PCMH, ACOs and Accountable Care Communities

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1 PCMH, ACOs and Accountable Care Communities JULY 12, 2016 Bob Rauner, MD, MPH, FAAFP

3 Don Klitgaard, MD Future IAFP President

4 Bob Wergin, MD Future AAFP President

5 Steve Ornstein, MD

6 Ogallala Trenton North Platte Curtis Lexington Kearney Eustis Elwood Hastings McCook Broken Bow Sargent Columbus Shelby Stromsburg David City York Geneva Bellevue Lincoln

7 Patient-Centered Medical Home

8 Patient-Centered Medical Home 1. Core Principles (Evidence-Based) 2. Soft Factors & Leadership 3. DMAIC vs. NCQA 4. Staffing for Quality Improvement

9 First contact care, which requires accessibility and responsibility for reducing unnecessary specialist care, Person focused care over time delivered by the patient's chosen physician, who assumes responsibility over long periods for all health care, Comprehensiveness of care, and Coordination of care when people have to go elsewhere for problems outside the competence of the primary care practitioner. PCMH Core Principles Barbara Starfield, MD 4 Pillars 1. First Contact Care 2. Continuity of Care 3. Comprehensive Care 4. Coordination of Care

10 Leadership - Michigan PCMH Lessons 1. Physician leadership is key 1. Lead Physicians need to be present and involved 2. Lead Physicians need to delegate appropriately 2. Measure, but keep it simple 3. The whole team needs to be involved 4. The team needs planning time Mary Ellen Benzik, MD

11 DMAIC Communicate Nimptsch U & Mansky T. Quality Measurement Combined With Peer Review Improved German In- Hospital Mortality Rates for Four Disease. Health Affairs, 32, no. (2013):

12 Effect of Disease Registries Pneumovax 2005 Effect of Disease Registries Pneumovax 2005

13 Improve 03/18/13 06/25/13 Total Pts Immun % Total Pts Immun % Dr A % % Dr B % % Dr C % % Dr D % % Dr E % % Dr F % % Dr G % % Clinic % 1, %

15 Smart Draw DMAIC - Summary Define: Improve rates of screening Communicate: Share your results Measure: What are my current screening rates Improve: Test new processes Analyze: Why does your current process produce these results?

16 Johns Hopkins Public Health Problem Solving Model 1. Define the problem. 2. Measure its magnitude. 3. Understand the key determinants. 4. Develop a conceptual framework of the relationships between the key determinants. 5. Identify and develop intervention strategies. 6. Set priorities among the intervention options. 7. Understand the barriers to implementation and evaluation. 8. Develop an effective communication strategy. Catholic vs. Buddhist Approach

17 Staffing for Quality Improvement Adapted from: How Physicians Can Fix Health Care: One Innovation at a Time, by Chris Trimble Slack Time 0.8 Fraction of Time Ongoing % 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% People

1 0.9 Slack Time 0.8 Fraction of Time 0.7 0.6 0.5 0.4 0.

19 1 0.9 Slack Time 0.8 Fraction of Time Ongoing % 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% People

20 Fraction of Time Ongoing Full Time % 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% People Part Time 0.8 Fraction of Time Ongoing Operations % 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% People

1 0.9 0.8 Slack Time Part Time: Lead Physician 0.7 Fraction of Time 0.6 0.5 0.4 Ongoing Operations Full Time: Care Coordinators 0.3 0.2 0.

21 Slack Time Part Time: Lead Physician 0.7 Fraction of Time Ongoing Operations Full Time: Care Coordinators % 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% People First contact care, which requires accessibility and responsibility for reducing unnecessary specialist care, Person focused care over time delivered by the patient's chosen physician, who assumes responsibility over long periods for all health care, Comprehensiveness of care, and Coordination of care when people have to go elsewhere for problems outside the competence of the primary care practitioner.

22 Barriers to Change

23 Most organizations have a big, powerful constituency for what is but almost no constituency for what could be. Remember that those on top have made it in the current system, and they see little personal value in changing what they know and can succeed in. From The Power of Positive Deviancy by Pascale, Sternin, & Sternin It is difficult to get a man to understand something when his salary depends on his not understanding it. Upton Sinclair

24 Questions Bob Rauner, MD, MPH Community Level Intervention Case Study

27 Nebraska Physicians Cancer Screening Initiative The Problem Nebraska has a higher than average incidence of Colorectal Cancer Nebraska has a lower than average screening rate for Colorectal Cancer

Physicians Initial Organizational Support: OneHealth Nebraska Lincoln UniNet

28 80% by 2018 Lincoln Physicians Cancer Screening Initiative Common Effort to Improve Cancer Screening In Lincoln Involve Majority of Lincoln s Primary Care Physicians Initial Organizational Support: OneHealth Nebraska Lincoln UniNet (CHI/Physician Network) Bryan Health Connect LMEP Peoples Health Center SERPA ACO Lincoln Clinics

29 Goals of the Initiative 1. Increase the percentage of people eligible for colorectal cancer screening who have been adequately screened to >65% by December 2016 as defined by National Quality Forum measure # Increase the percentage of women eligible for breast cancer screening who have received mammograms to >65% by December 2016 as defined by National Quality Forum Measure #2372. Then 80% by 2017!

30 TOTAL PARTICIPATING PROVIDERS Total PAs/NPs 45 Total Physicians 134 PROVIDER AFFILIATION Safety Net 41 SERPA-ACO 14 Unaffiliated 3 TPN 47 Bryan 22 One Health Nebraska 52

31 90.0% COLORECTAL CANCER SCREENING - ROUND 1 DATA 80.0% 66.2% 68.8% 70.0% 60.0% 50.0% 40.0% 30.0% 25.3% 25.8% 34.2% 32.0% 56.6% 59.1% 49.4% 52.5% 52.6% 54.9% 51.5% 44.9% 47.1% 50.0% 45.8% 49.8% 45.9% 45.5% 40.3% 40.1% 20.0% 8.0% 10.0% 0.0% Clinics 90.00% BREAST CANCER SCREENING - ROUND 1 DATA 75.11% 80.00% 65.60% 70.00% 60.00% 50.00% 40.00% 30.00% 11.49% 39.47% 40.91% 37.32% 41.20% 39.48% 33.56% 34.69% 31.24% 30.91% 29.30% 30.20% 30.37% 32.27% 52.28% 54.83% 49.54% 49.00% 57.86% 64.83% 20.00% 10.00% 0.00% Clinics

32 Nebraska Mammography Rates Medicare Beneficiaries Age Medicare Claims Data: 1Q2014 through 4Q2015 Sioux 50.0% Scotts Bluff 48.7% Banner 33.3% Kimball 43.2% Dawes 50.6% Box Butte 43.2% Morrill 45.2% Cheyenne 42.9% Sheridan 50.7% Garden 47.5% Deuel 46.8% Grant 52.3% Arthur 60.0% Keith 55.1% Perkins 63.6% Chase 57.4% Dundy 49.4% Cherry 59.2% Hooker 50.0% McPherson 77.8% Hayes 47.1% Hitchcock 54.3% Thomas 64.9% Lincoln 52.1% Logan 55.3% Frontier 55.4% Red Willow 57.7% Brown 59.7% Blaine 57.1% Keya Paha 52.6% Rock 59.2% Loup Boyd 63.5% Holt 57.6% 55.6% 57.3% 66.7% Custer 54.6% Dawson 57.8% Gosper 74.2% Furnas 63.8% Phelps Garfield Wheeler Sherman Howard 60.3% 59.3% Buffalo 67.0% Kearney Hall Knox 63.1% Antelope 67.4% Boone Pierce Cedar 54.4% 49.8% Wayne 59.4% Dakota 58.5% Thurston 39.7% Madison Stanton Cuming 60.8% 56.9% Valley Greeley 72.3% Platte Colfax 45.8% 56.5% 63.3% 64.2% 65.1% 69.4% 65.3% 63.4% 65.2% 62.1% 57.6% 60.7% 70.1% 58.1% 48.6% Burt 66.7% 62.4% Lancaster 62.9% 60.1% 58.2% 49.7% 66.1% Adams Nance 53.0% Hamilton Clay Polk 53.4% 64.3% York Fillmore Butler Seward Saline 61.1% Harlan Franklin Webster Nuckolls Thayer Jefferson Dodge 68.1% Washington Saunders 63.1% Gage 63.1% Douglas 64.0% Sarpy 60.9% Cass 58.7% Otoe 61.9% Johnson 57.4% 57.3% 61.3% Pawnee 61.6% Nemaha Richardson 49.7% Lowest Group 2 nd Group Middle Group State Rate 61.3% 4th Group Highest Group Analysis provided by Great Plains Quality Innovation Network June 2016 Nebraska Colorectal Cancer Screening Rates Medicare Claims Data (Age 50-75): 1Q2015 through 4Q2015 Sioux 27.3% Scotts Bluff 37.4% Banner 36.6% Kimball 42.8% Dawes 36.5% Box Butte 32.4% Morrill 42.2% Cheyenne 39.8% Sheridan 30.9% Garden 43.6% Deuel 37.0% Grant 36.0% Arthur 42.5% Keith 45.7% Perkins 56.3% Chase 44.9% Dundy 41.8% Cherry 40.1% Hooker 43.8% McPherson 40.0% Hayes 30.4% Hitchcock 40.5% Thomas 39.2% Lincoln 44.6% Logan 42.5% Frontier 43.5% Red Willow 36.8% Brown 37.8% Blaine 33.9% Keya Paha 49.4% Rock 41.4% Loup Boyd 37.8% Holt 44.3% 29.6% 33.5% 47.4% Custer 38.2% Dawson 38.6% Gosper 50.2% Furnas 40.7% Phelps Garfield Wheeler Sherman Howard 39.9% 35.8% Buffalo 43.9% Kearney Hall Knox 43.8% Antelope 42.3% Boone Pierce Cedar 42.3% 38.0% Wayne 37.5% Dakota 43.3% Thurston 36.5% Madison Stanton Cuming 36.5% 41.3% Valley Greeley 45.5% Platte Colfax 36.8% 41.0% 44.1% 47.3% 42.2% 40.3% 43.2% 44.8% 44.5% 39.5% 37.1% 44.6% 49.2% 42.8% 37.0% Burt 38.9% 42.8% Lancaster 42.6% 41.8% 43.0% 34.5% 48.3% Adams Nance 39.9% Hamilton Clay Polk 47.2% 41.8% York Fillmore Butler Seward Saline 43.5% Harlan Franklin Webster Nuckolls Thayer Jefferson Dodge 49.7% Washington Saunders 45.8% Gage 43.5% Douglas 45.9% Sarpy 47.3% Cass 44.2% Otoe 43.8% Johnson 43.8% Pawnee 35.7% Nemaha 38.4% 41.7% Richardson 42.1% *Screening tests included: Colonoscopy, Sigmoidoscopy, FOBT, Barium Enema Lowest Group 2 nd Group Middle Group State Rate 44.1% 4th Group Highest Group Analysis provided Great Plains Quality Innovation Network April 2016

33 Questions Bob Rauner, MD, MPH

Table of Contents. 2 P age. Susan G. Komen

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