A Text-Mining Algorithm for Identifying Drug Pairs Potentially Involved in Prescribing Cascades: Development and Internal Validation Within the Prescribing Inappropriateness Assessment (PINA) Digital Tool - Scorecard - MDSpire
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An Algorithm for Text Mining to Detect Potential Drug Pair Interactions in Prescribing Cascades: Development and Internal Validation Using the Prescribing Inappropriateness Assessment (PINA) Tool
Clinical Scorecard: An Algorithm for Text Mining to Detect Potential Drug Pair Interactions in Prescribing Cascades: Development and Internal Validation Using the Prescribing Inappropriateness Assessment (PINA) Tool
At a Glance
Category
Detail
Condition
Prescribing cascades (PCs)
Key Mechanisms
Identification of drug pairs potentially representing prescribing cascades during medication review.
Target Population
Patients exposed to polypharmacy, particularly older adults.
Care Setting
Medication review services in clinical settings.
Key Highlights
Algorithm developed to identify drug pairs for potential prescribing cascades.
Internal validation showed few spurious matches.
Broader terminology-based matching enhanced detection of drug pairs.
Tool prioritizes candidate drug pairs for clinical assessment.
Does not confirm prescribing cascades or initiate automatic deprescribing.
Guideline-Based Recommendations
Diagnosis
Accurate determination of prescribing cascades depends on temporal ordering of prescriptions.
Management
Utilization of the PINA tool for structured medication reviews.
Monitoring & Follow-up
Assessment of candidate drug pairs for potential adverse drug reactions.
Risks
Increased risks of drug-drug interactions and adverse drug reactions due to polypharmacy.
Patient & Prescribing Data
Older patients with multimorbidity and polypharmacy.
Medication burden complicated by over-the-counter medications and supplements.
Clinical Best Practices
Incorporate structured medication reviews in clinical practice.
Utilize established instruments like Beers Criteria and START/STOPP.