Solutions We scripted the prototype s backend and frontend entirely in R, enabling phased rapid application development, within the R Shiny web-framework (for Phase 1), in a FedRAMP certified, cloud-based Linux environment; Accordingly, we scale data acquisition, analysis, and dissemination with our architecture to the size of any real time data, in real time The data streams we integrate enable users to analyze pathogenic situations and acquire an integrated perspective and assessment of: The Present State of Pathogenic Events The Historical Correlation between Pathogens and Trends in Disparate Data Geographic Forecasts of Future Pathogenic Events Measures of Confidence in the Estimates and Forecasts Policies and Courses of Action (COAs) for user-defined events We enhance the power and utility of the Workbench by comparing CDC Epidemiological known after the fact as a confidence test with other disparate, unstructured data sources, including social media, climatic data, and 911 Call Centers Volume Massive increases in data collection, to include Electronic Health Records (EHR), are having significant impacts on storage, access, speed of computation, and data security requirements We employ the FedRAMP certified, Amazon EC2 which enables us to rapidly scale storage and analysis to the size and spectrum of increasing data sources in order to meet growing needs as they emerge, as well to as easily integrate into the BSVE

18 We are integrating multiple data streams, both novel and commonly used for health surveillance Hypothesis: If you utilize a multiple independent variable input approach, then you will achieve a higher level of confidence in your results Data Source Description 911 Calls Data collected by 911 call centers, including location, symptoms and outcome. BioSense Biowatch Laboratory Results Census Syndromic Surveillance data from participating US hospitals, including location and syndrome (gastroenteritis, fever, etc.). A sentinel surveillance system for environmental sampling with laboratory confirmed results from 34 US cities. Census data to inform potential magnitude of events. Environmental Genomic Pharmaceutical Poison Control Center Social and Traditional Media Weather patterns as influencers on vector-borne disease and spread of aerosolized pathogens. Human genomic data submitted to public repositories including NCBI's Sequence Read Archive (SRA) during relevant time period. Drug data, including location, class type, and indication. Data collected by local poison centers, including location, symptoms, and outcome. Real-time and historic social and traditional data to detect or correlate signal, including location, symptoms and magnitude

19 Filename/RPS Number 18 Case Study #2: Connected Health Personalized Communication and Analytics Concept

20 19 With continued adoption and use, patient generated data (PGD) could become a core element in the future of personalized health care 1 2 Patient Generated Data Existing forms of data become a new type of data Healthcare market challenge: How to use this new form of data? Self Entered Data Data quality issues? Patient Generated Data Overloaded workflow? Access to view a patient s activity over the year Sensor 3 Future Opportunity: How to analyze and use PGD in the care delivery workflow, for personalized care, with EHR data, etc.?

21 Potential Value 20 Advanced data analytics capabilities will facilitate delivery of personalized care offerings that improve behavior modification and improve health outcomes Analytics Capability Maturity Model Sample Analytics Questions What happened? Descriptive Analytics Supports summarization and display of relevant historical information Identify best practices, problem areas, root causes, etc. based on historical observations What will happen next? Predictive Analytics Advanced analytics techniques for detection of future events, trends, and patterns Deep insights for early warning and monitoring based on relevant data What actions can I take? Prescriptive Analytics (Decision Support) Ability to identify best course of action often based on competing objectives and limited resources Conduct trade-off and scenario analysis to support evidence and datadriven decision making Descriptive What were the trends, patterns and outliers in my historical vital sign readings? What areas of my health & wellness can I improve? Predictive Which patients have or are at risk of acute and/or chronic health conditions? Which patients are in most need of early intervention and coordination? Prescriptive What messaging techniques are most effective to influence health behavior and decisions? What treatments options are most effective for different patient populations and symptoms? Analytics Maturity

22 Filename/RPS Number 21 Case Study #3: Partnership Example IT Enabled Longitudinal Care Management

23 22 Healthcare Providers Doing More, with More Data Healthcare providers and provider systems are operating in an environment where data are in abundance, yet data systems and analytics solutions are not yet providing game changing services to significantly drive care transformation Managing data and information sharing across the care continuum is critical to drive high quality, cost effective patient care, decrease hospital readmissions and improve patient satisfaction Hospital Long Term / Extended Care Facilities Outpatient Care (Primary and Specialty) Home Health Care

24 23 Partnership Example IT-Enabled Longitudinal Care Management Partner Challenge High Burden of Ischemic Heart Disease (IHD): #1 cause of death and disability; 1 in 6 health care dollars $444 billion in 2010 Model for Longitudinal Care Delivery: Effective primary and secondary prevention; Shared responsibility of primary/specialty care Target for Value-Based Purchasing: Prepare for future reimbursement models Problems to Solve: Patients receive care in many ways, from many healthcare business entities; Data is not integrated across health care organizations, so is not available for consolidated analysis or other purposes; Communication to providers across business entities is fractured and difficult; A single outcome and management view that crosses business entities has never previously been developed Program Goals Create an integrated, longitudinal data platform for patients with IHD Implement a longitudinal, evidence-based care management program for patients with IHD that is coordinated with all of the patient s health care providers Evaluate the impact of the program on the health, health care, and costs for patients with IHD Potential Solutions Import data from disparate data systems; EHR, multi-payer claims Into a health data lake Create analytic models and visualizations to drive insights to improve quality, lower costs and improve the patient experience (cost per patient with IHD, utilization over time, quality measures, readmissions, patient satisfaction)

25 Wrap Up 24

26 25 Conclusion A sharp increase in the available healthcare data and a transforming clinical landscape are creating demand for new ways to use data to improve care delivery Exciting new cloud analytics technologies are working to harness data and turn it into information and insights Booz Allen is investing in new tools and applications to derive value from information in key healthcare sectors

27 Questions? 26

28 27 Contact Information Lynda Rowe Senior Associate James Owen Lead Associate Kaajal Singh Senior Associate Thank you!