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Search All Research Studies
AHRQ Research Studies Date
Topics
- Adverse Drug Events (ADE) (1)
- (-) Adverse Events (6)
- (-) Data (6)
- Electronic Health Records (EHRs) (1)
- Healthcare-Associated Infections (HAIs) (1)
- Health Information Technology (HIT) (2)
- Hospitals (1)
- Injuries and Wounds (1)
- Medical Devices (1)
- Medical Errors (3)
- Medication (1)
- Patient Safety (5)
- Quality Improvement (1)
- Quality Measures (1)
- Quality of Care (2)
- Registries (1)
- Surgery (1)
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 DisplayedWang E, Kang H, Gong Y
Generating a health information technology event database from FDA MAUDE reports.
This study examined using a health information technology (HIT) event database to identify patient safety events (PSEs) or medical errors. The study used the FDA Manufacturer and User Facility Device Experience (MAUDE) database to extract HIT events. Classic and CNN models were utilized on a test set. The model was capable of identifying HIT event with about a 90% accuracy.
AHRQ-funded; HS022895.
Citation: Wang E, Kang H, Gong Y .
Generating a health information technology event database from FDA MAUDE reports.
Stud Health Technol Inform 2019 Aug 21;264:883-87. doi: 10.3233/shti190350..
Keywords: Health Information Technology (HIT), Medical Devices, Adverse Events, Data, Medical Errors, Patient Safety
Yao B, Kang H, Gong Y
Data quality assessment of narrative medication error reports.
This study examined the data quality of patient safety event (PSE) reports that are used to analyze the root causes of PSE. If the data quality is poor then the reporting and root cause analysis (RCA) will also be poor. Incomplete or missing data is the most prevalent problem in these reports. The researchers used an adapted taxonomy to assess the data quality of PSE reports, and extracted sample reports based on eight error types. The extracts were scored by experts. They found that most structured fields were ignored by reporters, but the narrative parts of the reports contained rich and valuable information. The results show that the adapted taxonomy could be a promising tool for report quality assessment and improvement.
AHRQ-funded; HS022895.
Citation: Yao B, Kang H, Gong Y .
Data quality assessment of narrative medication error reports.
Stud Health Technol Inform 2019 Aug 9;265:101-06. doi: 10.3233/shti190146..
Keywords: Adverse Drug Events (ADE), Medication, Medical Errors, Adverse Events, Data, Patient Safety
Liang C, Gong Y
Automated classification of multi-labeled patient safety reports: a shift from quantity to quality measure.
The capacity for extracting useful information from patient safety reports remains limited. This study investigated the multi-labeled nature of patient safety reports as a key to disclose the complex relations between many components during the courses and development of medical errors. The authors developed automated multi-label text classifiers to process patient safety reports. The experiments demonstrated feasibility and efficiency of a combination of multi-label algorithms in the benchmark comparison.
AHRQ-funded; HS022895.
Citation: Liang C, Gong Y .
Automated classification of multi-labeled patient safety reports: a shift from quantity to quality measure.
Stud Health Technol Inform 2017;245:1070-74..
Keywords: Adverse Events, Data, Patient Safety, Quality Measures
Liang C, Gong Y
Predicting harm scores from patient safety event reports.
The Harm Scale developed by the AHRQ is widely used in the US hospitals. However, recent studies have indicated a moderate to poor inter-rater reliability of the scale across a number of US hospitals. This study proposed that key information to identify and refine the severity of harm is contained in the narrative data in patient safety reports. The researchers found that using automated text classification to categorize harm score provided reduced subjective judgments and improved efficiency.
AHRQ-funded; HS022895.
Citation: Liang C, Gong Y .
Predicting harm scores from patient safety event reports.
Stud Health Technol Inform 2017;245:1075-79..
Keywords: Adverse Events, Data, Hospitals, Patient Safety
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
Kang H, Gong Y
A novel schema to enhance data quality of patient safety event reports.
In this study, the researchers designed a patient safety event (PSE) similarity searching model based on semantic similarity measures, and proposed a novel schema of PSE reporting system which can effectively learn from previous experiences and timely inform the subsequent actions. Their system will not only help promote the report qualities but also serve as a knowledge base and education tool to guide healthcare providers in terms of preventing the recurrence of PSEs.
AHRQ-funded; HS022895.
Citation: Kang H, Gong Y .
A novel schema to enhance data quality of patient safety event reports.
AMIA Annu Symp Proc 2017 Feb 10;2016:1840-49.
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Keywords: Quality of Care, Patient Safety, Data, Adverse Events, Medical Errors