Finally, the performance of the CDSS in detecting DDIs was evaluated iteratively by a multidisciplinary research team. A multidisciplinary team decided the design characteristics of pDDI-alerts in a CDSS considering the international recommendations and the inputs from our clinical context. Our clinically relevant pDDIs were checked with the Dutch “G-Standard”. We performed semi-structured interviews with five nephrologists and one informant nurse. Alongside the Stockley’s drug interactions reference, our clinicians were consulted with respect to the clinical relevance of detected pDDIs. The Medscape multi-drug interaction checker tool was used to detect pDDIs. Prescriptions of five nephrologists were prospectively recorded through non-participatory observations for two months. In this paper, we reported a methodology through which we applied knowledge from the clinical context and the international recommendations to develop a potential drug-drug interaction (pDDI) CDSS in the field of kidney transplantation. The effectiveness of the clinical decision support systems (CDSSs) is hampered by frequent workflow interruptions and alert fatigue because of alerts with little or no clinical relevance. Nevertheless, the authors believe that the extra expenditure of time employing a CDSS is outweighed by their benefits, including reduced ADR risks and safer clinical drug management. According to the pain physicians the CDSS AID was chosen as the preferred tool.Īpplying a CDSS to examine a patients drug regimen for potential DDIs causes an average extra expenditure of work time of 2:09 min, which extends patient treatment time by 25 % on average.
The CDSS AID required a total of 3:12:27 h for 97 patients with an average time of analysis of 0:01:59 h and the discovery of 170 DDIs. Using the Medscape interaction checker required a total of 1:28:35 h for 38 patients with an average of 0:01:58 h and a yield of 178 interactions. The AU tool took a total of 3:55:45 h with an average of 0:02:32 h for 93 analyzed patient regimens and led to the discovery of 261 DDIs. Additionally, a qualitative evaluation of the used check systems was performed, employing a questionnaire asking five pain physicians to compare and rate the performance and practicability of the three CDSSs. Only patient pharmacotherapies with at least two prescribed drugs and fitting the criteria of the corresponding CDSS were analyzed. CDSS were compared with respect to the expenditure of time and usability.
Measurements included the time needed for running the analysis and printing the results. The time needed to analyze patient pharmacotherapy for DDIs was taken with a stopwatch. Patient-specific medications were checked for potential drug-drug interactions (DDI) using two publicly available CDSS, Apotheken Umschau (AU) and Medscape (MS), and a commercially available CDSS AiDKlinik® (AID). Estimate the expenditure of computer-related worktime resulting from the use of clinical decision support systems (CDSS) to prevent adverse drug reactions (ADR) among patients undergoing chronic pain therapy and compare the employed check systems with respect to performance and practicability.ĭata were collected retrospectively from 113 medical records of patients under chronic pain therapy during 2012/2013.
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