Original Article

Holiday Discharges Are Associated with Higher 30-Day General Internal Medicine Hospital Readmissions at an Academic Medical Center

Authors: Ajay Dharod, MD, Brian J. Wells, MD, PhD, Kristin Lenoir, MPH, Wesley G. Willeford, MD, Michael W. Milks, MD, Hal H. Atkinson, MD, MS


Objective: Academic medical centers face unique challenges in educating physician trainees in effective discharge practices to prevent readmissions. Meanwhile, residents must handle high workloads coupled with frequent rotations to different services. This study aimed to determine whether daily service census, service turnover, time of discharge, and day of discharge increase the risk of 30-day readmission.

Methods: All of the discharges from two academic general internal medicine teaching services between October 1, 2013 and September 30, 2014 were included in this observational data analysis. Variables were fit to a 30-day, all-cause readmission outcome using multiple logistic regression with inverse probability of treatment weighting and multiple imputations with chained equations. The following potential confounding variables were included in the model: health system utilization, demographics, laboratory values, and comorbidities.

Results: Among 1935 total discharges, 258 patients (13.3%) were readmitted within 30 days of the index discharge. Turnover, service census, weekend discharge, and time of discharge were not significantly associated with the risk of readmission. Patients discharged during holiday periods had higher odds of readmission (odds ratio 2.56, 95% confidence interval 2.01–3.25), whereas patients discharged on an intern switch day had lower odds of readmission (odds ratio 0.33, 95% confidence interval 0.27–0.41).

Conclusions: Patients who are discharged during holiday periods are at a higher risk of readmission after adjusting for potential confounders. These results also suggest that discharge on an intern switch day had a protective effect on readmission. Further work is needed to examine whether these findings can be replicated, and, if confirmed, to determine to what extent these associations are causal.

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1. Fletcher KE, Singh S, Schapira MM, et al. Inpatient housestaff discontinuity of care and patient adverse events. Am J Med 2016;129:341-347.
2. Au AG, Padwal RS, Majumdar SR, et al. Patient outcomes in teaching versus nonteaching general internal medicine services: a systematic review and meta-analysis. Acad Med 2014;89:517-523.
3. Duong JA, Jensen TP, Morduchowicz S, et al. Exploring physician perspectives of residency holdover handoffs: a qualitative study to understand an increasingly important type of handoff. J Gen Intern Med 2017;32:654-659.
4. Burns R, Nichols LO. Factors predicting readmission of older general medicine patients. J Gen Intern Med 1991;6:389-393.
5. Marcantonio ER, McKean S, Goldfinger M, et al. Factors associated with unplanned hospital readmission among patients 65 years of age and older in a Medicare managed care plan. Am J Med 1999;107:13-17.
6. Librero J, Peiro S, Ordinana R. Chronic comorbidity and outcomes of hospital care: length of stay, mortality, and readmission at 30 and 365 days. J Clin Epidemiol 1999;52:171-179.
7. Hasan O, Meltzer DO, Shaykevich SA, et al. Hospital readmission in general medicine patients: a prediction model. J Gen Intern Med 2010;25:211-219.
8. Kansagara D, Englander H, Salanitro A, et al. Risk prediction models for hospital readmission: a systematic review. JAMA 2011;306:1688-1698.
9. Tinsley A, Naymagon S, Mathers B, et al. Early readmission in patients hospitalized for ulcerative colitis: incidence and risk factors. Scand J Gastroenterol 2015;50:1103-1109.
10. Mueller SK, Donzé J, Schnipper JL. Intern workload and discontinuity of care on 30-day readmission. Am J Med 2013;126:81-88.
11. Kostis WJ, Demissie K, Marcella SW, et al. Weekend versus weekday admission and mortality from myocardial infarction. N Engl J Med 2007;356:1099-1109.
12. Tickoo S, Fonarow GC, Hernandez AF, et al. Weekend/holiday versus weekday hospital discharge and guideline adherence (from the American Heart Association’ Get with the Guidelines-Coronary Artery Disease database). Am J Cardiol 2008;102:663-667.
13. Attenello FJ, Wen T, Cen SY, et al. Incidence of “never events” among weekend admissions versus weekday admissions to US hospitals: national analysis. BMJ 2015;350:9.
14. Quan H, Li B, Duncan Saunders L, et al. Assessing validity of ICD-9-CM and ICD-10 administrative data in recording clinical conditions in a unique dually coded database. Health Serv Res 2008;43:1424-1441.
15. van Buuren S, Groothuis-Oudshoorn K. MICE: multivariate imputation by chained equations in R. J Stat Softw 2011;45:1-67.
16. Moons KGM, Donders RART, Stijnen T, et al. Using the outcome for imputation of missing predictor values was preferred. J Clin Epidemiol 2006;59:1092-1101.
17. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika 1983;70:41-55.
18. Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav Res 2011;46:399-424.
19. Curtis LH, Hammill BG, Eisenstein EL, et al. Using inverse probability-weighted estimators in comparative effectiveness analyses with observational databases. Med Care 2007;45:S103-S107.
20. Huber PJ. Robust statistics. In: Lovric M, ed. International Encyclopedia of Statistical Science. Berlin:Springer; 2011. p. 1248-1251.
21. Austin PC. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Stat Med 2009;28:3083-3107.
22. Rosenbaum PR, Rubin DB. Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. Am Stat 1985;39:33-38.
23. Austin PC, Stuart EA. Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Stat Med 2015;34:3661-3679.
24. Normand SLT, Landrum NB, Guadagnoli E, et al. Validating recommendations for coronary angiography following acute myocardial infarction in the elderly: a matched analysis using propensity scores. J Clin Epidemiol 2001;54:387-398.
25. Austin PC. Using the standardized difference to compare the prevalence of a binary variable between two groups in observational research. Commun Stat Simul Comput 2009;38:1228-1234.
26. Little RJ, Rubin DB. Statistical Analysis with Missing Data. New York: John Wiley & Sons; 2014.
27. Joynt KE, Orav EJ, Jha AK. Thirty-day readmission rates for Medicare beneficiaries by race and site of care. JAMA 2011;305:675-681.
28. Keenan PS, Normand SLT, Lin ZQ, et al. An administrative claims measure suitable for profiling hospital performance on the basis of 30-day all-cause readmission rates among patients with heart failure. Circ Cardiovasc Qual Outcomes 2008;1:29-U56.
29. Hernandez AF, Greiner MA, Fonarow GC, et al. Relationship between early physician follow-up and 30-day readmission among Medicare beneficiaries hospitalized for heart failure. JAMA 2010;303:1716-1722.
30. Park L, Andrade D, Mastey A, et al. Institution specific risk factors for 30 day readmission at a community hospital: a retrospective observational study. BMC Health Serv Res 2014;14:6.