Scientific Reports (Feb 2023)

Modeling acute care utilization: practical implications for insomnia patients

  • Farid Chekani,
  • Zitong Zhu,
  • Rezaul Karim Khandker,
  • Jizhou Ai,
  • Weilin Meng,
  • Emma Holler,
  • Paul Dexter,
  • Malaz Boustani,
  • Zina Ben Miled

DOI
https://doi.org/10.1038/s41598-023-29366-6
Journal volume & issue
Vol. 13, no. 1
pp. 1 – 11

Abstract

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Abstract 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.