Practical Predictive Analytics. John Cuddeback, MD, PhD AMA IPPS November 11, 2016

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1 Practical Predictive Analytics John Cuddeback, MD, PhD AMA IPPS November 11, 2016

AMGA s Work in Analytics Advocacy: Align payment incentives around population health Programs: Help members redesign delivery systems to manage population health 430 member organizations 170,000

2 AMGA s Work in Analytics Advocacy: Align payment incentives around population health Programs: Help members redesign delivery systems to manage population health 430 member organizations 170,000 physicians Average group size is 400 FTE physicians, median 150 FTE physicians Fully integrated, patient-centered, team-based care emphasis on care coordination Early adopters of electronic health records and e-prescribing Comparative Clinical Data Collaborative Shared Learning Performance improvement Copyright 2016 AMGA Analytics LLC and OptumInsight Inc. All rights reserved. 1

3 Aggregate data across the continuum Clean, normalize and validate data Transform data into insight Make insights actionable Clinical claims & scheduling data Source system agnostic Automated extraction Personcentric MPI Validation Mapping NLP Normalization Shared report library Predictive modeling Benchmarking Disease models AMGA Shared Learning, Research and Translation Copyright 2016 AMGA Analytics LLC and OptumInsight Inc. All rights reserved. 2

4 Users of Optum One among AMGA Members: Instrumented Practices 15% of AMGA members 25% of patients Optum One users Other AMGA members Copyright 2016 AMGA Analytics LLC and OptumInsight Inc. All rights reserved. 3

5 Practical Predictive Models Patients with COPD and HF: risk of hospital admission Switch from reactive to pro-active model of care Type 2 diabetes: transitions in glycemic control Identify patients considered safe who are at risk of slipping out of control Prediabetes: Diabetes Prevention Program study Heterogeneity of treatment effect Copyright 2016 AMGA Analytics LLC. All rights reserved. 4

6 Descending probability of IP admission (percentile) Optum Analytics Predictive Models 1,200,000 pts. w/ CHF 2,000,000 pts. w/ COPD 5,900,000 pts. w/ Diabetes Demographics Comorbid conditions Vital signs Clinical observations Lab results Medications Historical utilization Organization s Active Patients with COPD Actual Proportion Over Next 6 Months: Pts. w/ COPD-Related IP Admissions Logistic Regression Probability of at least one disease-related inpatient admission in next 6 months Copyright 2016 AMGA Analytics LLC and OptumInsight Inc. All rights reserved. 5

7 Descending probability of IP admission (percentile) Optum Analytics Predictive Models 1,200,000 pts. w/ CHF 2,000,000 pts. w/ COPD 5,900,000 pts. w/ Diabetes Demographics Comorbid conditions Vital signs Clinical observations Lab results Medications Historical utilization Organization s Active Patients with CHF Actual Proportion Over Next 6 Months: Pts. w/ CHF-Related IP Admissions Logistic Regression Probability of at least one disease-related inpatient admission in next 6 months Copyright 2016 AMGA Analytics LLC and OptumInsight Inc. All rights reserved. 6

Patient is discharged from hospital Patient has an episode

8 Aurora: The Old Model Patient sees primary care provider in office Patient is scheduled to see PCP Treatment Regimen Patient is discharged from hospital Patient has an episode Patient is admitted as Inpatient Patient goes to ER 7 Aurora Health Care, Inc.

9 Aurora: The New Model Health Coach RN performs outreach call Health Coach RN coordinates team members for the pt. (Pharmacy, Home Care etc.) Divide patient list among Health Coach RN Patient arrives for primary care visit Validate pt. inclusion with local providers Health Coach RN continues to follow schedule & work with patient daily or weekly Analytic reporting for target group Patient sees Health Coach RN & Provider (co-visit) Team develops tx plan and visit schedule 8 Aurora Health Care, Inc.

10 Clinical Successes Drop in hospital admission rates: 30% reduction in all-cause admissions among HF patients 60% reduction in heart-failure-related admissions Decrease in ER utilization. Increase in Patient Wellness (moving to lower risk). Increase in Patient Satisfaction Enhanced Care Coordination model with expanded primary care delivery team. This required a fundamental shift from a reactive to a pro-active care process for the highest-risk 20% of patients identified by Optum s predictive model. 9 Aurora Health Care, Inc.

11 AMGA s Role in Translation Predictive models allow/require a fundamental change in care process Reactive proactive New roles, deeper protocols Different relationship with patient Many elements of clinical translation can benefit from shared learning Culture (and strategy) Leadership development Communication Relationships within and among care teams Engaging patients and families Payers, finances, comp models IT support Monitoring and evaluation Copyright 2016 AMGA Analytics LLC and OptumInsight Inc. All rights reserved. 10

Translation is a Simple, Linear Process (Not!

14 Practical Predictive Models Patients with COPD and HF: risk of hospital admission Switch from reactive to pro-active model of care Type 2 diabetes: transitions in glycemic control Identify patients considered safe who are at risk of slipping out of control Prediabetes: Diabetes Prevention Program study Heterogeneity of treatment effect Copyright 2016 AMGA Analytics LLC. All rights reserved. 13

Changes in A1c Control, Year over Year 414,000 patients aged 18 75 with type 2 diabetes, across 34 AMGA members, who had HbA1c measured in 2013, 2014, and 2015 2013 22.1% 2014 2015 14.1% 8.0% 8.6% 10.

16 Changes in A1c Control, Year over Year 414,000 patients aged with type 2 diabetes, across 34 AMGA members, who had HbA1c measured in 2013, 2014, and % % 8.0% 8.6% 10.5% 3.7% 2.5% 5.6% 4.2% 4.4% 5.7% % 26.0% A1c 8.0 (All 3 years) Transitioning Patients (A1c < & 8.0) 77.9% 69.3% 63.6% 63.6% A1c < 8.0 (All 3 years) Data from the Optum One population health analytics platform Optum is a Principal Corporate Collaborator for Together 2 Goal Copyright 2016 AMGA Analytics LLC and OptumInsight Inc. All rights reserved. 15

Changes in A1c Control over 12 15 Months 241,000 patients aged 18 75 with type 2 diabetes, across 34 AMGA members, who had HbA1c measured in 2013, 2014, and 2015 Plurality of care (24 months) with

17 Changes in A1c Control over Months 241,000 patients aged with type 2 diabetes, across 34 AMGA members, who had HbA1c measured in 2013, 2014, and 2015 Plurality of care (24 months) with primary care, endocrinology, cardiology, or nephrology Baseline All Groups 23% Mo. Later All Groups Baseline A1c Out of Control Stay Out Baseline A1c In Control Move Out Baseline A1c Out of Control Move In Last A1c Out of Control (22%) Last A1c In Control (78%) 77% Baseline A1c In Control Stay In Copyright 2016 AMGA Analytics LLC and OptumInsight Inc. All rights reserved. 16

18 Changes in A1c Control over Months 241,000 patients aged with type 2 diabetes, across 34 AMGA members, who had HbA1c measured in 2013, 2014, and 2015 Plurality of care (24 months) with primary care, endocrinology, cardiology, or nephrology All Groups Copyright 2016 AMGA Analytics LLC and OptumInsight Inc. All rights reserved. 17

19 Distribution of A1c over months 241,000 patients aged with type 2 diabetes, across 34 AMGA members, who had HbA1c measured in 2013, 2014, and 2015 Plurality of care (24 months) with primary care, endocrinology, cardiology, or nephrology Move In Move Out Absolute A1c Median 1.7% Mean 2.1% Absolute A1c Median +1.5% Mean +1.8% Copyright 2016 AMGA Analytics LLC and OptumInsight Inc. All rights reserved. 18

A1c Control at 12 15 Months, by Baseline A1c 241,000 patients aged 18 75 with type 2 diabetes, across 34 AMGA members, who had HbA1c measured in 2013, 2014, and 2015

20 A1c Control at Months, by Baseline A1c 241,000 patients aged with type 2 diabetes, across 34 AMGA members, who had HbA1c measured in 2013, 2014, and 2015 Plurality of care (24 months) with primary care, endocrinology, cardiology, or nephrology Copyright 2016 AMGA Analytics LLC and OptumInsight Inc. All rights reserved. 19

21 Predictive Model Work in Progress Patients with Baseline A1c % All Patients (Deciles: Ranked by Predicted Risk) Patients with Follow-up A1c 8% 10% 10% 10% 10% 10% 10% 10% 10% 10% 10% Copyright 2016 AMGA Analytics LLC and OptumInsight Inc. All rights reserved. 20

22 Practical Predictive Models Patients with COPD and HF: risk of hospital admission Switch from reactive to pro-active model of care Type 2 diabetes: transitions in glycemic control Identify patients considered safe who are at risk of slipping out of control Prediabetes: Diabetes Prevention Program study Heterogeneity of treatment effect Copyright 2016 AMGA Analytics LLC. All rights reserved. 21

23 Heterogeneity of Treatment Effect Reanalysis of data from randomized trials subsets of patient populations PCORI-funded Tufts, University of Michigan, Veterans Health Administration Diabetes Prevention Program Study (DPP) 3,234 adults with pre-diabetes o Impaired fasting glucose, impaired glucose tolerance o BMI 24 (or 22 in Asians) Conducted , stopped one year early Two interventions reduced the risk of progression to overt diabetes o 14.2% for intensive lifestyle intervention o 07.1% for metformin 850 mg/d Sussman JB, Kent DM, Nelson JP, Hayward RA. Improving diabetes prevention with benefit-based tailored treatment: Risk-based reanalysis of Diabetes Prevention Program. BMJ 350 (February 2015): h454. Copyright 2016 AMGA Analytics LLC. All rights reserved. 22

Heterogeneity of Treatment Effect: Diabetes Prevention Program Study Intensive Lifestyle Intervention Metformin

25 Practical Predictive Models Patients with COPD and HF: risk of hospital admission Switch from reactive to pro-active model of care Type 2 diabetes: transitions in glycemic control Identify patients considered safe who are at risk of slipping out of control Prediabetes: Diabetes Prevention Program study Heterogeneity of treatment effect Copyright 2016 AMGA Analytics LLC. All rights reserved. 24

Using Analytics for Value-Based Care

Using Analytics for Value-Based Care John Cuddeback, MD, PhD Elizabeth Ciemins, PhD, MPH, MA AMGA Northwest Regional Meeting February 3, 2017 Seattle A Fundamental Change Is Underway Fee for Service MIPS