Using machine learning algorithms to guide rehabilitation planning for home care clients

This study is a secondary analysis of data on 24,724 longer-term clients from eight home care programs in Ontario. Data were collected with the RAI-HC assessment system, in which the Activities of Daily Living Clinical Assessment Protocol (ADLCAP) is used to identify clients with rehabilitation potential. For study purposes, a client is defined as having rehabilitation potential if there was: i) improvement in ADL functioning, or ii) discharge home. SVM and KNN results are compared with those obtained using the ADLCAP. For comparison, the machine learning algorithms use the same functional and health status indicators as the ADLCAP.The KNN and SVM algorithms achieved similar substantially improved performance over the ADLCAP, although false positive and false negative rates were still fairly high (FP > .18, FN > .34 versus FP > .29, FN. > .58 for ADLCAP). Results are used to suggest potential revisions to the ADLCAP.Machine learning algorithms achieved superior predictions than the current protocol. Machine learning results are less readily interpretable, but can also be used to guide development of improved clinical protocols.Targeting older clients for rehabilitation is a clinical challenge and a research priority [1]. For clients being assessed for home care services, the decision to provide rehabilitation (especially physical or occupational therapy) has major implications for the client's future quality of life and independence, as well as major resource implications. There is considerable evidence of the feasibility and effectiveness of rehabilitation in home-based settings [2-5]; there is also evidence that many home care clients who would benefit from rehabilitation services do not receive them [6].Resource constraints will inevitably limit the provision of rehabilitation services, but gaps in service also reflect gaps and shortcomings in the management and use of available health information. More appropriate targeting of rehabilitation therapy could be ach