Jane Brock, MD, MSPH Colorado Foundation for Medical Care March 11, 2013 Reducing Readmissions This material was prepared by the Colorado Foundation for Medical Care (CFMC), the Integrating Care for Populations & Communities National Coordinating Center, under contract with the Centers for Medicare & Medicaid Services (CMS), an agency of the U.S. Department of Health and Human Services. The contents presented do not necessarily reflect CMS policy. PM CO 2013

Results of the Care Transitions Theme Some interesting insights – Number of ways to measure readmissions – ‘cross continuum teams’ and community coalitions – A lesson in quality improvement What’s now/next Overview

Journal of the American Medical Association Brock, J; Mitchell, J; Irby, K; Stevens, B; Archibald, T; Goroski, A; Lynn, J. Association Between Quality Improvement for Care Transitions in Communities and Rehospitalizations Among Medicare Beneficiaries. Journal of the American Medical Association 309: ; Jan 23, 2013.

Where it started, 2006 – 2008 VALUE Recruit hospitals Reduce non-beneficial utilization We provided utilization data Transitions of Care Recruit hospitals + others Implement the CTI

5

6 FFS Medicare beneficiaries living in zip codes of interest Target Population FFS beneficiaries discharged from hospitals of interest 2% 2% absolute reduction in readmissions The Care Transitions Theme, ,157

We started with hospitals..

Why are people readmitted? No Community infrastructure for achieving common goals Unreliable system support Lack of standard and known processes Unreliable information transfer Unsupported patient activation during transfers Provider-Patient interface U nmanaged condition worsening Use of suboptimal medication regimens Return to an emergency department

The Care Transitions Intervention RED Transitional care nursing Interact 9 Interventions

Interim Quarterly Results 10

New Measures Population-Based Readmission Measure: Number of rehospitalizations within 30 days of discharge from an acute care hospital for all residents of the defined community Number of eligible FFS beneficiaries living in the defined community X 1000 Number of hospitalizations for all residents of the defined community Number of eligible FFS beneficiaries living in the defined community X 1000 Population-Based Admission Measure:

Population size (census) Poverty % (census) Healthcare Intensity Index of hospitals > 400 beds ( In-state vs out-of-state 10 possible matches QIOs could choose To account for secular trend.. [Δpopulation + Δpoverty + 2(ΔHCI)] ÷ 4

State Target Community* Comparison Communities† Community 1Community 2Community 3Community 4 ALTuscaloosa HRRGulfport, MSLafayette, LA Gadsden, AL ‡ Montgomery, AL ‡ CONW DenverMadison, WISanta Rosa, CA Aurora, CO ‡ Fort Collins, CO ‡ FLMiamiBrooklyn, NYFlushing, NYPhiladelphia, PA- GAAtlantaRaleigh, NCBaltimore, MD Marietta, GA ‡ - INEvansville HSATopeka, KS Terre Haute, IN ‡ Lafayette, IN ‡ - LABaton RougeKnoxville, TNJackson, MS Metairie, LA ‡ Shreveport, LA ‡ MIGreater LansingErie, PAChattanooga, TN Saginaw, MI ‡ Flint, MI ‡ NEOmahaCharleston, SCLouisville, KYRichmond, VAAkron, OH NJSW New JerseyWilmington, DEDenton, TX Toms River, NJ ‡ Lakewood, NJ ‡ NYUpper CapitalSpringfield, IL Kingston, NY ‡ Buffalo, NY ‡ Binghamton, NY ‡ PAWestern PAOrlando, FL Wilkes-Barre, PA ‡ Scranton, PA ‡ - RI Providence- Kent-Newport Nashville, TNTulsa, OKBoston, MA - TXHarlingen HRRNew Orleans, LAMobile, ALBakersfield, CA El Paso, TX ‡ WAWhatcomNampa, IDYakima, WA Richland, WA ‡ - Comparison Communities 31 – best matched 13 – lower on the list 6 – outside of the list

-5.7% (p<.001) -2.1% (p=.08) P=.03 (difference) Rehospitalization, Intervention vs Comparison

-5.7% (p<.001) -3.1% (p<.001) P=.01 (difference) Hospitalization, Intervention vs Comparison

Readmissions/Discharges Intervention – 18.97% – 18.91% = (p=.5) Comparison – 18.76% – 18.91% = (p=.18) P (difference) =.14

Assesses variation in the outcome A change worth investigating: – Reduced variation (increased control) – Significant change in the value of the outcome Process control limits = 3sd from the mean variation during ‘baseline’ ‘Significant’: 6 points above/below the mean OR A single point above/below the process control limit Present/Extending into the intervention period Statistical process control

Community Results Rehospitalizations InterventionComparison Special cause decrease10/14 (71%)22/50 (44%) Special cause increase2/14 (14%)13/50 (26%) Hospitalizations InterventionComparison Special cause decrease13/14 (93%)31/50 (63%) Special cause increase0/14 (0%)8/50 (16%)

6 points below the mean + 4 points below the LCL

8 points below the mean + 3 points below LCL

Special Cause Increase + 5 points below the mean..

Colorado 22

Summary of results 5.7% ↓ (1 hospitalization for every 1000 Medicare beneficiaries) 2.7x that experienced by comparison communities Rehospitalizations Hospitalizations 5.74% ↓ (5 hospitalization for every 1000 beneficiaries) 1.8x that experienced by comparison communities $4,000,000$1,000,000 vs.

Improvement for whole communities is a promising strategy – Providers engaged based on relevance – QIOs in the role of convener/supporter – Included community and social services Unadjusted geographic population data allows easy data display/sharing Summary of results

#1: The Challenges of Publishing QI Risk adjustment Changing the measures Allowing QIO selection/not matching Process control statistics ‘What worked’? 25

Research vs. QI Intervention trial Known important associations Static characteristics Known confounders Intervene on the outcome Test the static effects of interventions Control for all known confounders Match on important characteristics Quality Improvement Intervene on the system that produces the outcome System is complex (can’t assume you know all the important characteristics System is dynamic (can’t assume any specific characteristics endure) Can’t ‘match’ Aren’t sampling a known universe

Assess water quality Repeated samples Assess features associated with good quality (eg O2, microbes) Analyze by regression Control for known confounders (eg temperature, season) Make a statement about the quality of water in this pond Compare this pond to others Improve water quality Repeated samples Upstream interventions (eg filtration, divert agricultural runoff) Track together Correlate interventions with changes in samples Predict future water quality Compare this pond to itself over time

What would we do if we were selling something instead of trying to reduce/save something? 28 #2: Community, Coalitions and Cross-Continuum Teams

Kania and Kramer: Embracing Emergence. Need Measures of Collaboration

Community Organizing 30

#3: What’s a readmission? 31 Neither readmission nor index admission April 1st 2006 April 25th April 10th May 1st May 10th May 30th Index Admission Readmission Index Admission 30 days must elapse between index admissions Readmission and a new index admission Readmission and a new index admission April 1st 2006 April 25th April 10th May 1st May 10th May 30th Index Admission Readmission and a new index admission READMISSION – MATHEMATICA (PIHOEM)READMISSION(S) - CFMC

Hospital Compare (readmissions/discharges) – Disease specific – Risk adjusted – Standardized to the national mean Readmissions/1000 – Sensitive to change – Unadjusted – Can be displayed publicly – Captures gains due to non-medical providers 32

QIOs – 400+ community coalitions – Community organizing approach – Collecting process improvement data – Encouraging application to sustainable payment programs CCTP – Payment mechanism for communities with established successful programs Current expansion

ICPC Early Results: as of September 30, 2012 # of engaged communities340 # of beneficiaries in statewide coalition7,171,106 # hospitals802 # SNF1335 # HH758 # Hospice305 # outpatient physicians1504

12.7%* 31.1%* * compared to