scholarly journals Reach, Adoption and Sustainability of a Mobile Clinical Decision Support Tool Paired with a National Quality Improvement Project Targeting Emergency Department Febrile Infant Management

2020 ◽  
Author(s):  
Ellen K Kerns ◽  
Russell McCulloh ◽  
Hongying Dai
Keyword(s):  
Quality Improvement ◽  
Clinical Practice ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Confounding Factors ◽  
Children's Hospitals ◽  
Improvement Project ◽  
Children’S Hospitals ◽  
National Quality

Abstract Background: Electronic clinical decision support (ECDS) tools are often developed within quality improvement (QI) projects to increase adherence with the latest clinical practice guidelines (CPGs).However, scalability and sustainability of ECDS beyond the time and location of their associated project are very limited. Deploying ECDS using a mobile app (mECDS) has shown the potential to be a viable method of overcoming these limitations. However, it is unclear what pattern the spread of uptake and use of such a tool might follow.Methods: In 2016, our team released a freely available mECDS as part of a national multi-site project entitled Reducing Variation in Infant Sepsis Evaluation (REVISE). For this study, we evaluated trends in weekly mECDS usage measures defined as 1) REVISE metric related screen views (MetricHits), 2) unique designated market areas (DMA) where the mECDS was used (Unique DMAs), 3) density of use or MetricHits/Unique DMAs (HitsPerDMA). Linear regressions were performed to examine the app usage trends measures across three time periods (during REVISE, 1-year post, 2-year post). Separate regression analyses were performed among DMAs that contained a REVISE site and those that did not. The number of REVISE sites in the DMA, the number of children’s hospitals in the DMA, DMA population, and season were also evaluated as confounding factors.Results: Strong growth in the number of unique DMAs and MetricHits occurred during the period of 1-year post the REVISE project. The overall usage continued to be relatively stable during the period of 2-years post. HitsPerDMA had stronger growth in DMAs with a REVISE site than those without. MetricHits were higher in DMAs with a larger population, more REVISE sites, and more children’s hospitals. There were also more MetricHits in the summer than in the winter months. Conclusions: Both temporal and spatial increases in mECDS app usage were found as evidence of a contagion method of spread. Other confounding factors may also play a role in app reach, adoption, and sustainability. Further research is needed to determine the factors driving passive diffusion and its impact on clinical practice and patient outcomes.

scholarly journals Reach, Adoption and Sustainability of a Mobile Clinical Decision Support Tool Paired With a National Quality Improvement Project Targeting Emergency Department Febrile Infant Management

2020 ◽  
Author(s):  
Ellen K Kerns ◽  
Russell McCulloh ◽  
Hongying Dai
Keyword(s):  
Quality Improvement ◽  
Clinical Practice ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Confounding Factors ◽  
Children's Hospitals ◽  
Improvement Project ◽  
Children’S Hospitals ◽  
National Quality

Abstract Background: Electronic clinical decision support (ECDS) tools are often developed within quality improvement (QI) projects to increase adherence with the latest clinical practice guidelines (CPGs).However, scalability and sustainability of ECDS beyond the time and location of their associated project are very limited. Deploying ECDS using a mobile app (mECDS) has shown the potential to be a viable method of overcoming these limitations. However, it is unclear what pattern the spread of uptake and use of such a tool might follow.Methods: In 2016, our team released a freely available mECDS as part of a national multi-site project entitled Reducing Variation in Infant Sepsis Evaluation (REVISE). For this study, we evaluated trends in weekly mECDS usage measures defined as 1) REVISE metric related screen views (MetricHits), 2) unique designated market areas (DMA) where the mECDS was used (Unique DMAs), 3) density of use or MetricHits/Unique DMAs (HitsPerDMA). Linear regressions were performed to examine the app usage trends measures across three time periods (during REVISE, 1-year post, 2-year post). Separate regression analyses were performed among DMAs that contained a REVISE site and those that did not. The number of REVISE sites in the DMA, the number of children’s hospitals in the DMA, DMA population, and season were also evaluated as confounding factors.Results: Strong growth in the number of unique DMAs and MetricHits occurred during the period of 1-year post the REVISE project. The overall usage continued to be relatively stable during the period of 2-years post. HitsPerDMA had stronger growth in DMAs with a REVISE site than those without. MetricHits were higher in DMAs with a larger population, more REVISE sites, and more children’s hospitals. There were also more MetricHits in the summer than in the winter months. Conclusions: Both temporal and spatial increases in mECDS app usage were found as evidence of a contagion method of spread. Other confounding factors may also play a role in app reach, adoption, and sustainability. Further research is needed to determine the factors driving passive diffusion and its impact on clinical practice and patient outcomes.

scholarly journals Reach, Adoption and Sustainability of a Mobile Clinical Decision Support Tool Paired With a National Quality Improvement Project Targeting Emergency Department Febrile Infant Management

2021 ◽  
Author(s):  
Ellen K Kerns ◽  
Russell McCulloh ◽  
Hongying Dai
Keyword(s):  
Quality Improvement ◽  
Clinical Practice ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Confounding Factors ◽  
Children's Hospitals ◽  
Improvement Project ◽  
Children’S Hospitals ◽  
National Quality

Abstract Background: Electronic clinical decision support (ECDS) tools are often developed within quality improvement (QI) projects to increase adherence with the latest clinical practice guidelines (CPGs).However, scalability and sustainability of ECDS beyond the time and location of their associated project are very limited. Deploying ECDS using a mobile app (mECDS) has shown the potential to be a viable method of overcoming these limitations. However, it is unclear what pattern the spread of uptake and use of such a tool might follow.Methods: In 2016, our team released a freely available mECDS as part of a national multi-site project entitled Reducing Variation in Infant Sepsis Evaluation (REVISE). For this study, we evaluated trends in weekly mECDS usage measures defined as 1) REVISE metric related screen views (MetricHits), 2) unique designated market areas (DMA) where the mECDS was used (Unique DMAs), 3) density of use or MetricHits/Unique DMAs (HitsPerDMA). Linear regressions were performed to examine the app usage trends measures across three time periods (during REVISE, 1-year post, 2-year post). Separate regression analyses were performed among DMAs that contained a REVISE site and those that did not. The number of REVISE sites in the DMA, the number of children’s hospitals in the DMA, DMA population, and season were also evaluated as confounding factors.Results: Strong growth in the number of unique DMAs and MetricHits occurred during the period of 1-year post the REVISE project. The overall usage continued to be relatively stable during the period of 2-years post. HitsPerDMA had stronger growth in DMAs with a REVISE site than those without. MetricHits were higher in DMAs with a larger population, more REVISE sites, and more children’s hospitals. There were also more MetricHits in the summer than in the winter months. Conclusions: Both temporal and spatial increases in mECDS app usage were found as evidence of a contagion method of spread. Other confounding factors may also play a role in app reach, adoption, and sustainability. Further research is needed to determine the factors driving passive diffusion and its impact on clinical practice and patient outcomes.

scholarly journals Estimating the impact of deploying an electronic clinical decision support tool as part of a national practice improvement project

2019 ◽  
Vol 26 (7) ◽  
pp. 630-636 ◽  
Author(s):  
Ellen K Kerns ◽  
Vincent S Staggs ◽  
Sarah D Fouquet ◽  
Russell J McCulloh
Keyword(s):  
Clinical Practice ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Market Area ◽  
Improvement Project ◽  
Support Tool ◽  
Febrile Infant ◽  
The Impact ◽  
Electronic Clinical Decision Support

Abstract Objective Estimate the impact on clinical practice of using a mobile device–based electronic clinical decision support (mECDS) tool within a national standardization project. Materials and Methods An mECDS tool (app) was released as part of a change package to provide febrile infant management guidance to clinicians. App usage was analyzed using 2 measures: metric hits per case (metric-related screen view count divided by site-reported febrile infant cases in each designated market area [DMA] monthly) and cumulative prior metric hits per site (DMA metric hits summed from study month 1 until the month preceding the index, divided by sites in the DMA). For each metric, a mixed logistic regression model was fit to model site performance as a function of app usage. Results An increase of 200 cumulative prior metric hits per site was associated with increased odds of adherence to 3 metrics: appropriate admission (odds ratio [OR], 1.12; 95% confidence interval [CI], 1.06-1.18), appropriate length of stay (OR, 1.20; 95% CI, 1.12-1.28), and inappropriate chest x-ray (OR, 0.82; 95% CI, 0.75-0.91). Ten additional metric hits per case were also associated: OR were 1.18 (95% CI, 1.02-1.36), 1.36 (95% CI, 1.14-1.62), and 0.74 (95% CI, 0.62-0.89). Discussion mECDS tools are increasingly being implemented, but their impact on clinical practice is poorly described. To our knowledge, although ecologic in nature, this report is the first to link clinical practice to mECDS use on a national scale and outside of an electronic health record. Conclusions mECDS use was associated with changes in adherence to targeted metrics. Future studies should seek to link mECDS usage more directly to clinical practice and assess other site-level factors.

scholarly journals Sinusitis Treatment Guideline Adherence in the e-visit Setting

2016 ◽  
Vol 07 (02) ◽  
pp. 299-307
Author(s):  
Dang Tran ◽  
Bonnie Westra ◽  
Kevin Smith
Keyword(s):  
Quality Improvement ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Guideline Adherence ◽  
Clinical Guideline ◽  
Clinical Decision ◽  
Care Providers ◽  
Post Intervention ◽  
Improvement Project ◽  
Time Period

SummaryStudies demonstrate poor guideline adherence by health care providers for the treatment of upper respiratory infections, particularly acute bacterial rhinosinusitis (ABRS), in the appropriate prescribing of antibiotic medications.The purpose of this quality improvement project was to evaluate the effect of implementing interventions for improving adherence to a clinical practice guideline for the management of ABRS for patients treated in the e-visit setting. Interventions included: providing a report to providers of their adherence to the ABRS clinical guideline prior to the intervention, providing updated education on the ABRS guideline, and implementing a clinical decision support system reminder.A pre and post intervention evaluation design was used. Data were obtained from a retrospective electronic health record (EHR) data extract of all 316 diagnoses for ABRS in the preintervention 2-month time period and all 368 diagnoses of ABRS in the post-intervention 2-month time period. A review of the structured clinical data elements was performed to determine whether the provider adhered to the clinical guideline, meaning that only patients meeting the criteria for ABRS were to receive an antibiotic prescription.The interventions resulted in a 3.3% improvement in adherence to the ABRS clinical guideline from 95.25% adherence pre-intervention to 98.4% post-intervention. These results demonstrated that the use of an educational intervention and clinical decision support resulted in improved adherence to the ABRS clinical guideline in the e-visit setting. The implications for practice could be significant in that these quality improvement interventions improve guideline adherence and reduce unnecessary prescribing of antibiotics.

scholarly journals Current State of Antimicrobial Stewardship in Children’s Hospital Emergency Departments

2017 ◽  
Vol 38 (4) ◽  
pp. 469-475 ◽  
Author(s):  
Rakesh D. Mistry ◽  
Jason G. Newland ◽  
Jeffrey S. Gerber ◽  
Adam L. Hersh ◽  
Larissa May ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Antimicrobial Stewardship ◽  
Clinical Care ◽  
Clinical Decision ◽  
Antibiotic Prescribing ◽  
Children's Hospitals ◽  
Children's Hospital ◽  
Children’S Hospitals ◽  
Care Guidelines

BACKGROUNDAntimicrobial stewardship programs (ASPs) effectively optimize antibiotic use for inpatients; however, the extent of emergency department (ED) involvement in ASPs has not been described.OBJECTIVETo determine current ED involvement in children’s hospital ASPs and to assess beliefs and preferred methods of implementation for ED-based ASPs.METHODSA cross-sectional survey of 37 children’s hospitals participating in the Sharing Antimicrobial Resistance Practices collaboration was conducted. Surveys were distributed to ASP leaders and ED medical directors at each institution. Items assessed included beliefs regarding ED antibiotic prescribing, ED prescribing resources, ASP methods used in the ED such as clinical decision support and clinical care guidelines, ED participation in ASP activities, and preferred methods for ED-based ASP implementation.RESULTSA total of 36 ASP leaders (97.3%) and 32 ED directors (86.5%) responded; the overall response rate was 91.9%. Most ASP leaders (97.8%) and ED directors (93.7%) agreed that creation of ED-based ASPs was necessary. ED resources for antibiotic prescribing were obtained via the Internet or electronic health records (EHRs) for 29 hospitals (81.3%). The main ASP activities for the ED included production of antibiograms (77.8%) and creation of clinical care guidelines for pneumonia (83.3%). The ED was represented on 3 hospital ASP committees (8.3%). No hospital ASPs actively monitored outpatient ED prescribing. Most ASP leaders (77.8%) and ED directors (81.3%) preferred implementation of ED-based ASPs using clinical decision support integrated into the EHR.CONCLUSIONSAlthough ED involvement in ASPs is limited, both ASP and ED leaders believe that ED-based ASPs are necessary. Many children’s hospitals have the capability to implement ED-based ASPs via the preferred method: EHR clinical decision support.Infect Control Hosp Epidemiol 2017;38:469–475

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.

Clinical Decision Support Systems Question and Answering

2018 ◽  
pp. 146-157 ◽  
Author(s):  
David José Murteira Mendes ◽  
Irene Pimenta Rodrigues ◽  
César Fonseca
Keyword(s):  
Clinical Practice ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Medical Science ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Discourse Representation ◽  
Discourse Representation Theory

A question answering system to help clinical practitioners in a cardiovascular healthcare environment to interface clinical decision support systems can be built by using an extended discourse representation structure, CIDERS, and an ontology framework, Ontology for General Clinical Practice. CIDERS is an extension of the well-known DRT (discourse representation theory) structures, intending to go beyond single text representation to embrace the general clinical history of a given patient represented in an ontology. The Ontology for General Clinical Practice improves the currently available state-of-the-art ontologies for medical science and for the cardiovascular specialty. The chapter shows the scientific and philosophical reasons of its present dual structure with a deeply expressive (SHOIN) terminological base (TBox) and a highly computable (EL++) assertions knowledge base (ABox). To be able to use the current reasoning techniques and methodologies, the authors made a thorough inventory of biomedical ontologies currently available in OWL2 format.

Clinical Decision Support Systems Question and Answering

2021 ◽  
pp. 543-554
Author(s):  
David José Murteira Mendes ◽  
Irene Pimenta Rodrigues ◽  
César Fonseca
Keyword(s):  
Clinical Practice ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Medical Science ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Discourse Representation ◽  
Discourse Representation Theory

A question answering system to help clinical practitioners in a cardiovascular healthcare environment to interface clinical decision support systems can be built by using an extended discourse representation structure, CIDERS, and an ontology framework, Ontology for General Clinical Practice. CIDERS is an extension of the well-known DRT (discourse representation theory) structures, intending to go beyond single text representation to embrace the general clinical history of a given patient represented in an ontology. The Ontology for General Clinical Practice improves the currently available state-of-the-art ontologies for medical science and for the cardiovascular specialty. The chapter shows the scientific and philosophical reasons of its present dual structure with a deeply expressive (SHOIN) terminological base (TBox) and a highly computable (EL++) assertions knowledge base (ABox). To be able to use the current reasoning techniques and methodologies, the authors made a thorough inventory of biomedical ontologies currently available in OWL2 format.

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