Risk management in POCT blood glucose monitoring: FMEA approach aligned with ISO 15189:2022

Abstract

Objective

Point-of-care testing (POCT) blood glucose meters provide rapid and convenient monitoring for clinical care and chronic disease management. However, their accuracy is often compromised by risks associated with personnel, equipment, and procedural inconsistencies. This study systematically assesses these risks using the Failure Mode and Effects Analysis (FMEA) method and proposes control measures aligned with ISO 15189:2022 standards.

Methods

This study evaluated the risks associated with POCT blood glucose meters in clinical laboratory settings, encompassing the pre-analytical, analytical, and post-analytical phases. A multidisciplinary team employed FMEA to identify potential failure modes and their impacts. A risk matrix classified risks based on probability and severity, with “unacceptable” risks prompting targeted control measures. A follow-up assessment conducted three months later evaluated the effectiveness of these measures through feedback collection and quality control data analysis, ensuring effective risk mitigation in POCT practices.

Results

The risk assessment identified distinct issues at each hospital: Peking University Shenzhen Hospital faced significant risks related to inadequate performance verification prior to hospital entry, insufficient personnel training, and data management problems, while Wuhan Third Hospital primarily encountered challenges with inadequate training and insufficient calibration and inadequate quality control. Control measures implemented at Peking University Shenzhen Hospital included stringent validation protocols, comprehensive training systems, and automated data management. At Wuhan Third Hospital, the focus was on enhancing training oversight and establishing rigorous quality control measures and calibration Schedule. These interventions effectively reduced unacceptable risks and improved the safety and reliability of the monitoring process.

Conclusion

Integrating FMEA with ISO 15189:2022 provides a structured approach for identifying and mitigating risks in the use of POCT blood glucose meters. Implementing tailored measures significantly enhances POCT accuracy and reliability, offering clinical institutions effective strategies to improve quality and ensure better patient outcomes.

Introduction

Point-of-care testing (POCT) blood glucose meters are essential in clinical care and chronic disease management due to their rapid testing capabilities and portability, facilitating immediate decision-making at the patient’s bedside [1]. This convenience enhances diagnostic efficiency, enabling healthcare providers to promptly adjust treatment plans and support patient self-management, ultimately improving health outcomes [2,3]. However, these devices pose risks, including operational errors such as improper sample handling, inadequate calibration, and poor data management, which can compromise test accuracy and patient safety [4–6]. Therefore, robust quality management practices tailored to POCT blood glucose meters are imperative.

ISO 15189:2022 addresses these challenges with guidelines focusing on critical aspects of POCT services, including organizational management, equipment selection, personnel training, and quality assurance [7]. These standards mandate comprehensive training programs to ensure personnel proficiency and emphasize routine maintenance and calibration to guarantee accuracy [8]. Clear service agreements are required to delineate roles and responsibilities among departments, ensuring consistent use in clinical laboratory settings.

To address the diverse risks associated with POCT blood glucose meters, a structured risk assessment framework is essential. Failure Mode and Effects Analysis (FMEA) is a proactive approach to identify, assess, and prioritize potential failure modes within medical devices and laboratory practices [9,10]. FMEA evaluates various factors—such as personnel competency, instrument functionality, reagent stability, and quality control practices—allowing for a comprehensive assessment of potential failure locations and effective mitigation strategies [9,11–14].

Recent research underscores the significance of quality management in POCT practices. For example, implementing the Plan-Do-Check-Act (PDCA) cycle enhances both the accuracy of blood glucose measurements and patient satisfaction [15,16]. This cyclical approach ensures continuous improvement through planning interventions, monitoring outcomes, and refining procedures based on feedback. Establishing quality indicators (QIs) is also crucial for laboratory quality assurance, providing measurable benchmarks to monitor risks and assess the testing process in alignment with ISO 15189:2022 standards [17].

Clinical nurses and laboratory personnel are vital in maintaining the accuracy of POCT blood glucose tests. Nurses execute quality control measures, adhere to standard operating procedures, and participate in regular training [18]. Laboratory professionals select appropriate instruments, perform calibration, and troubleshoot discrepancies between POCT and central laboratory findings [19]. This multidisciplinary collaboration enhances risk management, ensuring that POCT results are reliable and clinically actionable.

Technological advancements have significantly improved POCT blood glucose meters, including developments like enzyme-free electrochemical sensors that enhance sensitivity and specificity. Additionally, integrating POCT devices with electronic information systems reduces manual entry errors and facilitates real-time data management [20,21]. However, challenges remain, particularly regarding discrepancies between POCT results and traditional laboratory methods for diagnosing complex conditions such as type 2 diabetes [22]. Addressing these challenges through a structured approach is essential for optimizing POCT use in clinical practice.

In this context, the study applies FMEA methodology, in accordance with ISO 15189:2022, to conduct a comprehensive risk assessment of POCT blood glucose meter usage in clinical laboratory settings. By systematically identifying, categorizing, and addressing these risks, the study proposes targeted control measures aimed at enhancing the safety, reliability, and effectiveness of POCT, thereby providing valuable evidence-based guidelines for quality improvement in clinical diagnostics.

Materials and methods

Materials

This study was conducted in a clinical laboratory setting to systematically evaluate the operational processes of point-of-care testing (POCT) blood glucose meters for potential risks. The assessment covered key elements across the pre-analytical, analytical, and post-analytical phases: personnel, instruments, reagents, methods, environment, and quality control. The POCT blood glucose meters utilized were standard models commonly employed in clinical practice. All reagents, calibration solutions, and consumables adhered to the specifications outlined in ISO 15189:2022. Personnel involved were certified clinical laboratory technicians with relevant experience, trained to comply with standardized operating procedures.

Methods

The study employed Failure Mode and Effects Analysis (FMEA) to assess the risks associated with the clinical use of POCT blood glucose meters. FMEA is a systematic, proactive tool that identifies potential failure modes, analyzes their impacts, and recommends corrective actions to minimize clinical risks. The risk assessment was conducted following ISO 15189:2022, emphasizing equipment calibration, personnel competence, and data integrity.

Risk identification

A multidisciplinary POCT Quality Safety and Risk Assessment Team conducted the risk identification process. The team comprised experienced POCT technicians from two hospitals, a clinical physician from a burn-specific ICU, a POCT manager, and nursing staff. This diverse group ensured a thorough evaluation of risks across different clinical laboratory settings.The team employed brainstorming sessions and a literature review to identify potential risks.

Risk matrix and risk assessment

A two-dimensional risk matrix was employed to classify and evaluate potential risks associated with the clinical use of POCT blood glucose meters. Each risk was evaluated according to its probability of harm and severity of harm, following expert consensus and ISO 15189:2022 standards. The matrix (Table 1), following EP23-A standards, provides a systematic approach for prioritizing risks, serving as an initial tool for assessing and prioritizing identified risks (see S1 File for details) [23]. Risks categorized as “unacceptable” necessitated targeted control measures.

[Figure omitted. See PDF.]

Risk control and effectiveness evaluation

Control measures were implemented for the identified unacceptable-risk factors based on their risk levels. A follow-up risk assessment conducted three months later evaluated the effectiveness of these measures in both hospitals. Control measures included feedback collection, quality control data analysis, and incident report reviews. This iterative process ensured the practicality and effectiveness of implemented controls in mitigating the risks associated with POCT blood glucose meters. In the effectiveness assessment, inadequate refers to conditions where the standard is entirely unmet, while insufficient refers to conditions where partial compliance exists but improvements are necessary.

Result

Risk assessment of the POCT blood glucose monitoring process

The risk assessment revealed that Peking University Shenzhen Hospital faces major risks in three areas: inadequate performance verification prior to hospital entry, insufficient personnel training, and data management issues. These risks can lead to inconsistencies and errors in the monitoring process, ultimately affecting result accuracy. In contrast, Wuhan Third Hospital primarily encounters risks related to insufficient personnel training, directly impacting operational safety and accuracy. Furthermore, Wuhan Hospital faces heightened risks associated with insufficient calibration and inadequate quality control. Identifying these distinct risk factors provides targeted information for subsequent control measures (see Table 2).

[Figure omitted. See PDF.]

Control measures for unacceptable risks and re-evaluation

To address the identified unacceptable factors, targeted control measures were implemented. At Peking University Shenzhen Hospital, measures focused on inadequate equipment performance verification involved stringent validation protocols to ensure thorough assessment of all equipment prior to use. Additionally, to combat insufficient personnel training, a comprehensive training system and assessment mechanism were established to ensure all operators are adequately qualified. In data management, the hospital introduced automated IT systems to minimize human errors and enhance data entry accuracy. Conversely, Wuhan Third Hospital emphasized reinforcing personnel training and oversight, ensuring new employees complete thorough training and assessments to mitigate risks stemming from inadequate training. For quality control, Wuhan Hospital strengthened daily quality control checks and established rigorous calibration plans, adjusting risk levels to acceptable. The implementation of these control measures and subsequent evaluations aims to reduce unacceptable-risk levels and ensure the safety and reliability of the POCT blood glucose monitoring process (see Table 3).

[Figure omitted. See PDF.]

Following these improvement measures, ongoing monitoring of the POCT blood glucose monitoring process has ensured that all associated risks remain within acceptable levels. This proactive approach enhances the overall reliability and safety of the monitoring process, thereby improving patient care outcomes.

Discussion

This study systematically employed Failure Mode and Effects Analysis (FMEA) to assess the clinical use of point-of-care testing (POCT) blood glucose meters at two hospitals, aligning with ISO 15189:2022. The analysis identified critical risks, including inadequate performance verification prior to hospital entry, insufficient personnel training, data management deficiencies, insufficient calibration and inadequate quality control (QC) [10,11]. Based on the evaluations provided by the multidisciplinary team during the follow-up period, the implementation of targeted control measures not only mitigates these risks but also enhances the overall reliability of POCT monitoring, underscoring the necessity of a structured risk management strategy.

Inadequate performance verification at Peking University Shenzhen Hospital highlighted the need for stringent protocols to ensure thorough assessment of all equipment before clinical use. Comprehensive performance verification protocols were established to ensure compliance with necessary standards, aiming to minimize risks of inaccurate readings and enhance patient safety.

Data management issues posed significant risks at Peking University Shenzhen Hospital due to its higher workload and previous reliance on manual processes. To enhance data integrity, integrating information technology (IT) was a critical improvement measure. Automated data entry systems and QC tracking were implemented, significantly reducing the potential for human error associated with manual data entry, which can compromise patient data integrity [24]. Due to limitations in resources and infrastructure availability at Wuhan Third Hospital, manual verification remains the current practice. To mitigate actual risks in resource-limited environments like Wuhan Third Hospital, the risk management team recommends maintaining manual verification, regular manual audits, and paper-based backups. These measures aim to ensure the accuracy and integrity of data management processes in the absence of fully implemented IT systems.

Personnel training emerged as a pivotal risk factor at both hospitals. Insufficient training can lead to improper use of POCT devices, resulting in unreliable test results. To address this, comprehensive training programs and ongoing education initiatives were instituted, equipping clinical staff to operate POCT devices correctly and adhere to standardized operating procedures [2]. These efforts emphasize the importance of continuous professional development in maintaining high-quality testing standards. As with the measures in data management, IT-driven solutions in personnel training can enhance the efficiency and accuracy of training management, ultimately improving the quality of POCT services. However, in resource-limited environments, the following measures remain essential to mitigate risks: manual tracking of personnel, enhanced supervision with mentorship, and ongoing performance assessments.

At Wuhan Third Hospital, inadequate calibration and quality control compounded risks. To address this, a comprehensive calibration schedule was established, incorporating daily internal quality control (IQC) checks, and performance verification tests [8]. A digital log system was implemented to track calibration activities, with alerts for upcoming requirements. This structured approach not only ensures compliance with ISO 15189:2022 standards but also reinforces the reliability of POCT results by enabling cross-verification with standard laboratory analyzers. Additionally, routine comparisons with biochemical analyzers and robust QC measures, including automated data logging and comprehensive internal and external QC protocols, effectively mitigated risks [25]. Post-control evaluations confirmed that insufficient QC measures were reduced to acceptable levels, demonstrating the value of ongoing quality control and multidisciplinary collaboration in POCT risk management.

Effective risk management requires interdisciplinary cooperation. Laboratory professionals are crucial in developing quality control protocols, calibrating instruments, and selecting appropriate POCT devices [22]. Their expertise in identifying potential interferences ensures the accuracy and reliability of POCT results [26]. Nurses and physicians rely on laboratory professionals to investigate discrepancies between POCT and central laboratory results, involving cross-checking device performance and assessing factors that may influence test accuracy.

However, the current study faces several limitations. Firstly, the Failure Mode and Effects Analysis (FMEA) method used in this study relies on clinical expertise and consensus-based brainstorming by a multidisciplinary team. This approach focuses on qualitative risk identification rather than extensive quantitative data collection, which is a recognized limitation of FMEA. Secondly, the data used in this study were obtained from a clinical laboratory setting, which simulates a real-world clinical environment but does not fully capture the variability and complexities of actual clinical practice. While the control measures implemented were effective, future studies should focus on performance indicators such as error rates, accuracy, and patient outcomes to further validate these measures and inform best practices for POCT use. Moreover, future research should explore emerging risks related to the integration of POCT devices into electronic health records (EHRs) and hospital information systems, including challenges related to data security and system interoperability [21]. Leveraging big data analytics and artificial intelligence (AI) could enable real-time monitoring and proactive risk detection [27], ultimately improving the reliability of POCT.

Conclusions

This study highlights the effectiveness of Failure Mode and Effects Analysis (FMEA) in identifying and mitigating risks associated with point-of-care testing (POCT) blood glucose meters. Key improvements, including structured performance verification, comprehensive training, and enhanced data management through information technology, have noticeably improved accuracy and reliability. Interdisciplinary collaboration among laboratory professionals and clinical staff is essential for ensuring proper use and data integrity. Future research should focus on emerging risks with electronic health records and leverage big data analytics to further enhance POCT safety and patient care outcomes.

Supporting information

S1 File. Risk acceptability matrix discussion.

https://doi.org/10.1371/journal.pone.0319817.s001

S2 File. Complete risk assessment report: Pre-analytical, analytical, and post-analytical phases.

https://doi.org/10.1371/journal.pone.0319817.s002

Acknowledgments

We extend our gratitude to the medical staff and laboratory professionals at Peking University Shenzhen Hospital and Wuhan Third Hospital for their invaluable contributions and collaboration in this study. Their expertise was crucial in implementing the Failure Mode and Effects Analysis (FMEA) and enhancing the safety of point-of-care testing (POCT) blood glucose meters. We also thank our research team for their support, as well as our colleagues for their insightful feedback. Their expertise and cooperation were crucial to the risk assessment and quality control processes.

References

1. 1. Luppa PB, Müller C, Schlichtiger A, Schlebusch H. Point-of-care testing (POCT): current techniques and future perspectives. Trends Analyt Chem. 2011;30(6):887–98. pmid:32287536

* View Article

* PubMed/NCBI

* Google Scholar

2. 2. Nichols JH. Blood glucose testing in the hospital: error sources and risk management. J Diabetes Sci Technol. 2011;5(1):173–7. pmid:21303641

* View Article

* PubMed/NCBI

* Google Scholar

3. 3. Plebani M, Nichols JH, Luppa PB, Greene D, Sciacovelli L, Shaw J, et al. Point-of-care testing: state-of-the art and perspectives. Clin Chem Lab Med. 2024;63(1):35–51. pmid:38880779

* View Article

* PubMed/NCBI

* Google Scholar

4. 4. Matteucci E, Giampietro O. Point-of-care testing in diabetes care. Mini Rev Med Chem. 2011;11(2):178–84. pmid:21222582

* View Article

* PubMed/NCBI

* Google Scholar

5. 5. Quinn AD, Dixon D, Meenan BJ. Barriers to hospital-based clinical adoption of point-of-care testing (POCT): a systematic narrative review. Crit Rev Clin Lab Sci. 2016;53(1):1–12. pmid:26292075

* View Article

* PubMed/NCBI

* Google Scholar

6. 6. Florkowski C, Don-Wauchope A, Gimenez N, Rodriguez-Capote K, Wils J, Zemlin A. Point-of-care testing (POCT) and evidence-based laboratory medicine (EBLM) - does it leverage any advantage in clinical decision making? Crit Rev Clin Lab Sci. 2017;54(7–8):471–94. pmid:29169287

* View Article

* PubMed/NCBI

* Google Scholar

7. 7. ISO 15189, Medical laboratories - Requirements for quality and competence. Geneva: International Organization for Standardization; 2022.

8. 8. Stavelin A, Sandberg S. Analytical performance specifications and quality assurance of point-of-care testing in primary healthcare. Crit Rev Clin Lab Sci. 2024;61(3):164–77. pmid:37779370

* View Article

* PubMed/NCBI

* Google Scholar

9. 9. Van Hoof V, Bench S, Soto AB, Luppa PP, Malpass A, Schilling UM, et al. Failure mode and effects analysis (FMEA) at the preanalytical phase for POCT blood gas analysis: proposal for a shared proactive risk analysis model. Clin Chem Lab Med. 2022;60(8):1186–201. pmid:35607775

* View Article

* PubMed/NCBI

* Google Scholar

10. 10. Liu H-C, Zhang L-J, Ping Y-J, Wang L. Failure mode and effects analysis for proactive healthcare risk evaluation: a systematic literature review. J Eval Clin Pract. 2020;26(4):1320–37. pmid:31849153

* View Article

* PubMed/NCBI

* Google Scholar

11. 11. Dawson A. A Practical guide to performance improvement: failure mode and effects analysis. AORN J. 2019;110(3):282–7. pmid:31465564

* View Article

* PubMed/NCBI

* Google Scholar

12. 12. Rah J-E, Manger RP, Yock AD, Kim G-Y. A comparison of two prospective risk analysis methods: traditional FMEA and a modified healthcare FMEA. Med Phys. 2016;43(12):6347. pmid:27908165

* View Article

* PubMed/NCBI

* Google Scholar

13. 13. Guiñón L, Soler A, Gisell Díaz M, Fernández RM, Rico N, Bedini JL, et al. Analytical performance assessment and improvement by means of the failure mode and effect analysis (FMEA). Biochem Med (Zagreb). 2020;30(2):020703. pmid:32292281

* View Article

* PubMed/NCBI

* Google Scholar

14. 14. Lee H, Lee H, Baik J, Kim H, Kim R. Failure mode and effects analysis drastically reduced potential risks in clinical trial conduct. Drug Des Devel Ther. 2017;11:3035–43. pmid:29089745

* View Article

* PubMed/NCBI

* Google Scholar

15. 15. Chen J, Cai W, Lin F, Chen X, Chen R, Ruan Z. Application of the PDCA cycle for managing hyperglycemia in critically ill patients. Diabetes Ther. 2023;14(2):293–301. pmid:36422801

* View Article

* PubMed/NCBI

* Google Scholar

16. 16. Meehan CD, Silvestri A, Street ED. Improving blood glucose monitoring in a hospital setting using the PDCA approach. Plan, do, check, act cycle. J Nurs Care Qual. 1993;7(4):56–63. pmid:8338967

* View Article

* PubMed/NCBI

* Google Scholar

17. 17. Nicolay CR, Purkayastha S, Greenhalgh A, Benn J, Chaturvedi S, Phillips N, et al. Systematic review of the application of quality improvement methodologies from the manufacturing industry to surgical healthcare. Br J Surg. 2012;99(3):324–35. pmid:22101509

* View Article

* PubMed/NCBI

* Google Scholar

18. 18. Makic MBF, Barton AJ. Point of care testing: ensuring accuracy. Clin Nurse Spec. 2015;29(6):306–7. pmid:26444505

* View Article

* PubMed/NCBI

* Google Scholar

19. 19. Khan A, Pratumvinit B, Jacobs E, Kost G, Kary H, Balla J, et al. Point-of-care testing performed by healthcare professionals outside the hospital setting: consensus based recommendations from the IFCC Committee on Point-of-Care Testing (IFCC C-POCT). Clin Chem Lab Med. 2023;61(9):1572–9.

* View Article

* Google Scholar

20. 20. Sabu C, Henna TK, Raphey VR, Nivitha KP, Pramod K. Advanced biosensors for glucose and insulin. Biosens Bioelectron. 2019;141:111201. pmid:31302426

* View Article

* PubMed/NCBI

* Google Scholar

21. 21. Tang L, Chang SJ, Chen CJ, Liu JT. Non-invasive blood glucose monitoring technology: a review. Sensors (Basel). 2020;20(23).

* View Article

* Google Scholar

22. 22. Siegrist KK, Rice MJ. Point-of-care blood testing: the technology behind the numbers. Anesth Analg. 2019;129(1):92–8. pmid:30973383

* View Article

* PubMed/NCBI

* Google Scholar

23. 23. Clinical and Laboratory Standards Institute. Laboratory quality control based on risk management. Approved guideline - 1st ed. EP23-A.Wayne, PA: Clinical and Laboratory Standards Institute; 2011.

24. 24. Hu J, Cui X, Gong Y, Xu X, Gao B, Wen T, et al. Portable microfluidic and smartphone-based devices for monitoring of cardiovascular diseases at the point of care. Biotechnol Adv. 2016;34(3):305–20. pmid:26898179

* View Article

* PubMed/NCBI

* Google Scholar

25. 25. Shaw J, Arnoldo S, Beach L, Bouhtiauy I, Brinc D, Brun M, et al. Establishing quality indicators for point of care glucose testing: recommendations from the Canadian Society for Clinical Chemists Point of Care Testing and Quality Indicators Special Interest Groups. Clin Chem Lab Med. 2023;61(7):1280–7.

* View Article

* Google Scholar

26. 26. Myers GJ, Browne J. Point of care hematocrit and hemoglobin in cardiac surgery: a review. Perfusion. 2007;22(3):179–83. pmid:18018397

* View Article

* PubMed/NCBI

* Google Scholar

27. 27. Arya SS, Dias SB, Jelinek HF, Hadjileontiadis LJ, Pappa A-M. The convergence of traditional and digital biomarkers through AI-assisted biosensing: a new era in translational diagnostics?. Biosens Bioelectron. 2023;235:115387. pmid:37229842

* View Article

* PubMed/NCBI

* Google Scholar

Citation: Luan X, Ke L, Feng M, Peng W, Luo H, Xue H, et al. (2025) Risk management in POCT blood glucose monitoring: FMEA approach aligned with ISO 15189:2022. PLoS ONE 20(3): e0319817. https://doi.org/10.1371/journal.pone.0319817

About the Authors:

Xiagang Luan

Contributed equally to this work with: Xiagang Luan, Lingling Ke, Minxuan Feng

Roles: Conceptualization, Data curation, Investigation, Writing – original draft

Affiliation: Department of Burns, Wuhan Third Hospital, Tongren Hospital of Wuhan University, Wuhan, China

Lingling Ke

Contributed equally to this work with: Xiagang Luan, Lingling Ke, Minxuan Feng

Roles: Conceptualization, Data curation, Investigation, Writing – original draft

Affiliation: Department of Laboratory, Wuhan Third Hospital, Tongren Hospital of Wuhan University, Wuhan, China

Minxuan Feng

Contributed equally to this work with: Xiagang Luan, Lingling Ke, Minxuan Feng

Roles: Investigation, Writing – original draft

Affiliations: Department of Laboratory Medicine, Peking University Shenzhen Hospital, Shenzhen, China, The First School of Clinical Medicine, Guangdong Medical University, Zhanjiang, China

Weiqun Peng

Roles: Project administration

Affiliation: Department of Endocrinology, Peking University Shenzhen Hospital, Shenzhen, China

Houlong Luo

Roles: Project administration

Affiliation: Department of Laboratory Medicine, Peking University Shenzhen Hospital, Shenzhen, China

Hao Xue

Roles: Conceptualization, Writing – review & editing

E-mail: [email protected] (HX); [email protected] (YX)

Affiliation: Department of Laboratory Medicine, Peking University Shenzhen Hospital, Shenzhen, China

Yong Xia

Roles: Conceptualization, Writing – review & editing

E-mail: [email protected] (HX); [email protected] (YX)

Affiliation: Department of Laboratory Medicine, Peking University Shenzhen Hospital, Shenzhen, China

ORICD: https://orcid.org/0000-0003-4589-9702

[/RAW_REF_TEXT]

[/RAW_REF_TEXT]

References

1. Luppa PB, Müller C, Schlichtiger A, Schlebusch H. Point-of-care testing (POCT): current techniques and future perspectives. Trends Analyt Chem. 2011;30(6):887–98. pmid:32287536

2. Nichols JH. Blood glucose testing in the hospital: error sources and risk management. J Diabetes Sci Technol. 2011;5(1):173–7. pmid:21303641

3. Plebani M, Nichols JH, Luppa PB, Greene D, Sciacovelli L, Shaw J, et al. Point-of-care testing: state-of-the art and perspectives. Clin Chem Lab Med. 2024;63(1):35–51. pmid:38880779

4. Matteucci E, Giampietro O. Point-of-care testing in diabetes care. Mini Rev Med Chem. 2011;11(2):178–84. pmid:21222582

5. Quinn AD, Dixon D, Meenan BJ. Barriers to hospital-based clinical adoption of point-of-care testing (POCT): a systematic narrative review. Crit Rev Clin Lab Sci. 2016;53(1):1–12. pmid:26292075

6. Florkowski C, Don-Wauchope A, Gimenez N, Rodriguez-Capote K, Wils J, Zemlin A. Point-of-care testing (POCT) and evidence-based laboratory medicine (EBLM) - does it leverage any advantage in clinical decision making? Crit Rev Clin Lab Sci. 2017;54(7–8):471–94. pmid:29169287

7. ISO 15189, Medical laboratories - Requirements for quality and competence. Geneva: International Organization for Standardization; 2022.

8. Stavelin A, Sandberg S. Analytical performance specifications and quality assurance of point-of-care testing in primary healthcare. Crit Rev Clin Lab Sci. 2024;61(3):164–77. pmid:37779370

9. Van Hoof V, Bench S, Soto AB, Luppa PP, Malpass A, Schilling UM, et al. Failure mode and effects analysis (FMEA) at the preanalytical phase for POCT blood gas analysis: proposal for a shared proactive risk analysis model. Clin Chem Lab Med. 2022;60(8):1186–201. pmid:35607775

10. Liu H-C, Zhang L-J, Ping Y-J, Wang L. Failure mode and effects analysis for proactive healthcare risk evaluation: a systematic literature review. J Eval Clin Pract. 2020;26(4):1320–37. pmid:31849153

11. Dawson A. A Practical guide to performance improvement: failure mode and effects analysis. AORN J. 2019;110(3):282–7. pmid:31465564

12. Rah J-E, Manger RP, Yock AD, Kim G-Y. A comparison of two prospective risk analysis methods: traditional FMEA and a modified healthcare FMEA. Med Phys. 2016;43(12):6347. pmid:27908165

13. Guiñón L, Soler A, Gisell Díaz M, Fernández RM, Rico N, Bedini JL, et al. Analytical performance assessment and improvement by means of the failure mode and effect analysis (FMEA). Biochem Med (Zagreb). 2020;30(2):020703. pmid:32292281

14. Lee H, Lee H, Baik J, Kim H, Kim R. Failure mode and effects analysis drastically reduced potential risks in clinical trial conduct. Drug Des Devel Ther. 2017;11:3035–43. pmid:29089745

15. Chen J, Cai W, Lin F, Chen X, Chen R, Ruan Z. Application of the PDCA cycle for managing hyperglycemia in critically ill patients. Diabetes Ther. 2023;14(2):293–301. pmid:36422801

16. Meehan CD, Silvestri A, Street ED. Improving blood glucose monitoring in a hospital setting using the PDCA approach. Plan, do, check, act cycle. J Nurs Care Qual. 1993;7(4):56–63. pmid:8338967

17. Nicolay CR, Purkayastha S, Greenhalgh A, Benn J, Chaturvedi S, Phillips N, et al. Systematic review of the application of quality improvement methodologies from the manufacturing industry to surgical healthcare. Br J Surg. 2012;99(3):324–35. pmid:22101509

18. Makic MBF, Barton AJ. Point of care testing: ensuring accuracy. Clin Nurse Spec. 2015;29(6):306–7. pmid:26444505

19. Khan A, Pratumvinit B, Jacobs E, Kost G, Kary H, Balla J, et al. Point-of-care testing performed by healthcare professionals outside the hospital setting: consensus based recommendations from the IFCC Committee on Point-of-Care Testing (IFCC C-POCT). Clin Chem Lab Med. 2023;61(9):1572–9.

20. Sabu C, Henna TK, Raphey VR, Nivitha KP, Pramod K. Advanced biosensors for glucose and insulin. Biosens Bioelectron. 2019;141:111201. pmid:31302426

21. Tang L, Chang SJ, Chen CJ, Liu JT. Non-invasive blood glucose monitoring technology: a review. Sensors (Basel). 2020;20(23).

22. Siegrist KK, Rice MJ. Point-of-care blood testing: the technology behind the numbers. Anesth Analg. 2019;129(1):92–8. pmid:30973383

23. Clinical and Laboratory Standards Institute. Laboratory quality control based on risk management. Approved guideline - 1st ed. EP23-A.Wayne, PA: Clinical and Laboratory Standards Institute; 2011.

24. Hu J, Cui X, Gong Y, Xu X, Gao B, Wen T, et al. Portable microfluidic and smartphone-based devices for monitoring of cardiovascular diseases at the point of care. Biotechnol Adv. 2016;34(3):305–20. pmid:26898179

25. Shaw J, Arnoldo S, Beach L, Bouhtiauy I, Brinc D, Brun M, et al. Establishing quality indicators for point of care glucose testing: recommendations from the Canadian Society for Clinical Chemists Point of Care Testing and Quality Indicators Special Interest Groups. Clin Chem Lab Med. 2023;61(7):1280–7.

26. Myers GJ, Browne J. Point of care hematocrit and hemoglobin in cardiac surgery: a review. Perfusion. 2007;22(3):179–83. pmid:18018397

27. Arya SS, Dias SB, Jelinek HF, Hadjileontiadis LJ, Pappa A-M. The convergence of traditional and digital biomarkers through AI-assisted biosensing: a new era in translational diagnostics?. Biosens Bioelectron. 2023;235:115387. pmid:37229842