Integrating Predictive Analytics Into Clinical Practice For Improved Outcomes & Financial Performance
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- Norma Peters
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1 Transforming the HHS Experience Improving the relationship between payers, providers and consumers Integrating Predictive Analytics Into Clinical Practice For Improved Outcomes & Financial Performance June 11 th, 2015 Presenters: Mike Lardieri, AVP Strategic Program Development, North Shore LIJ Health System Ravi Ganesan, President Core Solutions, Inc.
2 Agenda An overview of various predictive analytics tools The role of EHRs in predictive analytics Practical real world examples for improving clinical practice Using predictive analytics in population health management
3 Source: Health Information Management Kshira Saagar APR 2, 2015 Pyramid for Analytical Need
4 Pyramid for Analytical Need Level 1 Questions/Curiosity (Physiological) - More Questions Than Answers Level 2 Data (Safety) - You Need Data to Make Decisions Level 3 People (Love/Belonging) - You Need People who Love Data and are Adaptable to the Changing Nature of Technology & Analytics Level 4 Socialization (Esteem) - Share the Data Make Sure it Can be Understood by Senior Leaders as Well as the Rank & File Level 5 Artificial Intelligence/Smart Systems - (Self Actualization)
5 Source: Halo Business Intelligence Maturity Model
6 Large Vendors Challengers (Acquired by Microsoft)
7 Key Trends Availability of Software as a Service Model Prices are dropping Improved usability for business users Increasing specialization on verticals healthcare, customer service etc.
8 Explorsys
9 IBM Watson & Healthcare
10 Role of EHRs in Predictive Analytics 10
11 The Health Information Technology Pyramid Big Data Devices Apps and Point of Care Solutions Specialized Clinical & Financial Tools Electronic Health Record
12 The Role of The EHR Data Collection - Standardized, Validated Data Demographics Clinical Quality Measures (MU) Behavioral Health Quality Measures Analytics - Integration Seamless integration with Predictive tools through APIs Intelligence - Real-time Feedback Configurable business rules and workflows to inform and educate users and direct care.
13 EHR Checklist Meaningful Use Certified Stage 2 Plans for Stage 3 Clinical Tools Integration of standardized behavioral health tools Single Integrated Database Modern Technology with User Configurability
14 Gen 1 vs. Gen 2 EHRs
15 Paradigm Shift EHR + Predictive Intelligence Reactive Care Proactive Care Healthcare Cost Savings Consumer Cost Savings Provider Centered Person Centered
16 Real world Examples For Improving Clinical Practice 16
17 Predictive Modeling Process What is the problem we are trying to solve? Gather as much data as available Evaluate Models Run Predictive Analytics
18 Suicide Predictive Model Challenge: 41,149 suicides reported in 2013 (CDC); 10th leading cause of death for Americans; After cancer and heart disease, suicide accounts for more years of life lost than any other cause of death; Over 19% suicide rate among people 45 to 64 years old; 77.9% were male and 22.1% were female.
19 Suicide Predictive Model
20 Value Based Purchasing CMS program that rewards quality over Quantity. A good indicator of what the future of reimbursements for behavioral health is going to look like.
21 Scheduling Efficiency Improving access to behavioral health services requires improvements in scheduling efficiencies. Same Day Access Step in the right direction. Third next available (TNA) appointment and office visit cycle time are validated measures, but not widely used study of the Massachusetts private sector reported wait times of 50 and 39 days for internal medicine and family practice respectively. Scheduling has a direct impact on customer experience.
22 Scheduling Efficiency
23 Revenue Maximization Opportunities: Impact of ACO on payer mix Identify unit costs and impact of various cost components Self Pay/Bad Debt Management Transition from fee for service to value based reimbursements
24 Revenue Optimization
25 Using Predictive Analytics in Population Health Management 25
26 Key Principles for Population Management 1. Population-Based Care: Focus on caring for the whole population you are serving, not just the individuals actively seeking care. 2. Data-Driven Care: Utilize data and analytics in order to make informed decisions to serve those in your population who most need care. 3. Evidence-Based Care: Make use of the best available evidence to guide treatment decisions and delivery of care. 4. Care Management: Engage in actionable care management for the population you serve. Source:
27 Steps For Implementing Population Management 27
28 Key Principles for Population Management
29 Population Management For Blood Pressure Build a List Check Compliance Remote Monitoring Identify Care Gaps No Rx Coordinate Care Identify other gaps
30 Predicting Sickness Blue Cross Goal: Identify Patients Likely to be hospitalized in the next 3 months Algorithms based on claims, lab, Rx, height, weight, family history etc. to score risk. Assign Health Coach to coordinate care and reduce readmissions.
31 Projects At North Shore LIJ Health System 31
32 Opportunities Preventable Readmissions Length of Stay Hospital Acquired Conditions Chronic Care Management Predictive Illness / Disease Progressions Identification of High Cost Cases Wellness Program Management Micro-Segmentation & Plan Design
33 Q&A 33
34 Question and Answers THANK YOU! 34
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