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J Med Econ. 2017 Jun;20(6):646-651. doi: 10.1080/13696998.2017.1307203. Epub 2017 Apr 3.

Cost and mortality impact of an algorithm-driven sepsis prediction system.

Author information

1
a Dascena Inc. , Hayward , CA , USA.
2
b Department of Emergency Medicine , University of California San Francisco , San Francisco , CA , USA.
3
c Division of Critical Care Medicine, Department of Anesthesia and Perioperative Care , University of California San Francisco , San Francisco , CA , USA.
4
d Adult Critical Care, Advocate Health , Oak Brook , IL , USA.
5
e Department of Emergency Medicine , Kaiser Permanente South San Francisco Medical Center , South San Francisco , CA , USA.
6
f Department of Clinical Informatics, Stanford University School of Medicine , Stanford , CA , USA.
7
g Department of Emergency Medicine , Kaiser Permanente Redwood City Medical Center , Redwood City , CA , USA.
8
h Department of Bioengineering , University of California Berkeley , Berkeley , CA , USA.

Abstract

AIMS:

To compute the financial and mortality impact of InSight, an algorithm-driven biomarker, which forecasts the onset of sepsis with minimal use of electronic health record data.

METHODS:

This study compares InSight with existing sepsis screening tools and computes the differential life and cost savings associated with its use in the inpatient setting. To do so, mortality reduction is obtained from an increase in the number of sepsis cases correctly identified by InSight. Early sepsis detection by InSight is also associated with a reduction in length-of-stay, from which cost savings are directly computed.

RESULTS:

InSight identifies more true positive cases of severe sepsis, with fewer false alarms, than comparable methods. For an individual ICU with 50 beds, for example, it is determined that InSight annually saves 75 additional lives and reduces sepsis-related costs by $560,000.

LIMITATIONS:

InSight performance results are derived from analysis of a single-center cohort. Mortality reduction results rely on a simplified use case, which fixes prediction times at 0, 1, and 2 h before sepsis onset, likely leading to under-estimates of lives saved. The corresponding cost reduction numbers are based on national averages for daily patient length-of-stay cost.

CONCLUSIONS:

InSight has the potential to reduce sepsis-related deaths and to lead to substantial cost savings for healthcare facilities.

KEYWORDS:

Algorithm; Clinical decision support systems; Computer-assisted diagnosis; Length of stay; Medical informatics; Mortality reduction; Sepsis

PMID:
28294646
DOI:
10.1080/13696998.2017.1307203
[Indexed for MEDLINE]

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