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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
Results
1 to 3 of 3 Research Studies DisplayedKalenderian E, Bangar S, Yansane A
Identifying contributing factors associated with dental adverse events through a pragmatic electronic health record-based root cause analysis.
This study’s objective was to analyze harmful dental adverse events (AEs) to assess potential contributing factors. Harmful AEs were defined as those that resulted in temporary moderate to severe harm, required hospitalization, or resulted in permanent moderate to severe harm. The authors classified potential contributing factors according to (1) who was involved (person), (2) what were they doing (tasks), (3) what tools/technologies were they using (tools/technologies), (4) where did the event take place (environment), (5) what organizational conditions contributed to the event? (organization), (6) patient (including parents), and (7) professional-professional collaboration. A second review was conducted by a blinded panel of dental experts to confirm the presence of an AE. A total of 59 cases at 2 dental institutions had 1 or more harmful AEs. The most common harmful AE was pain (27.1%) followed by nerve injury (16.9%), hard tissue injury (15.2%), and soft tissue injury (15.2%). The most common contribution factor was the care provider (training, supervision, and fatigue at 31.5%) followed by patient ((noncompliance, unsafe practices at home, low health literacy, 17.1%), and professional-professional collaboration (15.3%).
AHRQ-funded; HS027268.
Citation: Kalenderian E, Bangar S, Yansane A .
Identifying contributing factors associated with dental adverse events through a pragmatic electronic health record-based root cause analysis.
J Patient Saf 2023 Aug 1; 19(5):305-12. doi: 10.1097/pts.0000000000001122..
Keywords: Dental and Oral Health, Adverse Events, Electronic Health Records (EHRs), Health Information Technology (HIT), Medical Errors, Patient Safety
Liberman AL, Wang Z, Zhu Y
Optimizing measurement of misdiagnosis-related harms using Symptom-Disease Pair Analysis of Diagnostic Error (SPADE): comparison groups to maximize SPADE validity.
The purpose of this paper was to clarify features of the Symptom-Disease Pair Analysis of Diagnostic Error (SPADE) approach to accurately measure diagnostic errors to assure that researchers utilize this method to yield valid results, as well as improve the validity of SPADE and related approaches to quantify diagnostic error in medicine. The researchers describe four types of comparators (intra-group and inter-group), detailing the reason for selecting one over the other and conclusions that can be drawn from these comparative analyses.
AHRQ-funded; HS027614.
Citation: Liberman AL, Wang Z, Zhu Y .
Optimizing measurement of misdiagnosis-related harms using Symptom-Disease Pair Analysis of Diagnostic Error (SPADE): comparison groups to maximize SPADE validity.
Diagnosis 2023 Aug 1; 10(3):225-34. doi: 10.1515/dx-2022-0130..
Keywords: Diagnostic Safety and Quality, Medical Errors, Adverse Events, Electronic Health Records (EHRs), Health Information Technology (HIT), Patient Safety
Zhang J, Kummerfield E, Hultman G
Application of causal discovery algorithms in studying the nephrotoxicity of remdesivir using longitudinal data from the EHR.
Researchers analyzed the role of remdesivir in the mechanism and optimal treatment of the development of acute kidney injury (AKI) in the setting of COVID. Applying causal discovery machine learning techniques, they built multifactorial causal models of COVID-AKI; risk factors and renal function measures were represented in a temporal sequence using longitudinal data from Electronic Health Records. Their results indicated a need for assessment of renal function on second- and third-day use of remdesivir, and also showed that remdesivir may pose less risk to AKI than existing conditions of chronic kidney disease.
AHRQ-funded; HS024532.
Citation: Zhang J, Kummerfield E, Hultman G .
Application of causal discovery algorithms in studying the nephrotoxicity of remdesivir using longitudinal data from the EHR.
AMIA Annu Symp Proc 2023 Apr 29; 2022:1227-36..
Keywords: COVID-19, Electronic Health Records (EHRs), Health Information Technology (HIT), Medication, Adverse Drug Events (ADE), Adverse Events