Implementation of a clinical decision support system to improve the medical data quality for hypertensive patients
M. V. Ionov,
E. V. Bolgova,
N. E. Zvartau,
N. G. Avdonina,
M. A. Balakhontceva,
S. V. Kovalchuk,
A. O. Konradi https://doi.org/10.17586/2226-1494-2022-22-1-217-222
Abstract
The digitalization of healthcare relies heavily on data analytics from medical information systems. Such systems aggregate information from heterogeneous sources, including electronic medical records. Improving the quality of data from electronic medical records is a modern challenge for developers of medical information systems. The authors have designed a decision support system with an expanded set of auxiliary functions to solve the problems of human-computer interaction, increasing the completeness and reliability of medical information. In this paper, the applicability of the existing decision-making system is investigated on the example of medical data of patients with arterial hypertension. The testing of the decision support system among medical specialists was carried out. The impact of the implementation of the system on the number of errors when filling out an electronic medical record was assessed. A software module was created integrated into the working version of the medical information system in the Almazov National Medical Research Centre. Test implementation of the system made it possible to reduce the number of errors and increase satisfaction with the information presented in patients with arterial hypertension.
Keywords
decision support systems,
value-based medicine,
human-computer interaction,
digitalization of healthcare
Supporting agencies: The research was supported by the Russian Science Foundation (project No. 17-15-01177).
About the Authors
M. V. Ionov
Almazov National Medical Research Centre
Russian Federation
Russian Federation
Mikhail V. Ionov — PhD, Junior Researcher
sc 57200520709
Saint Petersburg, 197341
E. V. Bolgova
ITMO University
Russian Federation
Russian Federation
Ekaterina V. Bolgova — PhD, Associate Professor
sc 57211535770
Saint Petersburg, 197101
N. E. Zvartau
Almazov National Medical Research Centre
Russian Federation
Russian Federation
Nadezhda E. Zvartau — PhD, Associate Professor, Senior Researcher
sc 6506439053
Saint Petersburg, 197341
N. G. Avdonina
Almazov National Medical Research Centre
Russian Federation
Russian Federation
Natalia G. Avdonina — Scientific Researcher
sc 57201382884
Saint Petersburg, 197341
M. A. Balakhontceva
ITMO University
Russian Federation
Russian Federation
Marina A. Balakhontceva — PhD, Senior Researcher
sc 57211535770
Saint Petersburg, 197101
S. V. Kovalchuk
ITMO University
Russian Federation
Russian Federation
Sergey V. Kovalchuk — PhD, Senior Researcher
sc 55382199400
Saint Petersburg, 197101
A. O. Konradi
Almazov National Medical Research Centre
Russian Federation
Russian Federation
Alexandra O. Konradi — D.Sc., Corresponding Member of RAS, Professor, Head of Department
sc 7004144504
Saint Petersburg, 197341
References
1. Shliakhto E.V., Konradi A.O., Zvartau N.E., Ratova L.G. Value-based medicine. St. Petersburg, Info-ra Publ., 2019, 92 p. (in Russian)
2. Cowie M.R., Bax J., Bruining N., Cleland J.G.F., Koehler F., Malik M., Pinto F., Van Der Velde E., Vardas P. e-Health: a position statement of the European Society of Cardiology // European Heart Journal. 2016. V. 37. N 1. P. 63–66. https://doi.org/10.1093/eurheartj/ehv416
3. Coorevits P., Sundgren M., Klein G.O., Bahr A., Claerhout B., Daniel C., Dugas M., Dupont D., Schmidt A., Singleton P., De Moor G., Kalra D. Electronic health records: new opportunities for clinical research // Journal of Internal Medicine. 2013. V. 274. N 6. P. 547–560. https://doi.org/10.1111/joim.12119
4. Jensen P.B., Jensen L.J., Brunak S. Mining electronic health records: towards better research applications and clinical care // Nature Reviews Genetics. 2012. V. 13. N 6. P. 395–405. https://doi.org/10.1038/nrg3208
5. Dugas M., Lange M., Müller-Tidow C., Kirchhof P., Prokosch H.-U. Routine data from hospital information systems can support patient recruitment for clinical studies // Clinical Trials. 2010. V. 7. N 2. P. 183–189. https://doi.org/10.1177/1740774510363013
6. Prokosch H.U., Ganslandt T. Perspectives for Medical Informatics. Reusing the electronic medical record for clinical research // Methods of Information in Medicine. 2009. V. 48. N 1. P. 38–44. https://doi.org/10.3414/ME9132
7. Turisco F., Keogh D., Stubbs C., Glaser J., Crowley W.F. Current status of integrating information technologies into the clinical research enterprise within US academic health centers: strategic value and opportunities for investment // Journal of Investigative Medicine. 2005. V. 53. N 8. P. 425–433. https://doi.org/10.2310/6650.2005.53806
8. Weiskopf N.G., Weng C. Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research // Journal of the American Medical Informatics Association. 2013. V. 20. N 1. P. 144–151. https://doi.org/10.1136/amiajnl-2011-000681
9. Birkhead G.S., Klompas M., Shah N.R. Uses of electronic health records for public health surveillance to advance public health // Annual Review of Public Health. 2015. V. 36. P. 345–359. https://doi.org/10.1146/annurev-publhealth-031914-122747
10. Zhou L., Soran C.S., Jenter C.A., Volk L.A., Orav E.J., Bates D.W., Simon S.R. The relationship between electronic health record use and quality of care over time // Journal of the American Medical Informatics Association. 2009. V. 16. N 4. P. 457–464. https://doi.org/10.1197/jamia.M3128
11. Ctarkov E.F. The support system of the making a decision in medicine. Journal of New Medical Technologies, 2006, no. 2, pp. 23– 24. (in Russian)
12. Williams B., Mancia G., Spiering W., Agabiti Rosei E., Azizi M., Burnier M. et al. 2018 ESC/ESH Guidelines for the management of arterial hypertension // European Heart Journal. 2018. V. 39. N 33. P. 3021–3104. https://doi.org/10.1093/eurheartj/ehy339
13. Wang C., Yuan Y., Zheng M., Pan A., Wang M., Zhao M., Li Y., Yao S., Chen S., Wu S., Xue H. Association of age of onset of hypertension with cardiovascular diseases and mortality // Journal of the American College of Cardiology. 2020. V. 75. N 23. P. 2921–2930. https://doi.org/10.1016/j.jacc.2020.04.038
14. Kaushal R., Shojania K.G., Bates D.W. Effects of computerized physician order entry and clinical decision support systems on medication safety: A systematic review // Archives of Internal Medicine. 2003. V. 163. N 12. P. 1409–1416. https://doi.org/10.1001/archinte.163.12.1409
15. Ray J.C., Kusumoto F. The transition to value-based care // Journal of Interventional Cardiac Electrophysiology. 2016. V. 47. N 1. P. 61– 68. https://doi.org/10.1007/s10840-016-0166-x
16. Christensen T.J. A framework for guiding efforts to reward value instead of volume // International Journal of Health Economics and Management. 2016. V. 16. N 2. P. 175–187. https://doi.org/10.1007/s10754-015-9178-9
17. 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 Association. 2001. V. 8. N 6. P. 527–534. https://doi.org/10.1136/jamia.2001.0080527
18. Sauro J., Dumas J.S. Comparison of three one-question, post-task usability questionnaires // Proc. of the 27th International Conference Extended Abstracts on Human Factors in Computing Systems (CHI). 2009. P. 1599–1608. https://doi.org/10.1145/1518701.1518946
Review
For citations:
Ionov M.V., Bolgova E.V., Zvartau N.E., Avdonina N.G., Balakhontceva M.A., Kovalchuk S.V., Konradi A.O. Implementation of a clinical decision support system to improve the medical data quality for hypertensive patients. Scientific and Technical Journal of Information Technologies, Mechanics and Optics. 2022;22(1):217-222. (In Russ.) https://doi.org/10.17586/2226-1494-2022-22-1-217-222
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