By Julie Berez Mentor: Matthew McHugh PhD JD, MPH, RN, CRNP

Transcription

1 Can Nurse Staffing Levels Improve Hospital Readmissions Performance? By Julie Berez Mentor: Matthew McHugh PhD JD, MPH, RN, CRNP

2 Presentation Outline Overview of Readmissions Reduction Program Study Significance Study Aims Methods: Non-Bipartite Matching Findings Lessons Learned and Future Steps

3 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. 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): Rau, J. (2012) Medicare to Penalize 2,211 Hospitals for Excess Readmissions. Kaiser Health News

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5 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? Kaiser Health News

6 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 1 Jack, B. W., V. K. Chetty, et al. (2009). "A reengineered hospital discharge program to decrease rehospitalization." Annals of Internal Medicine 150(3): Coleman, E. A., C. Parry, et al. (2006). "The care transitions intervention: results of a randomized controlled trial." Archives of Internal Medicine 166(17): 1822.

7 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.

8 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 Matching 1, can give additional validity to health services research studies. 1 Lu, B., R. Greevy, et al. (2011). "Optimal nonbipartite matching and its statistical applications." The American Statistician 65(1):

9 Data Overview Predicted readmissions penalties based on 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 ACS.

10 Data Overview 3014 Acute care non- federal hospitals All 50 States (+DC) RN hours per adjusted patient day Beds 0%-78% Revenue from Medicaid Distribution of Readmissions Penalties No Penalty 867 (29%) Max Penalty 472 (16%) Other Penalty 1674 (55%)

11 Matching: Find partner hospitals that are similar in all aspects but nurse staffing Technology Level Bed Count Ownership Teaching Status Profit Margin Urban/Rural Hospital A Socioeconomic Status Proportion Black Proportion Medicaid Proportion Hispanic Hospital B Well RN Staffed Hospital Poorly RN Staffed Hospital Readmissions Performance? Readmissions Performance?

12 How do you define a well staffed hospital?

13 Create 5 RN Staffing Categories

14 Non-Bipartite Matching: Match each hospital with a hospital in different RN staffing category High RN Staffing Group Low RN Staffing Group Best Nurse Staffing Worst Nurse Staffing 1 1

15 Now each hospital is paired with a similar hospital in a different RN staffing category Hospital A High RN Staffing Group Technology Level Ownership Profit Margin Socioeconomic Status Proportion Black Bed Count Teaching Status Urban/Rural Proportion Medicaid Proportion Hispanic Hospital B Low RN Staffing Group Well Staffed Hospital Poorly Staffed Hospital Readmissions Performance? Readmissions Performance?

16 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.

17 Findings Hospitals in the low nurse staffing group were 39% more likely to be penalized than hospitals in the high nurse staffing group. Number of Hospitals Distribution of Penalized Hospitals Penalized 1% 0.8% 0.6% 0.4% 0.2% 0% Readmissions Penalty

18 Findings Hospitals in the low nurse staffing group were 48% more likely to be fully penalized than hospitals in the high nurse staffing group. Number of Hospitals Fully Penalized Distribution of Penalized Hospitals 1% 0.8% 0.6% 0.4% 0.2% 0% Readmissions Penalty

19 Findings Traditional logistic regression without matching gave us similar results, giving us confidence in nonbipartite method.

20 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).

21 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.

22 Thank You! Dr. Matt McHugh Joanne Levy Lissy Madden Tara Kotagal Hoag Levins Renee Zawacki SUMR Scholars My Family