A critical evaluation of clinical decision support for the detection of drug–drug interactions

Smithburger, Pamela L.; Buckley, Mitchell S.; Bejian, Sharon; Burenheide, Katie; Kane‐Gill, Sandra L.

doi:10.1517/14740338.2011.583916

Cited by 106 publications

(101 citation statements)

References 37 publications

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“…Other studies of decision support systems showed that these do not perform as expected [29] . Inappropriate timing and need to click to access information [24] and alert fatigue [30] were reported as the main reasons. Improving the human-computer interface, providing recommendations for patients in addition to practitioners [31] , prioritizing and filtering recommendations for the user [32] can improve the effectiveness of CDSS interventions.…”

Section: Discussionmentioning

confidence: 99%

Effects of Computerized Decision Support Systems on Management of Atrial Fibrillation: A Scoping Review

Sheibani

Nabovati

Sheibani

et al. 2017

Journal of Atrial Fibrillation

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Background: Potential role of computerized decision support system on management of atrial fibrillation is not well understood. Objectives: To systematically review studies that evaluate the effects of computerized decision support systems and decision aids on aspects pertaining to atrial fibrillation.Data Sources: We searched Medline, Scopus and Cochrane database. Last date of search was 2016, January 10. Selection criteria: Computerized decision support systems that help manage atrial fibrillation and decision aids that provide useful knowledge for patients with atrial fibrillation and help them to self-care.Data collection and analysis: Two reviewers extracted data and summarized findings. Due to heterogeneity, meta-analysis was not feasible; mean differences of outcomes and confidence intervals for a difference between two Means were reported.Results: Seven eligible studies were included in the final review. There was one observational study without controls, three observational studies with controls, one Non-Randomized Controlled Trial and two Randomized Controlled Trials. The interventions were three decision aids that were used by patients and four computerized decision support systems. Main outcomes of studies were: stroke events and major bleeding (one article), Changing doctor-nurse behavior (three articles), Time in therapeutic International Normalized Ratio range (one article), decision conflict scale (two articles), patient knowledge and anxiety about stroke and bleeding (two articles).Conclusions: A computerized decision support system may decrease decision conflict and increase knowledge of patients with atrial fibrillation (AF) about risks of AF and AF treatments. Effect of computerized decision support system on outcomes such as changing doctornurse behavior, anxiety about stroke and bleeding and stroke events could not be shown.We need more studies to evaluate the role of computerized decision support system in patients with atrial fibrillation.

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Section: Discussionmentioning

confidence: 99%

Effects of Computerized Decision Support Systems on Management of Atrial Fibrillation: A Scoping Review

Sheibani

Nabovati

Sheibani

et al. 2017

Journal of Atrial Fibrillation

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“…Predictive values, especially the positive predictive value in the case of CPOEs, is closely linked to the prevalence of the alert considered. This has been demonstrated for drug-drug interactions decision support that has a very low positive predictive value [204,205]. Using the characteristics of the local population of patients of a given organization can provide precise and real-time prevalence, thus allowing to adapt decision support and improve its positive predictive value.…”

Section: H Clinical Practice and Research Integrationmentioning

confidence: 99%

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Meystre¹,

Lovis²,

Bürkle³

et al. 2017

Yearb Med Inform

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SummaryObjective: To perform a review of recent research in clinical data reuse or secondary use, and envision future advances in this field. Methods: The review is based on a large literature search in MEDLINE (through PubMed), conference proceedings, and the ACM Digital Library, focusing only on research published between 2005 and early 2016. Each selected publication was reviewed by the authors, and a structured analysis and summarization of its content was developed. Results: The initial search produced 359 publications, reduced after a manual examination of abstracts and full publications. The following aspects of clinical data reuse are discussed: motivations and challenges, privacy and ethical concerns, data integration and interoperability, data models and terminologies, unstructured data reuse, structured data mining, clinical practice and research integration, and examples of clinical data reuse (quality measurement and learning healthcare systems). Conclusion: Reuse of clinical data is a fast-growing field recognized as essential to realize the potentials for high quality healthcare, improved healthcare management, reduced healthcare costs, population health management, and effective clinical research.

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“…This approach, however, needs refinement as patient-specific information is often lacking and data on current medications rely on different sources, for example, patient, carer and doctor, particularly in the acute setting. Moreover, several drug--drug interactions are unlikely to be clinically significant, with potential for over-reporting [18,19]. At least two recently developed strategies, that is, medication reconciliation and trigger tools methodology, might help to mitigate the impact of ADRs in this group by enhancing the monitoring of drug therapy in the context of acute changes in clinical status and organ function.…”

Section: Potential Strategies To Mitigate the Impact Of Adverse Drugmentioning

confidence: 99%

Predicting and detecting adverse drug reactions in old age: challenges and opportunities

Mangoni

2012

Expert Opinion on Drug Metabolism & Toxicology

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A critical evaluation of clinical decision support for the detection of drug–drug interactions

Cited by 106 publications

References 37 publications

Effects of Computerized Decision Support Systems on Management of Atrial Fibrillation: A Scoping Review

Effects of Computerized Decision Support Systems on Management of Atrial Fibrillation: A Scoping Review

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Predicting and detecting adverse drug reactions in old age: challenges and opportunities

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