National Healthcare Quality and Disparities Report
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Search All Research Studies
Topics
- Adverse Events (3)
- Ambulatory Care and Surgery (2)
- Children/Adolescents (1)
- Data (1)
- Diagnostic Safety and Quality (1)
- (-) Electronic Health Records (EHRs) (6)
- (-) Healthcare-Associated Infections (HAIs) (6)
- Health Information Technology (HIT) (6)
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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 6 of 6 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
Bucher BT, Shi J, Ferraro JP
Portable automated surveillance of surgical site infections using natural language processing: development and validation.
The authors presented the development and validation of a portable natural language processing (NLP) approach for automated surveillance of surgical site infections (SSIs). Patient clinical text notes from EHRs following surgical procedures from two independent healthcare systems were abstracted. The authors found that automated surveillance of SSIs can be achieved using NLP of clinical notes with high sensitivity and specificity.
AHRQ-funded; HS025776.
Citation: Bucher BT, Shi J, Ferraro JP .
Portable automated surveillance of surgical site infections using natural language processing: development and validation.
Ann Surg 2020 Oct;272(4):629-36. doi: 10.1097/sla.0000000000004133..
Keywords: Surgery, Healthcare-Associated Infections (HAIs), Electronic Health Records (EHRs), Health Information Technology (HIT), Quality Improvement, Quality of Care
Colborn KL, Bronsert M, Amioka E
Identification of surgical site infections using electronic health record data.
The objective of this study was to develop an algorithm for identifying surgical site infections (SSIs) using independent variables from electronic health record data and outcomes from the American College of Surgeons National Surgical Quality Improvement Program to supplement manual chart review. The investigators concluded that they identified a model that accurately identified SSIs. They indicated that the framework presented can be easily implemented by other American College of Surgeons National Surgical Quality Improvement Program-participating hospitals to develop models for enhancing surveillance of SSIs.
AHRQ-funded; HS026019.
Citation: Colborn KL, Bronsert M, Amioka E .
Identification of surgical site infections using electronic health record data.
Am J Infect Control 2018 Nov;46(11):1230-35. doi: 10.1016/j.ajic.2018.05.011..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Healthcare-Associated Infections (HAIs), Injuries and Wounds, Patient Safety, Surgery
Karavite DJ, Miller MW, Ramos MJ
User testing an information foraging tool for ambulatory surgical site infection surveillance.
Surveillance for surgical site infections (SSIs) after ambulatory surgery in children requires a detailed manual chart review to assess criteria defined by the National Health and Safety Network. Electronic health records (EHRs) impose an inefficient search process. Using text mining and business intelligence software, the authors developed an information foraging application, the SSI Workbench, to visually present which postsurgical encounters included SSI-related terms and synonyms, antibiotic, and culture orders. This study compares the Workbench and EHR.
AHRQ-funded; HS020921.
Citation: Karavite DJ, Miller MW, Ramos MJ .
User testing an information foraging tool for ambulatory surgical site infection surveillance.
Appl Clin Inform 2018 Oct;9(4):791-802. doi: 10.1055/s-0038-1675179..
Keywords: Surgery, Ambulatory Care and Surgery, Children/Adolescents, Electronic Health Records (EHRs), Health Information Technology (HIT), Healthcare-Associated Infections (HAIs), Patient Safety
Grundmeier RW, Xiao R, Ross RK
Grundmeier RW, Xiao R, Ross RK, Ramos MJ, Karavite DJ, Michel JJ, Gerber JS, et al. Identifying surgical site infections in electronic health data using predictive models,.
The objective of this study was to prospectively derive and validate a prediction rule for detecting cases warranting investigation for surgical site infections (SSI) after ambulatory surgery. The investigators concluded that electronic health record data can facilitate SSI surveillance with adequate sensitivity and positive predictive value.
AHRQ-funded; HS020921.
Citation: Grundmeier RW, Xiao R, Ross RK .
Grundmeier RW, Xiao R, Ross RK, Ramos MJ, Karavite DJ, Michel JJ, Gerber JS, et al. Identifying surgical site infections in electronic health data using predictive models,.
J Am Med Inform Assoc 2018 Sep;25(9):1160-66. doi: 10.1093/jamia/ocy075..
Keywords: Healthcare-Associated Infections (HAIs), Injuries and Wounds, Surgery, Electronic Health Records (EHRs), Health Information Technology (HIT), Risk, Patient Safety, Adverse Events, Ambulatory Care and Surgery
Hu Z, Melton GB, Arsoniadis EG
Strategies for handling missing clinical data for automated surgical site infection detection from the electronic health record.
Proper handling of missing data is important for many secondary uses of electronic health record (EHR) data. Data imputation methods can be used to handle missing data, but their use for postoperative complication detection is unclear. Overall, models with missing data imputation almost always outperformed reference models without imputation that included only cases with complete data for detection of SSI overall achieving very good average area under the curve values.
AHRQ-funded; HS024532.
Citation: Hu Z, Melton GB, Arsoniadis EG .
Strategies for handling missing clinical data for automated surgical site infection detection from the electronic health record.
J Biomed Inform 2017 Apr;68:112-20. doi: 10.1016/j.jbi.2017.03.009.
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Keywords: Data, Electronic Health Records (EHRs), Healthcare-Associated Infections (HAIs), Registries, Surgery, Injuries and Wounds, Health Information Technology (HIT), Quality Improvement, Quality of Care, Adverse Events