Impact of expert knowledge on the detection of patients at risk of antimicrobial therapy failure by clinical decision support systems

Canovas-Segura, Bernardo; Morales, Antonio; Palácios, Francisco

doi:10.1016/j.jbi.2019.103200

Cited by 9 publications

(10 citation statements)

References 24 publications

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“…Another way of understanding CDS relevance was by directly soliciting feedback from users, for example using a Likert scale. A study of an antibiotic susceptibility CDS found that clinicians using a Likert scale felt that fewer than 30% of alerts were relevant [84]. Improved CDS relevance to individual patients was also high on the wish-lists of clinicians giving feedback on CDS alerts [29,97].…”

Section: Personalized Medicine (Alerts) -The Relevance Of Cds Promptsmentioning

confidence: 99%

“…Relatedly, how CDS incorporated into clinician workflow was commonly addressed. For example, one study found that a large number of antibiotic susceptibility alerts were already addressed by standard workflow (e.g., the clinician reviewing new culture results and adjusting empiric antibiotics appropriately), so early alerts just contributed to alert burden [84]. Another study noted the pharmacists using a tool for antibiotic stewardship were frustrated by automatic log-offs [33].…”

Section: Make It Easy -Workflow Efficiency and Integrationmentioning

confidence: 99%

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Clinical Decision Support and Implications for the Clinician Burnout Crisis

Janković

Chen

2020

Yearb Med Inform

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Objectives: This survey aimed to review aspects of clinical decision support (CDS) that contribute to burnout and identify key themes for improving the acceptability of CDS to clinicians, with the goal of decreasing said burnout. Methods: We performed a survey of relevant articles from 2018-2019 addressing CDS and aspects of clinician burnout from PubMed and Web of Science™. Themes were manually extracted from publications that met inclusion criteria. Results: Eighty-nine articles met inclusion criteria, including 12 review articles. Review articles were either prescriptive, describing how CDS should work, or analytic, describing how current CDS tools are deployed. The non-review articles largely demonstrated poor relevance and acceptability of current tools, and few studies showed benefits in terms of efficiency or patient outcomes from implemented CDS. Encouragingly, multiple studies highlighted steps that succeeded in improving both acceptability and relevance of CDS. Conclusions: CDS can contribute to clinician frustration and burnout. Using the techniques of improving relevance, soliciting feedback, customization, measurement of outcomes and metrics, and iteration, the effects of CDS on burnout can be ameliorated.

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Section: Personalized Medicine (Alerts) -The Relevance Of Cds Promptsmentioning

confidence: 99%

Section: Make It Easy -Workflow Efficiency and Integrationmentioning

confidence: 99%

Clinical Decision Support and Implications for the Clinician Burnout Crisis

Janković

Chen

2020

Yearb Med Inform

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“…Thus, Huibers et al, [9] transform the textual STOPP/START criteria into unambiguous definitions mapped to medical terminologies. Canovas et al, [10] formalize EUCAST expert rules as an ontology and production rules to detect antimicrobial therapies at risk of failure. Müller et al, [11] propose an open diagnostic knowledge base that can compete with commercial ones.…”

Section: Discussion and Outlookmentioning

confidence: 99%

Pragmatic Considerations on Clinical Decision Support from the 2019 Literature

Duclos

Bouaud

2020

Yearb Med Inform

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Objectives: To summarize significant research contributions published in 2019 in the field of computerized clinical decision support and select the best papers for the Decision Support section of the International Medical Informatics Association (IMIA) Yearbook. Methods: Two bibliographic databases were searched for papers referring to clinical decision support systems (CDSSs) and computerized provider order entry (CPOE) systems. From search results, section editors established a list of candidate best papers, which were then peer-reviewed by external reviewers. The IMIA Yearbook editorial committee finally selected the best papers on the basis of all reviews including the section editors’ evaluation. Results: A total of 1,378 articles were retrieved. Fifteen best paper candidates were selected, the reviews of which resulted in the selection of three best papers. One paper reports on a guideline modeling approach based on clinical decision trees, both clinically interpretable and suitable for implementation in CDSSs. In another paper, authors promote the use of extended Timed Transition Diagrams in CDSSs to formalize consistently recurrent medical processes for chronic diseases management. The third paper proposes a conceptual framework and a grid for assessing the performance of predictive tools based on the critical appraisal of published evidence. Conclusions: As showed by the number and the variety of works related to decision support, research in the field is very active. This year’s selection highlighted pragmatic works that promote transparency and trust required by decision support tools.

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“…This knowledge can be useful for many tasks such as the detection of incoherencies in laboratory results (e.g., bacteria that are found susceptible to an antibiotic to which they should be intrinsically resistant according to the EUCAST rules) and the detection of possible therapy failure because the infecting agent is found as resistant to all the antibiotics currently administered to the patient. In this last example, this knowledge has proved to be useful for increasing the number of cases detected and their clinical relevance [38].…”

Section: Incorporation Of Clinical Knowledgementioning

confidence: 97%

WASPSS: A Clinical Decision Support System for Antimicrobial Stewardship

Canovas-Segura¹,

Morales²,

Juárez³

et al. 2021

Recent Advances in Digital System Diagnosis and Management of Healthcare

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The increase of infections caused by resistant bacteria has become one of the major health-care problems worldwide. The creation of multidisciplinary teams dedicated to the implementation of antimicrobial stewardship programmes (ASPs) is encouraged by all clinical institutions to cope with this problem. In this chapter, we describe the Wise Antimicrobial Stewardship Program Support System (WASPSS), a CDSS focused on providing support for ASP teams. WASPSS gathers the required information from other hospital systems in order to provide decision support in antimicrobial stewardship from both patient-centered and global perspectives. To achieve this, it combines business intelligence techniques with a rule-based inference engine to integrate the data and knowledge required in this scenario. The system provides functions such as alerts, recommendations, antimicrobial prescription support and global surveillance. Furthermore, it includes experimental modules for improving the adoption of clinical guidelines and applying prediction models related with antimicrobial resistance. All these functionalities are provided through a multiuser web interface, personalized for each role of the ASP team.

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Impact of expert knowledge on the detection of patients at risk of antimicrobial therapy failure by clinical decision support systems

Cited by 9 publications

References 24 publications

Clinical Decision Support and Implications for the Clinician Burnout Crisis

Clinical Decision Support and Implications for the Clinician Burnout Crisis

Pragmatic Considerations on Clinical Decision Support from the 2019 Literature

WASPSS: A Clinical Decision Support System for Antimicrobial Stewardship

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