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Development and retrospective evaluation of a clinical decision support system for the efficient detection of drug-related problems by clinical pharmacists

BACKGROUND: Clinical decision support systems (CDSS) can help identify drug-related problems (DRPs). However, the alert specificity remains variable. Defining more relevant alerts for detecting DRPs would improve CDSS. AIM: Develop electronic queries that assist pharmacists in conducting medication...

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Autores principales: Skalafouris, Christian, Blanc, Anne-Laure, Grosgurin, Olivier, Marti, Christophe, Samer, Caroline, Lovis, Christian, Bonnabry, Pascal, Guignard, Bertrand
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10147748/
https://www.ncbi.nlm.nih.gov/pubmed/36515779
http://dx.doi.org/10.1007/s11096-022-01505-5
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author Skalafouris, Christian
Blanc, Anne-Laure
Grosgurin, Olivier
Marti, Christophe
Samer, Caroline
Lovis, Christian
Bonnabry, Pascal
Guignard, Bertrand
author_facet Skalafouris, Christian
Blanc, Anne-Laure
Grosgurin, Olivier
Marti, Christophe
Samer, Caroline
Lovis, Christian
Bonnabry, Pascal
Guignard, Bertrand
author_sort Skalafouris, Christian
collection PubMed
description BACKGROUND: Clinical decision support systems (CDSS) can help identify drug-related problems (DRPs). However, the alert specificity remains variable. Defining more relevant alerts for detecting DRPs would improve CDSS. AIM: Develop electronic queries that assist pharmacists in conducting medication reviews and an assessment of the performance of this model to detect DRPs. METHOD: Electronic queries were set up in CDSS using “triggers” from electronic health records: drug prescriptions, laboratory values, medical problems, vital signs, demographics. They were based on a previous study where 315 patients admitted in internal medicine benefited from a multidisciplinary medication review (gold-standard) to highlight potential DRPs. Electronic queries were retrospectively tested to assess performance in detecting DRPs revealed with gold-standard. For each electronic query, sensitivity, specificity, positive and negative predictive value were computed. RESULTS: Of 909 DRPs, 700 (77.8%) were used to create 366 electronic queries. Electronic queries correctly detected 77.1% of DRPs, median sensitivity and specificity reached 100.0% (IQRs, 100.0%–100.0%) and 99.7% (IQRs, 97.0%–100.0%); median positive predictive value and negative predictive value reached 50.0% (IQRs, 12.5%–100.0%) and 100.0% (IQRs, 100.0%–100.0%). Performances varied according to “triggers” (p < 0.001, best performance in terms of predictive positive value when exclusively involving drug prescriptions). CONCLUSION: Electronic queries based on electronic heath records had high sensitivity and negative predictive value and acceptable specificity and positive predictive value and may contribute to facilitate medication review. Implementing some of these electronic queries (the most effective and clinically relevant) in current practice will allow a better assessment of their impact on the efficiency of the clinical pharmacist. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11096-022-01505-5.
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spelling pubmed-101477482023-04-30 Development and retrospective evaluation of a clinical decision support system for the efficient detection of drug-related problems by clinical pharmacists Skalafouris, Christian Blanc, Anne-Laure Grosgurin, Olivier Marti, Christophe Samer, Caroline Lovis, Christian Bonnabry, Pascal Guignard, Bertrand Int J Clin Pharm Research Article BACKGROUND: Clinical decision support systems (CDSS) can help identify drug-related problems (DRPs). However, the alert specificity remains variable. Defining more relevant alerts for detecting DRPs would improve CDSS. AIM: Develop electronic queries that assist pharmacists in conducting medication reviews and an assessment of the performance of this model to detect DRPs. METHOD: Electronic queries were set up in CDSS using “triggers” from electronic health records: drug prescriptions, laboratory values, medical problems, vital signs, demographics. They were based on a previous study where 315 patients admitted in internal medicine benefited from a multidisciplinary medication review (gold-standard) to highlight potential DRPs. Electronic queries were retrospectively tested to assess performance in detecting DRPs revealed with gold-standard. For each electronic query, sensitivity, specificity, positive and negative predictive value were computed. RESULTS: Of 909 DRPs, 700 (77.8%) were used to create 366 electronic queries. Electronic queries correctly detected 77.1% of DRPs, median sensitivity and specificity reached 100.0% (IQRs, 100.0%–100.0%) and 99.7% (IQRs, 97.0%–100.0%); median positive predictive value and negative predictive value reached 50.0% (IQRs, 12.5%–100.0%) and 100.0% (IQRs, 100.0%–100.0%). Performances varied according to “triggers” (p < 0.001, best performance in terms of predictive positive value when exclusively involving drug prescriptions). CONCLUSION: Electronic queries based on electronic heath records had high sensitivity and negative predictive value and acceptable specificity and positive predictive value and may contribute to facilitate medication review. Implementing some of these electronic queries (the most effective and clinically relevant) in current practice will allow a better assessment of their impact on the efficiency of the clinical pharmacist. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11096-022-01505-5. Springer International Publishing 2022-12-14 2023 /pmc/articles/PMC10147748/ /pubmed/36515779 http://dx.doi.org/10.1007/s11096-022-01505-5 Text en © The Author(s) 2022, corrected publication 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research Article
Skalafouris, Christian
Blanc, Anne-Laure
Grosgurin, Olivier
Marti, Christophe
Samer, Caroline
Lovis, Christian
Bonnabry, Pascal
Guignard, Bertrand
Development and retrospective evaluation of a clinical decision support system for the efficient detection of drug-related problems by clinical pharmacists
title Development and retrospective evaluation of a clinical decision support system for the efficient detection of drug-related problems by clinical pharmacists
title_full Development and retrospective evaluation of a clinical decision support system for the efficient detection of drug-related problems by clinical pharmacists
title_fullStr Development and retrospective evaluation of a clinical decision support system for the efficient detection of drug-related problems by clinical pharmacists
title_full_unstemmed Development and retrospective evaluation of a clinical decision support system for the efficient detection of drug-related problems by clinical pharmacists
title_short Development and retrospective evaluation of a clinical decision support system for the efficient detection of drug-related problems by clinical pharmacists
title_sort development and retrospective evaluation of a clinical decision support system for the efficient detection of drug-related problems by clinical pharmacists
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10147748/
https://www.ncbi.nlm.nih.gov/pubmed/36515779
http://dx.doi.org/10.1007/s11096-022-01505-5
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