Clinical validation of clinical decision support systems for medication review: A scoping review

Damoiseaux-Volman, Birgit A.; Medlock, Stephanie; Meulen, Delanie M. van der; Boer, Jesse de; Romijn, Johannes A.; Velde, Nathalie van der; Abu‐Hanna, Ameen

doi:10.1111/bcp.15160

Cited by 11 publications

(6 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The overall sensitivity of 88.3% aligns with the recommendations that a CDSS should maximise specificity while sensitivity is kept above 75% [ 35 ]. In a scoping review, the clinical validation of several CDSSs for drug-related problems revealed a sensitivity of 28%–85% and a specificity of 42–75% [ 36 ], though these were not studies of CDSS for TW management. A general acceptance rate above 60% is suggested for action-oriented interventions [ 37 ]; our TW algorithm compliance rate was 77.7%, which compares favourably with this standard and with the acceptance rates reported for primary care TW CDSSs (30–82%) [ 26 , 28 , 29 ].…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Triple Whammy Prescriptions After the Implementation of a Drug Safety Algorithm

Dahmke,

Schelshorn,

Fiumefreddo

et al. 2024

Drugs - Real World Outcomes

View full text Add to dashboard Cite

Background and Objective The term triple whammy (TW) refers to the concomitant use of non-steroidal anti-inflammatory drugs, diuretics, and angiotensin system inhibitors; this combination significantly increases the risk of acute kidney injury (AKI). To prevent this serious complication, we developed an electronic algorithm that detects TW prescriptions in patients with additional risk factors such as old age and impaired kidney function. The algorithm alerts a clinical pharmacist who then evaluates and forwards the alert to the prescribing physician. Methods We evaluated the performance of this algorithm in a retrospective observational study of clinical data from all adult patients admitted to the Cantonal Hospital of Aarau in Switzerland in 2021. We identified all patients who received a TW prescription, had a TW alert, or developed AKI during TW therapy. Algorithm performance was evaluated by calculating the sensitivity and specificity as a primary endpoint and determining the acceptance rate among clinical pharmacists and physicians as a secondary endpoint. Results Among 21,332 hospitalized patients, 290 patients had a TW prescription, of which 12 patients experienced AKI. Overall, 216 patients were detected by the alert algorithm, including 11 of 12 patients with AKI; the algorithm sensitivity is 88.3% with a specificity of 99.7%. Physician acceptance was high (77.7%), but clinical pharmacists were reluctant to forward the alerts to prescribers in some cases. Conclusion The TW algorithm is highly sensitive and specific in identifying patients with TW therapy at risk for AKI. The algorithm may help to prevent AKI in TW patients in the future. Supplementary Information The online version contains supplementary material available at 10.1007/s40801-023-00405-y.

show abstract

Section: Discussionmentioning

confidence: 99%

Evaluation of Triple Whammy Prescriptions After the Implementation of a Drug Safety Algorithm

Dahmke,

Schelshorn,

Fiumefreddo

et al. 2024

Drugs - Real World Outcomes

View full text Add to dashboard Cite

show abstract

“…To the best of our knowledge, this is the first scoping review which attempts to identify the characteristics of studies in which different types of CDSSs were used to effectively support clinical decision in different settings. Previous scoping reviews have focused on CDSSs for medication review, rare-disease diagnosis, non-knowledge-based clinical decision support tools and on CDSSs to be used in nursing homes 6 19–21…”

Section: Discussionmentioning

confidence: 99%

Clinical decision support systems to improve drug prescription and therapy optimisation in clinical practice: a scoping review

Armando

Miglio

Cosmo³

et al. 2023

BMJ Health Care Inform

View full text Add to dashboard Cite

ObjectiveClinical decision support systems (CDSSs) can reduce medical errors increasing drug prescription appropriateness. Deepening knowledge of existing CDSSs could increase their use by healthcare professionals in different settings (ie, hospitals, pharmacies, health research centres) of clinical practice. This review aims to identify the characteristics common to effective studies conducted with CDSSs.Materials and methodsThe article sources were Scopus, PubMed, Ovid MEDLINE and Web of Science, queried between January 2017 and January 2022. Inclusion criteria were prospective and retrospective studies that reported original research on CDSSs for clinical practice support; studies should describe a measurable comparison of the intervention or observation conducted with and without the CDSS; article language Italian or English. Reviews and studies with CDSSs used exclusively by patients were excluded. A Microsoft Excel spreadsheet was prepared to extract and summarise data from the included articles.ResultsThe search resulted in the identification of 2424 articles. After title and abstract screening, 136 studies remained, 42 of which were included for final evaluation. Most of the studies included rule-based CDSSs that are integrated into existing databases with the main purpose of managing disease-related problems. The majority of the selected studies (25 studies; 59.5%) were successful in supporting clinical practice, with most being pre–post intervention studies and involving the presence of a pharmacist.Discussion and conclusionA number of characteristics have been identified that may help the design of studies feasible to demonstrate the effectiveness of CDSSs. Further studies are needed to encourage CDSS use.

show abstract

“…Firstly, real-world patients are complex, with multiple conditions and medications. Current tools however lack intelligent algorithms to account for multiple complex interactions in a patient, for example, different drug–food and drug–gene interactions (Finkelstein et al, 2016 ; Mehta et al, 2021 ; Westerbeek et al, 2021 ; Damoiseaux-Volman et al, 2022 ; Mouazer et al, 2022 ). Additionally, an important consideration in precision medicine is distinguishing between theoretical and clinically relevant drug interactions for a particular patient, which is another limitation.…”

Section: Current Approach To Precision Medicine In Polypharmacymentioning

confidence: 99%

Polypharmacy and precision medicine

Fujita

Masnoon

Mach

et al. 2023

Camb. prisms Precis. med.

View full text Add to dashboard Cite

Precision medicine is an approach to maximise the effectiveness of disease treatment and prevention and minimise harm from medications by considering relevant demographic, clinical, genomic and environmental factors in making treatment decisions. Precision medicine is complex, even for decisions about single drugs for single diseases, as it requires expert consideration of multiple measurable factors that affect pharmacokinetics and pharmacodynamics, and many patient-specific variables. Given the increasing number of patients with multiple conditions and medications, there is a need to apply lessons learned from precision medicine in monotherapy and single disease management to optimise polypharmacy. However, precision medicine for optimisation of polypharmacy is particularly challenging because of the vast number of interacting factors that influence drug use and response. In this narrative review, we aim to provide and apply the latest research findings to achieve precision medicine in the context of polypharmacy. Specifically, this review aims to (1) summarise challenges in achieving precision medicine specific to polypharmacy; (2) synthesise the current approaches to precision medicine in polypharmacy; (3) provide a summary of the literature in the field of prediction of unknown drug–drug interactions (DDI) and (4) propose a novel approach to provide precision medicine for patients with polypharmacy. For our proposed model to be implemented in routine clinical practice, a comprehensive intervention bundle needs to be integrated into the electronic medical record using bioinformatic approaches on a wide range of data to predict the effects of polypharmacy regimens on an individual. In addition, clinicians need to be trained to interpret the results of data from sources including pharmacogenomic testing, DDI prediction and physiological-pharmacokinetic-pharmacodynamic modelling to inform their medication reviews. Future studies are needed to evaluate the efficacy of this model and to test generalisability so that it can be implemented at scale, aiming to improve outcomes in people with polypharmacy.

show abstract

Clinical validation of clinical decision support systems for medication review: A scoping review

Cited by 11 publications

References 51 publications

Evaluation of Triple Whammy Prescriptions After the Implementation of a Drug Safety Algorithm

Evaluation of Triple Whammy Prescriptions After the Implementation of a Drug Safety Algorithm

Clinical decision support systems to improve drug prescription and therapy optimisation in clinical practice: a scoping review

Polypharmacy and precision medicine

Contact Info

Product

Resources

About