A systematic review of clinical decision support systems for antimicrobial management: are we failing to investigate these interventions appropriately?

Rawson, Timothy M; Moore, Luke; Hernandez, Bernard; Charani, Esmita; Castro‐Sánchez, Enrique; Herrero, Pau; Hayhoe, Benedict; Hope, William; Georgiou, Pantelis; Holmes, Alison

doi:10.1016/j.cmi.2017.02.028

Cited by 144 publications

(138 citation statements)

References 81 publications

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“…The literature which compares and contrasts available CDSSs for breast cancer treatment is very limited to the best of our knowledge. Initially, a total of nine reviews on general CDSSs were selected, using a systematic search explained in Section 2.2.1, to contextualise the study [8,[19][20][21][22][23][24][25][26]. The focus of reviewing these papers was to assess and characterise CDSSs in a general context.…”

Section: Related Work and Needs For The Reviewmentioning

confidence: 99%

Clinical Decision Support Systems in Breast Cancer: A Systematic Review

Mazo

Kearns

Mooney

et al. 2020

Cancers

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Breast cancer is the most frequently diagnosed cancer in women, with more than 2.1 million new diagnoses worldwide every year. Personalised treatment is critical to optimising outcomes for patients with breast cancer. A major advance in medical practice is the incorporation of Clinical Decision Support Systems (CDSSs) to assist and support healthcare staff in clinical decision-making, thus improving the quality of decisions and overall patient care whilst minimising costs. The usage and availability of CDSSs in breast cancer care in healthcare settings is increasing. However, there may be differences in how particular CDSSs are developed, the information they include, the decisions they recommend, and how they are used in practice. This systematic review examines various CDSSs to determine their availability, intended use, medical characteristics, and expected outputs concerning breast cancer therapeutic decisions, an area that is known to have varying degrees of subjectivity in clinical practice. Utilising the methodology of Kitchenham and Charter, a systematic search of the literature was performed in Springer, Science Direct, Google Scholar, PubMed, ACM, IEEE, and Scopus. An overview of CDSS which supports decision-making in breast cancer treatment is provided along with a critical appraisal of their benefits, limitations, and opportunities for improvement.

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Section: Related Work and Needs For The Reviewmentioning

confidence: 99%

Clinical Decision Support Systems in Breast Cancer: A Systematic Review

Mazo

Kearns

Mooney

et al. 2020

Cancers

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“…The review builds upon related work that highlights how academic and industry partners collaborate to develop machine learning products, 12 as well as the need for engagement from front-line clinicians and standard reporting. 13 Case studies were selected amongst 1,672 presentations at 9 informatics and machine learning conferences between January 2018 and October 2019. The conferences include American Medical Informatics Association (AMIA) Annual Symposia and Summits, MLHC, Health Information and Management Systems Society (HIMSS) Machine Learning and Artificial Intelligence Forum, and the Health AI Deployment Symposium.…”

Section: Introductionmentioning

confidence: 99%

A Path for Translation of Machine Learning Products into Healthcare Delivery

Sendak¹,

D’Arcy²,

Kashyap³

et al. 2020

EMJ Innov

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Despite enormous enthusiasm, machine learning models are rarely translated into clinical care and there is minimal evidence of clinical or economic impact. New conference venues and academic journals have emerged to promote the proliferating research; however, the translational path remains unclear. This review undertakes the first in-depth study to identify how machine learning models that ingest structured electronic health record data can be applied to clinical decision support tasks and translated into clinical practice. The authors complement their own work with the experience of 21 machine learning products that address problems across clinical domains and across geographic populations. Four phases of translation emerge: design and develop, evaluate and validate, diffuse and scale, and continuing monitoring and maintenance. The review highlights the varying approaches taken across each phase by teams building machine learning products and presents a discussion of challenges and opportunities. The translational path and associated findings are instructive to researchers and developers building machine learning products, policy makers regulating machine learning products, and health system leaders who are considering adopting a machine learning product.

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“…Yet, experience with PCT and education about its correct use remain essential prerequisites to optimize PCTguided treatment [57]. A clinical decision support system or including several decision cut-offs of PCT in the laboratory reports may lower the barrier of PCT-guided treatment for non-experts [58]. More clinical trials and a broader knowledge of real-world data in these countries would be of interest to assess clinical and health economic impact in the different countries.…”

Section: Discussionmentioning

confidence: 99%

Procalcitonin (PCT)-guided antibiotic stewardship in Asia-Pacific countries: adaptation based on an expert consensus meeting

Lee

Kwa

Apisarnthanarak

et al. 2020

Clinical Chemistry and Laboratory Medicine (CCLM)

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IntroductionRecently, an expert consensus on optimal use of procalcitonin (PCT)-guided antibiotic stewardship was published focusing mainly on Europe and the United States. However, for Asia-Pacific countries, recommendations may need adaptation due to differences in types of infections, available resources and standard of clinical care.MethodsPractical experience with PCT-guided antibiotic stewardship was discussed among experts from different countries, reflecting on the applicability of the proposed Berlin consensus algorithms for Asia-Pacific. Using a Delphi process, the group reached consensus on two PCT algorithms for the critically ill and the non-critically ill patient populations.ResultsThe group agreed that the existing evidence for PCT-guided antibiotic stewardship in patients with acute respiratory infections and sepsis is generally valid also for Asia-Pacific countries, in regard to proposed PCT cut-offs, emphasis on diagnosis, prognosis and antibiotic stewardship, overruling criteria and inevitable adaptations to clinical settings. However, the group noted an insufficient database on patients with tropical diseases currently limiting the clinical utility in these patients. Also, due to lower resource availabilities, biomarker levels may be measured less frequently and only when changes in treatment are highly likely.ConclusionsUse of PCT to guide antibiotic stewardship in conjunction with continuous education and regular feedback to all stakeholders has high potential to improve the utilization of antibiotic treatment also in Asia-Pacific countries. However, there is need for adaptations of existing algorithms due to differences in types of infections and routine clinical care. Further research is needed to understand the optimal use of PCT in patients with tropical diseases.

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A systematic review of clinical decision support systems for antimicrobial management: are we failing to investigate these interventions appropriately?

Cited by 144 publications

References 81 publications

Clinical Decision Support Systems in Breast Cancer: A Systematic Review

Clinical Decision Support Systems in Breast Cancer: A Systematic Review

A Path for Translation of Machine Learning Products into Healthcare Delivery

Procalcitonin (PCT)-guided antibiotic stewardship in Asia-Pacific countries: adaptation based on an expert consensus meeting

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