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Considering the Impact of Social Determinants on Readmissions

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

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Considering the Impact of Social Determinants on Readmissions

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  1. 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

  2. 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)

  3. Leppin et al. JAMA Int Med 2014

  4. Transitional Care Interventions to Prevent HF Readmissions • AHRQ-funded evidence report #133 • Examined 47 relevant trial-based studies evaluating reported interventions

  5. 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

  6. Association of SES with Readmissions JoyntK, Jha AK NEJM 2013

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

  8. 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?

  9. 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):269-82, 2013. • 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, 2014. • Hu, J. “Socioeconomic Status and Readmissions: Evidence form an Urban Teaching Hospital,” Health Affairs, 33(5):778-785, 2014. • McAlister, FA. “Decreasing Readmissions: It Can Be Done But One Size Does Not Fit All,” Qual Saf, 22:975-976, 2013. • Nagasako, EM et al. “Adding Socioeconomic Data to Hospital Readmissions Calculations may Produce More Useful Results,” Health Affairs, 33(5):786-791, 2014. • Joynt KE, Jha AK. A path forward on Medicare readmissions. NEJM 2013;368:1175-1177. • Leppin A, et al. Preventing 30-day hospital readmissions: a systematic review and meta-analysis. JAMA Int Med May 2014 (E pub)

  10. Care Navigator Pilot Study at Baylor Scott & White Health

  11. 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

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

  13. Care Navigator Intervention-90 Days

  14. Enrollment/Tracking Data 418 Eligible Patients Referred to BCC Irving Clinic December 2012-December 2013 341 Patients Established Clinic Follow-up with Data Available for Analysis 77 Patients “Unconnected” Randomization 86 Patients: Care Navigator Intervention 255 Patients: Usual Care Follow-up Period Follow-up Period 77 Patients (100%): 90 Days 72 Patients (94%): 180 Days 40 Patients (52%): 365 Days 341 Patients (100%): 90 Days 332 Patients (97%): 180 Days 208 Patients (61%): 365 Days

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

  16. 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)= 13 Masica et al. BSWH internal data

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

  18. Discussion Points • 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

  19. 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

  20. Open Forum

  21. We have reached our Gooooaaal!30 Day All Cause Readmissions 20.6% Reduction

  22. We have reached our Gooooaaal!30 Day Medicare Readmissions 20.5% Reduction

  23. Data Tables 30 Day All Cause Readmissions 30 Day Medicare Readmissions

  24. Reminders July 11: Falls & Immobility Affinity Call July 18: Leadership-Followership Webinar August 13: CLABSI Affinity Call Additional information available on the website at: http://www.henlearner.org/about/calendar/

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