• Daniel L Young, Elizabeth Colantuoni, FriedmanLisa AronsonLADivision of Pulmonary and Critical Care Medicine, School of Medicine, Johns Hopkins University, Baltimore, Maryland., Jason Seltzer, Kelly Daley, Bingqing Ye, Daniel J Brotman, and Erik H Hoyer.
• Department of Physical Medicine and Rehabilitation, Johns Hopkins University, Baltimore, Maryland.
• J Hosp Med. 2020 Sep 1; 15 (9): 540-543.

AbstractDelayed hospital discharges for patients needing rehabilitation in a postacute setting can exacerbate hospital-acquired mobility loss, prolong functional recovery, and increase costs. Systematic measurement of patient mobility by nurses early during hospitalization has the potential to help identify which patients are likely to be discharged to a postacute care facility versus home. To test the predictive ability of this approach, a machine learning classification tree method was applied retrospectively to a diverse sample of hospitalized patients (N = 761) using training and validation sets. Compared with patients discharged to home, patients discharged to a postacute facility were older (median, 64 vs 56 years old) and had lower mobility scores at hospital admission (median, 32 vs 41). The final decision tree accurately classified the discharge location for 73% (95% CI, 67%-78%) of patients. This study emphasizes the value of systematically measuring mobility in the hospital and provides a simple decision tree to facilitate early discharge planning.

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