Data Driven Organization: What does that mean? CenterPoint Board Training March 24, 2016 Presented by Lanier Cansler and Tara Larson Cansler Collaborative Resources, Inc.

Have you Asked? Do we even know agency wide what “data collection” systems are being used? Do we use the data to help to identify the risk to the agency? Is your staff equipped with the knowledge of the tools and the techniques of improvement and data management? Is it after the fact or early in the process on trends and signals from within the process? Do we know what the trends are indicating and why? Are we employing data for population health? 2

Data Analytics We recognize that there is LOTs of data in the organization, and within the system As we move to trying to do more with less, pay for what makes a difference, or control cost we find Data analytics is the discovery and communication of meaningful patterns in data Big data The collection of data sets so large and complex that it is difficult to process with database management tools or traditional data processing applications How does data use and capacity play out when defining your provider network? 3

Why Become a Data Driven Organization? Can make a difference with operational processes Can increase the rational for decision making – it is not the ONLY tool used for decision making To move forward the agency MUST: Be committed to use data Put the systems and technology to collect the data on an ongoing basis How to make sense of the data Hiring, partnering or contracting with the people who can decide what metrics are important, gathering the data and then analyzing it BE PREPARED TO ACT ON WHAT IS LEARNED, EVEN IF IT MEANS CHANGES TO THE POLICIES AND ORGANIZATIONAL STRUCTURE 4

Healthcare Analytics Adoption Model (HAAM) 1 Phase I – Data Collection The computerization by systems that are designed specifically for supporting transaction workflow and data collection In healthcare, this could be the adoption of the electronic health record (EHR) Other data sources integrated into your operations Phase II – Data Sharing Sharing data among members, however defined. This is where HIEs (health information exchange) come into play Phase III – Data Analytics The understanding that the data that is collected and shared can be used to analyze workflows (not just how you do business) 5

Progression It is not unusual for an organization to jump in to the process by buying “software” However, think progression of capability Data Sources Complexity Data Literacy Data Timeliness What do we need and why do we need it? 6

Eight Levels of Adoption (Health Catalyst Framework and Roadmap Model) Level 8: Personalize Medicine and Prescriptive AnalyticsLevel 7: Clinical Risk Intervention and Predictive Analysis Level 6: Population Health Management and Suggestive Analytics Level 5: Waste & Care Variability ReductionLevel 4: Automated External ReportingLevel 3: Automated Internal ReportingLevel 2: Standardized Vocabulary & Patient RegistriesLevel 1: Enterprise Data WarehouseLevel 0: Fragmented Solutions 7

Where does CenterPoint fall along the Continuum? Probably all through the continuum The functionality of Levels 6 through 8 are where the health care system is focusing and the more advanced providers/practitioners are at these levels or focusing on moving there This is an area where economies of scale and leverage of resources play a role. Analytics (tools and staff literacy) and IT solutions are not inexpensive and also highly competitive

Level 6: Population Health Management & Suggestive Analytics This is a “benchmark” of data driven organization Sustainable data culture and analytic environment is present This level is a must for ACO/MCOs as this level is forecasting risk, cost of bundled payments and ability to tie cost to clinical outcomes Supporting the Triple Aim or the Quadruple Aim as outlined in 1115 draft waiver application Quality of individual recipient care Population management Economics/Finance of care All sources of data have been considered, including remote health, home monitoring, and even more granular cost data Data uploaded daily 9

Level 7: Clinical Risk Intervention & Predictive Analytics This area of predictive analytics allows for the organization to expand the cost per capita of populations and the details of capitated payments The focus expands to the ability to manage episodes of care, with predictive modeling with risk stratification and forecasting Includes diagnoses based, fixed fees per capita models Ability to flag recipients that will impact findings such as disabilities, geography, religious restrictions, cultural, other social determinants Updated in real time or within one hour of changes 10

Level 8: Personalized Medicine & Prescriptive Analytics Utopia Tailoring patient care based upon population outcomes and even genetic data Based upon risk intervention, health improvement and prevention Algorithms that get into family history and environmental factors Ability for recipients to have more control and know “their” flags so the provider can develop a lifelong health optimization plan Treatment is tailored to the insights gained about the patient from the analytics and algorithms 11

Develop a Healthcare Analytics Strategy The strategy must be effective which means The right approach to gathering and organizing data Getting the right data to the right people to drive improvements Experienced Analytics expertise CAN be bought BUT be cautious about marketing Using a healthcare enterprise data that combines clinical and financial data is a good method for aggregating and optimizing data for analysis. The infrastructure must allow for the delivery of the linked clinical and financial data. 12

Data Sources Today and Their Use Most data sources in today’s market are claims based Limited behavioral health sites are pulling information from EHRs or other case management systems CMS released a notice 3/21 about beginning support for behavioral health providers Increasing pressure to integrate care is bringing the need to merge MCO data/health data in real time Being used for Population Management Financial and Forecasting Benchmarks and Outcomes Provider Management and Performance ratings 13

Sources of Data today… Data sources can be internal such as provider payment/tracking systems MCO systems such as ALPHA External from NC Tracks, NCFast, other payers Current Analytics used by most LMEs Community Care of NC (CCNC) CMT Examples of the CCNC and CMT 14

Some Areas of Policy for Data Analytics Population Health Financial MCO/LME costs, risks, allocation of resources Provider costs, network development Outcomes and Benchmarks Pay 4 performance or value based purchasing Provider monitoring and outlier management Network development and access requirements

Population Health Management (PHM) Not new but has gained momentum with onset of patient-centered medical homes and accountable care organizations (ACOs) It is required core competency in a post fee for service environment – i.e., our world today in NC While focusing partly on high risk patients and high cost patients, PHM systematically addresses preventive and chronic care needs. PHM has been one of CCNC’s functions for NC Medicaid Where is the most impact noted? 16

Population Health Management MCOs will look across providers and across service areas. Integration with and across the physical health side is key for controlling costs and for improving care Providers will look at their population Population health management requires aggregation of data across all types of episode of care – there are analytical tools that can address the trends There is no lack of data to accomplish the task The problem is the data is in silos and the sources aren’t integrated Your challenge is to figure ways to get and integrate the data and how to use the data 17

# 18 Risk Stratification via Diabetes Registry Members of Behavioral Health Home (31494 members) + Diabetes Diagnosis (3732 members) Service Claims Data + Use of 1 or more Antipsychotic Meds (411 members ) Pharmacy Claims + BMI > 30 (109 members) EMR Data + Antipsychotic medications contraindicated with Diabetes (95 members (24 members)

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A Successful Organization will use Analytics To manage their organization To identify trends and outliers easily and proactively rather than reactively To see data to guide process improvement To Tracks total cost vs. outcomes To Identify areas to target resources To manage their population To guide a risk management program To integrate in the overall health system in their community 20

In Conclusion Putting all the pieces together to become a data driven organization is tough work There are huge gains for moving forward It can streamline your operations Make you more responsive Increase speed and accuracy of your decision making Provide a more objective business case for decision making Clinical and Financial MUST be viewed together. Analyzing either in isolation is not a sound practice 21 Cansler Collaborative Resources, Inc.

Questions ? KEEP MOVING FORWARD! 22