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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 DisplayedLiang 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
Couture B, Fagan M, Gershanik E
Towards analytics of the patient and family perspective: a case study and recommendations for data capture of safety and quality concerns.
Patient Family Relations (PFR) programs provide the opportunity to capture patient/family safety concerns in the hospital. This study analyzed PFR concern submissions over a 20 month period, as well as a comparison of structured data fields to those of the AHRQ Common Format. The authors identified statistically significant differences in rates of concern submissions, methods of submission, and role of submitter across patient populations.
AHRQ-funded; HS023535.
Citation: Couture B, Fagan M, Gershanik E .
Towards analytics of the patient and family perspective: a case study and recommendations for data capture of safety and quality concerns.
AMIA Annu Symp Proc 2017 Apr 16;2017:615-24..
Keywords: Data, Quality of Care, Hospitals, 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.
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
Warren DK, Nickel KB, Wallace AE
Can additional information be obtained from claims data to support surgical site infection diagnosis codes?
The authors sought to confirm a claims algorithm to identify surgical site infections (SSIs) by examining the presence of clinically expected SSI treatment. They found that over 94% of patients identified by their claims algorithm as having an SSI received clinically expected treatment for infection, including antibiotics, surgical treatment, and culture, suggesting that this algorithm has very good positive predictive value. They concluded that their method may facilitate retrospective SSI surveillance and comparison of SSI rates across facilities and providers.
AHRQ-funded; HS019713.
Citation: Warren DK, Nickel KB, Wallace AE .
Can additional information be obtained from claims data to support surgical site infection diagnosis codes?
Infect Control Hosp Epidemiol 2014 Oct;35 Suppl 3:S124-32. doi: 10.1086/677830.
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Keywords: Data, Healthcare-Associated Infections (HAIs), Patient Safety, Surgery, Injuries and Wounds, Adverse Events