Using Analytics for Value-Based Care

Size: px

Start display at page:

Download "Using Analytics for Value-Based Care"

Ariel Jasmin Osborne
5 years ago
Views:

1 Using Analytics for Value-Based Care John Cuddeback, MD, PhD Elizabeth Ciemins, PhD, MPH, MA AMGA Northwest Regional Meeting February 3, 2017 Seattle

2 A Fundamental Change Is Underway Fee for Service MIPS Pay for Performance Advanced APMs Value-Based Payment Measures? You mean RVUs? Small up-side reward for quality measures, often process-based Pressure to enfranchise every specialty profusion of measures Clinical decision support Retrospective analytics Total cost of care is key need adjudicated claims for at-risk lives Need risk stratification, predictive modeling, and meaningful benchmarking to manage care and cost Outcomes (including the patient s perspective) are essential to ensure accountability for quality Need process measures internally, to manage the clinical business Copyright 2017 AMGA. All rights reserved. 1

3 AMGA Analytics for Improvement (A4i) Comparative Clinical Data Collaborative Shared Learning Performance improvement What to improve How to improve Managing chronic conditions, 1º and 2º prevention Total cost of care (cost-weighted utilization) Copyright 2017 AMGA. All rights reserved. 2

BP control < 140/90 for 12 month periods ended each quarter of 2013

4 AMGA Analytics for Improvement (A4i) Patients with diagnosis of essential hypertension in 2011 who had an ambulatory E&M visit in 2013 Data reflect BP control < 140/90 for 12 month periods ended each quarter of 2013 Copyright 2017 AMGA Analytics LLC and OptumInsight Inc. All rights reserved

5 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 4

6 Building Organizational Capability Hypothetical distribution of patients by overall cost of care Frequency Exception Management n Identify potential outliers, and provide individualized attention n Predictive modeling proactive case management Overall Cost of Care Frequency Care Process Redesign n Improve care for the typical patient and reduce variation, case-to-case n Comparative analytics process redesign Overall Cost of Care Copyright 2017 AMGA. All rights reserved. 5

7 Examples Predictive modeling to identify high-risk patients Heart failure and COPD: Risk of hospital admission Type 2 diabetes: Risk of slipping out of glycemic control Benchmarking to identify best performers Challenge of bundle measures Together 2 Goal Screening for diabetes Adult immunization Identifying and translating best practices Why is it so hard to operationalize? Mixed-methods research: quantitative + qualitative Copyright 2017 AMGA Analytics LLC and OptumInsight Inc. All rights reserved. 6

8 Predictive Models: Risk of Hospital Admission Over the next Six Months 7

9 Optum Analytics Predictive Models 1,200,000 pts. w/ HF 2,000,000 pts. w/ COPD 5,900,000 pts. w/ Diabetes n n n n n n n Demographics Comorbid conditions Vital signs Clinical observations Lab results Medications Historical utilization Logistic Regression Probability of at least one disease-related inpatient admission in next 6 months Descending probability of IP admission (percentile) Organization s Active Patients with COPD Actual Proportion Over Next 6 Months: Pts. w/ COPD-Related IP Admissions Copyright 2017 AMGA Analytics LLC and OptumInsight Inc. All rights reserved. 8

10 Optum Analytics Predictive Models 1,200,000 pts. w/ HF 2,000,000 pts. w/ COPD 5,900,000 pts. w/ Diabetes n n n n n n n Demographics Comorbid conditions Vital signs Clinical observations Lab results Medications Historical utilization Logistic Regression Probability of at least one disease-related inpatient admission in next 6 months Descending probability of IP admission (percentile) Organization s Active Patients with HF Actual Proportion Over Next 6 Months: Pts. w/ HF-Related IP Admissions Copyright 2017 AMGA Analytics LLC and OptumInsight Inc. All rights reserved. 9

11 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 10 Aurora Health Care, Inc.

12 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 11 Aurora Health Care, Inc.

13 Clinical Successes Drop in hospital admission rates: Ø 30% reduction in all-cause admissions among HF patients Ø 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, with expanded primary care 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. 12 Aurora Health Care, Inc.

14 AMGA s Role in Translation Predictive models allow/require a fundamental change in care process Reactive à 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 AMGA s assistance with translation builds upon excellent support from Optum Client Services for effective use of Optum One 13

15 Glycemic Control State Transitions: Identifying Patients Who Appear Safe But Are at Risk of Slipping Out of Control 14

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 2017 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 2017 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 2017 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 2017 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 2017 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 2017 AMGA Analytics LLC and OptumInsight Inc. All rights reserved. 20

22 Benchmarking to Identify Best Performers 21

23 AMGA Foundation

24 Together 2 Goal Measurement Tracks Basic Track Core Track HbA1c control < 8.0% x x BP control < 140/90 mmhg x Lipidmanagement (statin prescribed) x Medical attention to nephropathy x Bundle measure (all of above) x Copyright 2017 AMGA. All rights reserved. 23

27 Translating Best Practices 26

Putting Discoveries into Practice Copyright 2016 AMGA

29 Translation: The Science We often disseminate data or new evidence and hope for adoption. Or we simply train people. But this often fails. More intentional approaches are needed to improve uptake of new evidence and development of new processes. Copyright 2016 AMGA Analytics LLC and OptumInsight Inc. All rights reserved. 28

Translation is a Simple, Linear Process (Not!

Diffusion of Innovations (Everett Rogers, 1995) Diffusion of Innovations Model (DIM) Innovation offers a relative advantage over current model Compatible

32 Diffusion of Innovations (Everett Rogers, 1995) Diffusion of Innovations Model (DIM) Innovation offers a relative advantage over current model Compatible with current processes Not complex Easy to observe/test and measure results Copyright 2017 AMGA Analytics LLC and OptumInsight Inc. All rights reserved. 31

34 33

35 Atlas.ti Qualitative Research Software I think it s the culture of our system that we have, whether it be [the new EHR] or this or that issue. We even have in our physical space providers with nurses, and we re all together, we re a team, and so for us to have such a profound disruption, it s not surprising to me that we would work together as a team. So that s not really unique and that doesn t mean much to me, it s almost expected. Copyright 2017 AMGA Analytics LLC and OptumInsight Inc. All rights reserved. 34

37 36 36

38 Integrative Analysis: Qualitative & Quantitative task taxonomy + ethnographic observations QUALITATIVE time & motion (ipad) content software design QUANTITATIVE 37

Time & Motion Data Collection Copyright 2017 AMGA

40 Integrative Analysis: Qualitative & Quantitative task taxonomy + ethnographic observations QUALITATIVE time & motion (ipad) content software design QUANTITATIVE semi-structured interviews content sampling frame (who) video QUALITATIVE where to focus quantitatively QUANTITATIVE 39

43 Pneumococcal Vaccines Age 65+ (Any) Pre-Intervention vs. Intervention Period Rates 90% 80% 70% 60% 50% 55% 72% 72% 65% 48% 70% 50% 64% 56% 77% 76% 65% 61% 80% 40% 30% 20% 10% 0% n*= 42,333 24,463 16,399 32,240 12,217 27,278 34,771 Med Gp BA#1 Med BY Gp #2 Med Gp DQ#3 Med Gp KU#4 Med TU Gp #5 Med ZC Gp #6 Med ZU Gp #7 Group Identifier Pre-Intervention Vaccination Rate Intervention Period Vaccination Rate Copyright 2017 AMGA Analytics LLC and OptumInsight Inc. All rights reserved. 42

44 Adult Immunization Collaborative Example Quantitative data showed positive results in pneumococcal vaccinations for patients 65 Qualitative interviews explained why: Standing orders workforce empowerment Medicare Annual Wellness Visits Pneumococcal vaccines offered at flu shot clinics Work in progress Copyright 2017 AMGA Analytics LLC and OptumInsight Inc. All rights reserved. 43

45 Pneumococcal Vaccines Age High Risk (Any) Pre-Intervention vs. Intervention Period Rates 60% 50% 40% 32% 30% 20% 18% 26% 26% 24% 19% 12% 13% 16% 11% 22% 14% 17% 26% 10% 0% n*= 60,544 22,555 20,106 22,929 13,229 46,255 57,768 Med Gp BA#1 Med BY Gp #2 Med Gp DQ#3 Med Gp KU#4 Med TU Gp #5 Med ZC Gp #6 Med ZU Gp #7 Group Identifier Pre-Intervention Vaccination Rate n* = Represents Intervention Period Denominator Intervention Period Vaccination Rate Copyright 2017 AMGA Analytics LLC and OptumInsight Inc. All rights reserved. 44

46 Adult Immunization Collaborative Why so little improvement for high-risk patients under age 65? Qualitative interviews: Groups prioritized 65+ population instead of high risk conditions High-risk algorithms perceived as too complicated/confusing Care for high-risk patients centers on specialists Different workflow people and processes Assumptions about process ownership No refrigerators in specialty departments! Copyright 2017 AMGA Analytics LLC and OptumInsight Inc. All rights reserved. 45

Data make you credible; stories make you memorable.

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

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