From: Big data analysis techniques to address polypharmacy in patients – a scoping review
Author, Year | Country | Aim | No. of used observations | Method of data analysis | Outcome |
---|---|---|---|---|---|
Keine et al. 2019 [24] | USA | Evaluating a precision medicine platform to identify a multitude of polypharmacy problems in people with dementia and mild Alzheimer’s disease through the creation of personalized, multidomain treatment plans | 295 patients with a family history of Alzheimer’s disease or mild cognitive decline | Clinical decision support software (CDSS) with machine-learning algorithms | The system was able to identify a multitude of polypharmacy problems that individuals are currently facing. |
Kadra et al. 2015 [25] | UK | Extracting antipsychotic polypharmacy data from structured and free-text fields in electronic health records | 7201 patients with serious mental illness | Combination of natural language processing and a bespoke algorithm. | Individual instances of antipsychotic prescribing, 2 or more antipsychotics prescribed in any 6 week window; antipsychotic co-prescribing for 6 months |
Duke et al. 2010 [26] | USA | Creating a decision support system tailored to the evaluation of adverse reactions in patients on multiple medications | 16,340 unique drug and side-effect pairs, representing 250 common medications | A numeric score was assigned to reflect the strength of association between drug and effect. Based on this score, the system generates graphical adverse reaction maps for any user-selected combination of drugs. | This tool demonstrated a 60% reduction in time to complete a query (61 s vs. 155 s, p < 0.0001) with no decrease in accuracy |