Modeling acute care utilization: practical implications for insomnia patients

Machine learning models can help improve health care services. However, they need to be practical to gain wide-adoption. In this study, we investigate the practical utility of different data modalities and cohort segmentation strategies when designing models for emergency department (ED) and inpatient hospital (IH) visits. The data modalities include socio-demographics, diagnosis and medications. Segmentation compares a cohort of insomnia patients to a cohort of general non-insomnia patients under varying age and disease severity criteria. Transfer testing between the two cohorts is introduced to demonstrate that an insomnia-specific model is not necessary when predicting future ED visits, but may have merit when predicting IH visits especially for patients with an insomnia diagnosis. The results also indicate that using both diagnosis and medications as a source of data does not generally improve model performance and may increase its overhead. Based on these findings, the proposed evaluation methodologies are recommended to ascertain the utility of disease-specific models in addition to the traditional intra-cohort testing.

Automated summary

This study investigates the practical utility of different data modalities and cohort segmentation strategies in designing models for emergency department (ED) and inpatient hospital (IH) visits. The results show that designing an insomnia-specific model is not necessary for predicting ED visits, but may be beneficial for predicting IH visits, particularly for patients with an insomnia diagnosis. Additionally, using both diagnosis and medications as data sources does not generally improve model performance and may increase overhead.