Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter November 9, 2021

Real-time monitoring of drug laboratory test interactions: a proof of concept

Jasmijn A. van Balveren EMAIL logo , Wilhelmine P.H.G. Verboeket-van de Venne , Carine J.M. Doggen , Lale Erdem-Eraslan , Albert J. de Graaf , Johannes G. Krabbe , Ruben E.A. Musson , Wytze P. Oosterhuis , Yolanda B. de Rijke , Heleen van der Sijs , Andrei N. Tintu , Rolf J. Verheul , Rein M.J. Hoedemakers and Ron Kusters



For the correct interpretation of test results, it is important to be aware of drug-laboratory test interactions (DLTIs). If DLTIs are not taken into account by clinicians, erroneous interpretation of test results may lead to a delayed or incorrect diagnosis, unnecessary diagnostic testing or therapy with possible harm for patients. A DLTI alert accompanying a laboratory test result could be a solution. The aim of this study was to test a multicentre proof of concept of an electronic clinical decision support system (CDSS) for real-time monitoring of DLTIs.


CDSS was implemented in three Dutch hospitals. So-called ‘clinical rules’ were programmed to alert medical specialists for possible DLTIs based on laboratory test results outside the reference range in combination with prescribed drugs. A selection of interactions from the DLTI database of the Dutch society of clinical chemistry and laboratory medicine were integrated in 43 clinical rules, including 24 tests and 25 drugs. During the period of one month all generated DTLI alerts were registered in the laboratory information system.


Approximately 65 DLTI alerts per day were detected in each hospital. Most DLTI alerts were generated in patients from the internal medicine and intensive care departments. The most frequently reported DLTI alerts were potassium-proton pump inhibitors (16%), potassium-beta blockers (11%) and creatine kinase-statins (11%).


This study shows that it is possible to alert for potential DLTIs in real-time with a CDSS. The CDSS was successfully implemented in three hospitals. Further research must reveal its usefulness in clinical practice.

Corresponding author: Jasmijn A. van Balveren, MD, Laboratory for Clinical Chemistry and Haematology, Jeroen Bosch Hospital, Henri Dunantstraat 1, PO Box 90153, ’s-Hertogenbosch, The Netherlands; and Department of Health Technology and Services Research, Technical Medical Centre, University of Twente, Enschede, The Netherlands, Phone: +31 (0)73 553 27 64, Fax: +31 (0)73 5532958, LinkedIn: Jasmijn van Balveren, E-mail:

Funding source: Stichting Kwaliteitsgelden Medisch Specialisten (SKMS)

Award Identifier / Grant number: 42678870


We thank all IT specialists of the participating hospitals and Paul de Clercq (founder of Gaston Medical) for their effort in implementing the CDSS.

  1. Research funding: Funding from Stichting Kwaliteitsgelden Medisch Specialisten (SKMS), grant number 42678870.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: Authors state no conflict of interest.

  4. Informed consent: Not applicable.

  5. Ethical approval: The local Institutional Review Board deemed the study exempt from review.


1. Young, D. Effects of drugs on clinical laboratory tests, 5th ed. Washington: AACC Press; 2000.Search in Google Scholar

2. Vroemen, WH, van Doorn, WP, Kimenai, DM, Wodzig, WK, de Boer, D, Bekers, O, et al.. Biotin interference in high-sensitivity cardiac troponin T testing: a real-world evaluation in acute cardiac care. Cardiovasc Res 2019;115:1950–1. in Google Scholar

3. Yao, H, Rayburn, ER, Shi, Q, Gao, L, Hu, W, Li, H. Fda-approved drugs that interfere with laboratory tests: a systematic search of us drug labels. Crit Rev Clin Lab Sci 2016:1–17. in Google Scholar

4. Vlasveld, LT, van ’t Wout, J, Castel, A. False elevation of chromogranin a due to proton pump inhibitors. Neth J Med 2011;69:207.Search in Google Scholar

5. Perera, NJ, Stewart, PM, Williams, PF, Chua, EL, Yue, DK, Twigg, SM. The danger of using inappropriate point-of-care glucose meters in patients on icodextrin dialysis. Diabet Med 2011;28:1272–6. in Google Scholar

6. Sunderman, FWJr. Drug interference in clinical biochemistry. CRC Crit Rev Clin Lab Sci 1970;1:427–49. in Google Scholar

7. Wepler, R, Rommel, K. Drugs and parameters in the laboratory medicine. Dtsch Med Wochenschr 1973;98:2307–11. in Google Scholar

8. Groves, WE, Gajewski, WH. Use of a clinical laboratory computer to warn of possible drug interference with test results. Comput Progr Biomed 1978;8:275–82. in Google Scholar

9. van Balveren, JA. Clinical usefulness of drug-laboratory test interaction alerts: a multicentre survey. Clin Chem Lab Med 2021;59:1239–45. in Google Scholar PubMed

10. van Balveren, JA, Verboeket-van de Venne, W, Erdem-Eraslan, L, de Graaf, AJ, Loot, AE, Musson, REA, et al.. Impact of interactions between drugs and laboratory test results on diagnostic test interpretation – a systematic review. Clin Chem Lab Med 2018;56:2004–9. in Google Scholar PubMed

11. Werkgroep geneesmiddel-test interacties. Leidraad interactie klinisch-chemische parameters en geneesmiddelengebruik. Ned Tijdschr Klin Chem Lab 2017;42:37–49.Search in Google Scholar

12. Neubert, A, Dormann, H, Prokosch, HU, Burkle, T, Rascher, W, Sojer, R, et al.. E-pharmacovigilance: development and implementation of a computable knowledge base to identify adverse drug reactions. Br J Clin Pharmacol 2013;1:69–77. in Google Scholar PubMed PubMed Central

13. Helmons, PJ, Suijkerbuijk, BO, Nannan Panday, PV, Kosterink, JG. Drug-drug interaction checking assisted by clinical decision support: a return on investment analysis. J Am Med Inf Assoc 2015;22:764–72. in Google Scholar PubMed

14. de Clercq, PA, Blom, JA, Korsten, HH, Hasman, A. Approaches for creating computer-interpretable guidelines that facilitate decision support. Artif Intell Med 2004;31:1–27. in Google Scholar

15. de Clercq, PA, Hasman, A, Blom, JA, Korsten, HH. Design and implementation of a framework to support the development of clinical guidelines. Int J Med Inform 2001;64:285–318. in Google Scholar

16. Kailajarvi, M, Takala, T, Gronroos, P, Tryding, N, Viikari, J, Irjala, K, et al.. Reminders of drug effects on laboratory test results. Clin Chem 2000;46:1395–400.10.1093/clinchem/46.9.1395Search in Google Scholar

17. van der Sijs, H, Aarts, J, Vulto, A, Berg, M. Overriding of drug safety alerts in computerized physician order entry. J Am Med Inf Assoc 2006;13:138–47. in Google Scholar PubMed PubMed Central

18. Bates, DW, Kuperman, GJ, Wang, S, Gandhi, T, Kittler, A, Volk, L, et al.. Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. J Am Med Inf Assoc 2003;10:523–30. in Google Scholar PubMed PubMed Central

19. Horsky, J, Aarts, J, Verheul, L, Seger, DL, van der Sijs, H, Bates, DW. Clinical reasoning in the context of active decision support during medication prescribing. Int J Med Inform 2017;97:1–11. in Google Scholar PubMed

20. Friedman, RB, Young, DS, Beatty, ES. Automated monitoring of drug-test interactions. Clin Pharmacol Ther 1978;24:16–21. in Google Scholar PubMed

21. Rudolf, JW, Dighe, AS. Decision support tools within the electronic health record. Clin Lab Med 2019;39:197–213. in Google Scholar PubMed

22. Procop, GW, Weathers, AL, Reddy, AJ. Operational aspects of a clinical decision support program. Clin Lab Med 2019;39:215–29. in Google Scholar PubMed

Received: 2021-07-12
Accepted: 2021-10-28
Published Online: 2021-11-09
Published in Print: 2022-01-27

© 2021 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 9.12.2022 from
Scroll Up Arrow