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

Zahra Niazkhani, Mahsa Fereidoni, Parviz Rashidi Khazaee, Afshin Shiva, Khadijeh Makhdoomi, Andrew Georgiou, Habibollah Pirnejad

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
32 Downloads (Pure)


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 and 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.

Original languageEnglish
Article number196
Pages (from-to)1-13
Number of pages13
JournalBMC Medical Informatics and Decision Making
Issue number1
Publication statusPublished - 20 Aug 2020

Bibliographical note

Copyright the Author(s) 2020. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


  • Clinical decision support systems
  • Drug-lab interactions
  • Kidney transplant
  • Medication errors
  • Patient safety


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