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Research Studies is a monthly compilation of research articles funded by AHRQ or authored by AHRQ researchers and recently published in journals or newsletters.
Results1 to 4 of 4 Research Studies Displayed
Wang 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.
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.
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
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.
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.
Keywords: Quality of Care, Patient Safety, Data, Adverse Events, Medical Errors
Liang C, Gong Y
Enhancing patient safety event reporting by K-nearest neighbor classifier.
The debate on structured or unstructured data entry reveals not only a trade-off problem among data accuracy, completeness, and timeliness, but also a technical gap on text mining. The reesarchers suggested a text classification method for predicting subject categories. Their results demonstrated the feasibility of their system and indicated the advantage of such an application to raise data quality and clinical decision support in reporting patient safety events.
Citation: Liang C, Gong Y . Enhancing patient safety event reporting by K-nearest neighbor classifier. Stud Health Technol Inform 2015;218:40603.
Keywords: Adverse Events, Medical Errors, Patient Safety, Public Reporting, Clinical Decision Support (CDS), Health Information Technology (HIT), Data