scholarly journals Direct and Electronic Health Record Access to the Clinical Decision Support for Immunizations in the Minnesota Immunization Information System

2016 ◽  
Vol 8s2 ◽  
pp. BII.S40208
Author(s):  
Sripriya Rajamani ◽  
Aaron Bieringer ◽  
Stephanie Wallerius ◽  
Daniel Jensen ◽  
Tamara Winden ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Health Care Providers ◽  
Clinical Decision ◽  
Population Based ◽  
Direct Access ◽  
Care Providers ◽  
Study Objective ◽  
Vaccination Rates ◽  
Electronic Health

Immunization information systems (IIS) are population-based and confidential computerized systems maintained by public health agencies containing individual data on immunizations from participating health care providers. IIS hold comprehensive vaccination histories given across providers and over time. An important aspect to IIS is the clinical decision support for immunizations (CDSi), consisting of vaccine forecasting algorithms to determine needed immunizations. The study objective was to analyze the CDSi presentation by IIS in Minnesota (Minnesota Immunization Information Connection [MIIC]) through direct access by IIS interface and by access through electronic health records (EHRs) to outline similarities and differences. The immunization data presented were similar across the three systems examined, but with varying ability to integrate data across MIIC and EHR, which impacts immunization data reconciliation. Study findings will lead to better understanding of immunization data display, clinical decision support, and user functionalities with the ultimate goal of promoting IIS CDSi to improve vaccination rates.

Clinical Decision Support Systems

2021 ◽  
pp. 1-7
Author(s):  
Andreas Teufel ◽  
Harald Binder
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Health Care Providers ◽  
Support Systems ◽  
Medical Knowledge ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Current Status ◽  
Care Providers

<b><i>Background:</i></b> By combining up-to-date medical knowledge and steadily increasing patient data, a new level of medical care can emerge. <b><i>Summary and Key Messages:</i></b> Clinical decision support systems (CDSSs) are an arising solution to handling rich data and providing them to health care providers in order to improve diagnosis and treatment. However, despite promising examples in many areas, substantial evidence for a thorough benefit of these support solutions is lacking. This may be due to a lack of general frameworks and diverse health systems around the globe. We therefore summarize the current status of CDSSs in medicine but also discuss potential limitations that need to be overcome in order to further foster future development and acceptance.

scholarly journals Evaluation of User-Interface Alert Displays for Clinical Decision Support Systems for Sepsis

2018 ◽  
Vol 38 (4) ◽  
pp. 46-54 ◽  
Author(s):  
Devida Long ◽  
Muge Capan ◽  
Susan Mascioli ◽  
Danielle Weldon ◽  
Ryan Arnold ◽  
...  
Keyword(s):  
Health Care ◽  
Decision Support ◽  
User Interface ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Health Care Providers ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Care Providers

BACKGROUND Hospitals are increasingly turning to clinical decision support systems for sepsis, a life-threatening illness, to provide patient-specific assessments and recommendations to aid in evidence-based clinical decision-making. Lack of guidelines on how to present alerts has impeded optimization of alerts, specifically, effective ways to differentiate alerts while highlighting important pieces of information to create a universal standard for health care providers. OBJECTIVE To gain insight into clinical decision support systems–based alerts, specifically targeting nursing interventions for sepsis, with a focus on behaviors associated with and perceptions of alerts, as well as visual preferences. METHODS An interactive survey to display a novel user interface for clinical decision support systems for sepsis was developed and then administered to members of the nursing staff. RESULTS A total of 43 nurses participated in 2 interactive survey sessions. Participants preferred alerts that were based on an established treatment protocol, were presented in a pop-up format, and addressed the patient’s clinical condition rather than regulatory guidelines. CONCLUSIONS The results can be used in future research to optimize electronic medical record alerting and clinical practice workflow to support the efficient, effective, and timely delivery of high-quality care to patients with sepsis. The research also may advance the knowledge base of what information health care providers want and need to improve the health and safety of their patients.

Clinical Validity Assessment of Integrated Dose Range Checking Tool in a Tertiary Care Hospital Using an Electronic Health Information System

2019 ◽  
pp. 001857871986766
Author(s):  
Abdulrazaq S. Al-Jazairi ◽  
Eman K. AlQadheeb ◽  
Lama K. AlShammari ◽  
Maha A. AlAshaikh ◽  
Abdulgader Al-Moeen ◽  
...  
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Dose Range ◽  
Clinical Decision ◽  
Inpatient Setting ◽  
Care Hospital ◽  
Care Providers ◽  
Clinical Validity ◽  
Electronic Health

Background/purpose: The electronic clinical decision support system (CDSS) is mainly used to assist health care providers in their decision-making process. CDSS includes the dose range checking (DRC) tool. This study aims to evaluate the clinical validity of the DRC tool and compare it to the institutional Formulary and Drug Therapy Guide powered by Lexi-Comp. Methods: This retrospective study analyzed DRC alerts in the inpatient setting. Alerts were assessed for their clinical validity when compared to recommendations of the institution’s formulary. Relevant data regarding patient demographics and characteristics were collected. A sample size of 3000 DRC alerts was needed to give a margin of error of 1% (using normal approximation to binomial distribution gives 30.26/3000 = 1%). Results: In our cohort, 1659 (55%) of the DRC alerts were generated for adult patients. A total of 1557 (52%) of all medication-related DRC alerts recommended renal dose adjustments, while 708 (24%) needed hepatic dose adjustments. Majority of alerts, 2844 (95%), were clinically invalid. A total of 2892 (96%) alerts were overridden by prescribers. In 997 (33%) cases, there was an overdose relative to the recommended dose, and in 1572 (52%) there was underdosing. Residents were more likely to accept the DRC alerts compared with other health provider categories ( P < .001). Conclusion: Using DRC as a clinical decision support tool with minimal integration yielded serious clinically invalid recommendations. This could increase medication-prescribing errors and lead to alert fatigue in electronic health care systems.

scholarly journals Economic Value of Data and Analytics for Health Care Providers: Hermeneutic Systematic Literature Review

10.2196/23315 ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. e23315
Author(s):  
Philip von Wedel ◽  
Christian Hagist
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Economic Impact ◽  
Clinical Decision Support ◽  
Health Care Providers ◽  
Clinical Decision ◽  
Economic Benefits ◽  
Economic Effects ◽  
Care Providers ◽  
Advanced Analytics

Background The benefits of data and analytics for health care systems and single providers is an increasingly investigated field in digital health literature. Electronic health records (EHR), for example, can improve quality of care. Emerging analytics tools based on artificial intelligence show the potential to assist physicians in day-to-day workflows. Yet, single health care providers also need information regarding the economic impact when deciding on potential adoption of these tools. Objective This paper examines the question of whether data and analytics provide economic advantages or disadvantages for health care providers. The goal is to provide a comprehensive overview including a variety of technologies beyond computer-based patient records. Ultimately, findings are also intended to determine whether economic barriers for adoption by providers could exist. Methods A systematic literature search of the PubMed and Google Scholar online databases was conducted, following the hermeneutic methodology that encourages iterative search and interpretation cycles. After applying inclusion and exclusion criteria to 165 initially identified studies, 50 were included for qualitative synthesis and topic-based clustering. Results The review identified 5 major technology categories, namely EHRs (n=30), computerized clinical decision support (n=8), advanced analytics (n=5), business analytics (n=5), and telemedicine (n=2). Overall, 62% (31/50) of the reviewed studies indicated a positive economic impact for providers either via direct cost or revenue effects or via indirect efficiency or productivity improvements. When differentiating between categories, however, an ambiguous picture emerged for EHR, whereas analytics technologies like computerized clinical decision support and advanced analytics predominantly showed economic benefits. Conclusions The research question of whether data and analytics create economic benefits for health care providers cannot be answered uniformly. The results indicate ambiguous effects for EHRs, here representing data, and mainly positive effects for the significantly less studied analytics field. The mixed results regarding EHRs can create an economic barrier for adoption by providers. This barrier can translate into a bottleneck to positive economic effects of analytics technologies relying on EHR data. Ultimately, more research on economic effects of technologies other than EHRs is needed to generate a more reliable evidence base.

Clinical Decision Support in the Management of Patients With Suspected Ebola Infection

2015 ◽  
Vol 9 (5) ◽  
pp. 591-594 ◽  
Author(s):  
Adam B. Landman ◽  
Eric Goralnick ◽  
Jonathan M. Teich
Keyword(s):  
Public Health ◽  
Health Care ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Health Care Providers ◽  
Clinical Decision ◽  
Health Care Facilities ◽  
Care Providers ◽  
Clinical Workflow ◽  
Health Threats

AbstractPatients with suspected public health threats, such as Ebola, must be quickly identified and isolated on presentation to health care facilities. Patients can be screened by intake staff or other health care providers; however, perfect compliance is difficult to achieve. Well-designed, carefully placed clinical decision support (CDS) within the electronic health record can be a reliable partner in helping to rapidly identify, isolate, and care for patients with suspected Ebola infection and other emerging public health threats. We describe how different types of CDS can be applied in the clinical workflow and share how we implemented CDS to force Ebola screening upon patient presentation to our emergency department. (Disaster Med Public Health Preparedness. 2015;9:591–594)

scholarly journals Economic Value of Data and Analytics for Health Care Providers: Hermeneutic Systematic Literature Review (Preprint)

2020 ◽  
Author(s):  
Philip von Wedel ◽  
Christian Hagist
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Economic Impact ◽  
Clinical Decision Support ◽  
Health Care Providers ◽  
Clinical Decision ◽  
Economic Benefits ◽  
Economic Effects ◽  
Care Providers ◽  
Advanced Analytics

BACKGROUND The benefits of data and analytics for health care systems and single providers is an increasingly investigated field in digital health literature. Electronic health records (EHR), for example, can improve quality of care. Emerging analytics tools based on artificial intelligence show the potential to assist physicians in day-to-day workflows. Yet, single health care providers also need information regarding the economic impact when deciding on potential adoption of these tools. OBJECTIVE This paper examines the question of whether data and analytics provide economic advantages or disadvantages for health care providers. The goal is to provide a comprehensive overview including a variety of technologies beyond computer-based patient records. Ultimately, findings are also intended to determine whether economic barriers for adoption by providers could exist. METHODS A systematic literature search of the PubMed and Google Scholar online databases was conducted, following the hermeneutic methodology that encourages iterative search and interpretation cycles. After applying inclusion and exclusion criteria to 165 initially identified studies, 50 were included for qualitative synthesis and topic-based clustering. RESULTS The review identified 5 major technology categories, namely EHRs (n=30), computerized clinical decision support (n=8), advanced analytics (n=5), business analytics (n=5), and telemedicine (n=2). Overall, 62% (31/50) of the reviewed studies indicated a positive economic impact for providers either via direct cost or revenue effects or via indirect efficiency or productivity improvements. When differentiating between categories, however, an ambiguous picture emerged for EHR, whereas analytics technologies like computerized clinical decision support and advanced analytics predominantly showed economic benefits. CONCLUSIONS The research question of whether data and analytics create economic benefits for health care providers cannot be answered uniformly. The results indicate ambiguous effects for EHRs, here representing data, and mainly positive effects for the significantly less studied analytics field. The mixed results regarding EHRs can create an economic barrier for adoption by providers. This barrier can translate into a bottleneck to positive economic effects of analytics technologies relying on EHR data. Ultimately, more research on economic effects of technologies other than EHRs is needed to generate a more reliable evidence base.

Bridging the gap between evidence and practice in asthma management by developing a mobile-based clinical decision support system for GINA asthma guideline

2020 ◽  
Author(s):  
Marsa Gholamzadeh ◽  
Hamidreza Abtahi ◽  
Shahideh Amini ◽  
Mehrnaz Asadi Gharabaghi
Keyword(s):  
Decision Support ◽  
Expert System ◽  
Clinical Decision Support ◽  
Health Care Providers ◽  
Clinical Decision Making ◽  
Asthma Management ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Care Providers ◽  
Asthma Guideline

Abstract Background Physicians’ compliance with clinical practice guidelines (CPG) remains insufficient. Guideline-based clinical decision support systems (CDSSs) can be beneficial to address this challenge. The principal objective of this research is to translate the Global Initiative for Asthma guideline (GINA) into a mobile-based CDSS to improve its utilization as a clinical decision-making tool.Methods Designing and development of our expert system were conducted in an iterative and stepwise approach by the multidisciplinary expert team. Translating and extracting the embedded knowledge in GINA was done according to the Knowledge to Action framework. Next, extracted knowledge was converted to decision tree models to design the knowledge-base of the desired system. The accuracy and proficiency of the expert system were calculated based on the predefined scenarios. The expert system usability was evaluated by the think-aloud protocol and the GUIDES questionnaire.Results Based on the analysis of the GINA guideline, more than 220 rules and 336 knowledge statements were extracted. Our knowledge-based expert system was devised based on production rules. After modification with feedback from six experts, the system was developed in the Android platform. The overall accuracy and efficiency of our CDSS were 100% and 100%, respectively.Conclusion The ginasthma mobile-based CDSS was developed for android smartphones to improve the adherence of health care providers to GINA guideline with high accuracy and efficiency. Further investigation is needed to evaluate the efficacy of this app in real practice.

scholarly journals Deploying Clinical Decision Support for Familial Hypercholesterolemia

ACI Open ◽  
2020 ◽  
Vol 04 (02) ◽  
pp. e157-e161
Author(s):  
Hana Bangash ◽  
Joseph Sutton ◽  
Justin H. Gundelach ◽  
Laurie Pencille ◽  
Ahmed Makkawy ◽  
...  
Keyword(s):  
Decision Support ◽  
Familial Hypercholesterolemia ◽  
Clinical Decision Support ◽  
Health Care Providers ◽  
Academic Medical Center ◽  
Genomic Medicine ◽  
Clinical Decision ◽  
Care Providers ◽  
Stage Of Development ◽  
Development And Validation

Abstract Objective Familial hypercholesterolemia (FH), a prevalent genomic disorder that increases risk of coronary heart disease, remains significantly underdiagnosed. Clinical decision support (CDS) tools have the potential to increase FH detection. We describe our experience in the development and implementation of a genomic CDS for FH at a large academic medical center. Methods CDS development and implementation were conducted in four phases: (1) development and validation of an algorithm to identify “possible FH”; (2) obtaining approvals from institutional committees to develop the CDS; (3) development of the initial prototype; and (4) use of an implementation science framework to evaluate the CDS. Results The timeline for this work was approximately 4 years; algorithm development and validation occurred from August 2018 to February 2020. During this 4-year period, we engaged with 15 stakeholder groups to build and integrate the CDS, including health care providers who gave feedback at each stage of development. During CDS implementation six main challenges were identified: (1) need for multiple institutional committee approvals; (2) need to align the CDS with institutional knowledge resources; (3) need to adapt the CDS to differing workflows; (4) lack of institutional guidelines for CDS implementation; (5) transition to a new institutional electronic health record (EHR) system; and (6) limitations of the EHR related to genomic medicine. Conclusion We identified multiple challenges in different domains while developing CDS for FH and integrating it with the EHR. The lessons learned herein may be helpful in streamlining the development and deployment of CDS to facilitate genomic medicine implementation.

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