Abstract
ObjectiveTo investigate whether the discharge destination for older adults can be predicted using functional mobility as measured by the Modified Elderly Mobility Scale (MEMS), associated with demographic and primary reason for admission variables.MethodsA retrospective cohort population audit of 257 patients admitted and discharged from four tertiary older adult rehabilitation wards in a three‐month period. A number of predictor variables were considered alongside the discharge destination.ResultsMultinomial statistical modelling established that MEMS prior to (P < 0.001), MEMS on completion (P = 0.009) of rehabilitation physiotherapy and primary reason for admission (P = 0.002) were significant variables to predict discharge destination. The model correctly predicted 71% of observed patient discharge destinations.ConclusionThe MEMS in conjunction with primary reason for admission was able to predict discharge destination with 71% accuracy in a heterogeneous population of older adults following rehabilitation.
Highlights
Hospitalisation rates increase as we age, and in particular, it has been found that hospitalisation rates increase shortly before older people are admitted to long-term care [1]
Lindenberg et al [11] found that diagnosis was not associated with discharge destination in a heterogeneous group of older patients undergoing rehabilitation who had previously lived at home
Studies have found that an array of functional, cognitive and social measures used together had a high chance of predicting discharge destination [11,12,13]
Summary
Hospitalisation rates increase as we age, and in particular, it has been found that hospitalisation rates increase shortly before older people are admitted to long-term care [1]. Kosse et al’s [4] systematic review found that early physical rehabilitation for hospitalised older adults resulted in functional benefits and reduced likelihood of discharge to residential care. Discharge planning for the older adult has been shown to reduce hospital readmissions, duration of hospital readmissions and all-cause mortality, and to improve quality of life [6,7,8]. Recent systematic reviews identified many social, physical and cognitive factors that predict discharge destination for stroke [9] and non-stroke patients [10]. Studies have found that an array of functional, cognitive and social measures used together had a high chance of predicting discharge destination [11,12,13]. From a clinician’s perspective, identifying a single, simple measure to assist in the prediction of discharge to a range of destinations would be more useful clinically than currently available tools that involve multiple assessments
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