In modern healthcare, diagnostics form a critical first step — the process of identifying the presence, absence, or nature of a disease or condition in a patient. However, the economics surrounding diagnostics are often overlooked: the focus tends to be on treatment costs, drugs, and long-term care. Yet, poor diagnostic practices — whether overdiagnosis, underdiagnosis, or misdiagnosis — carry an enormous human and financial cost. As systems scale and diagnostic technologies advance, rethinking diagnostic strategies with an eye toward cost-efficiency, access, and safety becomes critical. This blog draws on recent evidence to highlight the stakes and propose how to align financial sustainability with diagnostic quality.
The High Human and Economic Cost of Diagnostic Failure
According to OECD’s recent report, up to 80% of all harm caused by delayed or incorrect diagnosis is preventable. Among specific conditions, evidence reveals disturbing levels of both overdiagnosis and underdiagnosis. For example, childhood asthma in the UK has been estimated to be overdiagnosed in up to 15% of cases and underdiagnosed in as many as 40%. More broadly, globally, up to 70% of individuals with chronic respiratory diseases such as COPD or asthma may remain undiagnosed.
These statistics suggest that many people either receive unnecessary treatment (due to overdiagnosis) or miss out on needed care (due to underdiagnosis) — both with far-reaching consequences. Overdiagnosis can lead to patient anxiety, unnecessary treatments, and furthermore, a waste of laboratory resources. Underdiagnosis prevents patients from receiving timely treatment, worsens disease progression, and may even increase mortality or morbidity. At a population level, misdiagnosis contributes to large numbers of avoidable deaths and permanent disabilities, making diagnostic error one of the leading causes of serious harm from safety failures.
Heavy Cost for Health Systems and Societies
From an economic standpoint, diagnostic failures are not minor glitches — they represent a significant drain on resources. The OECD estimates that the direct cost of misdiagnosis, underdiagnosis, and overdiagnosis combined is a substantial fraction of total healthcare expenditure.
In a typical high-income health system, such costs may be in the hundreds of billions of dollars annually. The report suggests that halving diagnostic error rates could save around 8% of total health expenditure.
Costs arise due to multiple factors: repetitive testing, unnecessary treatments, extended hospitalizations, expensive corrective care after delayed diagnosis, complications from inappropriate therapies, and legal / malpractice claims. Overdiagnosis can trigger unnecessary interventions, anxiety, and iatrogenic harm.
Beyond direct costs, diagnostic failures fuel intangible burdens — patient suffering, loss of trust, reduced quality of life, missed economic productivity, and worse public health outcomes.
Hence, any serious discussion of how to make health systems cost-efficient must include diagnostics — not only in terms of test pricing or volume, but quality, safety, system design, and equity.
Why Does Diagnostic Failure Persist — Even As Diagnostics Expand?
Diagnostic failure persists because diagnosis is rarely a single event; it is an iterative, probabilistic process that unfolds across multiple encounters and requires integrating and analyzing symptoms, tests, imaging, context, and treatment response. Many conditions — including long-COVID, irritable bowel syndrome, and several neuropsychiatric or functional disorders — lack clear biomarkers or universally accepted criteria, making diagnosis inherently uncertain. Even when tests exist, thresholds are not absolute; interpreting results without clinical context can lead to false positives or missed disease.
Human and systemic factors further compound these limitations. Errors in history-taking, examination, test ordering, or interpretation contribute heavily to serious diagnostic mistakes. Cognitive biases — anchoring, premature closure, availability bias, or “satisficing” — can skew clinical judgment and drive misdiagnosis.
System-level gaps such as fragmented records, poor communication across care levels, inadequate follow-up, and information silos also undermine diagnostic safety. In resource-constrained environments, these problems intensify, making reliable diagnosis even more difficult.
Strategies To Improve Diagnostics Economics
Improving the economics of diagnostics requires more than increasing the number of tests or lowering their price. Because diagnostic error imposes enormous clinical and financial costs, health systems need a value-driven approach that promotes diagnostic accuracy, timeliness, appropriateness, equity, and safety.
- Make Diagnostic Safety a System Priority
Diagnostic failures stem largely from structural issues, not isolated mistakes. Leaders, policymakers, and payers should elevate diagnostic safety to the same level as surgical or medication safety. This includes investing in quality assurance, standardization, audits, and feedback loops across all care levels, and aligning incentives with diagnostic accuracy rather than test volume. - Focus on High-Value Diagnostics, Not More Diagnostics
Unnecessary imaging and testing waste significant resources. Health systems can reduce low-value diagnostics by implementing evidence-based guidelines, referral criteria, and decision-support tools built into clinical workflows. These help clinicians determine when diagnostics are truly necessary and discourage indiscriminate testing. - Strengthen Clinical Judgment and Multidisciplinary Diagnosis
Since diagnosis is fundamentally a clinical reasoning process, improving clinician training and collaboration is key. Strategies include multidisciplinary case discussions, continuous education, peer review for complex or ambiguous cases, and structured analysis of misdiagnoses to create learning loops. Better information-sharing and continuity between primary and secondary care also reduce errors, showing that system design matters as much as individual skill. - Monitor and Report Diagnostic Safety as a Core Quality Metric
Diagnostic safety has long been overlooked in quality monitoring. Integrating it into routine reporting promotes accountability and continuous improvement, and helps demonstrate that investing in better diagnostic processes yields long-term economic and clinical benefits. - Improve Equity and Access to Avoid the “Inverse Care Law”
Overuse of diagnostics often occurs in well-resourced populations, while underdiagnosis persists in underserved or rural areas. Any reimagined diagnostic model must address these imbalances by expanding access to necessary diagnostics in primary care and marginalized populations, while curbing overuse where access is abundant. - Transition to Value-Based Diagnostic Incentives
Payment models that reward volume encourage unnecessary tests. Value-based incentives would instead reward appropriateness, accuracy, timeliness, and diagnostic outcomes. - Use Technology Thoughtfully
AI and digital diagnostic tools can improve accuracy, reduce costs, and speed up interpretation. However, adoption must be cautious, ensuring validation, bias mitigation, transparency, and ongoing human oversight to prevent new forms of error or inequity.
How Clinical Laboratories Shape Diagnostic Value and System Efficiency
Much of the diagnostic process begins and evolves within clinical laboratories, where test data and interpretation influence every downstream decision. Because so many clinical actions rely on lab-derived data and insights, the way laboratories function directly affects diagnostic quality, patient outcomes, and overall healthcare costs.
One of the most significant contributions labs make is ensuring the appropriate use of tests. Through collaborative protocols, utilization alerts, and reflex-testing pathways, laboratories help clinicians avoid both under-testing — which delays diagnosis — and over-testing, which wastes resources. This stewardship ensures that diagnostics are deployed thoughtfully and consistently.
Operational strength within labs is another major driver of economic efficiency. Standardized workflows, strict quality assurance, and continuous proficiency testing reduce variability, prevent repeat testing, and make results dependable from the start. Quick turnaround times also support timely clinical action, preventing avoidable downstream costs tied to delayed treatment.
Integration is equally critical. When labs connect seamlessly with electronic health records (EHRs), they reduce diagnostic fragmentation. This helps prevent duplicated tests, strengthens continuity, and ensures that abnormal results trigger timely intervention rather than slipping through system gaps.
The top three challenges laboratories face that significantly impact quality:
- Limited staff: With vacancy rates between 7% and 11%, laboratories are particularly hard hit.
- Increased demand: The number of tests laboratories perform continues to grow with an increase in the tests being ordered, an increase in chronic diseases, and the availability of newer tests.
- Lack of automation: The laboratories unanimously agree that adopting automation would help them improve patient care and is the only way forward to keep up with the demands of testing.
How a Purpose-Built Diagnostic Lab Management System Supports Cost-Efficiency and Quality in the Lab
A well-designed clinical diagnostics LIMS is one of the most effective tools for improving cost-efficiency and diagnostic quality. By assigning each sample a unique identifier and tracking it throughout its lifecycle, a clinical diagnostics LIMS or diagnostic lab management system minimizes mix-ups, loss, and mislabeling — all common causes of costly repeat testing. Automated data capture and instrument integration further reduce transcription errors, helping ensure results are accurate, consistent, and clinically reliable. With complete test histories available in one place, labs also avoid redundant testing that wastes time and resources.
Automation is central to efficiency. A diagnostic lab management system automates routine tasks such as accessioning, scheduling, test assignment, reporting, and billing. This boosts throughput without requiring additional staff and shortens turnaround times so clinicians receive results sooner. Better resource utilization — from reagents to consumables and equipment — reduces waste and prevents unnecessary purchasing or stockouts.
Standardized workflows enforced through a LIMS enhance diagnostic reliability by reducing variability in processes and reporting. Built-in audit trails, version control, and role-based access support compliance with regulatory frameworks, lowering the risk of non-compliance or legal challenges. Quality modules that monitor instrument performance and track QC trends help labs detect issues early and maintain accuracy.
A LIMS also supports growth and informed decision-making. It scales with expanding test menus or multi-site operations, centralizes data for better collaboration, and provides analytics that highlight bottlenecks and opportunities for improvement. A LIMS can also be integrated with clinical analyzers and third-party software, such as EHRs, for centralized data management and to prevent manual errors. Furthermore, it can be integrated with AI-powered tools to accelerate the downstream interpretation of test results.
Although implementing a modern diagnostic lab management system requires some upfront investment, long-term savings from fewer errors, reduced labor, optimized inventory usage, and improved productivity consistently deliver strong ROI — turning diagnostics into a more efficient, sustainable, and high-quality service. A cloud-based SaaS LIMS eliminates the need for upfront investment in IT resources or subscription fee. Laboratories can pay as they go, minimizing the total cost of ownership and maximizing investment returns.
Conclusion
Diagnostics are central to patient outcomes and health system sustainability, yet poor-quality, redundant testing causes waste, harm, and long-term costs. Strengthening diagnostic quality through better processes, evidence-based use, and improved system design delivers far greater value than simply reducing test spend. A purpose-built, cloud-based SaaS diagnostic lab management system is a key enabler: improving accuracy, streamlining workflows, reducing waste, ensuring quality control, and supporting scalable, compliant operations. The challenge for leaders is to view diagnostics not as a commodity but as a strategic investment. The aim isn’t doing less, but doing right — enabling safer care, lower waste, and better health for entire populations.