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Search All Research Studies
Topics
- Adverse Events (2)
- Ambulatory Care and Surgery (1)
- Data (1)
- (-) Electronic Health Records (EHRs) (6)
- Healthcare-Associated Infections (HAIs) (3)
- Health Information Technology (HIT) (6)
- (-) Injuries and Wounds (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 DisplayedTignanelli CJ, Silverman GM, Lindemann EA
Natural language processing of prehospital emergency medical services trauma records allows for automated characterization of treatment appropriateness.
Incomplete prehospital trauma care is a significant contributor to preventable deaths. Current databases lack timelines easily constructible of clinical events. Temporal associations and procedural indications are critical to characterize treatment appropriateness. Natural language processing (NLP) methods present a novel approach to bridge this gap. In this study, the investigators sought to evaluate the efficacy of a novel and automated NLP pipeline to determine treatment appropriateness from a sample of prehospital EMS motor vehicle crash records.
AHRQ-funded; HS026379.
Citation: Tignanelli CJ, Silverman GM, Lindemann EA .
Natural language processing of prehospital emergency medical services trauma records allows for automated characterization of treatment appropriateness.
J Trauma Acute Care Surg 2020 May;88(5):607-14. doi: 10.1097/ta.0000000000002598.
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Keywords: Trauma, Injuries and Wounds, 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
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
LaFleur J, Steenhoek CL, Horne J
Comparing fracture absolute risk assessment (FARA) tools: an osteoporosis clinical informatics tool to improve identification and care of men at high risk of first fracture.
The researchers compared 2 fracture absolute risk assessment (FARA) tools for use with electronic health records (EHRs) to determine which would more accurately identify patients known to be high risk for fracture. They found that absolute fracture risk estimation with the VA-FARA is more predictive of a first fracture than the WHO’s eFRAX in male veterans when used in an EHR-based population screening tool.
AHRQ-funded; HS018582.
Citation: LaFleur J, Steenhoek CL, Horne J .
Comparing fracture absolute risk assessment (FARA) tools: an osteoporosis clinical informatics tool to improve identification and care of men at high risk of first fracture.
Ann Pharmacother 2015 May;49(5):506-14. doi: 10.1177/1060028015572819..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Injuries and Wounds, Osteoporosis, Risk
Unni S, Yao Y, Milne N
An evaluation of clinical risk factors for estimating fracture risk in postmenopausal osteoporosis using an electronic medical record database.
The researchers sought to identify variables in an EMR database for calculating fracture risk Assessment (FRAX) score in a cohort of postmenopausal women, to estimate absolute fracture risk. They found that mean 10-year risk for any major fracture was 11.1 percent when bone mineral density (BMD) was used and 11.2 percent when BMI was used.
AHRQ-funded; HS0018582.
Citation: Unni S, Yao Y, Milne N .
An evaluation of clinical risk factors for estimating fracture risk in postmenopausal osteoporosis using an electronic medical record database.
Osteoporos Int 2015 Feb;26(2):581-7. doi: 10.1007/s00198-014-2899-7..
Keywords: Electronic Health Records (EHRs), Injuries and Wounds, Risk, Osteoporosis, Health Information Technology (HIT)