Feasibility and indicator outcomes using computerized clinical decision support in pediatric traumatic brain injury anesthesia care

2019 ◽  
Vol 29 (3) ◽  
pp. 271-279 ◽  
Author(s):  
Ashley A. Colletti ◽  
Taniga Kiatchai ◽  
Vivian H. Lyons ◽  
Bala G. Nair ◽  
Rosemary M. Grant ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Decision Support ◽  
Brain Injury ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Anesthesia Care ◽  
Pediatric Traumatic Brain Injury

scholarly journals Development and Feasibility of a Real-Time Clinical Decision Support System for Traumatic Brain Injury Anesthesia Care

2017 ◽  
Vol 26 (01) ◽  
pp. 80-96 ◽  
Author(s):  
Taniga Kiatchai ◽  
Ashley Colletti ◽  
Vivian Lyons ◽  
Rosemary Grant ◽  
Monica Vavilala ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Decision Support ◽  
Brain Injury ◽  
Decision Support System ◽  
Real Time ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Anesthesia Care

Summary Background: Real-time clinical decision support (CDS) integrated with anesthesia information management systems (AIMS) can generate point of care reminders to improve quality of care. Objective: To develop, implement and evaluate a real-time clinical decision support system for anesthetic management of pediatric traumatic brain injury (TBI) patients undergoing urgent neurosurgery. Methods: We iteratively developed a CDS system for pediatric TBI patients undergoing urgent neurosurgery. The system automatically detects eligible cases and evidence-based key performance indicators (KPIs). Unwanted clinical events trigger and display real-time messages on the AIMS computer screen. Main outcomes were feasibility of detecting eligible cases and KPIs, and user acceptance. Results: The CDS system was triggered in 22 out of 28 (79%) patients. The sensitivity of detecting continuously sampled KPIs reached 93.8%. For intermittently sampled KPIs, sensitivity and specificity reached 90.9% and 100%, respectively. 88% of providers reported that CDS helped with TBI anesthesia care. Conclusions: CDS implementation is feasible and acceptable with a high rate of case capture and appropriate generation of alert and guidance messages for TBI anesthesia care.

Clinical Decision Support Intervention and Time to Imaging in Older Patients with Traumatic Brain Injury

2020 ◽  
Vol 231 (3) ◽  
pp. 361-367.e2
Author(s):  
Arthur S. Nguyen ◽  
Simon Yang ◽  
Brian V. Thielen ◽  
Kristina Techar ◽  
Regina M. Lorenzo ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Decision Support ◽  
Brain Injury ◽  
Clinical Decision Support ◽  
Older Patients ◽  
Clinical Decision ◽  
Support Intervention ◽  
Decision Support Intervention

scholarly journals MP38: Barriers and facilitators to physician use of computerized clinical decision support for mild traumatic brain injury and suspected pulmonary embolism

CJEM ◽  
2018 ◽  
Vol 20 (S1) ◽  
pp. S54-S54
Author(s):  
S. Arnold ◽  
D. Grigat ◽  
J. E. Andruchow ◽  
A. D. McRae ◽  
G. Innes ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Pulmonary Embolism ◽  
Decision Support ◽  
Brain Injury ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Barriers And Facilitators ◽  
Evidence Based ◽  
Emergency Physicians ◽  
Suspected Pulmonary Embolism

Introduction: As utilization of CT imaging has risen dramatically, evidence-based decision rules and clinical decision support (CDS) tools have been developed to avoid unnecessary CT use in low risk patients. However, their ability to change physician practice has been limited to date, with a number of barriers cited. The purpose of this study was to identify the barriers and facilitators to CDS adoption following a local CDS implementation. Methods: All emergency physicians at 4 urban EDs and 1 urgent care center were randomized to voluntary evidence-based CT imaging CDS for patients with either mild traumatic brain injury (MTBI) or suspected pulmonary embolism (PE). CDS was integrated into the computerized physician order entry (CPOE) software and triggered whenever a CT scan for an eligible patient was ordered. Physicians in both the MTBI and PE arms were ranked according to their CDS use, and a stratified sampling strategy was used to randomly select 5 physicians from each of the low, medium and high CDS use tertiles in each study arm. Each physician was invited to participate in a 30-minute semi-structured interview to assess the barriers and facilitators to CDS use. Physician responses were reported using a thematic analysis. Results: A total of 202 emergency physicians were randomized to receive CDS for either MTBI or PE, triggering CDS 4561 times, and interacting with the CDS software 1936 times (42.4%). Variation in CDS use ranged from 0% to 88.9% of eligible encounters by physician. Fourteen physicians have participated in interviews to date, and data collection is ongoing. Physicians reported that CDS use was facilitated by their confidence in the evidence supporting the CDS algorithms and that it provided documentation to reduce medico-legal risk. CDS use was not impeded by concerns over missed diagnoses or patient expectations. Reported barriers to CDS use included suboptimal integration into the CPOE such as the inability to auto-populate test results, it disrupted the ordering process and was time consuming. A common concern was that CDS was implemented too late in workflow as most decision making takes place at the bedside. Physicians did not view CDS as infringing on physician autonomy, however they advised that CDS should be a passive educational option and should not automatically trigger for all physicians and eligible encounters. Conclusion: Physicians were generally supportive of CDS integration into practice, and were confident that CDS is an evidence-based way to reduce unnecessary CT studies. However, concerns were raised about the optimal integration of CDS into CPOE and workflow. Physicians also stated a preference to a passive educational approach to CDS rather than an automatic triggering mechanism requiring clinical documentation.

scholarly journals Use of Traumatic Brain Injury Prediction Rules With Clinical Decision Support

PEDIATRICS ◽  
2017 ◽  
Vol 139 (4) ◽  
pp. e20162709 ◽  
Author(s):  
Peter S. Dayan ◽  
Dustin W. Ballard ◽  
Eric Tham ◽  
Jeff M. Hoffman ◽  
Marguerite Swietlik ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Decision Support ◽  
Brain Injury ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Prediction Rules ◽  
Injury Prediction

A traumatic brain injury prognostic model to support in-hospital triage in a low-income country: a machine learning–based approach

2020 ◽  
Vol 132 (6) ◽  
pp. 1961-1969 ◽  
Author(s):  
Thiago Augusto Hernandes Rocha ◽  
Cyrus Elahi ◽  
Núbia Cristina da Silva ◽  
Francis M. Sakita ◽  
Anthony Fuller ◽  
...  
Keyword(s):  
Machine Learning ◽  
Traumatic Brain Injury ◽  
Decision Support ◽  
Brain Injury ◽  
Clinical Decision Support ◽  
Prognostic Model ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Support Tool ◽  
Clinical Decision Support Tool

OBJECTIVETraumatic brain injury (TBI) is a leading cause of death and disability worldwide, with a disproportionate burden of this injury on low- and middle-income countries (LMICs). Limited access to diagnostic technologies and highly skilled providers combined with high patient volumes contributes to poor outcomes in LMICs. Prognostic modeling as a clinical decision support tool, in theory, could optimize the use of existing resources and support timely treatment decisions in LMICs. The objective of this study was to develop a machine learning–based prognostic model using data from Kilimanjaro Christian Medical Centre in Moshi, Tanzania.METHODSThis study is a secondary analysis of a TBI data registry including 3138 patients. The authors tested nine different machine learning techniques to identify the prognostic model with the greatest area under the receiver operating characteristic curve (AUC). Input data included demographics, vital signs, injury type, and treatment received. The outcome variable was the discharge score on the Glasgow Outcome Scale–Extended.RESULTSThe AUC for the prognostic models varied from 66.2% (k-nearest neighbors) to 86.5% (Bayesian generalized linear model). An increasing Glasgow Coma Scale score, increasing pulse oximetry values, and undergoing TBI surgery were predictive of a good recovery, while injuries suffered from a motor vehicle crash and increasing age were predictive of a poor recovery.CONCLUSIONSThe authors developed a TBI prognostic model with a substantial level of accuracy in a low-resource setting. Further research is needed to externally validate the model and test the algorithm as a clinical decision support tool.

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