National Healthcare Quality and Disparities Report
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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
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1 to 2 of 2 Research Studies DisplayedZhu Y, Simon GJ, Wick EC
Applying machine learning across sites: external validation of a surgical site infection detection algorithm.
Surgical complications have tremendous consequences and costs. Complication detection is important for quality improvement, but traditional manual chart review is burdensome. Automated mechanisms are needed to make this more efficient. The purpose of the study was to understand the generalizability of a machine learning algorithm between sites; automated surgical site infection (SSI) detection algorithms developed at one center were tested at another distinct center.
AHRQ-funded; HS024532.
Citation: Zhu Y, Simon GJ, Wick EC .
Applying machine learning across sites: external validation of a surgical site infection detection algorithm.
J Am Coll Surg 2021 Jun;232(6):963-71.e1. doi: 10.1016/j.jamcollsurg.2021.03.026..
Keywords: Healthcare-Associated Infections (HAIs), Surgery, Adverse Events, Diagnostic Safety and Quality, Electronic Health Records (EHRs), Health Information Technology (HIT), Quality Improvement, Quality of Care
Aasen DM, Bronsert Rozeboom, PD
Relationships between predischarge and postdischarge infectious complications, length of stay, and unplanned readmissions in the ACS NSQIP database.
This study looked at the relationships between predischarge and postdischarge infectious complications, length of stay, and unplanned hospital readmissions after surgery. Data from the American College of Surgeons National Surgical Quality Improvement database from 2012 to 2017 across nine surgical specialties was used to analyze 30-day postoperative infectious complications including sepsis, surgical site infections, pneumonia, and urinary tract infections. Postoperative infectious complications were identified in 5.2% of cases, of which 59.8% were postdischarge. The specific postdischarge complications identified were 73.4% of surgical site infections, 34.9% of sepsis cases, 26.5% of pneumonia cases, and 53.2% of urinary tract infections. These postoperative infections were associated with an increased risk of readmission. Most infections were diagnosed postdischarge. The trend towards shorter length of stays postoperation also contribute to the increase in infections detected after discharge and the rate of unplanned related postoperative readmissions.
AHRQ-funded; HS026019.
Citation: Aasen DM, Bronsert Rozeboom, PD .
Relationships between predischarge and postdischarge infectious complications, length of stay, and unplanned readmissions in the ACS NSQIP database.
Surgery 2021 Feb;169(2):325-32. doi: 10.1016/j.surg.2020.08.009..
Keywords: Hospital Readmissions, Adverse Events, Healthcare-Associated Infections (HAIs), Infectious Diseases, Quality Improvement, Quality of Care, Surgery