Considering the Impact of Social Determinants on Readmissions June 26, 2014 Intermountain HEN Andrew Masica, MD, MSCI Chief Clinical Effectiveness Officer Baylor Scott & White Health

Readmissions within 30 Days of Discharge Common, costly, & potentially hazardous Major focus in virtually all hospitals/systems Effectiveness of many suggested interventions to reduce rates are often disappointing when rigorously evaluated … literature clearly shows that ‘one size does not fit all’ and implementation of readmission strategies should be accompanied by robust evaluations (McAlister, 2013)

Leppin et al. JAMA Int Med 2014

Transitional Care Interventions to Prevent HF Readmissions AHRQ-funded evidence report #133 Examined 47 relevant trial-based studies evaluating reported interventions HFAll Cause Intervention TypeReadmits Mortality Intensive Home Visits +++ Multidisciplinary HF Clinic -++ Structured Phone Support +-+ Telemonitoring --- Nurse-led Interventions ---

Understanding the relative effects of social factors on reported readmission rates may help hospitals better target improvement efforts at an organizational level. Nagasako et al., 2014

Joynt K, Jha AK NEJM 2013 Association of SES with Readmissions

Social Factors Influencing Readmission (Cavillo-King et al.)

Considering Cause & Effect Readmission rate as a quality metric & basis for financial penalties assumes that: – Readmissions are a result of poor quality, clinical care after adjustment for comorbidities and disease severity Socioeconomic factors at the patient and community levels are shown to be related to the probability of readmission – Individual level: Poverty, illiteracy, English proficiency, social support – Community level: poverty, housing vacancy, educational attainment rates Debate  Should we reformulate risk adjustment models and penalties?

Selected References Calvillo-King, L et al. “Impact of Social Factors on Risk of Readmission or Mortality in Pneumonia and Heart Failure: Systematic Review,” J Gen Intern Med, 28(2): , Feltner, C et al. Transitional Care Interventions to Prevent Readmission for People with Heart Failure, Comparative Effectiveness Review #133, AHRQ Publication No. 14-EHC021-EF, Rockville, MD, May, Hu, J. “Socioeconomic Status and Readmissions: Evidence form an Urban Teaching Hospital,” Health Affairs, 33(5): , McAlister, FA. “Decreasing Readmissions: It Can Be Done But One Size Does Not Fit All,” Qual Saf, 22: , Nagasako, EM et al. “Adding Socioeconomic Data to Hospital Readmissions Calculations may Produce More Useful Results,” Health Affairs, 33(5): , Joynt KE, Jha AK. A path forward on Medicare readmissions. NEJM 2013;368: Leppin A, et al. Preventing 30-day hospital readmissions: a systematic review and meta-analysis. JAMA Int Med May 2014 (E pub)

Care Navigator Pilot Study at Baylor Scott & White Health

Context Evidence to support the clinical benefits of medical homes Less clarity surrounding the financial impacts of these programs, particularly in underserved populations Current health care market (shifts in reimbursements, budget pressures, scarce resources) precipitated a need to examine the impact of the BSWH subsidized community clinics $50K of grant support awarded by the Irving Healthcare Foundation to formally investigate this question using a robust methodology

Baylor Irving Hospital Inpt/Obs/ER Encounter Pt. referral to Clinic Staff Clinic Staff enrolls eligible pts. Baseline data collected: Demographics Comorbidities Home status Other variables BCC Irving Medical Home “Connected” (Pts. establish follow-up in clinic) 1:3 Randomization Usual Care + Care Navigation Intervention Usual Care “Unconnected” (Pts. do not make follow-up visit) Outcomes Tracking Comparative Analyses BCC Irving Impact Evaluation Study Design

Care Navigator Intervention-90 Days

Enrollment/Tracking Data 418 Eligible Patients Referred to BCC Irving Clinic December 2012-December Patients Established Clinic Follow-up with Data Available for Analysis 77 Patients “Unconnected” 86 Patients: Care Navigator Intervention 255 Patients: Usual Care Randomization 341 Patients (100%): 90 Days 332 Patients (97%): 180 Days 208 Patients (61%): 365 Days Follow-up Period 77 Patients (100%): 90 Days 72 Patients (94%): 180 Days 40 Patients (52%): 365 Days 14

Study Population- CN Intervention vs. Control Group * Comorbidities also similar between groups 15

Preliminary Results I: Care Navigator vs. Usual Care P<0.05 considered as statistically significant Number of CN interventions needed to prevent 1 hospital admission (1/.075)= Masica et al. BSWH internal data

Preliminary Results II: Incremental Benefit of Support UnconnectedConnectedConnected + CN Hospital Admission Rate at 90-days after Index Encounter per 100 patients Hospital Admission Rate at 365-days after Index Encounter per 100 patients UnconnectedConnectedConnected + CN * *Care Navigator Intervention was 90-days in duration Masica et al. BSWH internal data

For patients establishing clinic follow-up, the Care Navigation intervention reduced hospital utilization rates at 90-days compared to usual care (matching the duration of the intervention) Hospital admission utilization converged between groups during the extended follow-up period without the Care Navigation intervention This intervention was successful in a high-risk population Discussion Points 18

Next Steps at BSWH Collect remaining follow-up data through 12/14 Cross-check readmissions with DFW Hospital Council database and assess subgroups Statistical adjustments Cost-effectiveness analyses Share the story with the outside world -National meetings, journal publication Consider operational use of care navigators at the community clinic sites 19

Open Forum

We have reached our Gooooaaal! 30 Day All Cause Readmissions

We have reached our Gooooaaal! 30 Day Medicare Readmissions

Data Tables 30 Day All Cause Readmissions 30 Day Medicare Readmissions

Reminders July 11: Falls & Immobility Affinity Call July 18: Leadership-Followership Webinar August 13:CLABSI Affinity Call Additional information available on the website at: