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A Conceptual Framework for Strengthening Diagnostic Quality Systems in Resource-Limited Clinical Laboratories
Subject area: Science,Engineering and Technology · Area of research: Diagnostic Quality Systems
Abstract
Clinical laboratories in low- and middle-income countries produce results that guide a large share of clinical decisions, yet very few meet internationally recognised quality standards, and this paper argues that the accreditation-preparation programmes that dominate improvement practice have raised audit scores without a demonstrated corresponding gain in the usefulness of the results laboratories deliver. Those programmes assess the laboratory as an isolated unit, so a facility can satisfy a checklist while its results arrive too late to change management, reach clinicians who cannot interpret them, or rest on infrastructure, power, and supply conditions the checklist never examines. This paper synthesises the laboratory strengthening, health systems, diagnostic infrastructure, engineering reliability, and governance literature to propose the Diagnostic Quality Systems Strengthening framework, which treats quality as a property of the diagnostic pathway rather than of the laboratory alone. The framework organises the determinants of diagnostic quality into an enabling foundation of governance, infrastructure, and financing, a technical core spanning the total testing process, an improvement engine of measurement and corrective action, and a clinical interface linking laboratory output to diagnostic decisions and patient outcomes, with workforce capability, information systems, and sustainable financing running through all four. A failure mode analysis maps thirty common causes of unusable results onto the domains, showing that a substantial proportion originate outside the analytical bench and outside the laboratory itself. Five maturity tiers, supported by a domain-level assessment rubric and a specified twelve-indicator starter set, describe progression from unmanaged practice to a self-sustaining learning laboratory, so that partial progress remains meaningful for facilities that will not achieve full accreditation soon. Three worked applications at district, regional, and national reference level illustrate how the same framework yields different priorities at different tiers. The framework is conceptual and requires validation, but it offers a planning and evaluation structure anchored in diagnostic usefulness rather than documentary compliance.
Keywords
laboratory quality management system; diagnostic quality; resource-limited settings; ISO 15189; SLIPTA; diagnostic infrastructure; total testing process; maturity model; health systems strengthening.
References
[1] Adaramola, T. S., Fadero, S., & Gideon, E. N. (2018). Predictive maintenance and condition monitoring in critical power and energy infrastructure. Iconic Research and Engineering Journals, 2(5), 454-477. Crossref
[2] Adeyelu, O. O. (2018). A predictive compliance monitoring framework for detecting systemic safety risks through aviation consumer complaint data. Iconic Research and Engineering Journals, 1(8), 250-276. Crossref
[3] Ahmed, K. S., & Odejobi, O. D. (2018a). Conceptual framework for scalable and secure cloud architectures for enterprise messaging. IRE Journals, 2(1), 1-15.
[4] Akeju, B., Edivri, J., Ogbole, J. I., Okoruwa, P. O., Fadayomi, O., & Abolaji, T. O. (2018). Conceptual model for insider threat classification and risk modeling in complex digital systems. Iconic Research and Engineering Journals, 1(9), 476-492. Crossref
[5] Akomolafe, O., & Agu, M. U. (2018). A conceptual model for enhancing internal audit quality through technology-enabled risk assessment frameworks. Iconic Research and Engineering Journals, 1(9), 458-475.
[6] Amayo, E. B., & Popoola, T. T. (2017b). Scientific study of telecommunication deployment in achieving quality network. International Journal of Trend in Research and Development, 4(5), 311-314.
[7] Aminu-Ibrahim, A. Y., Ogbete, J. C., & Ambali, K. B. (2018). Developing sustainable diagnostic laboratory infrastructure models for emerging and resource constrained health systems. Iconic Research and Engineering Journals, 1(8), 118-132.
[8] Ancker, J. S., Edwards, A., Nosal, S., Hauser, D., Mauer, E., & Kaushal, R. (2017). Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system. BMC Medical Informatics and Decision Making, 17(1), 36. Crossref
[9] Anderson BO, Cazap E, El Saghir NS, et al. (2011). Optimisation of breast cancer management in low-resource and middle-resource countries: executive summary of the Breast Health Global Initiative consensus. Lancet Oncol, 12(4):387-398.
[10] Andiric, L. R., & Massambu, C. G. (2015). Laboratory quality improvement in Tanzania. American Journal of Clinical Pathology, 143(4), 566-572.
[11] Arumosoye, O. M., & Obriki, O. D. (2018). Development of an integrated heat stress risk conceptual model for industrial operations in extreme environments. Iconic Research and Engineering Journals, 1(12), 141-160. Crossref
[12] Audu, R. A., Sylvester-Ikondu, U., Onwuamah, C. K., Salu, O. B., Ige, F. A., Meshack, E., Musa, A. Z., Odunukwe, N. N., Idigbe, E. O., & Kebede, A. (2014). Improving quality in national reference laboratories: The role of SLMTA and mentorship. African Journal of Laboratory Medicine, 3(2), Article 200.
[13] Azeez, L. O., & Badmus, O. B. (2018). Data-driven framework for predicting subsurface contamination pathways in complex remediation projects. Iconic Research and Engineering Journals, 2(5), 312-335.
[14] Barbé, B., Yansouni, C. P., Affolabi, D., & Jacobs, J. (2017). Implementation of quality management for clinical bacteriology in low-resource settings. Clinical Microbiology and Infection, 23(7), 426-433.
[15] Barth, J. H. (2012). Managing the pre- and post-analytical phases of the total testing process. Indian Journal of Clinical Biochemistry, 27(1), 2-9.
[16] Bender, D., & Sartipi, K. (2013). HL7 FHIR: An agile and RESTful approach to healthcare information exchange. In Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems (pp. 326-331). IEEE. IEEE Link
[17] Bobga, M. A., Boakye, K., Ogbona, C. S., & Yeboah, T. J. (2018). Pedagogical strategies for teaching students with learning difficulties in resource-constrained schools. Iconic Research and Engineering Journals, 2(4), 173-194. Crossref
[18] Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P. P., Irwig, L., Lijmer, J. G., Moher, D., Rennie, D., de Vet, H. C. W., Kressel, H. Y., Rifai, N., Golub, R. M., Altman, D. G., Hooft, L., Korevaar, D. A., & Cohen, J. F. (2015). STARD 2015: An updated list of essential items for reporting diagnostic accuracy studies. BMJ, 351, h5527.
[19] Clinical and Laboratory Standards Institute. (2010). EP28-A3c: Defining, establishing, and verifying reference intervals in the clinical laboratory (3rd ed.). CLSI.
[20] Clinical and Laboratory Standards Institute. (2012). EP17-A2: Evaluation of detection capability for clinical laboratory measurement procedures (2nd ed.). CLSI.
[21] Clinical and Laboratory Standards Institute. (2018). EP09c: Measurement procedure comparison and bias estimation using patient samples (3rd ed.). CLSI.
[22] Clinical and Laboratory Standards Institute. (2014a). EP05-A3: Evaluation of precision of quantitative measurement procedures (3rd ed.). CLSI.
[23] Clinical and Laboratory Standards Institute. (2014b). EP15-A3: User verification of precision and estimation of bias (3rd ed.). CLSI.
[24] Datema, T. A. M., Oskam, L., & Klatser, P. R. (2011). Review and comparison of quality standards, guidelines and regulations for laboratories. African Journal of Laboratory Medicine, 1(1), Article 3.
[25] Desautels, T., Calvert, J., Hoffman, J., Jay, M., Kerem, Y., Shieh, L., Shimabukuro, D., Chettipally, U., Feldman, M. D., Barton, C., Wales, D. J., & Das, R. (2016). Prediction of sepsis in the intensive care unit with minimal electronic health record data: A machine learning approach. JMIR Medical Informatics, 4(3), e28. Crossref
[26] Dogbatsey, E. A., & Ebhojie, O. (2018). Budget compliance and statutory reporting in Sub-Saharan Africa: A systematic review of frameworks, gaps, and reform pathways. Zenodo. Zenodo Link
[27] Donabedian, A. (1966). Evaluating the quality of medical care. The Milbank Memorial Fund Quarterly, 44(3, Suppl.), 166-206.
[28] Effler, P., Ching-Lee, M., Bogard, A., Ieong, M. C., Nekomoto, T., & Jernigan, D. (1999). Statewide system of electronic notifiable disease reporting from clinical laboratories: Comparing automated reporting with conventional methods. JAMA, 282(19), 1845-1850. JAMA Link
[29] Ejofodomi, O. A., Gideon, E. N., Oladipo, G. O., & Oshomah, E. R. (2014). Automated detection of architectural detection in mammograms using template matching. International Journal of Biomedical Science and Engineering, 2(1), 1-6. Crossref
[30] Fitzgibbons PL, Bradley LA, Fatheree LA, et al. (2014). Principles of analytic validation of immunohistochemical assays: guideline from the College of American Pathologists Pathology and Laboratory Quality Center. Arch Pathol Lab Med, 138(11):1432-1443.
[31] Fleming, K. A., Naidoo, M., Wilson, M., Flanigan, J., Horton, S., Kuti, M., Looi, L. M., Price, C., Ru, K., Ghafur, A., Wang, J., & Lago, N. (2017). An essential pathology package for low- and middle-income countries. American Journal of Clinical Pathology, 147(1), 15-32.
[32] Gbadamosi, I. T., & Obogo, S. F. (2013). Chemical constituents and in vitro antimicrobial activities of five botanicals used traditionally for the treatment of neonatal jaundice in Ibadan, Nigeria. Nature and Science, 11(10), 130-135.
[33] Gershy-Damet, G. M., Rotz, P., Cross, D., Belabbes, E. H., Cham, F., Ndihokubwayo, J. B., Fine, G., Zeh, C., Njukeng, P. A., Mboup, S., Sesse, D. E., Messele, T., & Birx, D. L. (2010). The World Health Organization African region laboratory accreditation process: Improving the quality of laboratory systems in the African region. American Journal of Clinical Pathology, 134(3), 393-400.
[34] Gil-Ozoudeh, I. D. S., Aransi, A. N., Nwafor, M. I., & Uduokhai, D. O. (2018). Socioeconomic determinants influencing the affordability and sustainability of urban housing in Nigeria. IRE Journals, 2(3), 164-169.
[35] Hawkins, R. C. (2007). Laboratory turnaround time. Clinical Biochemist Reviews, 28(4), 179-194.
[36] Horng, S., Sontag, D. A., Halpern, Y., Jernite, Y., Shapiro, N. I., & Nathanson, L. A. (2017). Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning. PLOS ONE, 12(4), e0174708. PLOS Link
[37] Horton, S., Sullivan, R., Flanigan, J., Fleming, K. A., Kuti, M. A., Looi, L. M., Pai, S. A., & Lawler, M. (2018). Delivering modern, high-quality, affordable pathology and laboratory medicine to low-income and middle-income countries: A call to action. The Lancet, 391(10133), 1953-1964.
[38] Inal, T. C., Goruroglu Ozturk, O., Kibar, F., Cetiner, S., Matyar, S., Daglioglu, G., & Yaman, A. (2018). Lean six sigma methodologies improve clinical laboratory efficiency and reduce turnaround times. Journal of Clinical Laboratory Analysis, 32(1), Article e22180.
[39] International Organization for Standardization. (2012). ISO 15189:2012. Medical laboratories: Requirements for quality and competence. ISO.
[40] Klompas, M., & Yokoe, D. S. (2009). Automated surveillance of health care-associated infections. Clinical Infectious Diseases, 48(9), 1268-1275. Crossref
[41] Kruk, M. E., Gage, A. D., Arsenault, C., Jordan, K., Leslie, H. H., Roder-DeWan, S., Adeyi, O., Barker, P., Daelmans, B., Doubova, S. V., English, M., García-Elorrio, E., Guanais, F., Gureje, O., Hirschhorn, L. R., Jiang, L., Kelley, E., Lemango, E. T., Liljestrand, J., … Pate, M. (2018). High-quality health systems in the Sustainable Development Goals era: Time for a revolution. The Lancet Global Health, 6(11), e1196-e1252.
[42] Ladapo, O. O., Dosunmu, A. A., Jooda, D., & Abolaji, T. O. (2018). Lessons learned from offline assessment of security-critical systems: The case of Microsoft Active Directory. Iconic Research and Engineering Journals, 2(6), 277-299. Crossref
[43] Lamidi, O. B. A., & Olamide, A. (2018). Spatially explicit risk modeling framework for tracking subsurface contaminant migration in data-limited remediation sites. Iconic Research and Engineering Journals, 2(6), 178-198.
[44] Mandel, J. C., Kreda, D. A., Mandl, K. D., Kohane, I. S., & Ramoni, R. B. (2016). SMART on FHIR: A standards-based, interoperable apps platform for electronic health records. Journal of the American Medical Informatics Association, 23(5), 899-908. Oxford Academic Link
[45] Masamha, J., Skaggs, B., Pinto, I., Mandlaze, A. P., Simbine, C., Chongo, P., Sitoe, N., Cunha, L., Nguenha, A., de Sousa, L., Gudo, E., & Samogudo, E. (2014). Working toward a sustainable laboratory quality improvement programme through country ownership: Mozambique's SLMTA story. African Journal of Laboratory Medicine, 3(2), Article 253.
[46] Mbonu, I. S., Aliliele, C., Iwuanyanwu, U., & Oluoha, O. M. (2018). A conceptual framework for legal and ethical risk modeling in enterprise data protection governance systems. Iconic Research and Engineering Journals, 2(2), 207-226. Crossref
[47] McDonald, C. J., Huff, S. M., Suico, J. G., Hill, G., Leavelle, D., Aller, R., Forrey, A., Mercer, K., DeMoor, G., Hook, J., Williams, W., Case, J., & Maloney, P. (2003). LOINC, a universal standard for identifying laboratory observations: A 5-year update. Clinical Chemistry, 49(4), 624-633. Crossref
[48] Nakhleh RE, Nosé V, Colasacco C, et al. (2016). Interpretive diagnostic error reduction in surgical pathology and cytology: guideline from the College of American Pathologists Pathology and Laboratory Quality Center and the Association of Directors of Anatomic and Surgical Pathology. Arch Pathol Lab Med, 140(1):29-40.
[49] Ndihokubwayo, J. B., Maruta, T., Ndlovu, N., Moyo, S., Yahaya, A. A., Coulibaly, S. O., Kasolo, F., Turgeon, D., & Abrol, A. P. (2016). Implementation of the World Health Organization Regional Office for Africa stepwise laboratory quality improvement process towards accreditation. African Journal of Laboratory Medicine, 5(1), Article 280.
[50] Nkengasong, J. N., Mesele, T., Orloff, S., Kebede, Y., Fonjungo, P. N., Timperi, R., & Birx, D. (2009). Critical role of developing national strategic plans as a guide to strengthen laboratory health systems in resource-poor settings. American Journal of Clinical Pathology, 131(6), 852-857.
[51] Nkengasong, J. N., Nsubuga, P., Nwanyanwu, O., Gershy-Damet, G. M., Roscigno, G., Bulterys, M., Schoub, B., DeCock, K. M., & Birx, D. (2010). Laboratory systems and services are critical in global health: Time to end the neglect? American Journal of Clinical Pathology, 134(3), 368-373.
[52] Nwafor, M. I., Uduokhai, D. O., Giloid, S., & Aransi, A. N. (2018). Comparative study of traditional and contemporary architectural morphologies in Nigerian settlements. Iconic Research and Engineering Journals, 1(7), 138-152.
[53] Nwafor, M. I., Uduokhai, D. O., Ifechukwu, G. O., Stephen, D., & Aransi, A. N. (2018). Impact of climatic variables on the optimization of building envelope design in humid regions. Iconic Research and Engineering Journals, 1(10), 322-335.
[54] Obriki, O. D., & Arumosoye, O. M. (2018). Conceptual modeling of data-driven occupational safety risk control in large-scale energy infrastructure projects. Iconic Research and Engineering Journals, 1(7), 169-189. Crossref
[55] Odejobi, O. D., & Ahmed, K. S. (2018a). Statistical model for estimating daily solar radiation for renewable energy planning. IRE Journals, 2(5), 1-12.
[56] Ogbete, J. C., Aminu-Ibrahim, A. Y., & Ambali, K. B. (2018). Optimizing laboratory spatial planning strategies to improve diagnostic accuracy, safety, and clinical throughput. Iconic Research and Engineering Journals, 2(1), 87-113.
[57] Okeke, I. N., Peeling, R. W., Goossens, H., Auckenthaler, R., Olmsted, S. S., de Lavison, J.-F., Zimmer, B. L., Perkins, M. D., & Nordqvist, K. (2011). Diagnostics as essential tools for containing antibacterial resistance. Drug Resistance Updates, 14(2), 95-106. ScienceDirect Link
[58] Okonkwo, C. S., Ogunwole, O., & Okeke, O. T. (2018a). Framework for strategic procurement optimization in oil and gas operations. Iconic Research and Engineering Journals, 1(7), 153-168. Crossref
[59] Okonkwo, C. S., Ogunwole, O., & Okeke, O. T. (2018b). Model for inventory availability and plant uptime improvement in energy facilities. Iconic Research and Engineering Journals, 2(4), 160-172. Crossref
[60] Ombelet, S., Ronat, J.-B., Walsh, T., Yansouni, C. P., Cox, J., Vlieghe, E., Martiny, D., Semret, M., Vandenberg, O., & Jacobs, J. (2018). Clinical bacteriology in low-resource settings: Today's solutions. The Lancet Infectious Diseases, 18(8), e248-e258. ScienceDirect Link
[61] Oshevire, P., Eyenubo, O. J., & Amayo, B. (2017). Voltage control in the presence of distributed generation. ATBU Journal of Science, Technology and Education, 5(2), 165-173.
[62] Overhage, J. M., Grannis, S., & McDonald, C. J. (2008). A comparison of the completeness and timeliness of automated electronic laboratory reporting and spontaneous reporting of notifiable conditions. American Journal of Public Health, 98(2), 344-350. Crossref
[63] Panackal, A. A., M'ikanatha, N. M., Tsui, F.-C., McMahon, J., Wagner, M. M., Dixon, B. W., Zubieta, J., Phelan, M., Mirza, S., Morgan, J., Jernigan, D., Pasculle, A. W., Rankin, J. T., Hajjeh, R. A., & Harrison, L. H. (2002). Automatic electronic laboratory-based reporting of notifiable infectious diseases at a large health system. Emerging Infectious Diseases, 8(7), 685-691. CDC Link
[64] Peter, T. F., Rotz, P. D., Blair, D. H., Khine, A. A., Freeman, R. R., & Murtagh, M. M. (2010). Impact of laboratory accreditation on patient care and the health system. American Journal of Clinical Pathology, 134(4), 550-555.
[65] Petti, C. A., Polage, C. R., Quinn, T. C., Ronald, A. R., & Sande, M. A. (2006). Laboratory medicine in Africa: A barrier to effective health care. Clinical Infectious Diseases, 42(3), 377-382.
[66] Plebani, M. (2006). Errors in clinical laboratories or errors in laboratory medicine? Clinical Chemistry and Laboratory Medicine, 44(6), 750-759. De Gruyter Link
[67] Plebani, M., & Carraro, P. (1997). Mistakes in a stat laboratory: Types and frequency. Clinical Chemistry, 43(8), 1348-1351.
[68] Plebani, M., & Lippi, G. (2011). Closing the brain-to-brain loop in laboratory testing. Clinical Chemistry and Laboratory Medicine, 49(7), 1131-1133.
[69] Plebani, M., Laposata, M., & Lundberg, G. D. (2011). The brain-to-brain loop concept for laboratory testing 40 years after its introduction. American Journal of Clinical Pathology, 136(6), 829-833.
[70] Rehm, H. L., Bale, S. J., Bayrak-Toydemir, P., Berg, J. S., Brown, K. K., Deignan, J. L., Friez, M. J., Funke, B. H., Hegde, M. R., & Lyon, E. (2013). ACMG clinical laboratory standards for next-generation sequencing. Genetics in Medicine, 15, 733-747.
[71] Sandberg, S., Fraser, C. G., Horvath, A. R., Jansen, R., Jones, G., Oosterhuis, W., Petersen, P. H., Schimmel, H., Sikaris, K., & Panteghini, M. (2015). Defining analytical performance specifications: Consensus statement from the 1st Strategic Conference of the European Federation of Clinical Chemistry and Laboratory Medicine. Clinical Chemistry and Laboratory Medicine, 53(6), 833-835.
[72] Sayed, S., Cherniak, W., Lawler, M., Tan, S. Y., El Sadr, W., Wolf, N., Silkensen, S., Brand, N., Looi, L. M., Pai, S. A., Wilson, M. L., Milner, D., Flanigan, J., & Fleming, K. A. (2018). Improving pathology and laboratory medicine in low-income and middle-income countries: Roadmap to solutions. The Lancet, 391(10133), 1939-1952.
[73] Schrijver, R., Stijntjes, M., Rodríguez-Baño, J., Tacconelli, E., Babu Rajendran, N., & Voss, A. (2018). Review of antimicrobial resistance surveillance programmes in livestock and meat in Europe, with a focus on humans. Clinical Microbiology and Infection, 24(6), 577-590. ScienceDirect Link
[74] Schroeder, L. F., & Amukele, T. (2014). Medical laboratories in sub-Saharan Africa that meet international quality standards. American Journal of Clinical Pathology, 141(6), 791-795.
[75] Shimabukuro, D. W., Barton, C. W., Feldman, M. D., Mataraso, S. J., & Das, R. (2017). Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: A randomised clinical trial. BMJ Open Respiratory Research, 4(1), e000234. BMJ Link
[76] Sips, M. E., Bonten, M. J. M., & van Mourik, M. S. M. (2017). Automated surveillance of healthcare-associated infections: State of the art. Current Opinion in Infectious Diseases, 30(4), 425-431. Crossref
[77] Sisay, A., Mindaye, T., Tesfaye, A., Abera, E., & Desale, A. (2015). Assessing the outcome of Strengthening Laboratory Management Towards Accreditation (SLMTA) on laboratory quality management system in city government of Addis Ababa, Ethiopia. Pan African Medical Journal, 20, Article 314.
[78] Sompuram SR, Vani K, Schaedle AK, Balasubramanian A, Bogen SA (2018). Quantitative assessment of immunohistochemistry laboratory performance by measuring analytic response curves and limits of detection. Arch Pathol Lab Med, 142(7):851-862.
[79] Sunday, E. A., & Omoegun, G. O. (2018). Integrating solar power solutions in small-scale manufacturing industries in Nigeria. International Journal of Scientific Research in Science, Engineering and Technology, 4(8), 832-853.
[80] Torlakovic EE, Cheung CC, D’Arrigo C, et al. (2017). Evolution of quality assurance for clinical immunohistochemistry in the era of precision medicine. Appl Immunohistochem Mol Morphol, 25(1-4). [Multi-part series].
[81] Torlakovic EE, Nielsen S, Francis G, et al. (2015). Standardization of positive controls in diagnostic immunohistochemistry: recommendations from the International Ad Hoc Expert Committee. Appl Immunohistochem Mol Morphol, 23(1):1-18.
[82] van Mourik, M. S. M., Moons, K. G. M., van Solinge, W. W., Berkelbach-van der Sprenkel, J. W., Regli, L., Troelstra, A., & Bonten, M. J. M. (2012). Automated detection of healthcare associated infections: External validation and updating of a model for surveillance of drain-related meningitis. PLOS ONE, 7(12), e51509. PLOS Link
[83] van Mourik, M. S. M., Perencevich, E. N., Gastmeier, P., & Bonten, M. J. M. (2018). Designing surveillance of healthcare-associated infections in the era of automation and reporting mandates. Clinical Infectious Diseases, 66(6), 970-976. Oxford Academic Link
[84] Vyberg M, Nielsen S (2016). Proficiency testing in immunohistochemistry: experiences from Nordic Immunohistochemical Quality Control (NordiQC). Virchows Arch, 468(1):19-29.
[85] Wilson, M. L., Fleming, K. A., Kuti, M. A., Looi, L. M., Lago, N., & Ru, K. (2018). Access to pathology and laboratory medicine services: A crucial gap. The Lancet, 391(10133), 1927-1938.
[86] Wolff AC, Hammond MEH, Allison KH, et al. (2018). Human epidermal growth factor receptor 2 testing in breast cancer: ASCO/CAP clinical practice guideline focused update. J Clin Oncol, 36(20):2105-2122.
[87] World Health Organization. (2011). Laboratory quality management system: Handbook. World Health Organization.
[88] World Health Organization. (2016). Diagnostic stewardship: A guide to implementation in antimicrobial resistance surveillance sites. WHO.
[89] World Health Organization Regional Office for Africa. (2015). WHO guide for the stepwise laboratory improvement process towards accreditation in the African region (SLIPTA). WHO-AFRO.
[90] Wurtz, R., & Cameron, B. J. (2005). Electronic laboratory reporting for the infectious diseases physician and clinical microbiologist. Clinical Infectious Diseases, 40(11), 1638-1643. Crossref
[91] Yao, K., Luman, E. T., & SLMTA Collaborating Authors. (2014a). Evidence from 617 laboratories in 47 countries for SLMTA-driven improvement in quality management systems. African Journal of Laboratory Medicine, 3(2), Article 262.
[92] Yao, K., McKinney, B., Murphy, A., Rotz, P., Wafula, W., Sendagire, H., Okui, S., & Nkengasong, J. N. (2010). Improving quality management systems of laboratories in developing countries: An innovative training approach to accelerate laboratory accreditation. American Journal of Clinical Pathology, 134(3), 401-409.
How to cite this paper
@article{1723090,
author = {Habeeb Damilola Yusuf, Kazeem Abdulrazaq},
title = {A Conceptual Framework for Strengthening Diagnostic Quality Systems in Resource-Limited Clinical Laboratories},
journal = {Iconic Research And Engineering Journals},
year = {2020},
volume = {4},
number = {6},
pages = {343-377},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1723090.pdf},
abstract = {Clinical laboratories in low- and middle-income countries produce results that guide a large share of clinical decisions, yet very few meet internationally recognised quality standards, and this paper argues that the accreditation-preparation programmes that dominate improvement practice have raised audit scores without a demonstrated corresponding gain in the usefulness of the results laboratories deliver. Those programmes assess the laboratory as an isolated unit, so a facility can satisfy a checklist while its results arrive too late to change management, reach clinicians who cannot interpret them, or rest on infrastructure, power, and supply conditions the checklist never examines. This paper synthesises the laboratory strengthening, health systems, diagnostic infrastructure, engineering reliability, and governance literature to propose the Diagnostic Quality Systems Strengthening framework, which treats quality as a property of the diagnostic pathway rather than of the laboratory alone. The framework organises the determinants of diagnostic quality into an enabling foundation of governance, infrastructure, and financing, a technical core spanning the total testing process, an improvement engine of measurement and corrective action, and a clinical interface linking laboratory output to diagnostic decisions and patient outcomes, with workforce capability, information systems, and sustainable financing running through all four. A failure mode analysis maps thirty common causes of unusable results onto the domains, showing that a substantial proportion originate outside the analytical bench and outside the laboratory itself. Five maturity tiers, supported by a domain-level assessment rubric and a specified twelve-indicator starter set, describe progression from unmanaged practice to a self-sustaining learning laboratory, so that partial progress remains meaningful for facilities that will not achieve full accreditation soon. Three worked applications at district, regional, and national reference level illustrate how the same framework yields different priorities at different tiers. The framework is conceptual and requires validation, but it offers a planning and evaluation structure anchored in diagnostic usefulness rather than documentary compliance.},
keywords = {laboratory quality management system; diagnostic quality; resource-limited settings; ISO 15189; SLIPTA; diagnostic infrastructure; total testing process; maturity model; health systems strengthening.},
month = {December},
}