scholarly journals A system uptake analysis and GUIDES checklist evaluation of the Electronic Asthma Management System: A point-of-care computerized clinical decision support system

2020 ◽  
Vol 27 (5) ◽  
pp. 726-737 ◽  
Author(s):  
Jeffrey Lam Shin Cheung ◽  
Natalie Paolucci ◽  
Courtney Price ◽  
Jenna Sykes ◽  
Samir Gupta ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Management System ◽  
Point Of Care ◽  
Action Plan ◽  
Asthma Management ◽  
Clinical Decision ◽  
User Feedback ◽  
Care Quality ◽  
System Usability Scale

Abstract Objective Computerized clinical decision support systems (CCDSSs) promise improvements in care quality; however, uptake is often suboptimal. We sought to characterize system use, its predictors, and user feedback for the Electronic Asthma Management System (eAMS)—an electronic medical record system–integrated, point-of-care CCDSS for asthma—and applied the GUIDES checklist as a framework to identify areas for improvement. Materials and Methods The eAMS was tested in a 1-year prospective cohort study across 3 Ontario primary care sites. We recorded system usage by clinicians and patient characteristics through system logs and chart reviews. We created multivariable models to identify predictors of (1) CCDSS opening and (2) creation of a self-management asthma action plan (AAP) (final CCDSS step). Electronic questionnaires captured user feedback. Results Over 1 year, 490 asthma patients saw 121 clinicians. The CCDSS was opened in 205 of 1033 (19.8%) visits and an AAP created in 121 of 1033 (11.7%) visits. Multivariable predictors of opening the CCDSS and producing an AAP included clinic site, having physician-diagnosed asthma, and presenting with an asthma- or respiratory-related complaint. The system usability scale score was 66.3 ± 16.5 (maximum 100). Reported usage barriers included time and system accessibility. Discussion The eAMS was used in a minority of asthma patient visits. Varying workflows and cultures across clinics, physician beliefs regarding asthma diagnosis, and relevance of the clinical complaint influenced uptake. Conclusions Considering our findings in the context of the GUIDES checklist helped to identify improvements to drive uptake and provides lessons relevant to CCDSS design across diseases.

scholarly journals Utilization and effects of mobile electronic clinical decision support on pediatric asthma care quality in the emergency department and inpatient setting

JAMIA Open ◽  
2021 ◽  
Vol 4 (2) ◽  
Author(s):  
Ellen Kerns ◽  
Russell McCulloh ◽  
Sarah Fouquet ◽  
Corrie McDaniel ◽  
Lynda Ken ◽  
...  
Keyword(s):  
Emergency Department ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Pediatric Asthma ◽  
Clinical Decision ◽  
Care Quality ◽  
Asthma Care ◽  
Mixed Effect ◽  
National Quality ◽  
Electronic Clinical Decision Support

Abstract Objective To determine utilization and impacts of a mobile electronic clinical decision support (mECDS) on pediatric asthma care quality in emergency department and inpatient settings. Methods We conducted an observational study of a mECDS tool that was deployed as part of a multi-dimensional, national quality improvement (QI) project focused on pediatric asthma. We quantified mECDS utilization using cumulative screen views over the study period in the city in which each participating site was located. We determined associations between mECDS utilization and pediatric asthma quality metrics using mixed-effect logistic regression models (adjusted for time, site characteristics, site-level QI project engagement, and patient characteristics). Results The tool was offered to clinicians at 75 sites and used on 286 devices; cumulative screen views were 4191. Children’s hospitals and sites with greater QI project engagement had higher cumulative mECDS utilization. Cumulative mECDS utilization was associated with significantly reduced odds of hospital admission (OR: 0.95, 95% CI: 0.92–0.98) and higher odds of caregiver referral to smoking cessation resources (OR: 1.08, 95% CI: 1.01–1.16). Discussion We linked mECDS utilization to clinical outcomes using a national sample and controlling for important confounders (secular trends, patient case mix, and concomitant QI efforts). We found mECDS utilization was associated with improvements in multiple measures of pediatric asthma care quality. Conclusion mECDS has the potential to overcome barriers to dissemination and improve care on a broad scale. Important areas of future work include improving mECDS uptake/utilization, linking clinicians’ mECDS usage to clinical practice, and studying mECDS’s impacts on other common pediatric conditions.

Real-Time Clinical Decision Support at the Point of Care

2019 ◽  
pp. 125-133
Author(s):  
Ana Margarida Pereira ◽  
Cristina Jácome ◽  
Rita Amaral ◽  
Tiago Jacinto ◽  
João A Fonseca
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Clinical Decision Support ◽  
Point Of Care ◽  
Clinical Decision

Red Book Point-of-Care Clinical Decision Support Tool

2018 ◽  
Keyword(s):  
Decision Support ◽  
Infectious Diseases ◽  
Clinical Decision Support ◽  
Health Care Professionals ◽  
Point Of Care ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Support Tool ◽  
Pediatric Health Care ◽  
Flip Chart

This convenient flip chart provides pediatric health care professionals with point-of-care guidance on the assessment, prevention, and treatment of childhood infectious diseases. https://shop.aap.org/red-book-pediatric-infectious-diseases-clinical-decision-support-chart/

scholarly journals A Quality Improvement Initiative to Decrease Platelet Ordering Errors and a Proposed Model for Evaluating Clinical Decision Support Effectiveness

2019 ◽  
Vol 10 (03) ◽  
pp. 505-512
Author(s):  
Julia Whitlow Yarahuan ◽  
Amy Billet ◽  
Jonathan D. Hron
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Health Care Quality ◽  
Platelet Transfusion ◽  
Tertiary Care ◽  
Clinical Decision ◽  
Care Quality ◽  
Inpatient Service ◽  
The Impact ◽  
Order Set

Background and Objectives Clinical decision support (CDS) and computerized provider order entry have been shown to improve health care quality and safety, but may also generate previously unanticipated errors. We identified multiple CDS tools for platelet transfusion orders. In this study, we sought to evaluate and improve the effectiveness of those CDS tools while creating and testing a framework for future evaluation of other CDS tools. Methods Using a query of an enterprise data warehouse at a tertiary care pediatric hospital, we conducted a retrospective analysis to assess baseline use and performance of existing CDS for platelet transfusion orders. Our outcome measure was the percentage of platelet undertransfusion ordering errors. Errors were defined as platelet transfusion volumes ordered which were less than the amount recommended by the order set used. We then redesigned our CDS and measured the impact of our intervention prospectively using statistical process control methodology. Results We identified that 62% of all platelet transfusion orders were placed with one of two order sets (Inpatient Service 1 and Inpatient Service 2). The Inpatient Service 1 order set had a significantly higher occurrence of ordering errors (3.10% compared with 1.20%). After our interventions, platelet transfusion order error occurrence on Inpatient Service 1 decreased from 3.10 to 0.33%. Conclusion We successfully reduced platelet transfusion ordering errors by redesigning our CDS tools. We suggest that the use of collections of clinical data may help identify patterns in erroneous ordering, which could otherwise go undetected. We have created a framework which can be used to evaluate the effectiveness of other similar CDS tools.

scholarly journals Current Status and Future Prospects for Electronic Point-of-Care Clinical Decision Support in Diabetes Care

2012 ◽  
Vol 13 (2) ◽  
pp. 172-176 ◽  
Author(s):  
Patrick J. O’Connor ◽  
Jay R. Desai ◽  
John C. Butler ◽  
Elyse O. Kharbanda ◽  
JoAnn M. Sperl-Hillen
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Diabetes Care ◽  
Point Of Care ◽  
Clinical Decision ◽  
Current Status ◽  
Future Prospects

From Adverse Drug Event Detection to Prevention

2014 ◽  
Vol 53 (06) ◽  
pp. 482-492 ◽  
Author(s):  
P. McNair ◽  
V. Kilintzis ◽  
K. Skovhus Andersen ◽  
J. Niès ◽  
J.-C. Sarfati ◽  
...  
Keyword(s):  
Patient Safety ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Adverse Drug Event ◽  
Support Services ◽  
Medication Safety ◽  
Point Of Care ◽  
Clinical Decision ◽  
Substantial Contribution ◽  
Drug Event

Summary Background: Errors related to medication seriously affect patient safety and the quality of healthcare. It has been widely argued that various types of such errors may be prevented by introducing Clinical Decision Support Systems (CDSSs) at the point of care. Objectives: Although significant research has been conducted in the field, still medication safety is a crucial issue, while few research outcomes are mature enough to be considered for use in actual clinical settings. In this paper, we present a clinical decision support framework targeting medication safety with major focus on adverse drug event (ADE) prevention. Methods: The novelty of the framework lies in its design that approaches the problem holistically, i.e., starting from knowledge discovery to provide reliable numbers about ADEs per hospital or medical unit to describe their consequences and probable causes, and next employing the acquired knowledge for decision support services development and deployment. Major design features of the frame-work’s services are: a) their adaptation to the context of care (i.e. patient characteristics, place of care, and significance of ADEs), and b) their straightforward integration in the healthcare information technologies (IT) infrastructure thanks to the adoption of a service-oriented architecture (SOA) and relevant standards. Results: Our results illustrate the successful interoperability of the framework with two commercially available IT products, i.e., a Computerized Physician Order Entry (CPOE) and an Electronic Health Record (EHR) system, respectively, along with a Web prototype that is independent of existing health-care IT products. The conducted clinical validation with domain experts and test cases illustrates that the impact of the framework is expected to be major, with respect to patient safety, and towards introducing the CDSS functionality in practical use. Conclusions: This study illustrates an important potential for the applicability of the presented framework in delivering contextualized decision support services at the point of care and for making a substantial contribution towards ADE prevention. None-theless, further research is required in order to quantitatively and thoroughly assess its impact in medication safety.

scholarly journals Clinical Decision Support Tool and Rapid Point-of-Care Platform for Determining Disease Severity in Patients with COVID-19

2020 ◽  
Author(s):  
Michael P. McRae ◽  
Glennon W. Simmons ◽  
Nicolaos J. Christodoulides ◽  
Zhibing Lu ◽  
Stella K. Kang ◽  
...  
Keyword(s):  
Decision Support ◽  
Disease Severity ◽  
Clinical Decision Support ◽  
Severity Score ◽  
Troponin I ◽  
Point Of Care ◽  
Clinical Decision ◽  
Adverse Outcomes ◽  
Number Of Patients ◽  
Severity Scores

AbstractSARS-CoV-2 is the virus that causes coronavirus disease (COVID-19) which has reached pandemic levels resulting in significant morbidity and mortality affecting every inhabited continent. The large number of patients requiring intensive care threatens to overwhelm healthcare systems globally. Likewise, there is a compelling need for a COVID-19 disease severity test to prioritize care and resources for patients at elevated risk of mortality. Here, an integrated point-of-care COVID-19 Severity Score and clinical decision support system is presented using biomarker measurements of C-reactive protein (CRP), N-terminus pro B type natriuretic peptide (NT-proBNP), myoglobin (MYO), D-dimer, procalcitonin (PCT), creatine kinase–myocardial band (CK-MB), and cardiac troponin I (cTnI). The COVID-19 Severity Score combines multiplex biomarker measurements and risk factors in a statistical learning algorithm to predict mortality. The COVID-19 Severity Score was trained and evaluated using data from 160 hospitalized COVID-19 patients from Wuhan, China. Our analysis finds that COVID-19 Severity Scores were significantly higher for the group that died versus the group that was discharged with median (interquartile range) scores of 59 (40–83) and 9 (6–17), respectively, and area under the curve of 0.94 (95% CI 0.89– 0.99). These promising initial models pave the way for a point-of-care COVID-19 Severity Score system to impact patient care after further validation with externally collected clinical data. Clinical decision support tools for COVID-19 have strong potential to empower healthcare providers to save lives by prioritizing critical care in patients at high risk for adverse outcomes.

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