Using longitudinal data and deep learning models to enhance resource allocation in home-based medical care

Abstract

Background

The aging population is driving increased healthcare demands and costs, prompting the need for effective home healthcare programs. Accurate patient assessment is essential for optimizing resource allocation and tailoring services.

Objective

This retrospective study explores the application of artificial intelligence (AI) in predicting home medical care stages to enhance care delivery.

Methods

Data from Taipei City Hospital (2015–2021) included inpatient, outpatient, and home medical care records. Three deep learning (DL) models—Transformer encoder-based, long short-term memory (LSTM), and gated recurrent unit (GRU)—were compared with three baseline machine learning (ML) models. Models were trained on 3, 5, and 10 consecutive visits for binary and multiclass classification. Performance was evaluated using accuracy, precision, recall, and the area under the receiver operating characteristic curve (AUC).

Results

The study included 4,343 patients with a mean age of 85.04 ± 11.47 years. While models trained on 10 visits generally exhibited higher performance, data from 5 visits were sufficient for accurate predictions. With five visits, the LSTM model achieved the highest AUC (0.908) for distinguishing between the absence (S0) and presence (S1–S3) of home medical care. Meanwhile, the Transformer achieved the best AUC (0.86) for classifying S0–S3, with individual stage AUCs of 0.90, 0.82, 0.81, and 0.94 for S0, S1, S2, and S3, respectively.

Conclusions

AI deep learning models show strong potential for accurately predicting home medical care stages. The best-performing model could be a promising tool for healthcare professionals to optimize resource allocation in home medical care settings.