Skip to main navigation menu Skip to main content Skip to site footer

Vol. 13 No. 06 (2026)
International Multidisciplinary Journal for Research & Development

Articles

PROMISING RESULTS OF SCIENTIFIC RESEARCH ON THE CENTRALIZATION OF KINETIC DIAGNOSTIC LABORATORY ANALYSES IN FAMILY CLINICS

Published 2026-06-13

  • Orifjon Abdumalikovich Aripov,Ilkhom Nazarovich Toshev,Mukhammad Sodiq Maqsud o'g'li Khojimurod

Orifjon Abdumalikovich Aripov,Ilkhom Nazarovich Toshev,Mukhammad Sodiq Maqsud o'g'li Khojimurod
Head of the Department of the Clinical Diagnostic Laboratory of the Center for the Development of Professional Qualifications of Medical Workers Senior Lecturer of the Department of Clinical Diagnostic Laboratory at the Center for the Development of Professional Qualifications of Medical Workers Center for the Development of Professional Qualifications of Medical Workers Department of Clinical Diagnostic Laboratory 2nd year basic doctoral student

Abstract

Numerous improvements have been developed in recent years with the goal of enhancing primary care diagnostic procedures. There is little data on how those who are directly involved in those procedures—patients, general practitioners, and medical experts like radiologists—perceive innovation in the primary care diagnostic process. End-user perspectives are essential to ensuring that activities aimed at improving the diagnostic process are successful. As a result, end users investigated possibilities and paths for the primary care diagnostic process with the help of change laboratory methodology, which uses conflicts and divergent perspectives as a source of motivation and education. Opportunities and directions were investigated in two study groups with nine and ten participants, respectively, who had four change lab sessions over the course of four months. The participants included patients, general practitioners, and medical experts. The Cultural-Historical Activity Theory served as the theoretical foundation for the analysis. This theory offers fresh perspectives for thinking, learning, and acting from and with one another by illuminating the various healthcare systems in which individuals find themselves and related conflicts and contradictions. Together with the participants, we identified conflicts and inconsistencies that exist both within and between various activity systems that are pertinent to primary care diagnostic procedures. Examples of these conflicts include those that may occur when more and quicker diagnostics are available in primary care or when cooperating parties have different motivations and interests in innovations. Participants have developed innovative directions and opportunities for the primary care diagnostic process by identifying these conflicts and inconsistencies. In addition to identifying specific artificial intelligence imaging techniques as promising to enhance the diagnostic process for acute complaints at the point-of-care, end users recognized a need for improved interchange and/or access to test results performed in hospitals to general practitioners. We developed criteria to be taken into consideration for recognizing successful innovation initiatives by exploring these paths and potential for improving and advancing the diagnostic procedure.By talking about conflicts and inconsistencies between systems, new factors for the diagnostic process's successful invention were found, and standards were developed that raise the possibility of producing innovative initiatives that show promise.

Keywords

Quality, laboratory practice, diagnostic error, diagnostic excellence, clinical microbiology, image analysis, machine learning, artificial intelligence

PDF

References

  1. Vaes EWP, de Groot E, Sabrkhany S, Bruining T, Cals JWL, Zwart D, van de Pol AC. Exploring the future of the diagnostic process in primary care : The value of making contradictions explicit using change lab methodology with healthcare professionals and patients. BMC Health Serv Res. 2025 Dec 29;26(1):160. doi: 10.1186/s12913-025-13915-y.
  2. Glasgow NJ, Douglas KA. The GP workforce: no room for complacency. Med J Aust. 2020;212(9):408–9. Islam QM. Innovation in primary healthcare in the twenty-first century. J Health Manage. 2021;23(1):129–42.
  3. Chatterjee S, Chaudhuri R, Mariani M, Fosso Wamba S. The consequences of innovation failure: an innovation capabilities and dynamic capabilities perspective. Technovation. 2023;128:102858.
  4. Forsman H. Innovation failure in smes: a narrative approach to understand failed innovations and failed innovators. Int J Innov Manag. 2021;25(09):2150104.
  5. Varpio L, O’Brien B, Rees CE, Monrouxe L, Ajjawi R, Paradis E. The applicability of generalisability and bias to health professions education’s research. Med Educ. 2021;55(2):167–73.
  6. Warkentin L, Scherer M, Kühlein T, Pausch F, Lühmann D, Muche-Borowski C, Hueber S. Evaluation of the German living guideline protection against the overuse and underuse of health Care – an online survey among German gps. BMC Prim Care. 2024;25(1):414.
  7. Sharp CA, Bresnen M, Austin L, McCarthy J, Dixon WG, Sanders C. Implementing disruptive technological change in UK healthcare: exploring development of a smart phone app for remote patient monitoring as a boundary object using qualitative methods. J Health Organ Manag. 2021;35(2):141–59.
  8. Haring M, Freigang F, Amelung V, Gersch M. What can healthcare systems learn from looking at tensions in innovation processes? A systematic literature review. BMC Health Serv Res. 2022;22(1):1299.
  9. Göttgens I, Oertelt-Prigione S. The application of Human-Centered design approaches in health research and innovation: A narrative review of current practices. JMIR Mhealth Uhealth. 2021;9(12):e28102.
  10. Han, GR., Goncharov, A., Eryilmaz, M. et al. Machine learning in point-of-care testing: innovations, challenges, and opportunities. Nat Commun 16, 3165 (2025). https://doi.org/10.1038/s41467-025-58527-6
  11. Cocker, D. et al. Healthcare as a driver, reservoir and amplifier of antimicrobial resistance: opportunities for interventions. Nat. Rev. Microbiol. 22, 636–649 (2024).
  12. Wiemken, T. L. & Kelley, R. R. Machine Learning in Epidemiology and Health Outcomes Research. Annu. Rev. Public Health. 41, 21–36 (2020).
  13. Tolley, A., Bansal, A., Murerwa, R. & Dicks, J. H. Cost-effectiveness of point-of-care diagnostics for AMR: a systematic review. J. Antimicrob. Chemother. 79, 1248–1269 (2024).
  14. Budd, J. et al. Lateral flow test engineering and lessons learned from COVID-19. Nat. Rev. Bioeng. 1, 13–31 (2023).
  15. Wilner, O. I., Yesodi, D. & Weizmann, Y. Point-of-care nucleic acid tests: assays and devices. Nanoscale 15, 942–952 (2022).
  16. Lubin IM, Astles JR, Shahangian S, Madison B, Parry R, Schmidt RL, Rubinstein ML. Bringing the clinical laboratory into the strategy to advance diagnostic excellence. Diagnosis (Berl). 2021 Jan 6;8(3):281-294. doi: 10.1515/dx-2020-0119.
  17. Palavecino EL, Williamson JC, Ohl CA. Collaborative antimicrobial stewardship: working with microbiology. Infect Dis Clin 2020;34:51–65.
  18. Whitehead NS, Williams L, Meleth S, Kennedy S, Ubaka-Blackmoore N, Kanter M, et al. The effect of laboratory test-based clinical decision support tools on medication errors and adverse drug events: a laboratory medicine best practices systematic review. J Appl Lab Med 2019;3:1035–48.
  19. Steinmetz JD, Seeher KM, Schiess N, et al. Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet Neurol 2024; 23: 344–381.
  20. Abdi IY, Hashish SA, El-Agnaf OA. Seeding amplification assay: Limitations and insights for enhanced clinical and research applications. Journal of Parkinson’s Disease. 2025;15(3):447-458. doi:10.1177/1877718X251325124
  21. Dien Bard J, Prinzi AM, Larkin PM, Peaper DR, Rhoads DD. Proceedings of the Clinical Microbiology Open 2024: artificial intelligence applications in clinical microbiology. J Clin Microbiol. 2025 Apr 9;63(4):e0180424. doi: 10.1128/jcm.01804-24.
  22. Tashkandi, S.A., Alenezi, A., Bakhsh, I. et al. Clinical laboratory services for primary healthcare centers in urban cities: a pilot ACO model of ten primary healthcare centers. BMC Fam Pract 22, 105 (2021). https://doi.org/10.1186/s12875-021-01449-1
  23. Ang TL, Choolani M, See KC, Poh KK. 2023. The rise of artificial intelligence: addressing the impact of large language models such as ChatGPT on scientific publications. Singapore Med J 64:219–221. doi: 10.4103/singaporemedj.SMJ-2023-055
  24. Burns BL, Rhoads DD, Misra A. 2023. The use of machine learning for image analysis artificial intelligence in clinical microbiology. J Clin Microbiol 61:e0233621. doi: 10.1128/jcm.02336-21
  25. Park SJ, Yoon J, Kwon JA, Yoon SY. 2021. Evaluation of the CellaVision advanced RBC application for detecting red blood cell morphological abnormalities. Ann Lab Med 41:44–50. doi: 10.3343/alm.2021.41.1.44
  26. Durant TJS, Dudgeon SN, McPadden J, Simpson A, Price N, Schulz WL, Torres R, Olson EM. 2021. Applications of digital microscopy and densely connected convolutional neural networks for automated quantification of babesia-infected erythrocytes. Clin Chem 68:218–229. doi: 10.1093/clinchem/hvab237

Downloads

Download data is not yet available.