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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 DisplayedLacson R, Gujrathi I, Healey M
Closing the loop on unscheduled diagnostic imaging orders: a systems-based approach.
This study looked at the impact of implementing a tool called SCORE (System for Coordinating Orders for Radiology Exams), whose objective is to manage unscheduled orders for outpatient diagnostic imaging in an electronic health record (EHR) with embedded computerized physician order entry. The rate of unscheduled imaging orders was compared before SCORE (October 2017 to September 2018) and after (October 2018 to June 2019). There was a 49% reduction in unscheduled orders after SCORE implementation at a large academic institution.
AHRQ-funded; HS024722.
Citation: Lacson R, Gujrathi I, Healey M .
Closing the loop on unscheduled diagnostic imaging orders: a systems-based approach.
J Am Coll Radiol 2021 Jan;18(1 Pt A):60-67. doi: 10.1016/j.jacr.2020.09.031..
Keywords: Imaging, Diagnostic Safety and Quality, Electronic Health Records (EHRs), Health Information Technology (HIT), Patient Safety
Cochon LR, Kapoor N, Carrodeguas E
Variation in follow-up imaging recommendations in radiology reports: patient, modality, and radiologist predictors.
The purpose of this study was to determine the incidence and to identify factors associated with follow-up recommendations in radiology reports from multiple modalities, patient care settings, and imaging divisions. A trained algorithm classified 318,366 report; the findings indicate that substantial interradiologist variation exists in the probability of recommending a follow-up examination in a radiology report.
AHRQ-funded; HS024722.
Citation: Cochon LR, Kapoor N, Carrodeguas E .
Variation in follow-up imaging recommendations in radiology reports: patient, modality, and radiologist predictors.
Radiology 2019 Jun;291(3):700-07. doi: 10.1148/radiol.2019182826..
Keywords: Decision Making, Diagnostic Safety and Quality, Imaging, Patient Safety, Quality of Care, Quality Improvement
Ablordeppey EA, Drewry AM, Theodoro DL
Current practices in central venous catheter position confirmation by point of care ultrasound: a survey of early adopters.
Although routine chest radiographs (CXR) to verify correct central venous catheter (CVC) position and exclude pneumothorax are commonly performed, emerging evidence suggests that this practice can be replaced by point of care ultrasound (POCUS). POCUS is advantageous over CXR because it avoids radiation while verifying correct placement and lack of pneumothorax without delay. In this study, they aimed to describe the current clinical practice regarding POCUS alone for CVC position confirmation and pneumothorax exclusion as compared with chest radiography.
AHRQ-funded; R18 HS025052.
Citation: Ablordeppey EA, Drewry AM, Theodoro DL .
Current practices in central venous catheter position confirmation by point of care ultrasound: a survey of early adopters.
Shock 2019 May;51(5):613-18. doi: 10.1097/shk.0000000000001218..
Keywords: Imaging, Diagnostic Safety and Quality, Patient Safety
Lacson R, Cochon L, Ip I
Classifying safety events related to diagnostic imaging from a safety reporting system using a human factors framework.
This study measured the prevalence of safety events related to diagnostic imaging reported to an electronic safety reporting system. The authors evaluated reports all system reports from 2015 at an academic medical center. Out of 11,570 safety reports submitted, only 7% were related to diagnostic imaging. The adverse event was reported as either result communication or harm during the imaging procedure itself. The harms were rates from 0 to 4 by the reporter. Harms from 2-4 were considered as “potential harm."
AHRQ-funded; HS024722.
Citation: Lacson R, Cochon L, Ip I .
Classifying safety events related to diagnostic imaging from a safety reporting system using a human factors framework.
J Am Coll Radiol 2019 Mar;16(3):282-88. doi: 10.1016/j.jacr.2018.10.015..
Keywords: Adverse Events, Diagnostic Safety and Quality, Imaging, Patient Safety, Medical Errors
Carrodeguas E, Lacson R, Swanson W
Use of machine learning to identify follow-up recommendations in radiology reports.
The aims of this study were to assess follow-up recommendations in radiology reports, develop and assess traditional machine learning (TML) and deep learning (DL) models in identifying follow-up, and benchmark them against a natural language processing (NLP) system. The investigators concluded that TML and DL were feasible methods to identify follow-up recommendations. They suggest that these methods have great potential for near real-time monitoring of follow-up recommendations in radiology reports.
AHRQ-funded; HS024722.
Citation: Carrodeguas E, Lacson R, Swanson W .
Use of machine learning to identify follow-up recommendations in radiology reports.
J Am Coll Radiol 2019 Mar;16(3):336-43. doi: 10.1016/j.jacr.2018.10.020..
Keywords: Diagnostic Safety and Quality, Imaging, Patient Safety