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Can Nurse Staffing Levels Improve Hospital Readmissions Performance?
By Julie Berez
Mentor: Matthew McHugh PhD JD, MPH, RN, CRNP
Presentation Outline • Overview of Readmissions Reduction
Program • Study Significance • Study Aims • Methods: Non-Bipartite Matching • Findings • Lessons Learned and Future Steps
Hospital Readmissions Reduction Program • 1 in 5 Medicare beneficiaries discharged from
hospitals are readmitted within 30 days.1
• Preventable readmissions are estimated to cost Medicare $17 billion annually. 2
• Beginning in October, hospitals will be penalized by up to 1% of Medicare reimbursements for excess readmissions among AMI, Heart Failure, and Pneumonia patients.
Overview Significance Aims Methods Findings Debrief
1 Jencks, S. F., M. V. Williams, et al. (2009). "Rehospitalizations among patients in the Medicare fee-for-service program." The New England Journal of Medicine 360(14): 1418-1428. 2 Rau, J. (2012) Medicare to Penalize 2,211 Hospitals for Excess Readmissions. Kaiser Health News
Overview Significance Aims Methods Findings Debrief
Controversy Surrounding Program • Can hospitals control what happens to patients
post-discharge?
• Are hospitals caring for minority populations or populations of lower socioeconomic status being unfairly penalized?
Overview Significance Aims Methods Findings Debrief
Kaiser Health News
How can we reduce readmissions rates?
Intervention Studies:
• Jack et. al. designed a successful intervention where nurse “discharge advocates” created comprehensive discharge plans to review with patients, and pharmacists followed up by phone to review medications. 1
• Coleman et. al. found that NP “transitional care coaches” who followed up with patients for 28 days post discharge including a home visit reduced readmissions. 2
Overview Significance Aims Methods Findings Debrief
1Jack, B. W., V. K. Chetty, et al. (2009). "A reengineered hospital discharge program to decrease rehospitalization." Annals of Internal Medicine 150(3): 178-187. 2Coleman, E. A., C. Parry, et al. (2006). "The care transitions intervention: results of a randomized controlled trial." Archives of Internal Medicine 166(17): 1822.
Study Significance • Many proposed interventions are complicated and
costly.
• Increasing nurse staffing levels is a simple, reasonable solution that has also been shown to improve outcomes and patient satisfaction.
• To our knowledge, we are the first study to use the Medicare readmissions penalties as an outcome.
Overview Significance Aims Methods Findings Debrief
Aims • To understand the relationship between registered
nurse staffing levels and performance in the Medicare Readmissions Reduction Program.
• To demonstrate how a new statistical technique, Non-Bipartite Matching1, can give additional validity to health services research studies.
Overview Significance Aims Methods Findings Debrief
1Lu, B., R. Greevy, et al. (2011). "Optimal nonbipartite matching and its statistical applications." The American Statistician 65(1): 21-30.
Data Overview • Predicted readmissions penalties based on 2008-
2010 Medicare discharge data for AMI, Pneumonia, and Heart Failure.
• Nurse staffing data from AHA 2009 Annual Survey and Provider of Services Medicare data.
• Covariate data from AHA, CMS MedPAR, Medicare Cost Reports, 2010 Census, and 2006-2010 ACS.
Overview Significance Aims Methods Findings Debrief
Overview Significance Aims Methods Findings Debrief
Data Overview • 3014 Acute care
non- federal hospitals
• All 50 States (+DC) • 0.15-20 RN hours
per adjusted patient day
• 10-1558 Beds • 0%-78% Revenue
from Medicaid
Max Penalty 472 (16%)
Other Penalty 1674 (55%)
No Penalty 867 (29%)
Distribution of Readmissions Penalties
Matching: Find partner hospitals that are similar in all aspects but nurse staffing
Well RN Staffed Hospital
Poorly RN Staffed Hospital
Bed Count
Teaching Status
Urban/Rural
Proportion Medicaid
Proportion Hispanic
Technology Level
Ownership
Profit Margin
Socioeconomic Status
Proportion Black
Readmissions Performance?
Readmissions Performance?
Hospital A Hospital B
Overview Significance Aims Methods Findings Debrief
How do you define a “well staffed” hospital?
Overview Significance Aims Methods Findings Debrief
Create 5 RN Staffing Categories
Overview Significance Aims Methods Findings Debrief
Non-Bipartite Matching: Match each hospital with a hospital in different RN staffing category
Worst Nurse
Staffing
Best Nurse
Staffing
High RN Staffing Group Low RN Staffing Group
3
4
5
2
3
4
1
5
2
1
Overview Significance Aims Methods Findings Debrief
Now each hospital is paired with a similar hospital in a different RN staffing category
Well Staffed Hospital
Poorly Staffed Hospital
Bed Count
Teaching Status
Urban/Rural
Proportion Medicaid
Proportion Hispanic
Technology Level
Ownership
Profit Margin
Socioeconomic Status
Proportion Black
Readmissions Performance?
Readmissions Performance?
Hospital A Hospital B
Overview Significance Aims Methods Findings Debrief
High RN Staffing Group Low RN Staffing Group
Analysis • Used conditional logistic regression on low vs. high
RN staffing groups to see the effect of RN staffing levels on readmission performance.
• Compared results of traditional logistic regression with non-bipartite matching method.
Overview Significance Aims Methods Findings Debrief
Findings • Hospitals in the low nurse staffing group were 39%
more likely to be penalized than hospitals in the high nurse staffing group.
020
040
060
080
010
00N
umbe
r of H
ospi
tals
1% 0.6%0.8% 0%0.2%0.4%Readmissions Penalty
Distribution of Penalized Hospitals
Penalized
Overview Significance Aims Methods Findings Debrief
Findings • Hospitals in the low nurse staffing group were 48%
more likely to be fully penalized than hospitals in the high nurse staffing group.
020
040
060
080
010
00N
umbe
r of H
ospi
tals
1% 0.6%0.8% 0%0.2%0.4%Readmissions Penalty
Distribution of Penalized Hospitals
Fully Penalized
Overview Significance Aims Methods Findings Debrief
Findings
• Traditional logistic regression without matching gave us similar results, giving us confidence in non-bipartite method.
Overview Significance Aims Methods Findings Debrief
Lessons Learned • How to take ownership of a study and persevere
through perceived roadblocks.
• That learning and experimenting with new statistical methods can be pretty cool.
• What Stata, SAS, and R are each useful for (and why it seems helpful to know them all).
Overview Significance Aims Methods Findings Debrief
Future Steps • See the study through the writing and publishing
process.
• Attempt to capitalize on recent media coverage on readmissions penalties in order to fast-track article into a journal.
Overview Significance Aims Methods Findings Debrief
Thank You! • Dr. Matt McHugh • Joanne Levy • Lissy Madden • Tara Kotagal • Hoag Levins • Renee Zawacki • SUMR Scholars • My Family
Overview Significance Aims Methods Findings Debrief
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