Developing and validating models for predicting nursing home admission using only RAI-HC instrument data

In recent years research has identified important predictors for nursing home admission (NHA). However, as far as we know, the previous risk models use complex variable sets from many sources and the output is a single risk value. The objective of this study was to develop an NHA risk model with a variable set from single data source and richer output information.

In this study, we developed a model selecting variables only from the RAI-HC (Resident Assessment Instrument - Home Care) system. Furthermore, we used principal component analysis and K-means clustering to target proper interventions for high-risk clients.

The performance of the model was close to the complex previous model (recall [Formula: see text] vs. [Formula: see text] and specificity [Formula: see text] vs. [Formula: see text]). For the risk clients, three intervention clusters (deficiency in physical functionality, deficiency in cognitive functionality and depression and mood disorders) were found.

The NHA risk model and intervention clusters are important because they enable the identification of proper interventions for the right clients. The fact that the model with RAI-HC data alone was accurate enough simplifies the integration of the NHA risk model into practice because it uses data from one system and the algorithm can be integrated easily into the source system.