Clinical Decision Support Systems

Wasylewicz, Arthur T M; Scheepers-Hoeks, A. M. J. W.

doi:10.1007/978-3-319-99713-1_11

Cited by 108 publications

(102 citation statements)

References 44 publications

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“…Analysis of big data gathered from EHRs can generate real-time evidence that helps end-users take better care of their patients [ 5 ]. Clinical decision support systems (CDSS) are a typical example of value provided to EHR users [ 6 ]. Such systems intervene in real-time to help users make appropriate decisions based on up-to-date information from EHRs.…”

Section: Introductionmentioning

confidence: 99%

Barriers and Facilitators to Implementation of Medication Decision Support Systems in Electronic Medical Records: Mixed Methods Approach Based on Structural Equation Modeling and Qualitative Analysis

Jung

Lee

et al. 2020

JMIR Med Inform

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Background Adverse drug events (ADEs) resulting from medication error are some of the most common causes of iatrogenic injuries in hospitals. With the appropriate use of medication, ADEs can be prevented and ameliorated. Efforts to reduce medication errors and prevent ADEs have been made by implementing a medication decision support system (MDSS) in electronic health records (EHRs). However, physicians tend to override most MDSS alerts. Objective In order to improve MDSS functionality, we must understand what factors users consider essential for the successful implementation of an MDSS into their clinical setting. This study followed the implementation process for an MDSS within a comprehensive EHR system and analyzed the relevant barriers and facilitators. Methods A mixed research methodology was adopted. Data from a structured survey and 15 in-depth interviews were integrated. Structural equation modeling was conducted for quantitative analysis of factors related to user adoption of MDSS. Qualitative analysis based on semistructured interviews with physicians was conducted to collect various opinions on MDSS implementation. Results Quantitative analysis revealed that physicians’ expectations regarding ease of use and performance improvement are crucial. Qualitative analysis identified four significant barriers to MDSS implementation: alert fatigue, lack of accuracy, poor user interface design, and lack of customizability. Conclusions This study revealed barriers and facilitators to the implementation of MDSS. The findings can be applied to upgrade MDSS in the future.

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Section: Introductionmentioning

confidence: 99%

Barriers and Facilitators to Implementation of Medication Decision Support Systems in Electronic Medical Records: Mixed Methods Approach Based on Structural Equation Modeling and Qualitative Analysis

Jung

Lee

et al. 2020

JMIR Med Inform

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“…Furthermore, the timeliness of the data in a CDSS is important, and there should be information on when it was last updated and maintained. CDSSs should be updated regularly to integrate a new balance of evidenced-base medicine or end user preferences [ 55 , 56 ]. For instance, the Roadmap for National Action on Clinical Decision Support of the American Medical Informatics Association recommends that CDSS knowledge bases and methods should be improved continuously [ 57 ].…”

Section: Discussionmentioning

confidence: 99%

Diagnosis of Rare Diseases: a scoping review of clinical decision support systems

Schaaf

Sedlmayr

Schaefer

et al. 2020

Orphanet J Rare Dis

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Background Rare Diseases (RDs), which are defined as diseases affecting no more than 5 out of 10,000 people, are often severe, chronic and life-threatening. A main problem is the delay in diagnosing RDs. Clinical decision support systems (CDSSs) for RDs are software systems to support clinicians in the diagnosis of patients with RDs. Due to their clinical importance, we conducted a scoping review to determine which CDSSs are available to support the diagnosis of RDs patients, whether the CDSSs are available to be used by clinicians and which functionalities and data are used to provide decision support. Methods We searched PubMed for CDSSs in RDs published between December 16, 2008 and December 16, 2018. Only English articles, original peer reviewed journals and conference papers describing a clinical prototype or a routine use of CDSSs were included. For data charting, we used the data items “Objective and background of the publication/project”, “System or project name”, “Functionality”, “Type of clinical data”, “Rare Diseases covered”, “Development status”, “System availability”, “Data entry and integration”, “Last software update” and “Clinical usage”. Results The search identified 636 articles. After title and abstracting screening, as well as assessing the eligibility criteria for full-text screening, 22 articles describing 19 different CDSSs were identified. Three types of CDSSs were classified: “Analysis or comparison of genetic and phenotypic data,” “machine learning” and “information retrieval”. Twelve of nineteen CDSSs use phenotypic and genetic data, followed by clinical data, literature databases and patient questionnaires. Fourteen of nineteen CDSSs are fully developed systems and therefore publicly available. Data can be entered or uploaded manually in six CDSSs, whereas for four CDSSs no information for data integration was available. Only seven CDSSs allow further ways of data integration. thirteen CDSS do not provide information about clinical usage. Conclusions Different CDSS for various purposes are available, yet clinicians have to determine which is best for their patient. To allow a more precise usage, future research has to focus on CDSSs RDs data integration, clinical usage and updating clinical knowledge. It remains interesting which of the CDSSs will be used and maintained in the future.

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“…CDS provides guidance that can either be active or passive. Passive CDS requires the user to perform an action to receive guidance 46,47 . For instance, selecting an order set that contains the institutional formulary medications, which would enhance formulary adherence, or using an electronic template in an electronic health record (EHR).…”

Section: Innovative Methods Of Formulary Managementmentioning

confidence: 99%

“…Alert fatigue occurs when the prescriber is inundated with numerous alerts from the CDS and becomes desensitized to the importance of the alerts. Strategies to decrease inappropriate overrides include combining multiple alerts, reducing the number of alerts by only allowing higher severity alerts, and by evaluating the decision tree or logic in the CDS to decrease the frequency of alert triggers 46 …”

Section: Innovative Methods Of Formulary Managementmentioning

confidence: 99%

Formulary management challenges and opportunities: 2020 and beyond ‐ an opinion paper of the drug information practice and research network of the American College of Clinical Pharmacy

Johnson

Gosser

Kier

et al. 2020

J Am Coll Clin Pharm

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Formulary management systems in hospitals and health‐systems serve to ensure that medications are rigorously evaluated for efficacy, safety, and value. The increased complexity of these systems, along with the broadening of items for formulary inclusion, poses unique challenges and opportunities for the development of innovative formulary management processes. Pharmacists are challenged to evaluate different types of medications that are not all small molecule entities as well as deal with medication shortages that can impact patient care. The authors review unique formulary aspects of biosimilars, gene therapy, rare disease treatments, outpatient therapies, and formulary‐related challenges/opportunities with drug shortages. Core strategies for formulary management and drug shortage mitigation techniques in closed formulary settings are discussed. This information is intended to guide formulary decision‐makers and other clinicians to effectively manage health‐system formularies in 2020 and beyond.

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Clinical Decision Support Systems

Cited by 108 publications

References 44 publications

Barriers and Facilitators to Implementation of Medication Decision Support Systems in Electronic Medical Records: Mixed Methods Approach Based on Structural Equation Modeling and Qualitative Analysis

Barriers and Facilitators to Implementation of Medication Decision Support Systems in Electronic Medical Records: Mixed Methods Approach Based on Structural Equation Modeling and Qualitative Analysis

Diagnosis of Rare Diseases: a scoping review of clinical decision support systems

Formulary management challenges and opportunities: 2020 and beyond ‐ an opinion paper of the drug information practice and research network of the American College of Clinical Pharmacy

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