The Philips eICU program is a transformational critical care telehealth program that delivers need-to-know information to caregivers, empowering them to care for the patients. It is a supplement — not a replacement — to the bedside team, and the data utilized by the remote caregivers is archived for research purposes. Through this work, it generated a large database which has the potential for facilitating additional research initiatives on patient outcomes, trends, and other best practice protocols in use today at most healthcare facilities.
The eICU Collaborative Research Database is a subset of a research data repository maintained by eRI. A stratified random sample of patients was used to select patients for inclusion in the public dataset. The selection was done as follows: first, all hospital discharges between 2014 and 2015 were identified, and a single index stay for each unique patient was extracted. The proportion of index stays in each hospital from the eRI data repository was used to perform a stratified sample of patient index stays based upon hospital; the aim was to maintain the distribution of the first ICU stays across the hospitals in the dataset. After a patient index stay was selected, all subsequent stays for that patient were also included in the dataset, regardless of the admitting hospital. A small proportion of patients only had stays in step down units or low acuity units, and these stays were removed.
The database comprises 200,859 patient unit encounters for 139,367 unique patients admitted between 2014 and 2015. Patients were admitted to one of 335 units at 208 hospitals located throughout the US.
The vitalPeriodic table contains data derived directly from these bedside monitors. Unlike other data elements in the database, the data collected in these tables are not entered or validated by providers of care: the periodic and aperiodic vital sign data have been automatically derived and archived with no human verification.
It is important to mention that coverage values do not necessarily represent reliability of data collection as the expected prevalence of documentation for each table varies. Coverage groups are: none (0%), low (0-20%), medium (20-60%), high (60-80%), and excellent (80-100%).