Short summary

Due to an increase in medication use and an expansion in the older generation, preventable medication errors has also become an increasing problem. These preventable medication errors adds to the expenses of the health care system and in worst case, it can give substantial damage to the patient. The aim of this study is to test selected algorithms designed to identify patients in high risk of drug related problems and readmissions.

Rationale

To identify these preventable medication errors, algorithms that detect patient groups at a high risk have been developed. The idea is that by identifying a patient group at high risk, a medication review can be performed on a much smaller population and the medication reviews will be able to detect a larger number of medication errors. Thus, the cost of pharmacist will be reduced with the decreasing number of medication reviews and the reviews will be concentrated at the patients with potentially drug-related problems. Thereby the algorithms will decrease overall expenses of the health care system and will prevent harm to the patient.This present study will compare and test the effect of selected algorithms, for detection of patients at high risk of medication errors, in a dataset from a randomized controlled study performed by SAF, the hospital pharmacy of Fyn. From these results, it will be discussed if any of these algorithms could be applied in a Danish environment and the results will be compared to previous validations. 

Description of the cohort

1499 patients were enrolled from September 2013 through April 2015 in four different acute admission wards in Denmark (regional hospital Viborg, Hospital Holbæk, Odense university hospital in Odense and Svendborg). Patients were included if they were 18 years or older, used five or more prescribed drugs, spoke and understood Danish and were new acute admissions. Patients were excluded if they were included in a similar study, if they were terminally ill, suicidal, in custody, in isolation precaution and if they had aphasia or severe dementia. 

Data and biological material

Data was collected on medication use, diagnoses, hospital stay, drug-related problems and readmissions. 

Collaborating researchers and departments

Hospital Pharmacy of Funen, Odense University Hospital

• Pharmacist Trine Graabæk Hansen, PhD