DA2 Update for CI 1 November 2013 Outline • Situation Analysis • Priorities for 2013 • New Technologies • Results 2 Situation Analysis Current State: Future State: Impact: Reporting Performance Metrics Analyzing Performance Metrics Efficiency, Quality, Costs Minimal Predictive Analytics Focus on Predictive Analytics Quality, Costs, Population Health Manual Data Abstraction Automated Data Abstraction Efficiency, Costs Multiple Data Silos Enterprise Data Warehouse (EDW) Efficiency, Quality No Data Governance Robust Data Governance Safety, Efficiency, Quality Minimal Self-Service Capacity User Driven Data Access Efficiency, Quality, Costs Limited Awareness of Analytics Data is a System Asset Data Quality Poor Data Quality High Quality Data/One Source of Truth Data Quality, Efficiency Unmet Analytic Needs High Client Satisfaction Efficiency, Quality, Costs 3 DA2 Priorities for 2013 1. Performance Metrics: Develop reliable and actionable leading indicators for clinical and non-clinical operational performance metrics 2. Quality and Value: Demonstrate CHS clinical quality, value and improved patient outcomes to payers and employers 3. Population Health: Provide predictive analytics using full range of data available to positively impact patient health by proactive delivery of preventative services. 4 Technology to Support Priorities Accelerators for Rapid Value Creation: Software Description Verisk Allows capability to conduct population analysis, employer group analysis, practice pattern variation, episode analysis on claims data Humedica Allows capability to conduct population analysis and provide medical home reporting using clinical (EMR) data. Can compare, analyze and identify clinical best practices near real time. Predixion Removes reliance on technical analysts to accelerate predictive analytics capability. Point of care predictive analytics through workflow. 5 Building an Analytic Competency Service Lines and Functional Units Requests Client Services Requests Estimates Models Reports Analytics Opportunity Analysis Demand Management Requests SLA Abstraction Services Shared Resource pool SLA Abstraction CoE Analytic Services Shared Resource Pool Requests Think Tank Leading metric development Key capabilities Data Services Requests Research Analytics CoE Color Key: Blue = Business Maroon = DA² Green = IT Pink = External Requests Data, SLA Services, SLA Data Marts, Extracts, Automation, SLA Shared Resource pool Information Architecture Data CoE Requests Services, SLA IT Infrastructure Services 6 DA² New Organization Structure Allen Naidoo VP Michael Dulin CCO Client Services Mike Trumbore AVP Melanie Spencer Research Director Analytic Services John Carew AVP Abstraction Svcs Angela Humphrey AVP Data Services Tim Reagan AVP Financial Services Director Rodney White Director Marcy Neale Director - Quality IT EDW Team Avery Ashby Director 7 Organization Impact INNOVATIVE LEADER DA² will apply innovative techniques and tools to the analysis of health information and will challenge the status quo uses of information and move the CHS culture forward to raise the collective CHS analytics IQ. ADVANCED ANALYTICS AND BUSINESS INTELLIGENCE DA² will apply a balanced approach to understanding business needs, promoting data governance and integrity, analyzing routine and complex data, and providing relevant and targeted intelligence to solve business problems. ELEVATE PATIENT OUTCOMES Quality of care is the cornerstone of the CHS business model and DA² is fully aligned to support existing and new initiatives to improve quality. PREDICT HEALTH NEEDS The true advancements will come from the modeling of data to predict the relationship of individuals with disease processes so proactive interventions can be employed. TRANSFORMATIVE SOLUTIONS DA² will focus on projects apt to move CHS in a material and enduring direction toward a successful future state. PROMOTE THE HEALTH OF OUR COMMUNITIES Population health management is an advanced state comprising all of the activities of CHS and DA² to improve the well‐being of the citizens in our services areas. 8 Results • DA2 Acute Care Predictive Analytics (Readmissions) • DA2 Ambulatory Analytics • Population Health – Population Segmentation and Stratification 9 DA2 Predictive Analytics Impacting Readmissions Readmission Risk Modeling and Patient Segmentation Readmission Risk Model 40 Key Predictors 200,000 Hospitalizations 600 Predictor Variables Patient Segmentation Segments Low Risk Medium Risk High Risk Very High Risk Total Volume 9 x 9 , Emergency and Medicare 3.90% 1.13% 3.42% 2.41% 10.86% Lengthy Stays, Large Number of Orders 2.84% 2.10% 3.80% 5.77% 14.51% New Patients, Longer Visits 3.44% 2.18% 7.32% 1.44% 14.37% New Patients, Shorter Visits 7.94% 11.98% 3.79% 0.15% 23.85% Older Frequent Flyers Simpler than 9 x 9, Medicine, Discharged Home 0.29% 0.72% 0.79% 8.57% 10.37% 1.02% 0.20% 0.50% 1.98% 3.70% Younger and Healthy 2.71% 4.70% 2.37% 0.54% 10.32% Younger Frequent Flyers 2.88% 1.98% 3.02% 4.15% 12.02% 25.00% 25.00% 25.00% 25.00% 100.00% Total Volume Care Management Analytics Application Care Management Analytics Application DA2 Ambulatory Analytics Using Analytics to Improve Outcomes for Patients with Diabetes Capabilities 15 Capabilities Outline Gaps in Care • Identify patients who are out of compliance for recommended tests or visits • Identify patients with heavy disease burden who are no being actively managed Predictive Modeling • Identify and stratify patients at high risk of hospitalization or disease complication Cost & Utilization Management • Track patients and providers with high utilization scores (ER or IP Visits within time period) • Monitor readmissions and post-discharge follow-up High Risk Patient Management • Profile poly-chronic patients • Discover actionable clinical insights by patient cohort Prescribing Patterns • Identify costly drug prescribing patterns in relation to clinical outcomes • Track compliance against medical protocols 16 Benchmarking Opportunities Anonymous Transparent 17 • Anonymous • Built into Humedica MinedShare® Ambulatory tool • CHSMG Ambulatory data compared to blinded comparator group • Comparator group includes all of Humedica’ s ambulatory clients, but can be filtered based on criteria such as region and size • Transparent • CHSMG is included in American Medical Group Association (AMGA) and the Anceta Collaborative through Humedica • Anceta uses data to identify opportunities for improvement and best performance • Collaborative members share and learn from each other Sample Output 18 Sample Output 19 DA2 Population Health Efforts Storyline Current Status Foundational Efforts Underway Potential Care Management Activities 21 Vital Statistics 1,524,282 Unique Patients* accessed CHS IP/OP Hospital Services and/or Medical Group Practice Services during August 2012 – July 2013 Medical Group Practice Services Charges $2.641B IP/OP Hospital Services Charges $10.171B * Unique patients are determined using IBM® Initiate Master Data Service® products looking across IDX, STAR, and Cerner 22 Patient & Charged Amount Distribution 1% Patients (15,243) $3,674,713,631 or 28.7% Total Charged Amount Mean = $242,299 4% Patients (60,971) $4,013,166,277 or 31.3% Total Charged Amount Mean = $65,820 25% Patients (381,071) $4,123,756,151 32.2% Total Charged Amount Mean = $10,821 70% Patients (1,066,997) $999,518,908 7.8% Total Charged Amount Mean = $985* 23 Core Market* Vital Statistics 1,226,372 Core Market Unique Patients accessed IP/OP Hospital Services and/or Medical Group Practice Services during August 2012 – July 2013 80% of Unique Patients 84% of Charged Amount 1% Patients (12,264) $3,011,776,739 or 27.9% Total Charged Amount Mean = $245,599 4% Patients (49,054) $3,348,738,254 or 31.1% Total Charged Amount Mean = $68,265 25% Patients (306,593) $3,576,082,446 33.2% Total Charged Amount Mean = $11,664 70% Patients (858,461) $847,618,912 7.8% Total Charged Amount (Mean = $1,038** ) ** Core Market defined by patient’s zip code (Management Company); CHS standard for all planning & reporting purposes ** Does not include patients with zero total charges in the calculation 24 8 County View – Unique Patient Counts* Anson Cabarrus Cleveland Gaston Lincoln Mecklenburg Stanly Union 10,531 142,825 68,272 74,285 41,208 530,244 23,625 120,567 * Unique patients utilizing CHS IP/OP Hospital Services and/or Medical Group Practice Services during Aug 2012 – Jul 2013 25 Aug 2012 – July 2013 26 Aug 2012 – July 2013 27 FOUNDATIONAL FRAMEWORK PCP Attribution Indigent Identification & Modeling DA2 Patient 360º Information 28 PCP Attribution Provider Level CPT Codes Visit Recency DIVISION Time Period Specialty Volume PROVIDER Note: Attribution is first determined in the most recent 12 months, and then if needed, looks within the previous 12 months. Future 29 state may include variation in the time frame used, based on patient demographics/conditions. Indigent Identification & Modeling • Common system-wide definition • Tagging of patients known to be indigent • Predicting patients of becoming charity or over due using social determinants/financial and other information • Micro-segmenting for outreach opportunities to enable primary care and community resource utilization 30 DA2 Patient 360º Information 31 POTENTIAL INITIATIVES FOR IMPACT DA2 Ambulatory Analytics Use Cases DA2 Predictive Analytics Use Cases DA2 Segmentation 32 DA2 Ambulatory Analytics Use Cases – Diabetes Focused Diabetic Outreach based on Lab Data http://www.diabetes24-7.com 33 DA2 Predictive Analytics Use Cases – Top 5% Readmission Risk at CMC Statistically significant differences between the top 5% and the remaining 95% for: • Insurance Type • Race • Age • Admission Type * Gender was not statistically different Patient segments will be identified, mapped across the CHS service area, and proper outreach developed 34 Patient Mapping There were 183 patients with 100 in Mecklenburg County 35 DA2 Research Asthma CER Table 1. Asthmatic Patients Seen in the CHS System (2008-2009) Number of Number of Clinic Patients Visits Total Number of Unique Patients with 38,634 77,582 an Asthma Diagnosis African American Race 14,168 34,551 Hispanic Ethnicity 2,043 4,596 37 Age < 18 11,058 21,357 Number within Mecklenburg County 16,458 41,961 Uninsured, Medicaid, or Medicare Number of Hospitalizations for Asthma Number Emergency Room Visits CHS School Children with Asthma 13,564 10,321 30,121 8,500 31,022 NA NA NA 95 Primary Care Practices (28,200 Asthmatic Patients and 49,692 Asthma Visits Per Year) AHRQ Asthma Group A Control - Usual Care n = 20 Practices (5,820 Patients) School System (8,500 Asthmatics) Group E Intervention - SBC 171 Schools (8,500 Patients) Group B Control - EAP n = 65 Practices (20,100 Patients) Time Delay* Group C Intervention - EAP + IAC n = 10 Practices at Start and 75 Practices after 18 months (3,050 Patients at Start and 23,150 at 18 months) Time Delay* Group D Intervention - EAP + IAC + SDM n = 4 Practices (2,580 Patients) Evaluate Outcomes for Each Group On: Healthcare Utilization, Medication Compliance, Costs, School Attendance, 40 School Performance, Clinical Outcomes, Quality of Life, & Perceptions of Care *Groups will be analyzed using a time delay study design EAP - Electronic Medical Record with Decision Support, Asthma Action Plan, & Population Management Reports IAC - Integrated Approach to Care SBC - School Based Care for Asthma SDM - Shared Decision Making Approach to Care Asthma Comparative Effectiveness (ACE) Research Database Outcome measures for 12,581 eligible asthma patients Service Utilization Medication Compliance Appropriate Care Quality of Life (QoL) Billing Data Patient Surveys Medicaid Claims ACER Clinical Data Spatial Data School Data ACE Database: Data Collection Patient-Level Measures – – – – – – Insurance QoL Score Visit History Demographics Medication Orders Comorbid Conditions Facility-Level Measures – Treatment Group – Intervention Dates Community-Level Measures • • • • • Crime Poverty Education Housing values Access to services 42 Shared Decision Making Results Time N Pre Post Chang e P-value 3 months (n=171) 171 18.70 % 9.40% -9.40% 0.011 6 months (n=121) 121 31.40 % 23.10 % -8.30% 0.114 43 AHRQ Asthma Population *Patients with first SDM visit in 2012Q1 or earlier Appropriate Care Measures 45