GIM, a dataset for predicting patient deterioration in the General Internal Medicine ward

@article{Health Data Nexus-smh-gim-1.0.1,
  author = {{(dev shell)}, Rutvik},
  title = {{GIM, a dataset for predicting patient deterioration in the General Internal Medicine ward}},
  journal = {{Health Data Nexus}},
  year = {2023},
  month = mar,
  note = {Version 1.0.1},
  doi = {10.57764/1w7f-kb56},
  url = {https://doi.org/10.57764/1w7f-kb56}
}

Abstract

The Data Science and Advanced Analytics (DSAA) team at Unity Health Toronto has developed and evaluated advanced patient monitoring and decision support systems to improve the efficiency, accuracy, and timeliness of clinical decision-making on the General Internal Medicine (GIM) inpatient ward at St. Michael’s Hospital. The GIM dataset was created through this work, and is comprised of de-identified health related data associated with over 22,000 patient encounters for 14,000 unique patients who were admitted under the GIM service at St. Michael’s Hospital between 2011 and 2019. The dataset was sourced from three distinct systems (Electronic Health Records, the Admit Discharge Transfer System and the Medication Administration Check System). Pre-processed datasets aggregating observations into fixed time windows are provided for convenience. A raw untransformed data set is also provided for researchers who wish to apply their own data transformations and includes demographics and outcome tables from the processed data. Patient outcomes available include ICU transfer, death, palliative entry, palliative discharge, and hospital discharge.

Ethics

The real dataset is governed by Unity Health REB protocol #21-206. This development shell contains no human-subject data of any kind.