Bedspacing and clinical outcomes in general internal medicine: A retrospective, multicenter cohort study.
Journal
Journal of hospital medicine
ISSN: 1553-5606
Titre abrégé: J Hosp Med
Pays: United States
ID NLM: 101271025
Informations de publication
Date de publication:
01 2022
01 2022
Historique:
revised:
08
12
2021
received:
26
08
2021
accepted:
09
12
2021
entrez:
3
5
2022
pubmed:
4
5
2022
medline:
6
5
2022
Statut:
ppublish
Résumé
Admitting hospitalized patients to off-service wards ("bedspacing") is common and may affect quality of care and patient outcomes. To compare in-hospital mortality, 30-day readmission to general internal medicine (GIM), and hospital length-of-stay among GIM patients admitted to GIM wards or bedspaced to off-service wards. Retrospective cohort study including all emergency department admissions to GIM between 2015 and 2017 at six hospitals in Ontario, Canada. We compared patients admitted to GIM wards with those who were bedspaced, using multivariable regression models and propensity score matching to control for patient and situational factors. Among 40,440 GIM admissions, 10,745 (26.6%) were bedspaced to non-GIM wards and 29,695 (73.4%) were assigned to GIM wards. After multivariable adjustment, bedspacing was associated with no significant difference in mortality (adjusted hazard ratio 0.95, 95% confidence interval [CI]: 0.86-1.05, p = .304), slightly shorter median hospital length-of-stay (-0.10 days, 95% CI:-0.20 to -0.001, p = .047) and lower 30-day readmission to GIM (adjusted OR 0.89, 95% CI: 0.83-0.95, p = .001). Results were consistent when examining each hospital individually and outcomes did not significantly differ between medical or surgical off-service wards. Sensitivity analyses focused on the highest risk patients did not exclude the possibility of harm associated with bedspacing, although adverse outcomes were not significantly greater. Overall, bedspacing was associated with no significant difference in mortality, slightly shorter hospital length-of-stay, and fewer 30-day readmissions to GIM, although potential harms in high-risk patients remain uncertain. Given that hospital capacity issues are likely to persist, future research should aim to understand how bedspacing can be achieved safely at all hospitals, perhaps by strengthening the selection of low-risk patients.
Sections du résumé
BACKGROUND
Admitting hospitalized patients to off-service wards ("bedspacing") is common and may affect quality of care and patient outcomes.
OBJECTIVE
To compare in-hospital mortality, 30-day readmission to general internal medicine (GIM), and hospital length-of-stay among GIM patients admitted to GIM wards or bedspaced to off-service wards.
DESIGN, PARTICIPANTS, AND MEASURES
Retrospective cohort study including all emergency department admissions to GIM between 2015 and 2017 at six hospitals in Ontario, Canada. We compared patients admitted to GIM wards with those who were bedspaced, using multivariable regression models and propensity score matching to control for patient and situational factors.
KEY RESULTS
Among 40,440 GIM admissions, 10,745 (26.6%) were bedspaced to non-GIM wards and 29,695 (73.4%) were assigned to GIM wards. After multivariable adjustment, bedspacing was associated with no significant difference in mortality (adjusted hazard ratio 0.95, 95% confidence interval [CI]: 0.86-1.05, p = .304), slightly shorter median hospital length-of-stay (-0.10 days, 95% CI:-0.20 to -0.001, p = .047) and lower 30-day readmission to GIM (adjusted OR 0.89, 95% CI: 0.83-0.95, p = .001). Results were consistent when examining each hospital individually and outcomes did not significantly differ between medical or surgical off-service wards. Sensitivity analyses focused on the highest risk patients did not exclude the possibility of harm associated with bedspacing, although adverse outcomes were not significantly greater.
CONCLUSIONS
Overall, bedspacing was associated with no significant difference in mortality, slightly shorter hospital length-of-stay, and fewer 30-day readmissions to GIM, although potential harms in high-risk patients remain uncertain. Given that hospital capacity issues are likely to persist, future research should aim to understand how bedspacing can be achieved safely at all hospitals, perhaps by strengthening the selection of low-risk patients.
Types de publication
Journal Article
Multicenter Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
3-10Commentaires et corrections
Type : CommentIn
Informations de copyright
© 2022 Society of Hospital Medicine.
Références
Bai AD, Srivastava S, Tomlinson GA, Smith CA, Bell CM, Gill SS. Mortality of hospitalised internal medicine patients bedspaced to non‐internal medicine inpatient units: Retrospective cohort study. BMJ Qual Saf. 2018;27(1):11‐20. doi:10.1136/bmjqs-2017-006925
Liu J, Griesman J, Nisenbaum R, Bell CM. Quality of care of hospitalized internal medicine patients bedspaced to non‐internal medicine inpatient units. PLoS One. 2014;9:e106763. doi:10.1371/journal.pone.0106763
Kohn R, Harhay MO, Bayes B, et al. Influence of bedspacing on outcomes of hospitalised medicine service patients: a retrospective cohort study. BMJ Qual Saf. 2020;30:116‐122. doi:10.1136/bmjqs-2019-010675
Song H, Tucker AL, Graue R, Moravick S, Yang JJ. Capacity pooling in hospitals: the hidden consequences of off‐service placement. Manage Sci. 2019;66(9):3825‐3842. doi:10.2139/ssrn.3186726
Verma AA, Guo Y, Kwan JL, et al. Patient characteristics, resource use and outcomes associated with general internal medicine hospital care: the General Medicine Inpatient Initiative (GEMINI) retrospective cohort study. CMAJ Open. 2017;5:E842‐E849. doi:10.9778/cmajo.20170097
Pasricha SV, Jung HY, Kushnir V, et al. Assessing the quality of clinical and administrative data extracted from hospitals: the General Medicine Inpatient Initiative (GEMINI) experience. medRxiv. 2020;62:851. doi:10.1101/2020.03.16.20036962
Healthcare Cost and Utilization Project. Beta Clinical Classifications Software (CCS) for ICD‐10‐CM/PCS. Agency for Healthcare Research and Quality. 2017. Accessed July 9, 2018. https://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp#pubs
Verma AA, Guo Y, Kwan JL, et al. Prevalence and costs of discharge diagnoses in inpatient general internal medicine: a multi‐center cross‐sectional study. J Gen Intern Med. 2018;33(11):1899‐1904. doi:10.1007/s11606-018-4591-7
Quan H, Li B, Couris CM, et al. Updating and validating the charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. Am J Epidemiol. 2011;173(6):676‐682. doi:10.1093/aje/kwq433
Escobar GJ, Greene JD, Scheirer P, et al. Risk‐adjusting hospital inpatient mortality using automated inpatient, outpatient, and laboratory databases. Med Care. 2008;46(3):232‐239. doi:10.1097/MLR.0b013e3181589bb6
van Walraven C, Escobar GJ, Greene JD, et al. The Kaiser Permanente inpatient risk adjustment methodology was valid in an external patient population. J Clin Epidemiol. 2010;63(7):798‐803. doi:10.1016/j.jclinepi.2009.08.020
Zhou B, Fine J, Laird G. Goodness‐of‐fit test for proportional subdistribution hazards model. Stat Med. 2013;32:3804‐3811. doi:10.1002/sim.5815
Grambsch PM, Therneau TM. Proportional hazards tests and diagnostics based on weighted residuals. Biometrika. 1994;81(3):515‐526. doi:10.1093/biomet/81.3.515
Koenker R. Quantile Regression. Cambridge University Press; 2005. doi:10.1017/CBO9780511754098
Huber P. Behavior of maximum likelihood estimates under nonstandard conditions. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1967.
Austin PC. Optimal caliper widths for propensity‐score matching when estimating differences in means and differences in proportions in observational studies. Pharm Stat. 2011;10(2):150‐161. doi:10.1002/pst.433
Ho DE, Imai K, King G, et al. MatchIt: nonparametric preprocessing for parametric causal inference. J Stat Softw. 2011;42(8):1‐28. doi:10.18637/jss.v042.i08
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(6):1228‐1234. doi:10.1080/03610910902859574
La Regina M, Guarneri F, Romano E, et al. What quality and safety of care for patients admitted to clinically inappropriate wards: a systematic review. J Gen Intern Med. 2019;34:1314‐1321. doi:10.1007/s11606-019-05008-4
Staples JA, Thiruchelvam D, Redelmeier DA. Site of hospital readmission and mortality: a population‐based retrospective cohort study. CMAJ Open. 2014;2(2):E77‐E85. doi:10.9778/cmajo.20130053