Winning at Care Coordination Using Data-Driven Partnerships
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1 Idriz Limaj, LNHA, RN Chief Operating Officer Winning at Care Coordination Using Data-Driven Partnerships Session #166, February 22, Steven Littlehale, MS, GCNS-BC EVP & Chief Clinical Officer
2 Speaker Introduction Idriz Limaj, LNHA, RN Chief Operating Officer LifeBridge Health Steven Littlehale, MS, GCNS-BC EVP & Chief Clinical Officer PointRight Inc. 2
3 Conflict of Interest Idriz Limaj, LNHA, RN Has no real or apparent conflicts of interest to report. Steven Littlehale, MS, GCNS-BC Has no real or apparent conflicts of interest to report. 3
4 Agenda What s going on in the post-acute world? What data is available from post-acute care? What are the steps for building and managing a high-performing post-acute provider network? How can predictive analytics play a role in managing the network? How can this advance population health management? 4
5 Learning Objectives Recognize the depth and breadth of available LTPAC data and what it can do for you public domain data, MDS data, OASIS Evaluate the critical components of a data-driven strategy for building and managing a smart network of LTPAC providers Discuss how predictive analytics can directly impact care coordination, reduce readmissions and optimize placements Develop the necessary steps for integrating LTPAC data into population health management programs 5
6 STEPS to Value An introduction to the benefits realized for the value of Health IT The value STEPS impacted were: Treatment/Clinical Patient Engagement & Population Management Savings Improved the Hospital discharge and admission process Improved the Nursing Home to Hospital transfer process Reduced overall risk-adjusted rehospitalization rate by 1.06 percentage points Improved LifeBridge Quality Score by 6.81% Improved overall Five-Star by 4.45% 6
7 What s Going On in the Post-Acute World? The journey to valuebased care New Measures HHVBP New Data New Measures Five-Star New Data New Data HVBP New Measures IMPACT Act New Measures Affordable Care 7 Act New Data SNFVBP New Measures New Data
8 There is an Alignment New Measures Five-Star New Data New Data IMPACT Act New Measures New Measures HHVBP New Data New Data HRRP New Data SNFVBP New Measures New Measures 8
9 HHS Payment Reform HHS announced targets to increase the number of payments that are linked to quality outcomes by We are setting clear goals and establishing a clear timeline for moving from volume to value in Medicare payments. Our first goal is for 30% of all Medicare provider payments to be in alternative payment models that are tied to how well providers care for their patients, instead of how much care they provide and to do it by Our goal would then be to get to 50% by Our second goal is for virtually all Medicare feefor-service payments to be tied to quality and value; at least 85% in 2016 and 90% in HHS Secretary Burwell Jan. 26,
10 Focus on Quality Payment is now aligning with quality. The Administration has set measurable goals and a timeline to move the Medicare program, and the health care system at large, toward paying providers based on the quality, rather than the quantity of care they give patients. The final rule includes policies that advance that vision and support building a health care system that delivers better care, spends health care dollars more wisely and results in healthier people. CMS 8/3/
11 Understanding post-acute data And knowing what it can do for you 11
12 Available Data: Home Health Agencies (HHAs) The Outcome and Assessment Information Set (OASIS) Home health quality measures (both outcome and process measures) Medicare claims Medicare claims data is used to calculate certain utilization-based home health quality measures. The patient experience of care survey uses the Home Health Consumer Assessment of Healthcare Providers and Systems (HHCAHPS) instrument. Medicare.gov/HomeHealthCompare 12
13 Available Data: Skilled Nursing Facilities (SNFs) CMS s health inspection database Nursing home characteristics and health deficiencies issued and data about staffing and penalties The Minimum Data Set (MDS) Clinical data for quality measures come from the MDS Repository Medicare claims Medicare claims data is used to calculate certain utilization-based home health quality measures Medicare.gov/NursingHomeCompare Many states have unique data sets/report cards 13
14 Available Data: Metrics in the Future IMPACT Act QRP Measures and C-CDA files Long-Term Care Hospitals (LTCHs): LTCH Care Data Set (LCDS) Skilled Nursing Facilities (SNFs): Minimum Data Set (MDS) Home Health Agencies (HHAs): OASIS Inpatient Rehabilitation Facilities (IRFs): IRF-Patient Assessment Instrument (IRF-PAI) 14
15 The critical components of a data-driven strategy Building and managing Smart LTPAC provider networks 15
16 Building a Narrow vs. Smart Network NARROW NETWORKS Efficient Geared toward the stakeholder SMART NETWORKS Customized to fit patients needs High Performance/Data-driven 16
17 Three Keys to Success Transparent Standardized Operational 17
18 LifeBridge Health Overview Leading provider of hospital services in northwestern Baltimore and all of Carroll County, Maryland $1.8B health system 4 hospitals 249 nursing home beds (1 free-standing, 1 TCU) ~550 employed physicians ~2,000 employed nurses 100+ locations 22 urgent care sites 18
19 SNF Collaborative A Preferred Network The SNF Collaborative was developed to: Create a network that is focused on quality and patient experience Enhance patient outcomes while reducing the cost of care Standardize and share best practices across systems Enhance problem-solving and collective navigation within today s healthcare landscape 19
20 Continuum of Care PRIMARY CARE, PREVENTION, WELLNESS AMBULATORY CARE ACUTE CARE, TERTIARY CARE POST-ACUTE CARE 20
21 Ongoing Performance Monitoring Metrics Quality Rehospitalization Patient and family satisfaction Goals Improve scores by 10% annually if below state average Improve scores by 20% annually if above state average 21
22 The Collaborative Dashboard Transparency. Standardization. Operational. 22
23 23
24 Pro30 Rehospitalization for Collaborative 20% 19% 18% 17% 16% 18.6% 18.1% 19.0% 19.1% 18.5% 18.5% 18.6% 18.7% 18.1% 18.2% 18.3% 18.2% 18.2% 17.8% 17.8% 17.8% 17.5% 17.0% 17.1% 16.8% 17.3% 17.0% 17.1% 16.8% 15% Sep-15 Oct-15 Nov-15 Dec-15 Jan-16 Feb-16 Mar-16 Apr-16 May-16 Jun-16 Jul-16 Aug-16 Observed Adjusted 24
25 SNF Outcomes to Date Transparency and data exchange drive change: Improved the Hospital discharge and admission process Improved the Nursing Home to Hospital transfer process Reduced overall adjusted rehospitalization rate by 1.06 percentage points Improved quality metrics by 6.81% Improved overall Five-Star by 4.45% 25
26 Next Steps for the SNF Collaborative Leverage a Smart network for discharge planning Discharge patients to the right setting based on outcomes Standardized clinical pathways based on best practices CHF, UTI, COPD, Pneumonia, Diabetes Reduce costs Monitor length of stay and quality outcomes Investigate acute care end of life admissions 26
27 The power of predictive analytics Knowing what to look for so you can better prepare for what lies ahead 27
28 Choose the Right Benchmarks When it comes to using your data, it s not just about choosing the right outcomes it s about choosing the right benchmarks AND proper risk adjustment. 28
29 Risk Adjusted Rehospitalization Rates Nation 15% and below 15% to 18% 18% and above Source: Original PointRight Research
30 Observed vs. Risk Adjusted Rehospitalization Rates California Observed Risk-Adjusted 12% and below 12% to 20% 20% and above Source: Original PointRight Research
31 Predictive Analytics = Better Partnerships PointRight Post-Acute Grades vs. the CMS Five-Star Ratings Chronic Obstructive Pulmonary Disease (COPD) 2% Points Difference 9% Points Difference CMS 5 Star Ranking PointRight Grade % 19.3% 19.5% 20.0% 21.1% A B C D E 16.0% 17.3% 19.0% 21.3% 24.9% 2% Points Difference 10% Points Difference CMS 5 Star Ranking PointRight Grade Post-Operative Care (All Procedures) % 19.1% 20.0% 19.1% 20.0% A B C D E 14.1% 17.0% 18.4% 20.8% 24.4% 33
32 1 and 2 Star SNFs Might be Best Choice in Some Markets
33 Care Processes at One SNF CARE PROCESSES AND OUTCOMES Measure Group State National Pain Management: % of Patients 50% 33% 38% 51% Advanced Directives: % of Patients 2% 20% 20% 59% Behavior Management Ratio 5% 40% 42% 80% Diagnosed with Depression: % of Patients 17% 35% 38% 41% Facility Acquired Contractors: % of Patients 6% 5% 6% 2% Hospice Care Utilization 8% 5% 5% 4% Respiratory Therapy: % of Patients 13% 7% 9% 13% Observed Drug Administration Errors No 36 Source: Original PointRight Research 2016
34 Average Length of Stay (ALOS) Year State Median 2012 Nation Nation Nation Nation 22 Year State Median 2015 Nation AL FL MD MA MS TX Source: Original PointRight Research 2016
35 What Impacts Average Length of Stay? State Cognitive Impairment Nation No 22 Nation Yes 25 Median State Incontinence Median Nation No 21 Nation Yes 28 State Pain Median Nation No 22 Nation Yes 24 State Depression Median Nation No 22 Nation Yes 27 Source: Original PointRight Research
36 What s next for LTPAC data? Accelerating population health management 39
37 Performance by Clinical Cohort 40
38 COPD at One SNF Source: 41
39 Source:
40 Summary Data is abundant; turning it into action requires an extra layer of intelligence ACTION Partnerships are best achieved through metrics that are: TRANSPARENT STANDARDIZED OPERATIONAL Alignment and communication improve outcomes INTELLIGENCE DATA 44
41 STEPS to Value A summary of the benefits realized for the value of Health IT The value STEPS impacted were: Treatment/Clinical Patient Engagement & Population Management Savings Improved the Hospital discharge and admission process Improved the Nursing Home to Hospital transfer process Reduced overall risk-adjusted rehospitalization rate by 1.06 percentage points Improved LifeBridge Quality Score by 6.81% Improved overall Five-Star by 4.45% 45
42 Questions Idriz Limaj, LNHA, RN Chief Operating Officer Steven Littlehale, MS, GCNS-BC EVP & Chief Clinical 46
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