Enhancing Precision in Autoimmune Diagnostics

Autoimmune diseases affect approximately 1 in 10 people, with about 80% of those affected being women. Polyautoimmunity, where patients have two or more autoimmune conditions, occurs in about 25% of individuals diagnosed with one autoimmune disease. This overlapping presence of diseases complicates diagnosis and management.

The diagnostic journey for autoimmune diseases is often long and complex. Patients typically see an average of four doctors over four years before receiving an accurate diagnosis. During this time, they can endure a wide range of symptoms such as fatigue, joint pain and muscle weakness. Fatigue alone affects 99% of individuals with autoimmune diseases, with 89% reporting it significantly hinders their ability to work. This prolonged delay not only worsens physical health but also contributes to increased mental health challenges, including anxiety and depression.

From a diagnostics standpoint, the rising prevalence of polyautoimmunity underscores the urgent need for more integrated and comprehensive testing strategies. Traditional workflows often struggle to detect or differentiate between coexisting autoimmune conditions. To address this, laboratories and clinicians must collaborate closely, leveraging advanced testing technologies and automated systems. These innovations help streamline workflows, improve diagnostic accuracy and ultimately provide faster, more actionable results for patients facing complex and often overlapping autoimmune disorders.

Laboratory autoimmunologists and clinical labs are central to identifying polyautoimmunity. The experts at these facilities conduct complex autoantibody tests and provide essential consulting services to medical staff who treat autoimmune diseases. They play an essential role in guaranteeing test results maintain technical precision while delivering meaningful clinical value for complete diagnostic assessment.

Polyautoimmunity often presents as subtle, overlapping or non-specific symptoms, and test results can sometimes be ambiguous. In these cases, lab specialists help guide the diagnostic process by recommending appropriate follow-up tests, clarifying the clinical relevance of antibody patterns, and ensuring that the right tests are used at the right time. Their collaboration with clinicians can significantly shorten the diagnostic journey and reduce the risk of misdiagnosis or delayed care.

To improve outcomes, it’s essential that clinical labs and healthcare providers establish more integrated communication channels. This means going beyond test results and enabling more active dialogue around test selection, interpretation and next steps. Automated systems and standardized testing platforms also play a role by helping labs manage growing testing volumes while ensuring consistency and accuracy.

Ultimately, improving outcomes in polyautoimmunity can go beyond technology and foster collaboration between labs and clinicians and build workflows that put the patient’s diagnostic journey at the center.

The process of achieving proper sensitivity and specificity levels in autoimmune diagnostics proves to be extremely difficult when healthcare providers need to diagnose multiple autoimmune conditions at once. The combination of high sensitivity and high specificity enables doctors to detect early disease indicators while minimizing incorrect test results, examinations and patient stress.

To achieve this balance, it’s important for labs to move away from a “more is better” mindset when it comes to test ordering. Instead, they should take a targeted, evidence-based approach – guided by clinical context and supported by collaboration with the ordering physician. The selective use of  EliA™ autoimmune tests allows for the detection of clinically relevant autoantibodies with high precision, avoiding the pitfalls of indiscriminate broad panels and ensuring results are meaningful and directly aligned with the patient’s presentation.

The use of standardized automated testing systems helps achieve this equilibrium because they eliminate human error and produce results that doctors can depend on. The combination of decision support tools with laboratory autoimmunologist consultation enables healthcare providers to perform tests that match patient symptoms and medical requirements.

The main objective involves obtaining precise diagnostic information for early autoimmune disease detection while preventing laboratory and patient information overload and clinical confusion. The successful implementation of smart testing strategies requires appropriate technologies and effective lab-clinician partnerships to achieve their goals.

Platforms like the Phadia Laboratory Systems are transforming autoimmune diagnostics by enabling laboratories to scale testing capacity while maintaining high standards for accuracy, consistency and efficiency. These fully automated systems are designed to handle the complexity of autoantibody testing – supporting parallel testing for multiple autoimmune markers with minimal hands-on time.

The process of autoimmune diagnostics requires consistent results because inconsistent test outcomes result in delayed medical diagnoses and incorrect patient assessments. The Phadia instruments achieve standardized results through their established reagent protocols and testing procedures, which minimize laboratory result differences between reference labs and hospital facilities of all sizes. The standardized testing process delivers maximum value to polyautoimmunity diagnosis because it enables precise detection of multiple biomarkers.

The instruments provide enhanced scalability as one of their main benefits. The growing need for testing services requires laboratories to implement platforms that maintain high performance levels while handling increased workload. The Phadia instruments operate at high testing speeds while optimizing workflow processes and minimizing human involvement in testing procedures. The Geisinger Medical Center achieved a 38% reduction in hands-on time after a workflow analysis resulting in instrument consolidation which also generated thousands of dollars in annual lab space savings. 

While there’s been significant progress made in advancing testing for autoimmune diseases, several hurdles remain when it comes to widespread adoption and scalability – particularly in the context of polyautoimmunity.

One major challenge is the lack of standardized protocols across laboratories. Autoimmune testing often varies in how it's implemented, which can lead to inconsistent results and complicated collaboration between labs and clinicians. The goal should be to support and promote harmonized workflows that offer solutions in delivering reliable, reproducible results – regardless of the lab’s size or location.

Another hurdle is integration with existing laboratory information systems and clinical decision-making tools. As laboratory testing workflows become more complex, it’s essential to ensure that data generated can be easily interpreted and acted upon. We're investing in enhanced data connectivity and informatics support to ensure that our platforms not only generate accurate results, but also help labs and clinicians make faster, more informed decisions.

Workforce training and change management are also key concerns. Adopting new testing workflows requires a shift in how labs approach autoimmune diagnostics. To support this transition, we provide robust education, training and implementation support to help labs scale confidently and efficiently.

Finally, there's a growing need for more clinically validated, disease-specific panels that can streamline test selection and interpretation. R&D teams need to focus on expanding current menus of autoantibody assays to better support detection of co-occurring conditions, helping labs and clinicians navigate polyautoimmunity with greater precision.

By addressing these barriers through innovation, education and collaboration, we aim to make comprehensive, scalable autoimmune diagnostics more accessible and impactful – ultimately improving outcomes for patients with complex, overlapping conditions.

The introduction to this interview includes text that has been created with the assistance of generative AI and has undergone editorial review before publishing. Technology Networks' AI policy can be found here.