By: Rocio Solano Padilla PCLP-NMF/GE Scholar Jul 23, 2012 2 - - PowerPoint PPT Presentation

PACE

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PACE Performance on Post-Discharge Primary Care Evaluations from Jan-Jun 2012

By: Rocio Solano Padilla PCLP-NMF/GE Scholar Jul 23, 2012

INTRODUCTION

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• 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

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What is PACE?

• Program of All-Inclusive Care for the Elderly
• 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

BACKGROUND

• According to the Medicare Payment Advisory Commission,

avoidable hospital readmissions cost Medicare $12 billion a year2

• The average costs for readmissions is 30-40% higher than the

average cost of acute hospital admissions3

• 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 reform3

• Moore et al. determined that 49% of patients experience at least
• ne medical error that is related to transitional care between

inpatient and outpatient settings4

• 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 readmitted5,6

BACKGROUND

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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

METHODS

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• Retrospective randomized chart review study

METHODS (cont…)

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• 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

RESULTS

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Based on number

• f admissions

Based on one admission per patient Readmission rate 24.1% 15.8% Patients seen within 72h 43.1% 40.6% Visits where Dx. was addressed 82.7% 81.3% 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)

15.8 15.9 16.1 24.1 22.5

5 10 15 20 25 30

Altamed (PB) California (2009) U.S. (2009) Altamed Chronic (2008)

30-day readmission rate (%)

RESULTS

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2 4 6 8 10 12 14 16 2 4 6 8 10 12 14 16 18 20

Number of patients

Days to PCP Appointment

Median: 4 Average: 5.3±0.61

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72 HOUR TIME WINDOW

Admission based Readmitted Not readmitted < 72 hours 5 20 > 72 hours 9 23 Patient based Readmitted Not readmitted < 72 hours 2 14 > 72 hours 5 19

P value = 0.54 P value = 0.68

0.05 0.1 0.15 0.2 Less than 72 hours More than 72 hours

0.13 0.19

Fraction of patients readmitted

44% 56% 40% 60%

DIAGNOSIS ADDRESSED

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Admission based Readmitted Not readmitted Dx discussed 10 38 Not discussed 4 5 Patient based Readmitted Not readmitted Dx discussed 4 29 Not discussed 2 5

P value = 0.2 P value = 0.27

0.05 0.1 0.15 0.2 0.25 0.3 0.35

Discussed Not discussed 12% 28.5% Fraction of patients readmitted

84% 16% 82% 18%

DIAGNOSIS ADDRESSED AND 72 HOUR TIME WINDOW

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0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Less than 72 hours More than 72 hours

0.97 0.7

Fraction of patients that discussed Dx

P value = 0.02

DISCUSSION

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• 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.

CONCLUSIONS

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There is an opportunity to improve the 72-h window performance Ongoing project… Future ideas: relative readmission rates

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

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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