PACE Performance on Post-Discharge Primary Care Evaluations from Jan-Jun 2012 PACE By: Rocio Solano Padilla PCLP-NMF/GE Scholar Jul 23, 2012 2
INTRODUCTION • Who am I? • Physician Assistant student – Towson/CCBC Essex, MD • What am I doing? • PCLP-NMF/GE Scholar • PACE Performance on Post-Discharge Primary Care Evaluations from Jan - Jun 2012 3
BACKGROUND What is PACE? • P rogram of A ll-Inclusive C are for the E lderly • Comprehensive medical, health, and social services that integrate acute and long-term care. • Patients 55 years of age or older living in the community and requiring nursing home care. • Strict regulation and auditing from CMS, CDHCS, Health Dep. “PACE organization should use organizational data to identify and improve areas of poor performance. The PACE organization must take actions that result in improvements in its performance in all types of care” 1 4
BACKGROUND - According to the Medicare Payment Advisory Commission, avoidable hospital readmissions cost Medicare $12 billion a year 2 - The average costs for readmissions is 30-40% higher than the average cost of acute hospital admissions 3 - According to Department of Health and Human Services the Obama administration and Congress have both named the reduction of readmissions as a target area for health reform 3 - Moore et al. determined that 49% of patients experience at least one medical error that is related to transitional care between inpatient and outpatient settings 4 - There is evidence in the medical literature that patients scheduled or who have seen a primary care provider (PCP) for post-hospital follow-up are less likely to be readmitted 5,6
OBJECTIVES • To determine performance for a 72-hour window between discharge and PCP. • To determine hospital diagnosis follow up by PCP. • To assess clinical data from Altamed in light of the current national data. • To participate in Altamed’s vision of leading community health services by contributing to the continuous evaluation of performance set at PACE 6
METHODS - Retrospective randomized chart review study 7
METHODS (cont…) -End Points - Time between discharge and PCP evaluation. - ER diagnosis followed up by PCP. - Early hospital readmission (30 days). - Statistical Analysis - Fisher’s exact test between: - 72-hr window rate and re-admission rate - Hospital diagnosis follow-up and re-admission rate -72-hr window rate and diagnosis f/u 8
RESULTS 30 30-day readmission rate (%) 24.1 Based on one Based on number 25 22.5 admission per of admissions patient 20 15.8 15.9 16.1 Readmission rate 24.1% 15.8% 15 Patients seen 10 43.1% 40.6% within 72h 5 Visits where Dx. 82.7% 81.3% was addressed 0 Altamed (PB) California U.S. (2009) Altamed Chronic (2009) (2008) California and U.S. data was retrieved from the Dartmouth Atlas of Health Care (Goodman et al. 2011). AHCRQ study on Chronic condition data was obtained from Podulka et al. (2008) 9
RESULTS 16 14 Number of patients 12 10 Median: 4 8 Average: 5.3 ± 0.61 6 4 2 0 2 4 6 8 10 12 14 16 18 20 Days to PCP Appointment 10
72 HOUR TIME WINDOW Patient based Readmitted Not Admission based Readmitted Not readmitted readmitted < 72 hours 2 14 < 72 hours 44% 5 20 40% > 72 hours 5 19 > 72 hours 56% 9 23 60% P value = 0.54 P value = 0.68 0.2 Fraction of patients readmitted 0.15 0.19 0.1 0.13 0.05 0 Less than 72 hours More than 72 hours 11
DIAGNOSIS ADDRESSED Admission based Readmitted Not Patient based Readmitted Not readmitted readmitted Dx discussed 84% 10 38 82% Dx discussed 4 29 Not discussed 16% 4 5 Not discussed 2 5 18% P value = 0.2 P value = 0.27 Fraction of patients readmitted 0.35 0.3 0.25 0.2 28.5% 0.15 0.1 12% 0.05 0 Discussed Not discussed 12
DIAGNOSIS ADDRESSED AND 72 HOUR TIME WINDOW Fraction of patients that discussed Dx 1 0.9 0.8 0.7 0.97 0.6 0.5 0.7 0.4 0.3 0.2 0.1 0 Less than 72 hours More than 72 hours P value = 0.02 13
DISCUSSION - Readmission data is consistent with result from regional and national centers. - Implications of the 72h window. - Challenges to addressing the diagnosis in the first visit. - Limitation of the study: sample size, EHR data collection/ time constraints. 14
CONCLUSIONS There is an opportunity to improve the 72-h window performance Ongoing project… Future ideas: relative readmission rates 15
ACKNOWLEDGEMENTS National Medical Fellowship General Electric Altamed – Dr. Martin Serota, Dr. Esiquio Casillas, Dr. Ricardo Puertas, PACE- East Los Angeles, Ulysses Garcia, Medical Team PACE- East Los Angeles
THANK YOU!
REFERENCES 1. CMS Manual System. Pub 100-11 Programs of all Inclusive Care for the Elderly (PACE). June 9, 2011. 2. Carrie A. et. al. Effect of Hospital Follow-up Appointment on Clinical Event Outcomes and Mortality. Arch Intern Med/ Vol 170 (No. 11), June 14, 2010 3. Measuring Hospital Readmission as an Outcome for Care Management Programs. DMAA: The Care Continuum Alliance Forum 2009. San Diego, CA 4. Moore, C. et. al. Medical errors related to discontinuity of care from and inpatient to an outpatient setting. J Gen Int Med. 2003. 18:646-641 5. Hernandez A. F. et. al. Relationship between early physician follow-up and 30-day readmission among Medicare beneficiaries hospitalized for heart failure. JAMA. 2010;303(17):1716 6. Misky G.J., Wald H.L., Coleman L.. Post-hospitalization transitions: Examining the effects of timing of primary care provider follow-up. EASOJ Hosp Med. 2010;5(7):392 18
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