Early Machine-human Interface around Sepsis Severity Identification: From Diagnosis to Improved Management?

Authors

  • Vikas Bansal Department Critical Care Medicine, Mayo Clinic, Jacksonville, FL
  • Emir Festić Department Critical Care Medicine, Mayo Clinic, Jacksonville, FL
  • Muhammad A. Mangi Department Critical Care Medicine, Mayo Clinic, Jacksonville, FL
  • Nicholl A. Decicco Department of Emergency Medicine, Mayo Clinic, Jacksonville, FL
  • Ashley N. Reid Department of Emergency Medicine, Mayo Clinic, Jacksonville, FL
  • Elizabeth L. Gatch Department of Emergency Medicine, Mayo Clinic, Jacksonville, FL
  • James M. Naessens Center for the Science of Healthcare Delivery, Mayo Clinic, Jacksonville, FL, Health Care Policy and Research, Mayo Clinic, Rochester, MN
  • Pablo Moreno-Franco Department of Transplant Critical Care Medicine, Mayo Clinic, Jacksonville, FL

DOI:

https://doi.org/10.5644/ama2006-124.212

Keywords:

Computerized decision, support, Sepsis, Algorithm

Abstract

Objective. To investigate the statistical measures of the performance of 2 interventions: a) early sepsis identification by a computerized sepsis “sniffer” algorithm (CSSA) in the emergency department (ED) and b) human decision to activate a multidisciplinary early resuscitation sepsis and shock response team (SSRT).

Methods. This study used a prospective and historical cohort study design to evaluate the performance of two interventions.

Intervention. A computerized sepsis sniffer algorithm (CSSA) to aid in early diagnosis and a multidisciplinary sepsis and shock response team (SSRT) to improve patient care by increasing compliance with Surviving Sepsis Campaign (SSC) bundles.

Results. The CSSA yielded a sensitivity of 100% (95% CI, 99.13-100%) and a specificity of 96.2% (95% CI, 95.55-96.45%) to identifying sepsis in the ED (Table 1). The SSRT resource was activated appropriately in 34.1% (86/252) of patients meeting severe sepsis or septic shock criteria; the SSRT was inappropriately activated only three times in sepsis-only patients. In 53% (134/252) of cases meeting criteria for SSRT activation, the critical care team was consulted as opposed to activating the SSRT resource.

Conclusion: Our two-step machine-human interface approach to patients with sepsis utilized an outstandingly sensitive and specific electronic tool followed by more specific human decision-making.

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Published

2018-06-25

How to Cite

Bansal, V., Festić, E., Mangi, M. A., Decicco, N. A., Reid, A. N., Gatch, E. L., Naessens, J. M., & Moreno-Franco, P. (2018). Early Machine-human Interface around Sepsis Severity Identification: From Diagnosis to Improved Management?. Acta Medica Academica, 47(1), 27–38. https://doi.org/10.5644/ama2006-124.212

Issue

Section

Clinical Science