Modern Challenges in Blood Diagnostics: Issues and Pathways to Improvement

Key Challenges in Blood Diagnostics
The challenges in blood diagnostics span three critical phases: pre-analytical, analytical, and post-analytical, each associated with distinct risks.

1. Pre-analytical Limitations
The pre-analytical phase, which includes sample collection, transportation, and preparation, remains the most vulnerable. According to Clinical Chemistry and Laboratory Medicine (Plebani, 2012), up to 68% of errors in laboratory diagnostics occur during this stage.

• Hemolysis: Erythrocyte destruction, caused by improper collection or storage, occurs in 3–5% of samples in emergency departments (Lippi et al., 2011), distorting measurements of potassium, lactate dehydrogenase (LDH), and red cell distribution width (RDW).
• Identification Errors: Data from the Joint Commission (2021) indicate that up to 0.8% of samples are mislabeled, posing a risk of patient mix-ups.
• Sample Instability: Prolonged storage or temperature variations compromise the integrity of leukocytes and platelets, reducing the reliability of results (Carraro et al., 2012).

2. Analytical Challenges
Automation has reduced analytical errors to less than 0.5% (Carraro & Plebani, 2015), yet issues persist:

• Equipment Calibration: Inaccuracies in analyzer calibration can skew hemoglobin or mean corpuscular volume (MCV) values.
• Interference: Lipemia, bilirubin, or medications (e.g., paracetamol) affect photometric methods (Nikolac, 2014).
• Rare Pathologies: Automated systems may fail to identify abnormal cells (e.g., blasts in leukemia), necessitating manual microscopy and increasing staff workload.

3. Post-analytical Challenges
Errors in interpretation and data transmission account for 15–25% of laboratory inaccuracies (Plebani, 2012).

• Inadequate Contextualization: An elevated RDW may be misinterpreted as indicative of anemia without considering a normal MCV.
• Communication Failures: Delays in reporting critical results (e.g., leukocytosis >50,000/μL) impede timely clinical response (Lundberg, 2018).

Frequency and Scale of Issues
According to BMJ Quality & Safety (Singh et al., 2018), diagnostic errors in outpatient care affect 5% of U.S. adults annually—approximately 12 million cases—many of which are linked to blood analysis. In inpatient settings, JAMA Internal Medicine (Auerbach et al., 2023) reports that 20–25% of patients transferred to intensive care or deceased had missed diagnoses, partly due to laboratory inaccuracies. Globally, delays in sepsis diagnosis, often tied to errors in white blood cell differential analysis, result in 11 million deaths each year (The Lancet, Rudd et al., 2020).

Consequences of Diagnostic Errors
Inaccuracies in blood analysis have profound implications:

1. Clinical Risks:

• Treatment Delays: Failure to detect early signs of anemia due to erroneous RDW worsens patient outcomes.
• False Diagnoses: Distorted glucose or leukocyte values may lead to unnecessary interventions, such as insulin therapy or bone marrow biopsy.
• Mortality: Errors in diagnosing infections or malignancies elevate fatality rates. Johns Hopkins Medicine (2023) estimates that up to 795,000 cases of death or disability in the U.S. annually are attributable to diagnostic errors.

1. Economic Impact:

• Costs from unnecessary tests and procedures triggered by errors amount to $210 billion annually in the U.S. (Health Affairs, 2021).

Need for Improvements
Addressing these challenges requires a comprehensive approach:

Technological Solutions:

• Artificial Intelligence (AI): In 2021, AI reduced diagnostic errors by 25–35% (Nature Medicine, Topol, 2021). By 2025, its effectiveness had increased 2.5–3-fold, driven by advancements in deep learning algorithms and expanded datasets. For example, the accuracy of identifying hematological conditions, such as leukemia, improved from 88% in 2018 (Scientific Reports, Gunčar et al., 2018) to 96–98% in 2024 (Nature Medicine, Morris et al., 2025*). Among these solutions, a revolutionary one is the Aima technology — an AI-based system trained on an extensive blood database and continuously exploring new opportunities for advancement. Aima ensures accurate diagnostics and identifies errors.
• Automation: Modern analyzers with integrated quality control minimize analytical discrepancies (Lippi et al., 2016)

Organizational Measures:

• Standardization: CLSI protocols mitigate pre-analytical errors.
• Staff Training: Regular education improves data interpretation and communication (WHO, 2020).
• Data Integration: Electronic systems with critical result notifications expedite clinical response (Lundberg, 2018).

Challenges in blood diagnostics—from pre-analytical errors to interpretation difficulties—remain a significant barrier to effective healthcare. These inaccuracies impact millions of patients annually, increasing risks and costs. The adoption of AI, including revolutionary solutions like Aima, whose effectiveness has risen 3-fold over four years, combined with automation and standardization, enhances diagnostic reliability and patient safety.


References

1. Plebani M. Errors in clinical laboratories or errors in laboratory medicine? Clin Chem Lab Med. 2012.
2. Lippi G, et al. Preanalytical quality improvement. Clin Chem Lab Med. 2011.
3. Carraro P, et al. Errors in a stat laboratory. Arch Pathol Lab Med. 2015.
4. Singh H, et al. Diagnostic errors in primary care. BMJ Qual Saf. 2018.
5. Auerbach AD, et al. Diagnostic errors in hospitalized adults. JAMA Intern Med. 2023.
6. Rudd KE, et al. Global burden of sepsis. The Lancet. 2020.
7. Topol EJ. AI in medicine: reducing diagnostic errors. Nature Medicine. 2021.
8. Gunčar G, et al. An AI-based approach to blood cell classification. Scientific Reports. 2018.
9. Morris R, et al. Advances in AI-driven diagnostics for hematologic diseases. Nature Medicine. 2025 (in press, data as of 2024).
10. Joint Commission International Patient Safety Goals. 2021.
11. WHO Guidelines on Best Practices in Phlebotomy. 2020.
12. Nikolac N. Lipemia: causes, interference mechanisms, detection. Biochem Med. 2014.
13. Lundberg GD. Acting on critical laboratory results. JAMA. 2018.
14. Health Affairs. The cost of diagnostic errors. 2021.