Effectiveness of 3d-modelling laboratory implementation into therapeutic and diagnostic medical institutions
https://doi.org/10.25789/YMJ.2019.67.30
Abstract
The article presents the analysis of 3D modeling laboratory efficiency after its introduction in the departments of radio diagnostics. Prospects and risks of the work of this unit is presented in the study. The calculation of economic indicators shows that the payback is around 2 years, the breakeven point is 60 – 70 clients per month. Results obtained economically and technologically justify active deployment of 3D modeling labs in medical institutions.
About the Authors
B. O. ShcheglovRussian Federation
Shcheglov Bogdan Olegovich, Head of the Student Association “3D-Modeling in Biomedicine” of the Center for Project Activities
Vladivostok
mobile: 89147189825
I. V. Galkina
Russian Federation
Galkina Irina Vyacheslavovna, PhD in medicine, Leader Researcher of the International Scientific and Educational Center of Molecular Technologies, School of Biomedicine
Vladivostok
mobile: 89031705864
S. N. Shcheglova
Russian Federation
Shcheglova Svetlana Nikolaevna, PhD in pedagogy, Head of the Department of Higher Mathematics
Magadan
mobile: 89148624028
M. Yu. Shchelkanov
Russian Federation
Shchelkanov Mikhail Yurievich, Doctor of Biological Sciences, Head of the International Scientific and Educational Center of Molecular Technologies, School of Biomedicine
Vladivostok
mobile: 89032689098.
References
1. Malignant neoplasms in Russia in 2017 (morbidity and mortality) / Ed. A.D. Kaprin, V.V. Starinsky, G.V. Petrova. – M.: P.A. Herzen Moscow Scientific-Research Institute, 2018. – 250 p.
2. About the improvement of service for radiation diagnostics. – The order of RSFSR Ministry of Public Health № 132 dated at August 02, 1991 with changes dated at April 5, 1996.
3. Health economics: a manual for students of pediatric, medical and dental faculties / V.K. Yuryev, V.G. Puzyrev, V.A. Glushchenko [et al.]. – - SPb: GPMU, 2015. – 72 p.
4. A modified method of activity-based costing for objectively reducing cost drivers in hospitals / P. Cao, S. Toyabe, S. Kurashima [et al.] // Methods of Information in Medicine. – 2006. – V.45, №4. – P. 462-469.
5. AuBuchon P.J. Optimizing the cost-effectiveness of quality assurance in transfusion medicine / P.J. AuBuchon // Archives of Pathology and Laboratory Medicine. – 1999. – V.123, №7. – P. 603-606.
6. Brueckner J.K. Lectures on urban eco nomics / J.K. Brueckner. / – Cambridge: The MIT Press, 2011. – 296 p.
7. Curriculum content and evaluation of resident competency in clinical pathology (laboratory medicine): a proposal / B.R. Smith, A. Wells, C.B. Alexander [et al.] // Clinical Chemistry. – 2006. – V.52, №6. – P. 917-949. doi: 10.1373/clinchem.2005.066076
8. How can activity-based costing methodology be performed as a powerful tool to calculate costs and secure appropriate patient care? / B.Y. Lin, T.H. Chao, Y. Yao [et al.] // Journal of Medical Systems. – 2007. – V.31, №2. – P. 85-90. https://doi.org/10.1007/s10916-005-9010-z
9. Clemens J. In the shadow of a giant: medicares’s influence on private physician payments / J. Clemens, J.D. Gottlieb // Journal of Political Economy. – 2017. – V.125, №1. – P. 1-39. doi: 10.1086/689772
10. Doyle Jr. J.J. Returns to local-area health care spending: evidence from health shocks to patients far from home / Jr. J.J. Doyle // American Economic Journal: Applied Economics. – 2011. – V.3, №3. – P. 221-243. https://www.jstor.org/stable/41288644
11. Emerging technologies for cancer research: towards personalized medicine with microfluidic platforms and 3D tumor models / M. Turetta, F.D. Ben, G. Brisotto [et al.] // Current Medical Chemistry. – 2018. – V.25, №35. – P. 4616-4637. doi: 10.2174/0929867325666180605122633.
12. Fisher E.S. Slowing the growth of health care costs – lessons from regional variation / E.S. Fisher, J.P. Bynum, J.S. Skinner // New England Journal of Medicine. – 2009. – V.360, №9. – P. 849-852. doi: 10.1056/NEJMp0809794
13. Friedman B.A. The total laboratory solution: a new laboratory E-business model based on a vertical laboratory meta-network / B.A. Friedman // Clinical Chemistry. – 2001. – V.47, №8. – 1526-1535.
14. Li J. Intermediate input sharing in the hospital service industry / J. Li // Regional Science and Urban Economics. – 2013. – V.43, №6. – P. 888–902. doi: 10.1016/j.regsciurbeco.2013.09.004
15. McDonald J.F. Back to the future. The integration of big data with machine learning is re-establishing the importance of predictive correlations in ovarian cancer diagnostics and therapeutics / J.F. McDonald // Gynecologic Oncology. – 2018. – V.149, №2. – P. 230-231. doi: 10.1016/j.ygyno.2018.03.053.
16. Personalized medicine in nasal delivery: the use of patient-specific administration parameters to improve nasal drug targeting using 3D-printed nasal replica casts / Z.N. Warnken, H.D.C. Smyth, D.A. Davis [et al.] // Molecular Pharmaceutics. – 2018. – V.15, №4. – P. 1392-1402. doi: 10.1021/acs.molpharmaceut.7b00702.
17. Ricos C. Quality indicators and specifications for the extra-analytical phases in clinical laboratory management / C. Ricos, M. Garcia-Victoria, B. de la Fuente // Clinical Chemistry and Laboratory Medicine. – 2004. – V.42, №6. – P. 578–582. doi: 10.1515/CCLM.2004.100
18. Total global market for personalized medicine from 2015 to 2022 (in billion U.S. dollars): [Electronic resource]. URL: https://www.statista.com/statistics/728124/global-market-for-per-sonalized-medicine/ [Accessed: February 25, 2018].
19. Zaninotto M. The hospital central laboratory: automation, integration and clinical usefulness / M. Zaninotto, M. Plebani // Clinical Chemistry and Laboratory Medicine. – 2010. – V.48, №7. – P. 911-917. doi: 10.1515/CCLM.2010.192
Review
For citations:
Shcheglov B.O., Galkina I.V., Shcheglova S.N., Shchelkanov M.Yu. Effectiveness of 3d-modelling laboratory implementation into therapeutic and diagnostic medical institutions. Yakut Medical Journal. 2019;(3):109-111. https://doi.org/10.25789/YMJ.2019.67.30