scholarly journals 1297. Examining the Successes and Challenges of Implementing HIV Testing Clinical Decision Support in the Emergency Department

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S467-S468
Author(s):  
Mariah Powell ◽  
Michael Gierlach ◽  
Sandra L Werner ◽  
David S Bar-Shain ◽  
Ann Avery
Keyword(s):  
Emergency Department ◽  
Decision Support ◽  
Hiv Testing ◽  
Clinical Decision Support ◽  
Screening Tool ◽  
Clinical Decision ◽  
The United States ◽  
Hiv Screening ◽  
Implementation Challenges ◽  
Start Date

Abstract Background In 2016, MetroHealth System (MHS) launched the FOCUS (Frontlines of Communities in the United States) project to routinize HIV testing in the emergency department (ED). Before 2016, clinical decision support (CDS) for HIV testing was not in place, nor was there a policy to support the importance of opt-out, nontargeted screening. The purpose of this study was to outline the progress of HIV testing after the integration of CDS, as well as describe the implementation challenges, and how certain events impacted HIV testing. Methods HIV testing data from MHS EDs were collected from October 1, 2015 to March 31, 2019 and graphed into a run chart. The dataset was mapped with the following events: project start date, ED testing begins (without CDS), CDS implementation, the staffing of the ED Testing Coordinator (EDTC), and optimization of CDS (Figure 1). To determine whether observed variation in the dataset is due to random or special cause variation, these run chart rules were applied: Run, Shift (Figure 2), and Trend. Results There were 42 data points and 4 runs. With 42 points, the lower limit of runs was 16 and the upper limit of runs was 28. This signals that one or more special cause variations were present. A total of three distinct shifts were observed indicating special cause variation. The run chart did not include any downward or upward trends. Testing increased as much as 3971% (7 tests in October 2015 vs. 285 tests in March 2018). Conclusion HIV testing increased from 7 tests to 86 tests (Shift 1). This coincided with establishment of an ED testing policy in April 2016. Testing increased to 266 tests in October 2016 (Shift 2). This directly related to implementation of CDS in the ED. December 2017 displayed the lowest testing with 117 tests. This was due to lack of policy awareness, and to the rarely-visited location of the HIV screening tool during the triage process. Staff was re-educated and the HIV screening tool was moved to a more visible location. This resulted in 227 tests in February 2018, and was followed by the highest testing month with 285 tests (Shift 3). Continued challenges prohibit sustained upward trends in ED testing. A control chart may be the appropriate next step to identify new control limits Disclosures All authors: No reported disclosures.

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.

scholarly journals Clinical decision support alert malfunctions: analysis and empirically derived taxonomy

2017 ◽  
Vol 25 (5) ◽  
pp. 496-506 ◽  
Author(s):  
Adam Wright ◽  
Angela Ai ◽  
Joan Ash ◽  
Jane F Wiesen ◽  
Thu-Trang T Hickman ◽  
...  
Keyword(s):  
Decision Support ◽  
Electronic Health Record ◽  
Medical Informatics ◽  
Clinical Decision Support ◽  
Quantitative Methods ◽  
Clinical Decision ◽  
The United States ◽  
Health Record ◽  
Qualitative And Quantitative Methods ◽  
Electronic Health

Abstract Objective To develop an empirically derived taxonomy of clinical decision support (CDS) alert malfunctions. Materials and Methods We identified CDS alert malfunctions using a mix of qualitative and quantitative methods: (1) site visits with interviews of chief medical informatics officers, CDS developers, clinical leaders, and CDS end users; (2) surveys of chief medical informatics officers; (3) analysis of CDS firing rates; and (4) analysis of CDS overrides. We used a multi-round, manual, iterative card sort to develop a multi-axial, empirically derived taxonomy of CDS malfunctions. Results We analyzed 68 CDS alert malfunction cases from 14 sites across the United States with diverse electronic health record systems. Four primary axes emerged: the cause of the malfunction, its mode of discovery, when it began, and how it affected rule firing. Build errors, conceptualization errors, and the introduction of new concepts or terms were the most frequent causes. User reports were the predominant mode of discovery. Many malfunctions within our database caused rules to fire for patients for whom they should not have (false positives), but the reverse (false negatives) was also common. Discussion Across organizations and electronic health record systems, similar malfunction patterns recurred. Challenges included updates to code sets and values, software issues at the time of system upgrades, difficulties with migration of CDS content between computing environments, and the challenge of correctly conceptualizing and building CDS. Conclusion CDS alert malfunctions are frequent. The empirically derived taxonomy formalizes the common recurring issues that cause these malfunctions, helping CDS developers anticipate and prevent CDS malfunctions before they occur or detect and resolve them expediently.

scholarly journals Comparison of Computer-based Clinical Decision Support Systems and Content for Diabetes Mellitus

2011 ◽  
Vol 02 (03) ◽  
pp. 284-303 ◽  
Author(s):  
A. Wright ◽  
M. Burton ◽  
G. Fraser ◽  
M. Krall ◽  
S. Maviglia ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Health Care ◽  
Decision Making ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Diabetes Care ◽  
Clinical Decision ◽  
The United States ◽  
Computer Based

SummaryBackground: Computer-based clinical decision support (CDS) systems have been shown to improve quality of care and workflow efficiency, and health care reform legislation relies on electronic health records and CDS systems to improve the cost and quality of health care in the United States; however, the heterogeneity of CDS content and infrastructure of CDS systems across sites is not well known.Objective: We aimed to determine the scope of CDS content in diabetes care at six sites, assess the capabilities of CDS in use at these sites, characterize the scope of CDS infrastructure at these sites, and determine how the sites use CDS beyond individual patient care in order to identify characteristics of CDS systems and content that have been successfully implemented in diabetes care.Methods: We compared CDS systems in six collaborating sites of the Clinical Decision Support Consortium. We gathered CDS content on care for patients with diabetes mellitus and surveyed institutions on characteristics of their site, the infrastructure of CDS at these sites, and the capabilities of CDS at these sites.Results: The approach to CDS and the characteristics of CDS content varied among sites. Some commonalities included providing customizability by role or user, applying sophisticated exclusion criteria, and using CDS automatically at the time of decision-making. Many messages were actionable recommendations. Most sites had monitoring rules (e.g. assessing hemoglobin A1c), but few had rules to diagnose diabetes or suggest specific treatments. All sites had numerous prevention rules including reminders for providing eye examinations, influenza vaccines, lipid screenings, nephropathy screenings, and pneumococcal vaccines.Conclusion: Computer-based CDS systems vary widely across sites in content and scope, but both institution-created and purchased systems had many similar features and functionality, such as integration of alerts and reminders into the decision-making workflow of the provider and providing messages that are actionable recommendations.

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