Prediction of Disposition Within 48 Hours of Hospital Admission Using Patient Mobility Scores

Document Type

Article

Publication Date

12-18-2019

Publication Title

Journal of Hospital Medicine

Volume

15

Issue

9

First page number:

540

Last page number:

543

Abstract

Delayed 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.

Keywords

Delayed hospital discharges; Hospital patients; Hospital-acquired mobility loss; Patient mobility

Disciplines

Medicine and Health Sciences | Patient Safety | Public Health

Language

English

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