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