Results of Testing a Clinical Decision Support System for Management of Cognitive Disorders in Older Patients

Background. Differential diagnosis of combined cognitive and extrapyramidal disorders in older patients remains a complex clinical challenge requiring integration of large amounts of clinical and imaging data.

Objective. To evaluate diagnostic accuracy, organizational, clinical, and economic effects of the health infor­mation system (HIS) “Diagnosis and Treatment of Cognitive Disorders”.

Materials and Methods. The validation study included 196 patients (mean age 72.0 ± 7.0 years) with verified cognitive and/or motor disorders. For each observation, a profile of 147 features across 27 domains was loaded into the HIS. Diagnostic performance was determined by comparing the system’s leading hypothesis with the clinical reference diagnosis, as well as sensitivity, specificity, and positive and negative predictive values. In parallel, two groups (intervention, n=97 with HIS; control, n=99 without HIS) were compared regarding time to diagnosis, pharmacotherapy parameters, satisfaction of physicians and patients, and direct costs.

Results. The diagnoses were in complete agreement in 81.6% of cases; the reference diagnosis was among the first three hypotheses in 92.3%. Specific­ity for major nosological entities was 94-100%. In the HIS group, time to diagnosis decreased by 26.3 days (p < 0.001), number of visits from 4.0 to 2.3 (p < 0.001), physician’s time per case by 5.5 minutes (p < 0.001). The proportion of personalized treatment regimens increased from 47.5% to 86.6%, and the incidence of adverse drug events decreased from 28.3% to 10.3% (p = 0.001). Direct cost savings reached 7,231 rubles per patient per year, with a return on investment (ROI) of 3.8.

Conclusion. The HIS demonstrates high diagnostic accu­racy, contributes to faster patient routing, improves the quality and safety of pharmacotherapy, and achieves positive economic outcomes.

1. Livingston G., Huntley J., Sommerlad A. et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. The Lancet. 2020;396(10248):413-446. https://doi.org/10.1016/S0140-6736(20)30367-6

2. Wimo A., Seeher K., Cataldi R. et al. The worldwide costs of dementia in 2019. Alzheimer’s & Dementia. 2023;19(7):2865-2873. https://doi.org/10.1002/alz.12901

3. Trunkova K.S., Tuillet P.S., Tatarinova T.A. Promoting Mental Health of City Residents: Stress Management and The Role of Salutogenic Design. City Healthcare. 2024;5(3):92-105. (In Russ.) https://doi.org/10.47619/2713-2617.zm.2024.v.5i3;92-105

4. Parsadanyan N.E., Kuznetsova A.M. Current Issues of Safety and Quality of In-home Medical Care Provided to Adult Population. City Healthcare. 2025;6(1):119-126. (In Russ.) https://doi.org/10.47619/2713-2617.zm.2025.v.6i1;119-126

5. McKeith I.G., Boeve B.F., Dickson D.W. et al. Diagnosis and management of dementia with Lewy bodies: Fourth consensus report of the DLB Consortium. Neurology. 2017;89(1):88-100. https://doi.org/10.1212/WNL.0000000000004058

6. Postuma R.B., Berg D., Stern M. et al. MDS clinical diagnostic criteria for Parkinson’s disease. Movement Disorders. 2015;30(12):1591-1601. https://doi.org/10.1002/mds.26424

7. Höglinger G.U., Respondek G., Stamelou M. et al. Clinical diagnosis of progressive supranuclear palsy: The movement disorder society criteria. Movement Disorders. 2017;32(6):853-864. https://doi.org/10.1002/mds.26987

8. Gilman S., Wenning G.K., Low P.A. et al. Second consensus statement on the diagnosis of multiple system atrophy. Neurology. 2008;71(9):670-676. https://doi.org/10.1212/01.wnl.0000324625.00404.15

9. Dudchenko N.G., Mkhitaryan E.A. Management of Patients with Pre-Dementia Cognitive Impairment: Literature Review. Russian Journal of Geriatric Medicine. 2026;(3):326-334. (In Russ.) https://doi.org/10.37586/2686-8636-3-2026-326-333

10. Myszczynska M.A., Ojamies P.N., Lacoste A.M.B. et al. Applications of machine learning to diagnosis and treatment of neurodegenerative diseases. Nature Reviews Neurology. 2020;16(8):440-456. https://doi.org/10.1038/s41582-020-0377-8

11. Gusev A.V., Zarubina T.V. Clinical Decision Support in medical information systems of a medical organization. Physician and Information Technologies. 2017;(2):60-72. (In Russ.) Available from: https://cyberleninka.ru/article/n/podderzhka-prinyatiya-vrachebnyh-resheniy-v-meditsinskih-informatsionnyh-sistemah-meditsinskoy-organizatsii (accessed 2026 Sep 10)

12. Atkov O.Yu., Kudryashov Yu.Yu., Prokhorov A.A. et al. Clinical decision support system. Physician and Information Technologies. 2013;(6):67-75. (In Russ.) Available from: https://cyberleninka.ru/article/n/sistema-podderzhki-prinyatiya-vrachebnyh-resheniy (accessed 2026 Sep 10)

13. Smirnova E.K. Introducing Digital Mentoring into Outpatient Healthcare Organizations. City Healthcare. 2026;7(2):162-170. (In Russ.) https://doi.org/10.47619/2713-2617.zm.2026.v.7i2;162-170

14. Abdulayeva M.R. Digital Literacy Among Different Age Groups and the Issue of Digital Divide. City Healthcare. 2026;7(2):188-195. (In Russ.) https://doi.org/10.47619/2713-2617.zm.2026.v.7i2;188-195

15. Gurwitz J.H., Field T.S., Harrold L.R. et al. Incidence and Preventability of Adverse Drug Events Among Older Persons in the Ambulatory Setting. JAMA. 2003;289(9):1107-1116. https://doi.org/10.1001/jama.289.9.1107

16. Shekelle P.G., Morton S.C., Keeler E.B. Costs and Benefits of Health Information Technology. Evidence Report/Technology Assessment (Full Report). 2006;(132):1-71. https://doi.org/10.23970/ahrqepcerta132

17. Ostroukhova N.G. Improving the economic efficiency of healthcare through building a unified patient pathway. Eliminating duplications at different levels of the system: expert review. Moscow: Research Institute for Healthcare Organization and Medical Management; 2026. (In Russ.) Available from: https://niioz.ru/moskovskaya-meditsina/izdaniya-nii/obzory/povyshenie-ekonomicheskoy-effektivnosti-zdravookhraneniya-za-schet-vystraivaniya-edinogo-marshruta-p/ (accessed 2026 Jul 25)

18. 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

19. Bossuyt P.M., Reitsma J.B., Bruns D.E. et al. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. BMJ. 2015;351:h5527. https://doi.org/10.1136/bmj.h5527

20. World Health Organization. Global strategy on digital health 2020–2025. Geneva: WHO; 2021. Available from: https://www.who.int/publications/i/item/9789240020924