Preview

Clinical and experimental thyroidology

Advanced search

Efficacy of clinical decision support systems: methods and estimates

https://doi.org/10.14341/ket12377

Abstract

Clinical decision support (CDS) systems are the medical technologies that go through their life cycle. Evaluation of effectiveness and safety should be carried out at its various stages – at the development, in clinical trials, licensing, clinical and economic analysis, health technologies assessment. To date, the effectiveness and safety of CDS systems vary and are ambiguous – there are both successes and failures. Hundreds of clinical trials are carried out, and more than a hundred of systematic reviews are published. When evaluating the efficacy and safety of CDS systems, two types of outcomes are usually estimated: indicators of medical care (volume, time, costs, etc.), and patient outcomes (clinical and surrogate). A slight increase in physicians’ adherence to clinical guidelines has been observed, but it had very small influence on surrogate outcomes, and there is no effect on clinical patient outcomes. A slight increase in risk with respect to patient outcomes was found in only a few studies. However, the methodological quality of the evidence is very low. In this regard, a few products based on artificial intelligence have so far approached the licensing phase. The field of CDS systems is developing, but not yet sufficiently studied, and there is a long way to real successes ahead. Meanwhile, there is a wide gap between the postulated and empirically demonstrated benefits of CDS systems.

About the Author

Olga Yu. Rebrova
Endocrinology Research Centre; The Russian National Research Medical University named after N.I. Pirogov
Russian Federation

MD, PhD



References

1. Гусев А.В., Зарубина Т.В. Поддержка принятия врачебных решений в медицинских информационных системах медицинской организации // Врач и информационные технологии. 2017. №2. С. 60-72. [Gusev A.V., Zarubina T.V. Clinical Decisions Support in medical information systems of a medical organization // Vrach i informacionnye tehnologii. 2017;(2):60-72. (In Russ.)]

2. Реброва О.Ю. Жизненный цикл систем поддержки принятия врачебных решений как медицинских технологий // Врач и информационные технологии. 2020. №1. С. 27-37. [Rebrova O.Yu. Life cycle of decision support systems as medical technologies // Vrach i informacionnye tehnologii. 2020;(1): 27-37. (In Russ.)]

3. Письмо Росздравнадзора “О программном обеспечении” от 13.02.2020. [Pis’mo Roszdravnadzora “O programmnom obespechenii” ot 13.02.2020. (In Russ.)] https://www.roszdravnadzor.ru/i/upload/images/ 2020/2/14/1581670651.93473-1-10822.pdf

4. Black AD, 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. https://doi.org/10.1371/journal.pmed.1000387.

5. Bright TJ, Wong A, Dhurjati R, et al. Effect of clinical decision-support systems. A systematic review. Ann Intern Med. 2012;157:29-43. doi: 10.7326/0003-4819-157-1-201207030-00450.

6. Moja L, Kwag KH, 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(12):e12–e22. https://doi.org/10.2105/AJPH.2014.302164.

7. Varghese J, Kleine M, Gessner SDI, et al. Effects of computerized decision support system implementations on patient outcomes in inpatient care: a systematic review. J Am Med Inform Assoc. 2018;25(5):593-602. https://doi.org/10.1093/jamia/ocx100.

8. 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. https://doi.org/10.1016/j.artmed.2013.11.005.

9. Tomaselli Muensterman E, Tisdale JE. Predictive analytics for identification of patients at risk for QT interval prolongation: a systematic review. Pharmacotherapy. 2018;38(8):813-821. https://doi.org/10.1002/phar.2146.

10. Arani LA, Hosseini A, Asadi F, et al. Intelligent computer systems for multiple sclerosis diagnosis: a systematic review of reasoning techniques and methods. Acta Inform Med. 2018;26(4):258-264. https://doi.org/10.5455/aim.2018.26.258-264.

11. Cresswell K, Callaghan M, Khan S, et al. Investigating the use of data-driven artificial intelligence in computerised decision support systems for health and social care: a systematic review. Health Informatics J. 1460458219900452, 2020 Jan 22 [Online ahead of print]

12. Dissanayake PI, Colicchio TK, Cimino JJ. Using clinical reasoning ontologies to make smarter clinical decision support systems: a systematic review and data synthesis. J Am Med Inform Assoc. 2020;27(1):159-174. https://doi.org/10.1093/jamia/ocz169.

13. Liu X, Faes L, Kale AU, 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. https://doi.org/10.1016/S2589-7500(19)30123-2.

14. Electronic health records vendor to pay largest criminal fine in Vermont history and a total of $145 million to resolve criminal and civil investigations. Department of Justice, U.S. Attorney’s Office, District of Vermont // https://www.justice.gov/usao-vt/pr/electronic-health-records-vendor-pay-largest-criminal-fine-vermont-history-and-total-145.

15. Roshanov PS, Fernandes N, Wilczynski JM, et al. Features of effective computerised clinical decision support systems: meta-regression of 162 randomised trials. BMJ. 346, f657, 2013 Feb 14. https://doi.org/10.1136/bmj.f657.

16. Государственный реестр медицинских изделий и организаций (индивидуальных предпринимателей), осуществляющих производство и изготовление медицинских изделий. [Gosudarstvennyj reestr medicinskih izdelij i organizacij (individual’nyh predprinimatelej), osushhestvljajushhih proizvodstvo i izgotovlenie medicinskih izdelij. (In Russ.)] https://www.roszdravnadzor.ru/services/misearch


Supplementary files

1. Picture. Degrees of influence of DSS on the indicators of medical care and patient outcomes (according to [4]).
Subject
Type Other
View (41KB)    
Indexing metadata ▾

Review

For citations:


Rebrova O.Yu. Efficacy of clinical decision support systems: methods and estimates. Clinical and experimental thyroidology. 2019;15(4):148-155. (In Russ.) https://doi.org/10.14341/ket12377

Views: 4521


ISSN 1995-5472 (Print)
ISSN 2310-3787 (Online)