Jan 28, 2022

At Nihon Kohden Digital Health Solutions Inc. we focus on making value added software, hardware, and data driven solutions that complement Nihon Kohden’s leading hardware technologies. Our technology connects the patients’ information to the medical professionals that are caring for them.

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CoMET® (Continuous Monitoring of Event Trajectories) is a predictive analytical tool that provides early warning of a patient’s risk for critical events, often up to 12 hours before symptoms are evident, allowing proactive diagnosis and treatment. The application displays continuously updated trajectories of a patient’s risk for developing adverse conditions such as sepsis, hemorrhage, respiratory failure, mortality, cardiovascular collapse, and others.

  • Outperforms common early warning scores in predicting significant adverse events. Use of the application in a surgical ICU was associated with a 50% reduction septic shock.i

  • Intuitive user interface integrates into the clinical setting, can be access remotely and is mobile enabled.

  • Supports a wide range of care areas (e.g., Acute Care, remote ICU, PICU, MICU, ED and others)

  • Interfaces with most hospital EMR and continuous monitoring systems

  • Delivers continuous risk estimates for future adverse clinical events that most impact patient outcomes.

Example Screen

CoMET image

System Overview
HK-HiQ Enterprise Gateway System diagram


i Ruminski, Caroline M., Matthew T. Clark, Douglas E. Lake, Rebecca R. Kitzmiller, Jessica Keim-Malpass, Matthew P. Robertson, Theresa R. Simons, J. Randall Moorman, and J. Forrest Calland. "Impact of predictive analytics based on continuous cardiorespiratory monitoring in a surgical and trauma intensive care unit." Journal of clinical monitoring and computing 33, no. 4 (2019): 703-711.

Blackwell, J. N., Keim-Malpass, J., Clark, M. T., Kowalski, R. L., Najjar, S. N., Bourque, J. M., Lake, D. E., & Moorman, J. R. (2020). Early Detection of In-Patient Deterioration: One Prediction Model Does Not Fit All. Critical care explorations, 2(5), e0116.

Glass, G., Hartka, T. R., Keim-Malpass, J., Enfield, K. B., & Clark, M. T. (2021). Dynamic data in the ED predict requirement for ICU transfer following acute care admission. Journal of clinical monitoring and computing, 35(3), 515-523.

Moss, T. J., Clark, M. T., Calland, J. F., Enfield, K. B., Voss, J. D., Lake, D. E., & Moorman, J. R. (2017). Cardiorespiratory dynamics measured from continuous ECG monitoring improves detection of deterioration in acute care patients: A retrospective cohort study. PloS one, 12(8), e0181448.

Spaeder, M. C., Moorman, J. R., Tran, C. A., Keim-Malpass, J., Zschaebitz, J. V., Lake, D. E., & Clark, M. T. (2019). Predictive analytics in the pediatric intensive care unit for early identification of sepsis: capturing the context of age. Pediatric research, 86(5), 655-661.