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Reducing patient mortality, length of stay and readmissions through machine learning-based sepsis prediction in the emergency department, intensive care unit and hospital floor units
  1. Andrea McCoy1,
  2. Ritankar Das2
  1. 1 Cape Regional Medical Center, Cape May Court House, New Jersey, USA
  2. 2 Dascena, Hayward, California, USA
  1. Correspondence to Mr Ritankar Das; ritankar{at}dascena.com

Abstract

Introduction Sepsis management is a challenge for hospitals nationwide, as severe sepsis carries high mortality rates and costs the US healthcare system billions of dollars each year. It has been shown that early intervention for patients with severe sepsis and septic shock is associated with higher rates of survival. The Cape Regional Medical Center (CRMC) aimed to improve sepsis-related patient outcomes through a revised sepsis management approach.

Methods In collaboration with Dascena, CRMC formed a quality improvement team to implement a machine learning-based sepsis prediction algorithm to identify patients with sepsis earlier. Previously, CRMC assessed all patients for sepsis using twice-daily systemic inflammatory response syndrome screenings, but desired improvements. The quality improvement team worked to implement a machine learning-based algorithm, collect and incorporate feedback, and tailor the system to current hospital workflow.

Results Relative to the pre-implementation period, the post-implementation period sepsis-related in-hospital mortality rate decreased by 60.24%, sepsis-related hospital length of stay decreased by 9.55% and sepsis-related 30-day readmission rate decreased by 50.14%.

Conclusion The machine learning-based sepsis prediction algorithm improved patient outcomes at CRMC.

  • information technology
  • PDSA
  • quality improvement

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

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Footnotes

  • Contributors Both authors listed in this manuscript contributed to the design and implementation of this quality improvement initiative. AMC oversaw on-site implementation of the machine learning algorithm, patient safety measures, workflow integration and facilitation of clinician feedback. RD contributed to the implementation of the machine learning algorithm, as well as updates made to the algorithm during the quality improvement initiative. RD and AMC contributed to data collection and analysis. Both authors assisted in drafting and editing of the manuscript. Both authors have had the opportunity to draft and revise this manuscript and have approved it in this final form.

  • Competing interests RD is an employee of Dascena.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data sharing statement No data obtained from Cape Regional Medical Center in this study can be shared or made available for open access.

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