scholarly journals Clinical Decision Support for Immunization Uptake and Use in Immunization Health Information Systems

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Lauren Shrader ◽  
Stuart Myerburg ◽  
Eric Larson
Keyword(s):  
United States ◽  
Decision Support ◽  
Information Systems ◽  
Health Information ◽  
Clinical Decision Support ◽  
Health Information Systems ◽  
Clinical Decision ◽  
The United States ◽  
Precise Integration ◽  
Vaccine Recommendations

Context: In the United States, immunization recommendations and their associated schedules are developed by the Advisory Committee on Immunization Practices (ACIP). To assist with the translation process and better harmonize the outcomes of existing clinical decision support tools, the Centers for Disease Control and Prevention (CDC) created clinical decision support for immunization (CDSi) resources for each set of ACIP recommendations. These resources are continually updated and refined as new vaccine recommendations and clarifications become available and will be available to health information systems for a coronavirus disease 2019 (COVID-19) vaccine when one becomes available for use in the United States. Objectives: To assess awareness of CDSi resources, whether CDSi resources were being used by immunization-related health information systems, and perceived impact of CDSi resources on stakeholders’ work.Design: Online surveys conducted from 2015–2019 including qualitative and quantitative questions.Participants: The main and technical contact from each of the 64 CDC-funded immunization information system (IIS) awardees, IIS vendors, and electronic health record vendors. Results: Awareness of at least one resource increased from 75% of respondents in 2015 to 100% in 2019. Use of at least one CDSi resource also increased from 47% in 2015 to 78% in 2019. About 80% or more of users of CDSi are somewhat or very highly satisfied with the resources and report a somewhat or very positive impact from using them. Conclusion: As awareness and use of CDSi resources increases, the likelihood that patients receive recommended immunizations at the right time will also increase. Rapid and precise integration of vaccine recommendations into health information systems will be particularly important when a COVID-19 vaccine becomes available to help facilitate vaccine implementation.

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 CATalyst for Change: How Members of WellSpan’s Central Alert Team (CAT) Are Revolutionizing Sepsis Care

2021 ◽  
pp. 74-79
Author(s):  
Caitlyn Allen
Keyword(s):  
United States ◽  
Decision Support ◽  
Early Identification ◽  
Clinical Decision ◽  
The United States ◽  
Mortality Rates ◽  
Bundle Compliance ◽  
The Cost ◽  
State 1 ◽  
Sepsis Care

In the United States, almost 1 million patients with sepsis are admitted to hospitals annually, and the cost of managing sepsis admissions is higher than any other disease state.1 Early identification and treatment are critical for survival, though both are notoriously difficult as symptoms are often nonspecific. Four years ago, WellSpan Health asked, “What if there were a way to provide real-time, meaningful clinical decision support to bedside providers to identify sepsis sooner and start lifesaving treatment?” Meet Margaret D’Ercole, Patricia Everett, Dana Gaultney, Angela Mays, Brenna Simcoe, and Cynthia Yascavage, who share how their Central Alert Team decreased mortality rates, increased bundle compliance, and proved there is a better way.

scholarly journals IT-Enabled Clinical Decision Support: An Empirical Study on Antecedents and Mechanisms

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Rogier van de Wetering
Keyword(s):  
Clinical Practice ◽  
Decision Support ◽  
Health Information ◽  
Clinical Decision Support ◽  
Information Exchange ◽  
Large Scale ◽  
Health Information Exchange ◽  
Clinical Decision ◽  
Future Research ◽  
Cross Sectional

Modern hospitals increasingly make use of innovations and information technology (IT) to improve workflow and patient’s clinical journey. Typical innovative solutions include patient records and clinical decision support systems to enhance the process of decision making by doctors and other healthcare practitioners. However, currently, it remains unclear how hospitals could facilitate and enable such a decision support capability in clinical practice. We ground our work on the resource-based view of the firm and put forth the notion of IT-enabled capabilities which emphasizes critical IT investment and capability development areas that hospitals could exploit in their quest to improve clinical decision support. We develop a research model that explains how “health information exchange” and enhanced “information capability” collectively drive a hospital’s “clinical decision support capability.” We used partial least squares path modeling on large-scale cross-sectional data from 720 European hospitals. Outcomes suggest that health information exchange positively impacts information capability. In turn, information capability complementary partially mediates the relationship between information exchange and clinical decision support. Hence, this research contributes to the literature on clinical decision support and provides valuable insights into how to support such innovative technologies and capabilities in clinical practice. We conclude with a discussion and conclusion. Also, we outline the inherent limitations of this study and outline directions for future research.

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.

scholarly journals Informatics and interaction: Applying human factors principles to optimize the design of clinical decision support for sepsis

2019 ◽  
Vol 26 (1) ◽  
pp. 642-651
Author(s):  
Laura Schubel ◽  
Danielle L Mosby ◽  
Joseph Blumenthal ◽  
Muge Capan ◽  
Ryan Arnold ◽  
...  
Keyword(s):  
Information Technology ◽  
Decision Support ◽  
Human Factors ◽  
Organ Failure ◽  
Health Information Technology ◽  
Health Information ◽  
Clinical Decision Support ◽  
Clinical Care ◽  
Clinical Decision ◽  
Wide Range

In caring for patients with sepsis, the current structure of electronic health record systems allows clinical providers access to raw patient data without imputation of its significance. There are a wide range of sepsis alerts in clinical care that act as clinical decision support tools to assist in early recognition of sepsis; however, there are serious shortcomings in existing health information technology for alerting providers in a meaningful way. Little work has been done to evaluate and assess existing alerts using implementation and process outcomes associated with health information technology displays, specifically evaluating clinician preference and performance. We developed graphical model displays of two popular sepsis scoring systems, quick Sepsis Related Organ Failure Assessment and Predisposition, Infection, Response, Organ Failure, using human factors principles grounded in user-centered and interaction design. Models will be evaluated in a larger research effort to optimize alert design to improve the collective awareness of high-risk populations and develop a relevant point-of-care clinical decision support system for sepsis.

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.

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