СLINICAL DECISION SUPPORT SYSTEMS: FOREIGN EXPERIENCE (LITERATURE REVIEW)
DOI:
https://doi.org/10.32782/health-2023.4.4Keywords:
clinical decision support systems, medical worker, computer technologiesAbstract
The article analyzes the foreign experience of using various clinical decision support systems. Clinical decision support systems are computing or technology systems designed to meet specific healthcare requirements. A traditional clinical decision support system consists of software designed to directly aid clinical decisions, in which individual patient characteristics are matched against a computerized clinical knowledge base, and patient-specific assessments and recommendations are then provided to the clinician for decision-making. Their advantages related to facilitating the work of a medical worker, opportunities to reduce the workload, optimizing the amount of time for diagnosis and treatment of diseases, and choosing the most optimal ways of solving the tasks are considered. Clinical decision support systems, when used correctly, can significantly improve the work of hospitals and accelerate the establishment of the correct diagnosis. The importance of information and technological progress and cybernetic opportunities for the implementation of clinical procedures is clarified. The “critical points” of using artificial intelligence and machines capable of self-learning are discussed, because data from clinical decision support systems is full of problems, including problems with understanding the logic used by artificial intelligence to create recommendations (black boxes), as well as problems with data availability. In our opinion, the moderate and professional attitude of the medical staff towards clinical decision support systems is important, as well as opportunities to improve their knowledge and skills in the “smart” use of computer technologies. We believe that it is expedient to train medical workers in new medical informatics by conducting courses and internships to implement clinical decision-making support systems in all healthcare institutions of Ukraine. The development and analysis of already existing domestic clinical decision-making support systems for the implementation of the abovementioned conclusions remains a promising direction.
References
Osheroff J.A., Teich J., Levick D., Saldana L., Velasco F., Sittig D., Rogers K., Jenders R. Improving Outcomes with Clinical Decision Support: An Implementer’s Guide, 2012. HIMSS Publishing, 2012.
Sim I., Gorman P., Greenes R.A., Haynes R.B., Kaplan B., Lehmann H., Tang P.C. Clinical decision support systems for the practice of evidence-based medicine. Journal of the American Medical Informatics. 2001. № 8 (6). Р. 527–534. https://doi.org/10.1136/jamia.2001.0080527.
Silveira D.V., Marcolino M.S., Machado E.L., et al. Development and Evaluation of a Mobile Decision Support System for Hypertension Management in the Primary Care Setting in Brazil: Mixed-Methods Field Study on Usability, Feasibility, and Utility. JMIR Mhealth Uhealth. 2019. Vol. 25. № 7 (3). Р. e9869. DOI: 10.2196/mhealth.9869. https://mhealth.jmir.org/2019/3/e9869/.
Wang J., Bao B., Shen P., et al. Using electronic health record data to establish a chronic kidney disease surveillance system in China: protocol for the China Kidney Disease Network (CK-NET)-Yinzhou Study. BMJ Open. 2019. Vol. 28.
№ 9 (8). Р. e030102. DOI: 10.1136/bmjopen-2019-030102. https://bmjopen.bmj.com/lookup/pmidlookup?view=long&pmid=31467053.
Adnan M., Peterkin D., McLaughlin A., Hill N. HL7 Middleware Framework for Laboratory Notifications for Notifiable Diseases. Stud Health Technol Inform. 2015. № 214. Р. 1–7.
Courbis A., Murray R.B., Arnavielhe S., et al. Electronic Clinical Decision Support System for allergic rhinitis management: MASK e-CDSS. Clin Exp Allergy. 2018. Vol. 20. № 48 (12). Р. 1640–1653. DOI: 10.1111/cea.13230.
Schuh C., de Bruin J.S., Seeling W. Clinical decision support systems at the Vienna General Hospital using Arden Syntax: Design, implementation, and integration. Artif Intell Med. 2018. № 92. Р. 24–33. DOI: 10.1016/j.artmed.2015.11.002.
Ely J.W., Osheroff J.A., Chambliss M.L. et al. Answering Physicians Clinical Questions: Obstacles and Potential Solutions. Journal of the American Medical Informatics Association. 2005. Vоl. 12 (2). P. 217–224. DOI: 10.1197/jamia.m1608.
Beeler P., Bates D., Hug B. Clinical decision support systems. Swiss Medical Weekly. 2014. Vol. 144. DOI: 10.4414/smw.2014.14073.
Wright A., Sittig D.F., Ash J.S. et al. Development and evaluation of a comprehensive clinical decision support taxonomy: Comparison of front-end tools in commercial and internally developed electronic health record systems. Journal of the American Medical Informatics Association. 2011. Vоl. 18 (3). P. 232–242. DOI: 10.1136/amiajnl-2011-000113.
De Dombal F. Computers, diagnoses and patients with acute abdominal pain. Arch. Emerg. Med. 1992. № 9. Р. 267–270.
Middleton B., Sittig D.F. Wright A. Clinical decision support: a 25 year retrospective and a 25 year vision. Yearbook of Medical Informatics. 2016. № 25 (S 01). Р. 103–116.
Dias D. Wearable health devices – vital sign monitoring, systems and technologies. 2018. Р. s18082414. https://doi.org/10.3390/s18082414.
Nuckols T.K., Smith-Spangler C., Morton S.C., et al. The effectiveness of computerized order entry at reducing preventable adverse drug events and medication errors in hospital settings: a systematic review and meta-analysis. Syst Rev. 2014. № 3. Р. 56.
Wolfstadt J.I., Gurwitz J.H., Field T.S., et al. The effect of computerized physician order entry with clinical decision support on the rates of adverse drug events: a systematic review. J Gen Intern Med. 2008. № 23 (4). Р. 451–458.
Deo R.C. Machine learning in medicine. Circulation. 2015. Р. 132.
Omididan Z., Hadianfar A. The role of clinical decision support systems in healthcare (1980–2010): a systematic review study. Jentashapir Sci. -Res Q. 2. 2011. Р. 125–134.
Kabane S.M. Healthcare and the Effect of Technology: Developments, Challenges and Advancements. Medical Information Science Reference. 2010.
Eckelt F., Remmler J., Kister T., et al. Improved patient safety through a clinical decision support system in laboratory medicine. Internist (Berl). 2020. Vol. 27. № 61 (5). Р. 452–459. DOI: 10.1007/s00108-020-00775-3.
Kjeldsen K. Hypokalemia and sudden cardiac death. Exp Clin Cardiol. 2010. № 15 (4). Р. e96–9. http://europepmc.org/abstract/MED/21264075.
Isern D., Moreno A.A. Systematic Literature Review of Agents Applied in Healthcare. J Med Syst. 2016 Vol. 21. № 40 (2). Р. 43. DOI: 10.1007/s10916-015-0376-2.
Isern D., Moreno A., Sánchez D., et al. Agent-based execution of personalised home care treatments. Appl Intell. 2009. Vol. 24. № 34 (2). Р. 155–180. DOI: 10.1007/s10489-009-0187-6.
Börner K., Scrivner O., Cross L.E., et al. Mapping the co-evolution of artificial intelligence, robotics, and the internet of things over 20 years (1998–2017). PLoS One. 2020. № 15 (12). Р. e0242984. Published 2020 Dec 2. DOI: 10.1371/journal.pone.0242984.
Charles Megan, DelVecchio Alex. Сlinical decision support system (CDSS). Content Development Strategist. This was last updated in July 2018. URL: https://www.techtarget.com/searchhealthit/definition/clinical-decision-support-system-CDSS.