TY - JOUR
T1 - Prediction of disposition within 48 hours of hospital admission using patient mobility scores
AU - Young, Daniel L.
AU - Colantuoni, Elizabeth
AU - Friedman, Lisa Aronson
AU - Seltzer, Jason
AU - Daley, Kelly
AU - Ye, Bingqing
AU - Brotman, Daniel J.
AU - Hoyer, Erik H.
N1 - Publisher Copyright:
© 2020 Society of Hospital Medicine
PY - 2020/9
Y1 - 2020/9
N2 - Delayed hospital discharges for patients needing validation sets. Compared with patients discharged rehabilitation in a postacute setting can exacerbate to home, patients discharged to a postacute facility hospital-acquired mobility loss, prolong functional were older (median, 64 vs 56 years old) and had recovery, and increase costs. Systematic measurement lower mobility scores at hospital admission (median, of patient mobility by nurses early during 32 vs 41). The final decision tree accurately classified hospitalization has the potential to help identify which the discharge location for 73% (95% CI, 67%-78%) patients are likely to be discharged to a postacute care of patients. This study emphasizes the value of facility versus home. To test the predictive ability of systematically measuring mobility in the hospital and this approach, a machine learning classification tree provides a simple decision tree to facilitate early method was applied retrospectively to a diverse sample discharge planning. Journal of Hospital Medicine of hospitalized patients (N = 761) using training and 2020;15:540-543.
AB - Delayed hospital discharges for patients needing validation sets. Compared with patients discharged rehabilitation in a postacute setting can exacerbate to home, patients discharged to a postacute facility hospital-acquired mobility loss, prolong functional were older (median, 64 vs 56 years old) and had recovery, and increase costs. Systematic measurement lower mobility scores at hospital admission (median, of patient mobility by nurses early during 32 vs 41). The final decision tree accurately classified hospitalization has the potential to help identify which the discharge location for 73% (95% CI, 67%-78%) patients are likely to be discharged to a postacute care of patients. This study emphasizes the value of facility versus home. To test the predictive ability of systematically measuring mobility in the hospital and this approach, a machine learning classification tree provides a simple decision tree to facilitate early method was applied retrospectively to a diverse sample discharge planning. Journal of Hospital Medicine of hospitalized patients (N = 761) using training and 2020;15:540-543.
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U2 - 10.12788/jhm.3332
DO - 10.12788/jhm.3332
M3 - Article
C2 - 31869298
AN - SCOPUS:85090792593
VL - 15
SP - 540
EP - 543
JO - Journal of hospital medicine (Online)
JF - Journal of hospital medicine (Online)
SN - 1553-5606
IS - 9
ER -