Translation of evidence into kidney transplant clinical practice: managing drug-lab interactions by a context-aware clinical decision support system

Abstract

Background Drug-laboratory (lab) interactions (DLIs) are a common source of preventable medication errors. Clinical decision support systems (CDSSs) are promising tools to decrease such errors by improving prescription quality in terms of lab values. However, alert fatigue counteracts their impact. We aimed to develop a novel user-friendly, evidence-based, clinical context-aware CDSS to alert nephrologists about DLIs clinically important lab values in prescriptions of kidney recipients . Methods For the most frequently prescribed medications identified by a prospective cross-sectional study in a kidney transplant clinic, DLI-rules were extracted using main pharmacology references and clinical inputs from clinicians. A CDSS was then developed linking a computerized prescription system and lab records. The system performance was tested using data of both fictitious and real patients. The “Questionnaire for User Interface Satisfaction” was used to measure user satisfaction of the human-computer interface. Results Among 27 study medications, 17 needed adjustments regarding renal function, 15 required considerations based on hepatic function, 8 had drug-pregnancy interactions, and 13 required baselines or follow-up lab monitoring. Using IF & THEN rules and the contents of associated alert, a DLI-alerting CDSS was designed. To avoid alert fatigue, the alert appearance was considered as interruptive only when medications with serious risks were contraindicated or needed to be discontinued or adjusted. Other alerts appeared in a non-interruptive mode with visual clues on the prescription window for easy, intuitive notice. When the system was used for real 100 patients, it correctly detected 260 DLIs and displayed 249 monitoring, seven hepatic, four pregnancy, and none renal alerts. The system delivered patient-specific recommendations based on individual lab values in real-time. Clinicians were highly satisfied with the usability of the system. Conclusions To our knowledge, this is the first study of a comprehensive DLI-CDSS for kidney transplant care. By alerting on considerations in renal and hepatic dysfunctions, maternal and fetal toxicity, or required lab monitoring, this system can potentially improve medication safety in kidney recipients. Our experience provides a strong foundation for designing specialized systems to promote individualized transplant follow-up care.

Unlocking Therapeutic Symphonies: Innovations in Clinical Decision Support for Drug-Disease Interactions in Kidney Transplantation

“…This study was conducted in an outpatient kidney transplant clinic at Urmia University of Medical Sciences, Urmia, Iran, in spring 2023. A renal transplant management system (RTMS) is available in this clinic, equipped with a computerized provider order entry (CPOE) and CDSSs for DDIs and drug-lab interactions [3,20]. The study was reviewed and approved by the institutional research ethics committee of the university.…”

“…Our decision support rules were categorized for major and moderate DDSIs based on the seriousness of their patient outcomes and the required responses, such as 'complete avoidance' or 'prescription with caution needing close symptom monitoring.' In line with the previous CDSS modules in the RTMS, the alert display was considered interruptive and non-interruptive, respectively [3,20]. To create workflow diagrams, Unified Modeling Language (UML) was used, and the CDSS system was programmed using the C#.Net programming language and Structured Query Language (SQL) Server databases.…”

Context-Aware Medical Systems within Healthcare Environments: A Systematic Scoping Review to Identify Subdomains and Significant Medical Contexts

“…One application of this that has been field tested is looking at patients' lab values and drug prescriptions to decide whether the drug dosage is incorrect or the drug itself should not have been prescribed given the patient's current kidney and liver function. Niazkhani et al used clinical guidelines regarding drugs prescribed by nephrologists at a kidney transplant clinic to determine the proper dosages and prescribing rules given a patient's specific context, such as their kidney function, liver function, pregnancy status, and other demographic data [33]. Their system was then field tested in 100 patients and used these rules to alert physicians when problematic drug lab interactions (DLIs) existed, of which 260 DLIs were found [33].…”

“…Niazkhani et al used clinical guidelines regarding drugs prescribed by nephrologists at a kidney transplant clinic to determine the proper dosages and prescribing rules given a patient's specific context, such as their kidney function, liver function, pregnancy status, and other demographic data [33]. Their system was then field tested in 100 patients and used these rules to alert physicians when problematic drug lab interactions (DLIs) existed, of which 260 DLIs were found [33]. The largest field-tested study identified was a similar system tested in a 721-bed hospital over 14 months developed by Cornu et al that used contexts such as patients' current medications, age, sex, last potassium levels, and renal function to develop clinical decision rules that warn physicians about dangerous drug-drug interactions [31].…”

Clinical Decision Support System Based on Hybrid Knowledge Modeling: A Case Study of Chronic Kidney Disease-Mineral and Bone Disorder Treatment

“…Niazkhani et al [ 53 ] proposed a context-aware CDSS for managing drug-laboratory interactions in order to reduce medication errors. The main focus of the study was to develop a user-friendly CDSS to accommodate drug-laboratory interactions (DLIs) while reducing the alert fatigue of clinicians.…”