Decision support systems (DSS) in medicine can be classified into reference and intellectual, and the latter, in turn, into modeling and imitating human reasoning. Modeling systems are based on formalized expert knowledge, and imitating ones are based on models built by various multidimensional data analysis methods. DSS should be considered as medical technologies, therefore, after their development, assessing of analytical (technical) and clinical validity should follow, regardless of current national regulatory documents. Clinical validation have to be based on principles of evidence based medicine and demonstrate superiority, non-inferiority or equivalence to routine practice. Then a clinical and economic analysis can be carried out in order to justify the economic feasibility of DSS, and later health technology assessment can be performed.
References
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2. Garg A.X., Adhikari N. K.J., McDonald H. et al. Effects of Computerized Clinical Decision Support Systems on Practitioner Performance and Patient Outcomes. A Systematic Review. JAMA. 2005; 293: 1223–1238.
3. Tan K., Dear P. R.F., Newell S. J. Clinical decision support systems for neonatal care. Cochrane Database of Systematic Reviews 2005, Issue 2. Art. № CD004211. DOI: 10.1002/14651858.CD004211.pub2.
4. Jamal A., McKenzie K., Clark M. The impact of health information technology on the quality of medical and health care: a systematic review. Health Inf Manag. 2009; 38(3): 26–37.
5. Black A.D., Car J., Pagliari C. et al. The Impact of eHealth on the Quality and Safety of Health Care: A Systematic Overview. PLoS Med. 2011, 8(1): e1000387. doi:10.1371/journal.pmed.1000387.
6. Bright T.J., Wong A., Dhurjati R. et al. Effect of Clinical Decision-Support Systems. A Systematic Review. Ann Intern Med. 2012; 157: 29–43.
7. Moja L., Kwag K. H., Lytras T. et al. Effectiveness of Computerized Decision Support Systems Linked to Electronic Health Records: A Systematic Review and Meta-Analysis. Am J Public Health. 2014; 104: e12–e22. doi:10.2105/AJPH.2014.302164.
8. Varghese J., Kleine M., Gessner SD.I. et al. Effects of computerized decision support system implementations on patient outcomes in inpatient care: a systematic review. Journal of the American Medical Informatics Association. 2018 ; 25(5) : 593–602. doi : 10.1093/ jamia /ocx100.
9. Pombo N., Araújo P., Viana J. Knowledge discovery in clinical decision support systems for pain management: a systematic review. Artif Intell Med. 2014; 60(1): 1–11. doi : 10.1016/j.artmed.2013.11.005.
10. Tomaselli Muensterman E., Tisdale J.E. Predictive Analytics for Identification of Patients at Risk for QT Interval Prolongation: A Systematic Review. Pharmacotherapy. 2018; 38(8): 813–821. doi : 10.1002/ phar.2146.
11. Arani L.A., Hosseini A., Asadi F., Masoud S.A., Nazemi E. Intelligent Computer Systems for Multiple Sclerosis Diagnosis: a Systematic Review of Reasoning Techniques and Methods. Acta Inform Med. 2018; 26(4): 258–264. doi : 10.5455/aim.2018.26.258–264.
12. Liu X., Faes L., Kale A.U. et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. The Lancet Digital Health. 2019 ; 1 ( 6 ): e271-e297. doi : 10.1016/S2589–7500 (19) 30123 2.
13. Software as a Medical Device (SAMD): Clinical Evaluation Guidance for Industry and Food and Drug Administration Staff. Document issued on December 8, 2017. Available at: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/software-medical-device-samd-clinical-evaluation-guidance-industry-and-food-and-drug-administration. Last accessed on 15.12.2019.
14. Moore T.J., Zhang H., Anderson G., Alexander G. C. Estimated Costs of Pivotal Trials for Novel Therapeutic Agents Approved by the US Food and Drug Administration, 2015–2016. JAMA Intern Med. 2018; 178(11): 1451–1457. doi : 10.1001/jamainternmed.2018.3931.
15. Rebrova O.Y u., Fedyaeva V.K., Hachatryan G.R. Adaptaciya i validizaciya voprosnika dlya ocenki riska sistematicheskih oshibok v randomizirovannyh kontroliruemyh ispytaniyah. Medicinskie tekhnologii. Ocenka i vybor. 2015 ; 1 (19) : 9–17. (In Russ).
16. Rebrova O.Y u., Fedyaeva V.K. Ocenka riska sistematicheskih oshibok v odnomomentnyh issledovaniyah diagnosticheskih testov : russkoyazychnaya versiya voprosnika QUADAS. Medicinskie tekhnologii. Ocenka i vybor. 2017 ; 27 ( 1 ): 11–14. (In Russ).
17. Moher D., Hopewell S., Schulz K. F. et al. CONSORT 2010 Explanation and Elaboration: updated guidelines for reporting parallel group randomised trials. BMJ. 2010; 340: c869 doi : 10.1136/bmj.c869.
18. Bossuyt P.M., Reitsma J. B., Bruns D. E. et al. STARD2015: An Updated List of Essential Items for Reporting Diagnostic Accuracy Studies. BMJ. 2015; 351: h5527. doi : 10.1136/bmj.h5527.
19. Stolbov A.P. O klassifikacii riskov primeneniya medicinskogo programmnogo obespecheniya v Evrazijskom ekonomicheskom soyuze. Vrach i informacionnye tekhnologii. 2019 ; 3 : 22–31. (In Russ).
20. Postanovlenie Pravitel'stva RF ot 27 dekabrya 2012 g. № 1416 (red. ot 31.05.2018) «Ob utverzhdenii Pravil gosudarstvennoj registracii medicinskih izdelij ». Spravochnaya pravovaya sistema Konsul'tantPlyus. (In Russ). Available at: http://www.consultant.ru/document/cons_doc_LAW_140066/. Last accessed on 15.12.2019.
21. Reshenie Soveta Evrazijskoj ekonomicheskoj komissii ot 12.02.2016 № 29 «O Pravilah provedeniya klinicheskih i kliniko-laboratornyh ispytanij ( issledovanij ) medicinskih izdelij ». Spravochnaya pravovaya sistema Konsul'tantPlyus. (In Russ). Available at: http://www.consultant.ru/document/cons_doc_ LAW_197969/ Last accessed on 15.12.2019.
22. Prikaz Ministerstva zdravoohraneniya RF ot 9 yanvarya 2014 g. № 2n «Ob utverzhdenii Poryadka provedeniya ocenki sootvetstviya medicinskih izdelij v forme tekhnicheskih ispytanij , toksikologicheskih issledovanij , klinicheskih ispytanij v celyah gosudarstvennoj registracii medicinskih izdelij ». (In Russ). Available at: http://base.garant.ru/70631448. Last accessed on 15.12.2019.
23. Clinical Decision Support Software Draft Guidance for Industry and Food and Drug Administration Staff. Document issued on September 27, 2019. Available at: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/clinical-decision-support-software. Last accessed on 15.12.2019.
24. Nadezhdina E.Y., Rebrova O.Yu., Grigoriev A.Y. et al. Prediction of recurrence and remission within 3 years in patients with Cushing disease after successful transnasal adenomectomy. Pituitary. 2019 ; 22 ( 6 ): 574–580. doi : 10.1007/s11102 019 00985 5. (In Russ).
25. Nadezhdina E.Y u., Rebrova O. Y u., Antyuh M.S., Grigor'ev A.Y u. Prognozirovanie recidiva u pacientov s bolezn'yu Icenko-Kushinga posle uspeshnoj endoskopicheskoj transnazal'noj adenomektomii : nejrosetevaya model' i ee programmnaya realizaciya. Vrach i informacionnye tekhnologii. 2019 ; 4 : 65–71. (In Russ).
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