Pediatric Provider Utilization of a Clinical Decision Support Alert and Association with HIV Pre-exposure Prophylaxis Prescription Rates

2022 ◽  
Vol 13 (01) ◽  
pp. 030-036
Author(s):  
Carrie T. Chan ◽  
Megen Vo ◽  
Jennifer Carlson ◽  
Tzielan Lee ◽  
Marcello Chang ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Academic Community ◽  
Provider Education ◽  
Post Intervention ◽  
Support Tool ◽  
Hiv Test ◽  
Exposure Prophylaxis ◽  
Pediatric Providers

Abstract Objectives An electronic clinical decision support (CDS) alert can provide real-time provider support to offer pre-exposure prophylaxis (PrEP) to youth at risk for human immunodeficiency virus (HIV). The purpose of this study was to evaluate provider utilization of a PrEP CDS alert in a large academic-community pediatric network and assess the association of the alert with PrEP prescribing rates. Methods HIV test orders were altered for patients 13 years and older to include a hard-stop prompt asking if the patient would benefit from PrEP. If providers answered “Yes” or “Not Sure,” the CDS alert launched with options to open a standardized order set, refer to an internal PrEP specialist, and/or receive an education module. We analyzed provider utilization using a frequency analysis. The rate of new PrEP prescriptions for 1 year after CDS alert implementation was compared with the year prior using Fisher's exact test. Results Of the 56 providers exposed to the CDS alert, 70% (n = 39) responded “Not sure” to the alert prompt asking if their patient would benefit from PrEP, and 54% (n = 30) chose at least one clinical support tool. The PrEP prescribing rate increased from 2.3 prescriptions per 10,000 patients to 6.6 prescriptions per 10,000 patients in the year post-intervention (p = 0.02). Conclusion Our findings suggest a knowledge gap among pediatric providers in identifying patients who would benefit from PrEP. A hard-stop prompt within an HIV test order that offers CDS and provider education might be an effective tool to increase PrEP prescribing among pediatric providers.

scholarly journals Improving Primary Care Provider Practices in Youth Concussion Management

2017 ◽  
Vol 56 (9) ◽  
pp. 854-865 ◽  
Author(s):  
Kristy B. Arbogast ◽  
Allison E. Curry ◽  
Kristina B. Metzger ◽  
Ronni S. Kessler ◽  
Jeneita M. Bell ◽  
...  
Keyword(s):  
Primary Care ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Care Provider ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Post Intervention ◽  
Support Tool ◽  
Concussion Management ◽  
Clinical Decision Support Tool

Primary care providers are increasingly providing youth concussion care but report insufficient time and training, limiting adoption of best practices. We implemented a primary care–based intervention including an electronic health record–based clinical decision support tool (“SmartSet”) and in-person training. We evaluated consequent improvement in 2 key concussion management practices: (1) performance of a vestibular oculomotor examination and (2) discussion of return-to-learn/return-to-play (RTL/RTP) guidelines. Data were included from 7284 primary care patients aged 0 to 17 years with initial concussion visits between July 2010 and June 2014. We compared proportions of visits pre- and post-intervention in which the examination was performed or RTL/RTP guidelines provided. Examinations and RTL/RTP were documented for 1.8% and 19.0% of visits pre-intervention, respectively, compared with 71.1% and 72.9% post-intervention. A total of 95% of post-intervention examinations were documented within the SmartSet. An electronic clinical decision support tool, plus in-person training, may be key to changing primary care provider behavior around concussion care.

scholarly journals Challenges involved in establishing a web-based clinical decision support tool in community health centers

Healthcare ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 100488
Author(s):  
Rachel Gold ◽  
Mary Middendorf ◽  
John Heintzman ◽  
Joan Nelson ◽  
Patrick O'Connor ◽  
...  
Keyword(s):  
Decision Support ◽  
Community Health ◽  
Clinical Decision Support ◽  
Community Health Centers ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Health Centers ◽  
Web Based ◽  
Support Tool ◽  
Clinical Decision Support Tool

scholarly journals A multifaceted clinical decision support intervention to improve adherence to thromboprophylaxis guidelines

2021 ◽  
Author(s):  
Tessa Jaspers ◽  
Marjolijn Duisenberg-van Essenberg ◽  
Barbara Maat ◽  
Marc Durian ◽  
Roy van den Berg ◽  
...  
Keyword(s):  
Decision Support ◽  
Venous Thromboembolism ◽  
Clinical Decision Support ◽  
Guideline Adherence ◽  
Intervention Group ◽  
Clinical Decision ◽  
Multifaceted Intervention ◽  
Annual Number ◽  
Post Intervention ◽  
Mobile Phone Application

AbstractBackground Venous thromboembolism is a potentially fatal complication of hospitalisation, affecting approximately 3% of non-surgical patients. Administration of low molecular weight heparins to the appropriate patients adequately decreases venous thromboembolism incidence, but guideline adherence is notoriously low. Objective To determine the effect of a multifaceted intervention on thromboprophylaxis guideline adherence. The secondary objective was to study the effect on guideline adherence specifically in patients with a high venous thromboembolism risk. As an exploratory objective, we determined how many venous thromboembolisms may be prevented. Setting A Dutch general teaching hospital. Method A prospective study with a pre- and post-intervention measurement was conducted. A multifaceted intervention, consisting of Clinical Decision Support software, a mobile phone application, monitoring of duplicate anticoagulants and training, was implemented. Guideline adherence was assessed by calculating the Padua prediction and Improve bleeding score for each patient. The number of preventable venous thromboembolisms was calculated using the incidences of venous thromboembolism in patients with and without adequate thromboprophylaxis and extrapolated to the annual number of admitted patients. Main outcome measure Adherence to thromboprophylaxis guidelines in pre- and post-intervention measurements. Results 170 patients were included: 85 in both control and intervention group. The intervention significantly increased guideline adherence from 49.4 to 82.4% (OR 4.78; 95%CI 2.37–9.63). Guideline adherence in the patient group with a high venous thromboembolism risk also increased significantly from 54.5 to 84.3% (OR 2.46; 95%CI 1.31–4.62), resulting in the potential prevention of ± 261 venous thromboembolisms per year. Conclusions Our multifaceted intervention significantly increased thromboprophylaxis guideline adherence.

scholarly journals Reduced Length of Stay Using Clinical Decision Support Tool (ASAP) for Empiric Antibiotic Selection

2020 ◽  
Vol 41 (S1) ◽  
pp. s368-s368
Author(s):  
Mary Acree ◽  
Kamaljit Singh ◽  
Urmila Ravichandran ◽  
Jennifer Grant ◽  
Gary Fleming ◽  
...  
Keyword(s):  
Decision Support ◽  
Length Of Stay ◽  
Electronic Health Record ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Health Record ◽  
Antibiotic Selection ◽  
Support Tool ◽  
Empiric Antibiotic

Background: Empiric antibiotic selection is challenging and requires knowledge of the local antibiogram, national guidelines and patient-specific factors, such as drug allergy and recent antibiotic exposure. Clinical decision support for empiric antibiotic selection has the potential to improve adherence to guidelines and improve patient outcomes. Methods: At NorthShore University HealthSystem, a 4-hospital, 789 bed system, an automated point-of-care decision support tool referred to as Antimicrobial Stewardship Assistance Program (ASAP) was created for empiric antibiotic selection for 4 infectious syndromes: pneumonia, skin and soft-tissue infections, urinary tract infection, and intra-abdominal infection. The tool input data from the electronic health record, which can be modified by any user. Using an algorithm created with electronic health record data, antibiogram data, and national guidelines, the tool produces an antibiotic recommendation that can be ordered via a link to order entry. If the tool identifies a patient with a high likelihood for a multidrug-resistant infection, a consultation by an infectious diseases specialist is recommended. Utilization of the tool and associated outcomes were evaluated from July 2018 to May 2019. Results: The ASAP tool was executed by 140 unique, noninfectious diseases providers 790 times. The tool was utilized most often for pneumonia (194 tool uses), followed by urinary tract infection (166 tool uses). The most common provider type to use the tool was an internal medicine hospitalist. The tool increased adherence to the recommended antibiotic regimen for each condition. Antibiotic appropriateness was assessed by an infectious diseases physician. Antibiotics were considered appropriate when they were similar to the antibiotic regimen recommended by the ASAP. Inappropriate antibiotics were classified as broad or narrow. When antibiotic coverage was appropriate, hospital length of stay was statistically significantly shorter (4.8 days vs 6.8 days for broad antibiotics vs 7.4 days for narrow antibiotics; P < .01). No significant differences were identified in mortality or readmission. Conclusions: A clinical decision support tool in the electronic health record can improve adherence to recommended empiric antibiotic therapy. Use of appropriate antibiotics recommended by such a tool can reduce hospital length of stay.Funding: NoneDisclosures: None

scholarly journals The use of a clinical decision support tool to assess the risk of QT drug–drug interactions in community pharmacies

2021 ◽  
Vol 12 ◽  
pp. 204209862199609
Author(s):  
Florine A. Berger ◽  
Heleen van der Sijs ◽  
Teun van Gelder ◽  
Patricia M. L. A. van den Bemt
Keyword(s):  
Decision Support ◽  
Drug Interactions ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Community Pharmacies ◽  
Support Tool ◽  
Interacting Agents ◽  
Before And After ◽  
Structured Approach

Introduction: The handling of drug–drug interactions regarding QTc-prolongation (QT-DDIs) is not well defined. A clinical decision support (CDS) tool will support risk management of QT-DDIs. Therefore, we studied the effect of a CDS tool on the proportion of QT-DDIs for which an intervention was considered by pharmacists. Methods: An intervention study was performed using a pre- and post-design in 20 community pharmacies in The Netherlands. All QT-DDIs that occurred during a before- and after-period of three months were included. The impact of the use of a CDS tool to support the handling of QT-DDIs was studied. For each QT-DDI, handling of the QT-DDI and patient characteristics were extracted from the pharmacy information system. Primary outcome was the proportion of QT-DDIs with an intervention. Secondary outcomes were the type of interventions and the time associated with handling QT-DDIs. Logistic regression analysis was used to analyse the primary outcome. Results: Two hundred and forty-four QT-DDIs pre-CDS tool and 157 QT-DDIs post-CDS tool were included. Pharmacists intervened in 43.0% and 35.7% of the QT-DDIs pre- and post-CDS tool respectively (odds ratio 0.74; 95% confidence interval 0.49–1.11). Substitution of interacting agents was the most frequent intervention. Pharmacists spent 20.8 ± 3.5 min (mean ± SD) on handling QT-DDIs pre-CDS tool, which was reduced to 14.9 ± 2.4 min (mean ± SD) post-CDS tool. Of these, 4.5 ± 0.7 min (mean ± SD) were spent on the CDS tool. Conclusion: The CDS tool might be a first step to developing a tool to manage QT-DDIs via a structured approach. Improvement of the tool is needed in order to increase its diagnostic value and reduce redundant QT-DDI alerts. Plain Language Summary The use of a tool to support the handling of QTc-prolonging drug interactions in community pharmacies Introduction: Several drugs have the ability to cause heart rhythm disturbances as a rare side effect. This rhythm disturbance is called QTc-interval prolongation. It may result in cardiac arrest. For health care professionals, such as physicians and pharmacists, it is difficult to decide whether or not it is safe to proceed treating a patient with combinations of two or more of these QT-prolonging drugs. Recently, a tool was developed that supports the risk management of these QT drug–drug interactions (QT-DDIs). Methods: In this study, we studied the effect of this tool on the proportion of QT-DDIs for which an intervention was considered by pharmacists. An intervention study was performed using a pre- and post-design in 20 community pharmacies in The Netherlands. All QT-DDIs that occurred during a before- and after-period of 3 months were included. Results: Two hundred and forty-four QT-DDIs pre-implementation of the tool and 157 QT-DDIs post-implementation of the tool were included. Pharmacists intervened in 43.0% of the QT-DDIs before the tool was implemented and in 35.7% after implementation of the tool. Substitution of one of the interacting agents was the most frequent intervention. Pharmacists spent less time on handling QT-DDIs when the tool was used. Conclusion: The clinical decision support tool might be a first step to developing a tool to manage QT-DDIs via a structured approach.

scholarly journals Duplicate Laboratory Test Reduction Using a Clinical Decision Support Tool

2014 ◽  
Vol 141 (5) ◽  
pp. 718-723 ◽  
Author(s):  
Gary W. Procop ◽  
Lisa M. Yerian ◽  
Robert Wyllie ◽  
A. Marc Harrison ◽  
Kandice Kottke-Marchant
Keyword(s):  
Decision Support ◽  
Laboratory Test ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Support Tool ◽  
Clinical Decision Support Tool

41 Enhancing Burn Medical Care During a Disaster Using a Novel Augmented-Reality Application

2021 ◽  
Vol 42 (Supplement_1) ◽  
pp. S31-S31
Author(s):  
Sena Veazey ◽  
Maria SerioMelvin ◽  
David E Luellen ◽  
Angela Samosorn ◽  
Alexandria Helms ◽  
...  
Keyword(s):  
Decision Support ◽  
Augmented Reality ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Burn Injuries ◽  
Medical Decision ◽  
Support Tool ◽  
Burn Care ◽  
Clinical Decision Support Tool

Abstract Introduction In disaster or mass casualty situations, access to remote burn care experts, communication, or resources may be limited. Furthermore, burn injuries are complex and require substantial training and knowledge beyond basic clinical care. Development and use of decision support (DS) technologies may provide a solution for addressing this need. Devices capable of delivering burn management recommendations can enhance the provider’s ability to make decisions and perform interventions in complex care settings. When coupled with merging augmented reality (AR) technologies these tools may provide additional capabilities to enhance medical decision-making, visualization, and workflow when managing burns. For this project, we developed a novel AR-based application with enhanced integrated clinical practice guidelines (CPGs) to manage large burn injuries for use in different environments, such as disasters. Methods We identified an AR system that met our requirements to include portability, infrared camera, gesture and voice control, hands-free control, head-mounted display, and customized application development abilities. Our goal was to adapt burn CPGs to make use of AR concepts as part of an AR-enabled burn clinical decision support system supporting four sub-applications to assist users with specific interventional tasks relevant to burn care. We integrated relevant CPGs and a media library with photos and videos as additional references. Results We successfully developed a clinical decision support tool that integrates burn CPGs with enhanced capabilities utilizing AR technology. The main interface allows input of patient demographics and injuries with step-by-step guidelines that follow typical burn management care and workflow. There are four sub-applications to assist with these tasks, which include: 1) semi-automated burn wound mapping to calculate total body surface area; 2) hourly burn fluid titration and recommendations for resuscitation; 3) medication calculator for accurate dosing in preparation for procedures and 4) escharotomy instructor with holographic overlays. Conclusions We developed a novel AR-based clinical decision support tool for management of burn injuries. Development included adaptation of CPGs into a format to guide the user through burn management using AR concepts. The application will be tested in a prospective research study to determine the effectiveness, timeliness, and performance of subjects using this AR-software compared to standard of care. We fully expect that the tool will reduce cognitive workload and errors, ensuring safety and proper adherence to guidelines.

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