Sultana J, Cutroneo P, Trifirò G. Clinical and economic burden of adverse drug reactions. J Pharmacol Pharmacother [Internet]. 2013 Dec [cited 2016 Nov 28];4(Suppl 1):S73–7. http://www.ncbi.nlm.nih.gov/pubmed/24347988.

Aspinall SL, Vu M, Moore V, Jiang R, Au A, Bounthavong M, et al. Estimated costs of severe adverse drug reactions resulting in hospitalization in the Veterans health administration. JAMA Netw Open [Internet]. 2022 Feb 1 [cited 2022 Mar 1];5(2):e2147909–e2147909. https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2788929.

Formica D, Sultana J, Cutroneo PM, Lucchesi S, Angelica R, Crisafulli S, et al. The economic burden of preventable adverse drug reactions: a systematic review of observational studies. Expert Opin Drug Saf [Internet]. 2018 [cited 2018 Jul 2];17(7):681–95. https://doi.org/10.1080/14740338.2018.1491547.

Pirnejad H, Amiri P, Niazkhani Z, Shiva A, Makhdoomi K, Abkhiz S, et al. Preventing potential drug-drug interactions through alerting decision support systems: A clinical context based methodology. Int J Med Inform [Internet]. 2019 Jul 1 [cited 2024 Mar 24];127:18–26. https://pubmed.ncbi.nlm.nih.gov/31128828/.

Gabriel MH, Powers C, Encinosa W, Bynum JPW. E-Prescribing and Adverse Drug Events: An Observational Study of the Medicare Part D Population With Diabetes. Med Care [Internet]. 2017 [cited 2024 Mar 24];55(5):456–62. https://pubmed.ncbi.nlm.nih.gov/28060051/.

Yeh ML, Chang YJ, Wang PY, Li YC, Hsu CY. Physicians’ responses to computerized drug-drug interaction alerts for outpatients. Comput Methods Programs Biomed [Internet]. 2013 Jul [cited 2024 Mar 24];111(1):17–25. https://pubmed.ncbi.nlm.nih.gov/23608682/.

Edrees H, Amato MG, Wong A, Seger DL, Bates DW. High-priority drug-drug interaction clinical decision support overrides in a newly implemented commercial computerized provider order-entry system: Override appropriateness and adverse drug events. J Am Med Inform Assoc [Internet]. 2020 Jun 1 [cited 2024 Mar 24];27(6):893–900. https://pubmed.ncbi.nlm.nih.gov/32337561/.

Bertsche T, Pfaff J, Schiller P, Kaltschmidt J, Pruszydlo MG, Stremmel W, et al. Prevention of adverse drug reactions in intensive care patients by personal intervention based on an electronic clinical decision support system. Intensive Care Med. 2010;36(4):665–72.

Article  PubMed  Google Scholar 

Hashemi F, van Gelder TG, Bollen CW, Liem YTB, Egberts TCG. The effect of a decision support system on the incidence of prescription errors in a PICU. J Clin Pharm Ther [Internet]. 2022 Mar 1 [cited 2024 Mar 24];47(3):330–44. https://pubmed.ncbi.nlm.nih.gov/34734650/.

Segal G, Segev A, Brom A, Lifshitz Y, Wasserstrum Y, Zimlichman E. Reducing drug prescription errors and adverse drug events by application of a probabilistic, machine-learning based clinical decision support system in an inpatient setting. J Am Med Inform Assoc [Internet]. 2019 Nov 15 [cited 2023 Nov 11];26(12):1560. /pmc/articles/PMC7647149/.

Ojeleye O, Avery A, Gupta V, Boyd M. The evidence for the effectiveness of safety alerts in electronic patient medication record systems at the point of pharmacy order entry: a systematic review. BMC Med Inform Decis Mak [Internet]. 2013 [cited 2024 Mar 24];13(1):69. /pmc/articles/PMC3702525/.

Damoiseaux-Volman BA, Medlock S, van der Meulen DM, de Boer J, Romijn JA, van der Velde N, et al. Clinical validation of clinical decision support systems for medication review: A scoping review. Br J Clin Pharmacol [Internet]. 2022 May 1 [cited 2024 Mar 24];88(5):2035. /pmc/articles/PMC9299995/.

Ferner RE, Coleman JJ. An algorithm for integrating contraindications into electronic prescribing decision support. Drug Saf [Internet]. 2010 [cited 2024 Mar 24];33(12):1089–96. https://pubmed.ncbi.nlm.nih.gov/20961164/.

Aufegger L, Serou N, Chen S, Franklin BD. Evaluating users’ experiences of electronic prescribing systems in relation to patient safety: a mixed methods study. BMC Med Inform Decis Mak [Internet]. 2020 Apr 3 [cited 2024 Mar 24];20(1). /pmc/articles/PMC7126479/.

Powers EM, Shiffman RN, Melnick ER, Hickner A, Sharifi M. Efficacy and unintended consequences of hard-stop alerts in electronic health record systems: a systematic review. J Am Med Inform Assoc. 2018;25(11):1556–66.

Article  PubMed  PubMed Central  Google Scholar 

When more information may not lead to better decisions | National Institutes of Health (NIH) [Internet]. [cited 2024 Mar 24]. https://www.nih.gov/news-events/nih-research-matters/when-more-information-may-not-lead-better-decisions.

Page N, Baysari MT, Westbrook JI. A systematic review of the effectiveness of interruptive medication prescribing alerts in hospital CPOE systems to change prescriber behavior and improve patient safety. Int J Med Inform. 2017;105:22–30.

Article  PubMed  CAS  Google Scholar 

Natsiavas P, Malousi A, Bousquet C, Jaulent MC, Koutkias V. Computational advances in drug safety: systematic and mapping review of knowledge engineering based approaches. Front Pharmacol [Internet]. 2019 May 17 [cited 2019 Jun 21];10:415. https://doi.org/10.3389/fphar.2019.00415/full.

Calvo-Cidoncha E, Camacho-Hernando C, Feu F, Pastor-Duran X, Codina-Jané C, Lozano-Rubí R. OntoPharma: ontology based clinical decision support system to reduce medication prescribing errors. BMC Med Inform Decis Mak [Internet]. 2022 Dec 1 [cited 2022 Oct 3];22(1):238. https://doi.org/10.1186/s12911-022-01979-3.

Bernonville S, Guillot B, Pedersen HG, Koutkias V, Beuscart R. The PSIP project for Adverse Drug Events prevention. IRBM [Internet]. 2013 Nov 1 [cited 2019 Feb 14];34(4–5):263–6. https://www.sciencedirect.com/science/article/pii/S1959031813000997.

Koutkias V, Kilintzis V, Stalidis G, Lazou K, Collyda C, Chazard E, et al. Constructing Clinical Decision Support Systems for Adverse Drug Event Prevention: A Knowledge-based Approach. AMIA Annual Symposium Proceedings [Internet]. 2010 [cited 2024 Mar 24];2010:402. /pmc/articles/PMC3041377/.

Melton BL, Zillich AJ, Saleem J, Russ AL, Tisdale JE, Overholser BR. Iterative development and evaluation of a pharmacogenomic-guided clinical decision support system for warfarin dosing. Appl Clin Inform [Internet]. 2016 [cited 2019 May 14];7(4):1088–106. http://www.ncbi.nlm.nih.gov/pubmed/27878205.

Fox J. A short account of Knowledge Engineering. Knowl Eng Rev [Internet]. 1984 Mar 7 [cited 2018 Dec 21];1(01):4. http://www.journals.cambridge.org/abstract_S0269888900000424.

Schreiber G. Chapter 25 knowledge engineering. Foundations of artificial intelligence. 2008 Jan 1;3:929–46.

Natsiavas P, Boyce RD, Jaulent MC, Koutkias V. OpenPVSignal: advancing information search, sharing and reuse on pharmacovigilance signals via FAIR principles and semantic web technologies. Front Pharmacol [Internet]. 2018 Jun 26 [cited 2018 Jun 26];9:609. https://doi.org/10.3389/fphar.2018.00609/full.

Herrero-Zazo M, Segura-Bedmar I, Hastings J, Martínez P. DINTO: using OWL ontologies and SWRL rules to infer drug–drug interactions and their mechanisms. J Chem Inf Model [Internet]. 2015 Aug [cited 2016 Jun 15];55(8):1698–707. https://doi.org/10.1021/acs.jcim.5b00119.

Declerck G, Hussain S, Daniel C, Yuksel M, Laleci GB, Twagirumukiza M, et al. Bridging data models and terminologies to support adverse drug event reporting using EHR data. Methods Inf Med [Internet]. 2015 Jan 22 [cited 2018 May 10];54(01):24–31. http://www.ncbi.nlm.nih.gov/pubmed/25487120.

Grammatikopoulou M, Lazarou I, Giannios G, Kakalou C, Zachariadou M, Zande M, et al. Electronic prescription systems in Greece: a large-scale survey of healthcare professionals’ perceptions. Arch Public Health 82, 68 (2024). https://doi.org/10.1186/s13690-024-01304-6

Kruchten P. Architectural blueprints—The “4+1” view model of software architecture. IEEE Softw. 1995;12:42–50.

Article  Google Scholar 

Chytas A, Dimitriadis V, Giannios G, Grammatikopoulou M, Nikolaidis G, Pliatsika J, et al. The PrescIT knowledge graph: supporting eprescription to prevent adverse drug reactions. Stud Health Technol Inform [Internet]. 2023 May 18 [cited 2024 Mar 24];302:551–5. https://pubmed.ncbi.nlm.nih.gov/37203746/.

Kilicoglu H, Shin D, Fiszman M, Rosemblat G, Rindflesch TC. SemMedDB: a PubMed-scale repository of biomedical semantic predications. Bioinformatics [Internet]. 2012 Dec 1 [cited 2018 Jul 23];28(23):3158–60. http://www.ncbi.nlm.nih.gov/pubmed/23044550.

Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, et al. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res [Internet]. 2006 Jan 1 [cited 2024 Mar 24];34(Database issue):D668. /pmc/articles/PMC1347430/.

Dimitriadis V, Chytas A, Grammatikopoulou M, Nikolaidis G, Pliatsika J, Zachariadou M, et al. Use of real-world data to support adverse drug reactions prevention during ePrescription. Stud Health Technol Inform [Internet]. 2023 Jun 29 [cited 2024 Mar 24];305:226–9. https://pubmed.ncbi.nlm.nih.gov/37387003/.

Grammatikopoulou M, Zachariadou M, Zande M, Giannios G, Chytas A, Karanikas H, et al. Evaluation of an electronic prescription platform: Clinicians’ feedback on three distinct services aiming to facilitate clinical decision and safer e-prescription. Research in Social and Administrative Pharmacy. 2024 Apr 17. https://doi.org/10.1016/j.sapharm.2024.04.004

Katsanos C, Tselios N, Liapis A. PSSUQ-GR: a first step towards standardization of the post-study system usability questionnaire in Greek. In: International conference of the ACM Greek SIGCHI Chapter. 2021 Nov 25.

Brooke J. SUS: a retrospective. J Usability Stud. 2013;8(2):29–40.

Google Scholar 

Brooke J. SUS: A “Quick and Dirty” Usability Scale. In: Jordan PW, Thomas B, McClelland IL, Weerdmeester B, editors. Usability evaluation in industry. 1st ed. London: CRC Press; 1996.

Natsiavas P, Stavropoulos TG, Pliatsios A, Karanikas H, Gavriilidis GI, Dimitriadis VK, et al. Using business process management notation to model therapeutic prescription protocols: the PrescIT approach. Public Health and Informatics: Proceedings of MIE 2021 [Internet]. 2021 Jul 1 [cited 2023 Jan 11];1089–90. https://doi.org/10.3233/SHTI210358.

Li RC, Asch SM, Shah NH. Developing a delivery science for artificial intelligence in healthcare. NPJ Digit Med [Internet]. 2020 Dec 21;3(1):107. http://www.nature.com/articles/s41746-020-00318-y.

Shah NH, Halamka JD, Saria S, Pencina M, Tazbaz T, Tripathi M, et al. A nationwide network of health AI assurance laboratories. JAMA [Internet]. 2024 Jan 16 [cited 2024 Mar 5];331(3):245–9. https://jamanetwork.com/journals/jama/fullarticle/2813425.