Using Advanced Analytics for Value-based Healthcare Delivery

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Promoting Value-based Healthcare Delivery

The fundamental principles of the Affordable Care Act recognize that the volume-based, fee-for-service payment model is unsustainable and that a value-based healthcare delivery system is essential. With the emergence of Accountable Care Organizations (ACOs), providers are incentivized to implement payment reforms and participate in shared savings programs that seek to balance quality of care, access to care and cost of care.

Our healthcare analytics payment model uses predictive analytics to assist ACOs in patient attribution, budget development, bench-marking and performance monitoring to maximize incentives through shared savings and quality improvements.

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Using Advanced Analytics for Value-based Healthcare Delivery

  1. 1. Using Advanced Analytics for Value-based Healthcare Delivery Copyright © Prime Dimensions 2013 All rights reserved.
  2. 2. We are a DC-based consulting firm that provides advanced analytics capabilities and implementation services  Prime Dimensions offers expertise in the processes, tools and techniques associated with:  Data Management  Business Intelligence  Advanced Analytics  We focus on operational aspects and emphasis on Big Data strategy and technology  We assist organizations in transforming data into actionable insights to improve performance, make informed decisions and achieve measurable results.  We partner closely with clients to ensure cost-effective implementations. Big Data requires a new generation of scalable technologies designed to extract insights from very large volumes of disparate, multi-structured data by enabling high velocity capture, discovery, and analysis. Copyright © Prime Dimensions 2013 All rights reserved. 1
  3. 3. Agenda  Challenges & Opportunities  Payment Reform  Big Data Enabling Technologies  Big Data Roadmap Copyright © Prime Dimensions 2013 All rights reserved. 2
  4. 4. Typical Client Challenges Copyright © Prime Dimensions 2013 All rights reserved. 3
  5. 5. Healthcare in the 21st Century “The transformational force that has brought affordability and accessibility to other industries is disruptive innovation. Today's health-care industry screams for disruption. Politicians are consumed with how we can afford health care. But disruption solves the more fundamental question: How do we make health care affordable?” Clayton M. Christensen, The Innovator's Prescription: A Disruptive Solution for Health Care National Challenges       Escalating Costs Dwindling Budgets More Oversight and Scrutiny Expectation of Transparency and Accountability New Laws, Regulations and Polices Focus on Performance Transformational Opportunities     Radical innovation Go beyond measuring outcomes to changing them Aggregate and/or discover data in ways that were not possible until recently Visualize problems in humanfriendly formats Copyright © Prime Dimensions 2013 All rights reserved. 4
  6. 6. Current U.S. Healthcare Situation Copyright © Prime Dimensions 2013 All rights reserved. 5
  7. 7. Medicare Accounts for Over 20% of Total U.S. Healthcare Spending in 2010 Note: Other health insurance programs includes DoD and VA Source: Medicare Payment Advisory Committee, June 2012 Data Book Copyright © Prime Dimensions 2013 All rights reserved. 6
  8. 8. Healthcare Spending as a Percentage of GDP In 2010, total U.S. healthcare spending was $2.6 trillion, representing 18% of Gross Domestic Product, doubling over the past 30 years. If this trend continues, estimates are that healthcare spending will reach a staggering $5 trillion, or 20% of GDP, by 2021. Moreover, patients’ outof-pocket costs have doubled over the past 10 years. Source: Medicare Payment Advisory Committee, June 2012 Data Book Copyright © Prime Dimensions 2013 All rights reserved. 7
  9. 9. Transforming the Healthcare Ecosystem Copyright © Prime Dimensions 2013 All rights reserved. 8
  10. 10. Agenda  Challenges & Opportunities  Payment Reform  Big Data Enabling Technologies  Big Data Roadmap Copyright © Prime Dimensions 2013 All rights reserved. 9
  11. 11. The DNA of Healthcare Data Copyright © Prime Dimensions 2013 All rights reserved. 10
  12. 12. Unsustainable Fee-for-Service (FFS) Payment Model  Current FFS payment structure results in redundant testing, medical errors, and over-utilization of the system     Maximizes providers’ fees and reimbursements based on volume Incentivizes multiple tests and procedures, regardless of necessity Limited coordination of care across provider network Medical errors and failed procedures result in higher fees for providers  Data overload: vital health information assets are not being leveraged  96% of Medicare dollars account for treating chronic illnesses, with only 3-5% for prevention (Source: CMS, Chronic Conditions Chartbook, 2012) Research indicates that payment reform would reduce spending by $1.33 Trillion by 2023 and significantly improve quality and outcomes. (Source: The Commonwealth Fund Commission on a High Performance Health System, 1/13) Copyright © Prime Dimensions 2013 All rights reserved. 11
  13. 13. Four Major Payment Reform Models Global Payments • A single PMPM payment is made for all services delivered to a patient, with payment adjustments based on measured performanc e and patient risk. Bundled Payments Patientcentered Medical Home • A single • A physician “bundled” practice or payment, w other hich may provider is include eligible to multiple receive providers in additional multiple payments if care medical settings, is home made for criteria are services met, based delivered on quality during an and cost Copyright Prime episode ©of Dimensions 2013 All rights reserved. performanc Accountab le Care Organizati ons • Groups of providers that voluntarily assume responsibilit y for the care of a population of patients share payer savings if they meet quality and 12 cost
  14. 14. ACO Implementation Process Copyright © Prime Dimensions 2013 All rights reserved. 13
  15. 15. Solution  We selected three CMS programs for analytics: Accountable Care Organizations (ACO), Bundled Payments, and Patient Centered Medical Home.  The focus of Phase 1 of the prototype is on ACOs. Sources: Bundled Payments for Care Improvement (BPCI) Initiative: General Information, http://innovation.cms.gov/initiatives/bundled-payments/ Against this backdrop, CMS has launched new delivery and payment initiatives aimed to improve patient outcomes and quality while containing costs. We have selected the following programs for this business case: Bundled Payments, Accountable Care Organizations, and Patient Centered Medical Home. Bundled Payments for Care Improvement (BPCI) Initiative: General Information, http://innovation.cms.gov/initiatives/bundled-payments/ Copyright © Prime Dimensions 2013 All rights reserved. 14
  16. 16. Payment Reform Solution  Helps healthcare payers find the optimal cost, revenue, and provider incentive models to improve health outcomes and contain patient cost  Embeds business rules and algorithms of the Medicare Shared Savings Program (MSSP) Accountable Care Organization (ACO).  Includes the following features and capabilities:  ACO benchmarks and budget based on historical cost baseline, trend estimates and risk adjustments  Performance monitoring of key measures and metrics related to cost, utilization and quality  Predictive modeling to determine the proper mix of inputs to maximize payment incentives  Dynamic dashboards and visualizations to perform trade-off analysis and scenario planning Copyright © Prime Dimensions 2013 All rights reserved. 15
  17. 17. Methodology and Scope  The team acquired and loaded public datasets from CMS and related sources  Phase 1 scope: In-patient Medicare data for 2010 Sampling of 5 metropolitan areas representing a range of regions and population sizes (CA, FL, MA, OH, GA)  Diabetes, hypertension, coronary disease, and heart failure measures  Readmissions and ER utilization    Created scenarios for comparative analysis and benchmarking  Population, cost, clinical and utilization data  Analytical models and predictive modeling Seeking input from SMEs  Applying industry best practices  Assessing technical solutions  Data Sources 1. Data Entrepreneurs’ Synthetic Public Use File 2. Institutional Provider & Beneficiary Summary 3. Data for ACO Applicant Share Calculations 4. CMS Statistics reference booklet for the Medicare and Medicaid health insurance programs 5. National Health Expenditure Accounts (health care goods and services, public health activities, government administration, the net cost of health insurance, and investment related to health care) 6. Premier Hospital Quality Incentive Demonstration (expanding the information available about quality of care and through direct incentives to reward the delivery of superior quality care) 7. Hospital Compare (including 27 quality measures of clinical process of care and outcomes) 8. Assorted files from the CMS Download Database 9. The Dartmouth Atlas of Health Care Copyright © Prime Dimensions 2013 All rights reserved. 16
  18. 18. Agenda  Challenges & Opportunities  Payment Reform  Big Data Enabling Technologies  Big Data Roadmap Copyright © Prime Dimensions 2013 All rights reserved. 17
  19. 19. Big Data Enabling Technologies  New systems that handle a wide variety of data from sensor data to web and social media data Big Data requires a new  Improved analytical capabilities (sometimes called generation of scalable advanced analytics) including event, predictive and technologies designed to extract meaning from very text analytics large volumes of  Operational business intelligence that improves disparate, multi-structured data by enabling high business agility by enabling automated real-time velocity actions and better decision making capture, discovery, and analysis.  Faster hardware ranging from faster multi-core processors and large memory spaces, to solid-state  Massively Parallel Processing drives and virtual data storage  Hadoop and MapReduce  Cloud computing including on-demand software-as- NoSQL Databases  a-service (SaaS) and platform-as-a-service (PaaS)  In-Memory Technology analytical solutions in public and private clouds Copyright © Prime Dimensions 2013 All rights reserved. 18
  20. 20. Massively Parallel Processing  Massively parallel processing (MPP) is the coordinated processing of a program by multiple processors that work on different parts of the program, with each processor using its own operating system and memory  Known as “loosely coupled” or “share nothing” systems  Enables “analytic offload”  Permits parallelized data loading, i.e. E-L-T vs. E-T-L Copyright © Prime Dimensions 2013 All rights reserved. 19
  21. 21. The Hadoop Ecosystem Compute Cluster Multi-structured Source Data Master Node NameNode DataNode Slave Node JobTracker TaskTracker DataNode TaskTracker Slave Node DataNode TaskTracker Slave Node MapReduce Engine DataNode Slave Node TaskTracker YARN DataNode TaskTracker Slave Node DataNode TaskTracker DATA LAYER WORKLOAD MANAGEMENT LAYER APPLICATION LAYER Copyright © Prime Dimensions 2013 All rights reserved. 20
  22. 22. The Hadoop Ecosystem Compute Cluster ApplicationMaster Multi-structured Source Data ResourceMaster Master Node NameNode DataNode Slave Node NodeMaster DataNode JobTracker TaskTracker TaskTracker Slave Node NodeMaster DataNode TaskTracker Slave Node NodeMaster DataNode Slave Node NodeMaster DataNode MapReduce Engine TaskTracker YARN MapReduce 2.0 TaskTracker Slave Node NodeMaster DataNode TaskTracker WORKLOAD MANAGEMENT LAYER Copyright © Prime Dimensions 2013 All rights reserved. 21
  23. 23. NoSQL (Not only SQL) Databases  Like HDFS, NoSQL databases have distributed, schema-less data structure and high scale out across low cost commodity hardware  NoSQL databases are not reliant on a relational, fixed data model and Structured Query Language (SQL)  NoSQL databases are not ACID-compliant and lack the strict data integrity of relational databases  NoSQL databases have BASE properties – Basically Available, Soft state, Eventual consistency  NoSQL databases are queried via REST APIs. Key-Value Columnar Document/Object Graph Increasing Data Complexity Copyright © Prime Dimensions 2013 All rights reserved. 22
  24. 24. In-memory Database   An in-memory database stores (IMDS) data entirely in main memory using NVDIMMs, as opposed to disk-based storage Storing and retrieving data in memory is much faster than writing to and reading from disc    Reduce data access and latency caused by I/O operations and significantly speeding query and response times. This trend toward in-memory is made possible by low memory prices and multi-core 64-bit processing capabilities. Data stored entirely in-memory is immediately available for analytic processing Copyright © Prime Dimensions 2013 All rights reserved. 23
  25. 25. Database Options Data Velocity Scale Up Analytic Databases Relational Databases NoSQL Databases Scale Out Data Variety Copyright © Prime Dimensions 2013 All rights reserved. 24
  26. 26. Agenda  Challenges & Opportunities  Payment Reform  Solution Prototype  Big Data Enabling Technologies  Big Data Roadmap Copyright © Prime Dimensions 2013 All rights reserved. 25
  27. 27. Our roadmap reduces risk while progressing towards a unified analytics environment Continuous improvement & innovation Changes to laws (e.g., ACA) Permanent budget pressure Information Management Health Assessor Design Thinking & Prototyping Mission Oriented Analytics Framework Unified Analytics Environment Evolving On-Demand Infrastructure Commodity IT Open source tools Copyright © Prime Dimensions 2013 All rights reserved. Smarter, mor e demanding data consumers & partners
  28. 28. Your Brain on Big Data Query & Reporting Discovery & Engagement Copyright © Prime Dimensions 2013 All rights reserved. 27
  29. 29. Growth of Data Exploding  Eric Schmidt: “Every 2 days we create as much information as we did up to 2003.”  Relational databases can not effectively ingest, store and query the increased volume, variety and velocity of Big Data Copyright © Prime Dimensions 2013 All rights reserved. 28
  30. 30. Begin with the End in Mind Copyright © Prime Dimensions 2013 All rights reserved. 29
  31. 31. Cycles of Design Thinking and Prototyping Copyright © Prime Dimensions 2013 All rights reserved. 30
  32. 32. Mission Oriented Analytics Framework Copyright © Prime Dimensions 2013 All rights reserved. 31
  33. 33. Evolving On-Demand Infrastructure for Big Data Advanced Analytics Analytic Offload Scale-up/Scale-out YARN Storm NoSQL Database Tez Analytic Applications REST/JSON APIs Dashboards & Visualizations Analytic Database E-L-T In-memory Columnar HCatalog Data Warehouse Augmentation OLAP Data Warehouse E-T-L Multi-structured And Stream Source Data Copyright © Prime Dimensions 2013 All rights reserved. Data Discovery Structured Source Data
  34. 34. Unified Analytics Environment Copyright © Prime Dimensions 2013 All rights reserved. 33
  35. 35. Data Management | Business Intelligence | Advanced Analytics Copyright © Prime Dimensions 2013 All rights reserved. 34
  36. 36. Questions? Our Contact Information Michael Joseph Managing Partner Direct: 703.861.9897 Email: mjoseph@primedimensions.com Richard Rowan Managing Director Direct: 703.201.2641 Email: rrowan@primedimensions.com Prime Dimensions, LLC www.primedimensions.com Data Management | Business Intelligence | Advanced Analytics Follow us @primedimensions Copyright © Prime Dimensions 2013 All rights reserved. 35

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