Negligible impact of highly patient-specific decision support for potassium-increasing drug-drug interactions – a cluster-randomised controlled trial

Beeler, Patrick E.; Eschmann, Emmanuel; Schneemann, Markus; Blaser, Jürg

doi:10.4414/smw.2019.20035

Cited by 6 publications

(6 citation statements)

References 31 publications

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“…Optimization strategies for monitoring prescribed medications attempted to identify and reduce adverse drug reactions 40 or drug-drug interactions. 39 , 40 In 1 study, CDS was combined with pharmacogenetic data to help identify drug therapy problems in polypharmacy patients, 40 whereas in another study, 5 features of an existing CDS were optimized to deliver patient-specific decision support for potassium-increasing drug-drug interactions. Neither of these studies were reported as having a positive effect on their respective primary end points.…”

Section: Resultsmentioning

confidence: 99%

Optimizing Hospital Electronic Prescribing Systems: A Systematic Scoping Review

et al. 2022

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Objective: Considerable international investment in hospital electronic prescribing (ePrescribing) systems has been made, but despite this, it is proving difficult for most organizations to realize safety, quality, and efficiency gains in prescribing. The objective of this work was to develop policy-relevant insights into the optimization of hospital ePrescribing systems to maximize the benefits and minimize the risks of these expensive digital health infrastructures.Methods: We undertook a systematic scoping review of the literature by searching MEDLINE, Embase, and CINAHL databases. We searched for primary studies reporting on ePrescribing optimization strategies and independently screened and abstracted data until saturation was achieved. Findings were theoretically and thematically synthesized taking a medicine life-cycle perspective, incorporating consultative phases with domain experts.Results: We identified 23,609 potentially eligible studies from which 1367 satisfied our inclusion criteria. Thematic synthesis was conducted on a data set of 76 studies, of which 48 were based in the United States. Key approaches to optimization included the following: stakeholder engagement, system or process redesign, technological innovations, and education and training packages. Single-component interventions (n = 26) described technological optimization strategies focusing on a single, specific step in the prescribing process. Multicomponent interventions (n = 50) used a combination of optimization strategies, typically targeting multiple steps in the medicines management process.Discussion: We identified numerous optimization strategies for enhancing the performance of ePrescribing systems. Key considerations for ePrescribing optimization include meaningful stakeholder engagement to reconceptualize the service delivery model and implementing technological innovations with supporting training packages to simultaneously impact on different facets of the medicines management process.

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Section: Resultsmentioning

confidence: 99%

Optimizing Hospital Electronic Prescribing Systems: A Systematic Scoping Review

et al. 2022

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“…However, when the side effects were low (p value = 0.02), it had effects on the continuous use of the medication. In addition, text messages had an effect on the physicians' knowledge in using medication with fatty foods (p value<0.0001) [59].…”

Section: The Effect Of Cdss On Malariamentioning

confidence: 98%

“…[49]; Avansino et al [51], and Bruxvoort et al [59]. Because of the limited number of patients in these trials, we decided to exclude them from our meta-analysis.…”

Section: Statistical and Sensitivity Analysismentioning

confidence: 99%

The effects of clinical decision support system for prescribing medication on patient outcomes and physician practice performance: a systematic review and meta-analysis

Moghadam

Sadoughi

Velayati

et al. 2020

Preprint

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Background: The clinical decision support systems (CDSSs) for prescribing is one of the technologies aimed at improving physician practice performance and patient outcomes by reducing medication prescription errors. This study, thus, was conducted to investigate the effect of various CDSSs on physician practice performance and patient outcomes.Methods: This systematic review was conducted by searching in PubMed, EMBASE, Web of Science, Scopus, and Cochrane Library from 2005 to 2019. Two researchers independently evaluated the studies. Any discrepancies over the eligibility of the studies between the two researchers were then resolved by consulting a third researcher. Finally, we extracted data from the articles. Then, we conducted a meta-analysis based on medication subgroups, CDSS type subgroups, and outcome categories. Also, we presented a narrative form of the findings. In the meantime, we applied random-effects model to estimate the effects of CDSS on patient outcomes and physician practice performance with 95% confidence interval. Q statistics and I2 were then used to measure heterogeneity.Results: Based on the inclusion criteria, 45 studies were considered eligible for the analysis in this review. The CDSS for prescribing medications/COPE were used for various diseases such as cardiovascular diseases, hypertension, diabetes, gastrointestinal and respiratory diseases, AIDS, appendicitis, kidney disease, malaria, high blood potassium, and mental diseases. Meanwhile, other cases such as the concurrent prescription of multiple drugs for patients and its effects on the above-mentioned outcomes were evaluated. The analysis shows that in some cases the application of CDSS provides positive effects on patient outcomes and physician practice performance (std diff in means = 0.114, 95% CI: 0.090 to 0.138). Also, it was statistically significant for outcome groups such as those showing improved outcomes on physician practice performance and patient outcome or both. However, no significant difference was observed between some other cases and conventional methods. We think that this could be due to the disease type, the quantity, and the type of CDSS requirements that influenced the comparison. Overall, all types of CDSSs have positive effects on outcomes including combinational types and other types that only cause messages when appropriate and necessary.Conclusions: Our findings suggest that positive effects of the CDSS can be attributed to factors such as user-friendliness, compliance with clinical guidelines, patient and physician cooperation, integration of electronic health records, CDSS and pharmaceutical systems, consideration of the views of physicians in assessing the importance of CDSS alerts, and the real-time alerts in the prescription.

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“…Thus, patient safety could be increased [48]. In another study, the time lapse in hyperkalemia monitoring (p-value=0.20) and the incidence rate of hyperkalemia (p-value=0.22) did not differ significantly even with use of three different kind of reminder and alert based CDSSs [49]. Table 11 shows the result briefly.…”

Section: The Effect Of Cdss On Increasing Blood Potassiummentioning

confidence: 99%

The effects of clinical decision support system for prescribing on patient outcomes and physician practice performance: A systematic review

Moghadam¹,

Velayati²,

Sadoughi³

et al. 2020

Preprint

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Background The clinical decision support systems(CDSSs) for prescription medications is one of the technologies aimed at improving physician practice behavior and patient outcomes by reducing drug prescription errors. This study was conducted to investigate the effect of various CDSSs on physician practice behavior and patient outcomes.Methods This systematic review was conducted by searching in PubMed, EMBASE, Web of Science, Scopus and Cochrane Library from January 2005 to November 2019. Two researchers independently evaluated the studies. Any discrepancies over the eligibility of the studies between the two researchers were then resolved by consulting a third researcher. Finally, data were extracted from the articles; however, we could not able to conduct meta-analysis due to the heterogeneity of the studies and the narrative form of the findings.Results Based on the inclusion criteria, 46 studies were considered eligible for the analysis in this review. The CDSS for prescription medications had been used for various diseases, namely cardiovascular diseases, hypertension, diabetes, gastrointestinal and respiratory diseases, AIDS, appendicitis, kidney disease, malaria, high blood potassium, mental disease. Meanwhile, other cases such as the concurrent prescription of multiple drugs for the patient and its effects on the above-mentioned outcomes were evaluated. The analysis shows that in some cases the application of CDSS provides positive effects on patient outcomes and physician practice behaviors. No significant difference was observed in comparison between some other cases and conventional methods. We think that this could be due to the disease type, the quantity and type of CDSS requirements that influence the comparison.Conclusions Our findings suggest that the positive effects of the CDSS are due to factors such as user-friendliness, compliance with clinical guidelines, patient and doctor cooperation, integration of electronic health records, CDSS and pharmaceutical systems, consideration of the views of physicians in assessing the importance of CDSS alerts and their real-time alerts in the prescription.Registration number on PROSPERO CRD42018079936.

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Negligible impact of highly patient-specific decision support for potassium-increasing drug-drug interactions – a cluster-randomised controlled trial

Cited by 6 publications

References 31 publications

Optimizing Hospital Electronic Prescribing Systems: A Systematic Scoping Review

Optimizing Hospital Electronic Prescribing Systems: A Systematic Scoping Review

The effects of clinical decision support system for prescribing medication on patient outcomes and physician practice performance: a systematic review and meta-analysis

The effects of clinical decision support system for prescribing on patient outcomes and physician practice performance: A systematic review

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Negligible impact of highly patient-specific decision support for potassium-increasing drug-drug interactions – a cluster-randomised controlled trial

Cited by 6 publications

References 31 publications

Optimizing Hospital Electronic Prescribing Systems: A Systematic Scoping Review

Optimizing Hospital Electronic Prescribing Systems: A Systematic Scoping Review

­The effects of clinical decision support system for prescribing medication on patient outcomes and physician practice performance: a systematic review and meta-analysis

The effects of clinical decision support system for prescribing on patient outcomes and physician practice performance: A systematic review

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The effects of clinical decision support system for prescribing medication on patient outcomes and physician practice performance: a systematic review and meta-analysis