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Search All Research Studies
Topics
- Antibiotics (1)
- (-) Clinical Decision Support (CDS) (5)
- Diagnostic Safety and Quality (3)
- Emergency Department (1)
- Emergency Medical Services (EMS) (1)
- Evidence-Based Practice (1)
- Health Information Technology (HIT) (3)
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- Medication (1)
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- (-) Sepsis (5)
AHRQ Research Studies
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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
Results
1 to 5 of 5 Research Studies DisplayedDutta S, McEvoy DS, Rubins DM
Clinical decision support improves blood culture collection before intravenous antibiotic administration in the emergency department.
This paper discusses the outcomes of using a clinical decision support (CDS) tool that was implemented in emergency departments (EDs) for sepsis patients to remind healthcare staff to take blood cultures before administration of intravenous (IV) antibiotics. The study compared timely blood culture collection outcomes prior to IV antibiotics for 54,538 adult ED patients 1 year before and after a CDS intervention implementation in the electronic health record. The baseline phase found that 46.1% had blood cultures prior to IV antibiotics, compared to 58.8% after the intervention. The CDS improved blood culture collection rates without increasing overutilization.
AHRQ-funded; HS02717.
Citation: Dutta S, McEvoy DS, Rubins DM .
Clinical decision support improves blood culture collection before intravenous antibiotic administration in the emergency department.
J Am Med Inform Assoc 2022 Sep 12;29(10):1705-14. doi: 10.1093/jamia/ocac115..
Keywords: Clinical Decision Support (CDS), Health Information Technology (HIT), Antibiotics, Emergency Department, Medication, Sepsis
Gale BM, Hall KK
The use of patient monitoring systems to improve sepsis recognition and outcomes: a systematic review.
This systematic review’s aim was to determine the impact of automated patient monitoring systems (PMSs) on sepsis recognition and outcomes. The review was conducted using articles published from 2008 through 2018. Nineteen articles were identified for inclusion with 4 systematic reviews and 15 individual studies. Study results for outcome measures were mixed, with more than half the studies showing a significant improvement in at least one outcome measure (eg mortality, intensive care unit length of stay, ICU transfer). Evidence for process measure improvement was of moderate strength across multiple hospital units.
AHRQ-funded; 233201500013I.
Citation: Gale BM, Hall KK .
The use of patient monitoring systems to improve sepsis recognition and outcomes: a systematic review.
J Patient Saf 2020 Sep;16(3S Suppl 1):S8-s11. doi: 10.1097/pts.0000000000000750..
Keywords: Clinical Decision Support (CDS), Sepsis, Diagnostic Safety and Quality, Screening, Outcomes, Patient-Centered Outcomes Research, Evidence-Based Practice
Bhattacharjee P, Edelson DP, Churpek MM
Identifying patients with sepsis on the hospital wards.
The goal of this review was to discuss recent advances in the detection of sepsis in patients on the hospital wards. The investigators discuss data highlighting the benefits and limitations of the systemic inflammatory response syndrome (SIRS) criteria for screening patients with sepsis, such as its low specificity, as well as newly described scoring systems, including the proposed role of the quick sepsis-related organ failure assessment (qSOFA) score.
AHRQ-funded; HS000078.
Citation: Bhattacharjee P, Edelson DP, Churpek MM .
Identifying patients with sepsis on the hospital wards.
Chest 2017 Apr;151(4):898-907. doi: 10.1016/j.chest.2016.06.020..
Keywords: Clinical Decision Support (CDS), Diagnostic Safety and Quality, Hospitalization, Sepsis
Taylor RA, Pare JR, Venkatesh AK
Prediction of in-hospital mortality in emergency department patients with sepsis: A local big data-driven, machine learning approach.
In this proof-of-concept study, a local, big data-driven, machine learning approach is compared to existing clinical decision rules (CDRs) and traditional analytic methods using the prediction of sepsis in-hospital mortality as the use case. It concluded that this approach outperformed existing CDRs as well as traditional analytic techniques for predicting in-hospital mortality of ED patients with sepsis.
AHRQ-funded; HS021271.
Citation: Taylor RA, Pare JR, Venkatesh AK .
Prediction of in-hospital mortality in emergency department patients with sepsis: A local big data-driven, machine learning approach.
Acad Emerg Med 2016 Mar;23(3):269-78. doi: 10.1111/acem.12876.
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Keywords: Emergency Medical Services (EMS), Mortality, Clinical Decision Support (CDS), Sepsis, Health Information Technology (HIT)
Makam AN, Nguyen OK, Auerbach AD
Diagnostic accuracy and effectiveness of automated electronic sepsis alert systems: a systematic review.
This review aimed to determine whether automated real-time electronic sepsis alerts can: (1) accurately identify sepsis and (2) improve process measures and outcomes. It found that automated sepsis alerts derived from electronic health data may improve care processes but tend to have poor positive predictive value and do not improve mortality or length of stay.
AHRQ-funded; HS022418.
Citation: Makam AN, Nguyen OK, Auerbach AD .
Diagnostic accuracy and effectiveness of automated electronic sepsis alert systems: a systematic review.
J Hosp Med 2015 Jun;10(6):396-402. doi: 10.1002/jhm.2347..
Keywords: Clinical Decision Support (CDS), Health Information Technology (HIT), Sepsis, Diagnostic Safety and Quality, Patient-Centered Outcomes Research