A clinical context based decision support system to prevent potential drug-drug interactions in kidney transplant recipients: a field study

Background:Drug-drug interactions (DDIs) are considered as one of the major causes of adverse drug events and can compromise patient safety. Clinical decision support systems (CDSS) can prevent DDIs and improve the quality of medication prescriptions. The purpose of this study was to design and implement an alerting CDSS for the most common DDIs occurring in prescriptions of kidney transplant recipients.Material and Method:A prospective, mix-method study was carried out at the kidney transplantation clinic of Urmia University of Medical Sciences in 2017. At first, a prospective cross-sectional study detected all serious and significant potential drug-drug interactions (pDDIs) occurring in the prescriptions of all transplant recipients visited during a 2-month period. A checklist including demographic and medication data of recipients was used to collect the data. The Medscape DDI checker tool was used to determine all serious and significant interactions among the recipients’ active medications. Data were analyzed using descriptive statistical methods. Then, a comprehensive qualitative study was conducted using interviews (with 6 clinicians) and observations of the prescription workflow in the kidney transplantation clinic (for 2 months) in order to model a pDDI CDSS. Based on the common pDDIs, our clinical context, and considering internationally published guidelines, a pDDI CDSS was designed and its performance in detecting pDDIs was evaluated in comparison to the Medscape and using medication lists of 100 randomly selected patients.Results:We collected all medication data of 595 kidney recipients, from which 384 (64.48%) were male. Mean transplant age at the time of visits was 6.45 ± 5.43 years. Among the 6524 detected pDDIs, we identified 2 contraindicated, 44 serious, and 219 significant types of pDDIs. Ninety five percent of recipients experienced at least one contraindicated or serious pDDIs. A majority of drug-drug interactions occurred between the ages of 50 to 59. The most common pDDIs pairs were Cyclosporine-Diltiazem, Cyclosporine-Mycophenolate Mofetil, Omeprazole-Mycophenolate Mofetil, Cyclosporine-Prednisolone, Diltiazem-Prednisolone and Diltiazem-Mycophenolate Mofetil. Based on our qualitative data, the requirements of the prescription process were used to design a pDDI alerting CDSS, which was incorporated into the computerized order entry module of a home-grown transplant clinical management system. In order to increase the acceptance and use of the pDDI-CDSS by clinicians and to reduce alert fatigue, the system was designed with capability of adjusting alerts appearing based on the physicians’ clinical judgment and their patients’ clinical condition. The system went through multiple tests in a lab till sensitivity, specificity and accuracy rates of 100% in comparison to the Medscape tool were achieved.Conclusion:Our study showed a high rate of pDDIs in kidney transplant recipients which merits further attention. Our pDDI-CDSS has the potential to support clinicians in the prescription process because it was designed based on the international guidelines and the local knowledge of pDDIs and has the flexibility to accommodate the needs of our clinical context.

Keywords:

Drug-drug Interactions , Clinical Decision Support Systems , Medication Errors , Kidney Transplantation