Healthcare is evolving rapidly, and value-based contracts create the economic alignment to drive optimized outcomes at a viable cost. Diagnostic exam choices have a tremendous impact on outcomes and cost. With the proper understanding of value and evidence-based standards, providers are driving toward this change, continuing to improve patients’ health. However, the cost of care is difficult to wrangle. Creating standards that care teams and systems adhere to ensures that diagnostic choices represent clinically appropriate and cost-effective care. Health systems, provider networks, and medical groups each play a role in leading this change, and they must work together. The way out of the labyrinth of cost is an unwavering and shared commitment to leverage the evidence.

Aligning Payment Models to Best Practice

“Value-based care” is defined in several different ways; it is an approach to care delivery that prioritizes and rewards quality of care, efficiency of service delivery, and patient satisfaction with the care received. The caregivers must also be engaged and satisfied with the model for it to be viable and sustainable. The idea of care centered around the patient and provider requires all members and processes of health care delivery to work in concert with each other to achieve high-quality, evidence-based, and accessible care. In value-based models, payment economics may even penalize the participants financially when goals are left unmet. The alignment of performance and payment is the most important difference from a traditional fee-for-service payment model. Yet, it is fair to assess that not all value models recognize the evidence completely. Productive critique is needed and drives the evolution of payment.

The way out of the labyrinth of cost is an unwavering and shared commitment to leverage the evidence.

The Importance of Evidence-Based Diagnostic Choices

Diagnostics testing is an essential step in the clinical care process. The utilization of diagnostics is wildly different from one model of care or provider to the next. Inconsistencies create variability in cost, and ultimately outcomes suffer. The question is, “Why does variability exist?” A degree of variability is always expected in medicine, but as clinical care and medical training has evolved, inconsistencies are exacerbated while providers chase the latest guidelines and evidence.

Additionally, patients often demand testing from providers who recognize the most efficient best practice may not alleviate the fear that a test might. Many disciplines of providers, clinical staff, finance leaders, analysts, and care teams are nobly charging headlong to solve the value equation within their silos. Research yields multidisciplinary, evidence-based protocols that should supersede individual experiences.

Collaboration to Create Clinical Standards

Health systems, networks, and provider groups must work together to develop and implement these diagnostic standards within clinical protocols. Just as critical as the clinician is the expertise of the diagnostics teams. Intentional effort should be made to engage leadership from the clinical laboratory, imaging, cardiology, neurology, pathology, genetics, endoscopy, electrophysiology, etc. This collaborative effort extends into the multidisciplinary teams of clinicians, analysts, researchers, and experts in value-based care who can review the existing evidence and collectively establish best practices. Regular updates and reviews of standards ensure they remain current with the latest medical advancements and research findings. Many top academic and care delivery institutions have taken this step forward, engaging systematically across disciplines. They are unlocking value, publishing additional evidence, and as they do, the improvement cycle continues.

Measure, Measure, Measure

Once clinical standards are agreed on, monitoring adherence and outcomes helps sustain momentum. “Cost to deliver care” and “cost charged to deliver the care” are two separate crucial indicators that frequently get confused—the time burden and cost of each care staff member and how that value gets maximized. There are various approaches to analyzing these costs; all of them are tedious, but they are still equally worthy. Until the cost of each moment of care, each turn of the cog, and each unit of resource is identified, the true cost opportunity is unknown. Understanding the “cost to deliver care” creates transparency and repeatable value, reducing waste in the system from overutilization, errors, and inefficient processes.

Summarizing Success: Evidence-based Diagnostic Cost Containment

1. Engage Multidisciplinary Teams: Involve diverse groups of experts, including diagnostic experts. Their combined expertise ensures the protocols are comprehensive.
2. Leverage the Evidence: Allow the evidence to drive the decision-making, avoiding the variability and cost associated with preferences and habits.
3. Education and Training: Provide ongoing education and training for healthcare providers to ensure they are familiar with and adhere to the standardized diagnostic pathways.
4. Find the “Cost to deliver care”: Implement costing analysis as a standard. Start small, gain competency, then scale. Differentiate “cost to deliver” versus “price charged.”
5. Data and Analytics: Leverage clinical data to analyze the effectiveness and costs of different diagnostic tests, refining clinical pathways.
6. Monitoring and Feedback: Ensure mechanisms for surveillance of implementing diagnostic standards and adherence. Create a path for users of the protocol to give feedback.
7. Improvement Cycle: Engage multidisciplinary teams to consistently review and update diagnostic standards to reflect the latest evidence.

Improving value and achieving diagnostic cost containment requires engagement from your stakeholders – be sure to invite your diagnostic leaders to the table. Health systems leaders, provider networks, and medical groups are uniquely positioned to leverage their operational sophistication and influence to drive change. A consistent feed of new evidence into the improvement cycle unlocks the value of diagnostic choices, elevating effective and efficient care. A shared commitment to the evidence must prevail.

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AI-powered diagnostic tools have revolutionized healthcare across multiple domains. In medical imaging analysis, AI algorithms enhance radiologists’ ability to detect abnormalities in X-rays, CT scans, MRI scans, and mammograms, leading to more accurate diagnoses and treatment plans. Pathology and histopathology benefit from AI’s capacity to analyze tissue samples, aiding pathologists in identifying cancerous cells and other abnormalities with greater precision. Dermatology has seen advancements with AI analyzing skin images to detect conditions such as melanoma, improving early detection rates. Ophthalmology utilizes AI to analyze retinal images for diseases like diabetic retinopathy and macular degeneration, enhancing early intervention strategies. AI also plays a crucial role in cardiology by analyzing ECG signals and cardiac imaging to diagnose heart conditions like arrhythmias and coronary artery disease more effectively. In genomics, AI analyzes genetic data to identify disease patterns and personalize treatment plans. Clinical decision support systems (CDSS) integrate AI to synthesize patient data and medical knowledge, assisting healthcare providers in making informed decisions.

By harnessing AI algorithms to analyze patient data remotely, telemedicine platforms can enhance diagnostic accuracy, expand access to specialized medical expertise, and improve patient outcomes.

Additionally, AI-powered remote monitoring systems analyze real-time patient data from wearable devices, enabling proactive health management and early intervention. These AI applications continue to evolve, promising to improve diagnostic accuracy, patient outcomes, and healthcare delivery efficiency. Equitable telemedicine continues to be a challenge, specifically in the underserved communities and geriatric populations. In some cases, it is believed to close the disparity gap by enhancing access in rural areas by utilizing eConsults, which can allow for specialized care, in areas where it was difficult to reach in the past.

AI diagnostics and telemedicine represent a powerful convergence that is reshaping healthcare delivery. AI enables telemedicine platforms to analyze patient data, including symptoms, vital signs, and medical history, to assist healthcare providers in making accurate diagnoses remotely. This integration facilitates more efficient and timely healthcare access, especially in remote or underserved areas where access to specialists may be limited. AI algorithms can interpret medical images, such as X-rays and CT scans, improving diagnostic accuracy in telemedicine consultations. Moreover, AI-driven chatbots and virtual assistants in telemedicine platforms can triage patients, provide preliminary assessments, and offer personalized health recommendations, thereby enhancing patient care and operational efficiency. As AI continues to evolve, its role in telemedicine is expected to further streamline healthcare delivery, improve patient outcomes, and expand access to quality care worldwide.

The uncertainty of reimbursement model in this new era of Telehealth/AI Diagnostics and the impacts of disruptive innovation have led to some uncertainties. Although data is still fuzzy around this, utilizing these platforms to deter long-term health cost consequences (preventing hospitalizations) in the risk-based value model as well as incremental cost savings in the fee-for-service model, seem promising. A fee that incentivizes the clinicians may be needed so that this type of virtual care is substitutive vs. additive in the grand scheme of things. They would also need to ease up on the regulations to improve continuum of care and transparency on a national level as the state-to-state restrictions have been challenging to overcome. In addition, a qualitative outcome worth assessing is the effect on clinician burnout. This will potentially play a role in either contributing to this or improving clinician satisfaction.

If the technology is accurate and reimbursements become more transparent, the next question will come down to equity. AI diagnostics have the potential to address healthcare equity by improving access to accurate and timely medical diagnoses across diverse populations. AI algorithms can analyze vast amounts of data efficiently, which is particularly beneficial in regions with limited access to healthcare professionals or specialized diagnostic services. By automating and standardizing diagnostic processes, AI can reduce disparities in healthcare outcomes caused by variations in access to resources or healthcare provider expertise.

However, there are challenges to ensuring equity in AI diagnostics. Biases in AI algorithms can perpetuate disparities if not addressed, as algorithms trained on biased datasets may produce inaccurate or inequitable results, particularly for underrepresented or marginalized groups. Ensuring diverse and representative datasets, along with rigorous testing and validation of AI models across different demographics, is crucial to mitigate biases and promote equity in AI diagnostics. Furthermore, the implementation of AI diagnostics must consider the digital divide, ensuring that all populations have access to the technology and infrastructure needed to benefit from AI-driven healthcare solutions. This includes considerations of internet access, digital literacy, and affordability of technology. Overall, while AI diagnostics hold promise in advancing healthcare equity by improving access to diagnostic capabilities, addressing biases and ensuring equitable access to AI technologies are essential steps towards realizing these benefits for all populations.

In summary, the integration of AI diagnostics into telemedicine represents a transformative advancement in healthcare delivery. By harnessing AI algorithms to analyze patient data remotely, telemedicine platforms can enhance diagnostic accuracy, expand access to specialized medical expertise, and improve patient outcomes. This synergy not only facilitates more efficient healthcare delivery but also addresses geographic and socioeconomic barriers to healthcare access. However, ensuring the ethical use of AI, addressing biases in algorithms, and bridging the digital divide are critical considerations to maximize the benefits of AI diagnostics in telemedicine while promoting equitable healthcare delivery for all populations. As AI technology continues to evolve, its role in telemedicine holds promise for shaping a more accessible, efficient, and patient-centered healthcare system globally.

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Chicago’s Rush University Medical Center’s (Rush) national leadership and vision for addressing healthcare’s past failings and today’s patients’ evolving expectations for how digital technology should shape healthcare delivery can combine to provide a compelling blueprint for how healthcare institutions can address the root causes of chronic diseases that shorten lives and cost the nation billions of dollars.   

Innovative population health-focused technology solutions are playing an essential role.   

In 2017, Rush became the nation’s first major hospital to establish health equity – everyone having the equal opportunity to be healthy – as a measurable, and accountable system strategy. At the core of this strategy was a mission shift to not only deliver the world-class patient care that has Rush consistently ranked among the nation’s top providers, but also to improve the health of the local communities Rush serves by thinking holistically about how health equity affects digital access and clinical outcomes.   

And Rush’s recent commitment to a transformative, multi-year digital experience strategy is accelerating efforts. Hence, patients are better able to access and navigate their health and wellness journeys while also working to improve digital health equity: Fair access to health technologies for those who need them most. 

Understanding how these parallel strategies’ reliance on new technologies that improve accurate clinical diagnostics intersects with population health efforts can help health systems better advance value-based care and health equity progress.   

More advanced technology not only collects and analyzes data, but also ensures that the insights derived are used to create inclusive and effective health solutions.

Addressing unacceptable gaps in lifespans 

Rush’s commitment to a health equity strategy is based on data showing a 14-year gap in lifespan between residents of downtown Chicago and those of primarily black neighborhoods just a few miles to the west. It recognizes the pivotal role of addressing the social determinants of health (SDOH) and lived experiences that shape many black and brown communities. Understanding these dynamics allows Rush to think about digital tools differently in terms of how they engage families on the economic spectrum. 

Partnering at the Intersection of value-based care and health equity 

Like most health systems, Rush has also been growing and improving value-based care approaches along several fronts. One of the highest profile and innovative is its partnership with a leading health solutions company which explores novel reimbursement models focused on health equity known as ACO REACH (Realizing Equity and Community Health), a pilot program from the Centers for Medicare and Medicaid Services (CMS) that tests alternative payment models. Traditional reimbursement models often fail to account for the unique needs and challenges faced by underserved populations. However, the ACO REACH model develops and implements digital strategies and partnerships that haven’t traditionally been attempted, especially addressing barriers to access, such as transportation and socioeconomic factors, which disproportionately affect marginalized communities. Aligning financial incentives with health equity goals is powered by analytics that provides insights on risk, member profiles, and actionable solutions related to demographics and evidence-based medicine. Population tools and analytics that assess the social and clinical risk factors allow our health system to apply accurate clinical diagnostics to the patients that need them the most.  

Rush has also joined forces with two leading population health technology providers to gain deeper insights into the health behaviors, social conditions, and care patterns of Medicaid populations. This allows for the development of targeted interventions addressing the root causes of chronic diseases, rather than just treating symptoms.  

An AI-powered analytics platform along with a healthcare technology company enables patients and providers to better manage chronic diseases that shorten countless lives in underserved communities. For example, we assume psychological factors are the drivers of Chicago’s lifespan gaps, but detailed analytics show that hypertension and other manageable chronic conditions steal thousands of life years annually.

For example, Rush care providers worked with a healthcare technology company to create tools for patients and providers that advanced data analytics capabilities, incentivizing populations to not only navigate their communities but be empowered to improve chronic conditions for themselves or loved ones. 

This collaboration is particularly significant in the context of what is known as digital health equity, or fair access to health technologies for those who need them most. More advanced technology not only collects and analyzes data, but also ensures that the insights derived are used to create inclusive and effective health solutions. For instance, a better understanding of the barriers that prevent populations from accessing care led to the design of more user-friendly mobile health tools that helps patients both embrace local resources and incents them to engage in behaviors proven to lower blood pressure.   

This holistic approach recognizes that technology, when used equitably and complementary to population-based tools, has the power to transform health systems and close the life expectancy gap. And doing so on Chicago’s West Side can also show that creating an equitable health system that reduces cost and improves the lives of individuals and communities alike is possible.   

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In the rapidly evolving healthcare landscape, strategy and technology adoption are pivotal in shaping the future of patient care, operational efficiency, and overall healthcare outcomes. As medical technologies advance and data management systems become increasingly sophisticated, the strategic approach to integrating these innovations can significantly impact the effectiveness of technology adoption.

The strategic adoption of technology holds the promise of truly transforming personalized patient care, improving operational efficiency, and driving innovation.

Strategic Framework for Technology Adoption

Strategy begins with a clear vision of how technology can enhance both patient outcomes and operational efficiency, requiring a multifaceted approach that encompasses several key components:

1. Needs Assessment: The first step in any strategy is conducting a thorough needs assessment. This involves evaluating current opportunities and challenges, such as gaps in patient care, inefficiencies in workflows, or outdated systems which can help prioritize technologies that address specific needs and align with overall goals.
2. Stakeholder Engagement: Successful technology adoption hinges on the involvement of various stakeholders, including clinical professionals, administrative staff, patients, and technology providers. Feedback from end-users is crucial in identifying potential obstacles and areas for improvement as well as identifying the likelihood of adoption and utilization.
3. Evidence-Based Decision Making: Decisions about technology adoption should be guided by evidence and data. This involves assessing the effectiveness of potential technologies through pilot programs, research studies, and cost-benefit analyses. Evidence-based decision-making helps mitigate risks and ensures that investments yield tangible benefits.
4. Integration and Interoperability: For technology to be effective, it must seamlessly integrate with existing systems and processes. Strategic planning should address how new technologies will interface with existing infrastructures.
5. Training and Support: Implementing new technologies requires comprehensive training and support for healthcare staff. A well-defined strategy includes training programs and resources to help staff adapt to new tools and workflows.
6. Scalability and Flexibility: Technology solutions should be scalable to accommodate future growth and adaptable to evolving healthcare needs. A strategic approach involves selecting technologies that can expand in functionality or scale as the organization grows. Vendors are constantly adding new capabilities and many organizations find inefficiency and duplication of services if they don’t consistently re-evaluate the scope of their technology solutions.

Understanding Artificial Intelligence (AI) and Machine Learning (ML) Innovation Cycle

The innovation product cycle describes the trajectory of emerging technologies as they move from initial excitement to mainstream acceptance. It consists of several phases:

1. Innovation Trigger: The cycle begins with a breakthrough or innovation, generating considerable excitement and attention. For AI and ML in healthcare, this phase was marked by the introduction of technologies like deep learning (DL) algorithms and natural language processing (NLP) that promised to transform diagnostic accuracy, patient management, and operational efficiencies.
2. Inflated Expectations: As interest and investment surge, expectations for AI and ML technologies become inflated. In healthcare, this period resulted in high hopes for AI systems to be capable of outperforming human clinicians in diagnosing diseases, predicting patient outcomes, and personalizing treatments, leading to unrealistic expectations.
3. Disillusionment: When early adopters encounter limitations, challenges, or underwhelming results, enthusiasm can wane, leading to disillusionment. This phase has involved issues such as algorithmic bias, data quality concerns, and the complexities of integrating AI systems with existing infrastructures. Early AI models sometimes fail to deliver on their promises due to inadequate data, lack of interpretability, or integration difficulties.
4. Enlightenment: As the technology matures and practical applications are refined, understanding grows, leading to more realistic expectations such as improved diagnostic accuracy in imaging, more effective predictive models for patient outcomes, and enhanced operational efficiencies through process automation.
5. Effectiveness & Productivity: At this stage, the technology achieves broad adoption and delivers consistent value. AI and ML tools become standardized and widely used in healthcare practices. For instance, AI-driven diagnostic tools for radiology and predictive analytics for patient management have become integral components of the care process and demonstrate clear benefits.

Strategic Adoption of AI and ML in Healthcare

Incorporating AI and ML into healthcare requires a strategic approach that aligns with the phases of the product cycle:

1. Assessing the Current Landscape: Organizations should begin by evaluating the current capabilities and limitations of AI and ML technologies.
2. Pilot Programs and Validation: To mitigate risks and validate the technology, healthcare organizations should initiate pilot programs. These trials can help assess the practical implications, such as accuracy, usability, and integration challenges.
3. Managing Expectations and Change: Effective communication is essential to manage expectations and foster a realistic understanding of AI and ML capabilities. Addressing concerns about bias, data security, and integration can help build trust and facilitate smoother adoption.
4. Scaling and Integration: This involves ensuring that AI and ML systems are interoperable with existing healthcare infrastructure and can handle increasing volumes of data and users.
5. Continuous Evaluation and Adaptation: AI and ML technologies evolve rapidly, and continuous evaluation is necessary to keep pace with advancements.

Conclusion

The strategic adoption of technology holds the promise of truly transforming personalized patient care, improving operational efficiency, and driving innovation. By carefully assessing needs, engaging stakeholders, making evidence-based decisions, and focusing on integration and support, healthcare organizations can harness the potential of technological advancements. As technology continues to evolve, ongoing adaptation and strategic planning will be essential to navigate the complex landscape of modern healthcare and achieve the best possible outcomes for patients and providers alike.

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The diagnostic laboratory has always been an important partner to every clinician. Information gleaned from the work in the laboratory can assist in clinical decision-making, taking some of the “guesswork” out of the diagnosis process. Over the last several years, personalized medicine has taken center stage in healthcare, and diagnostics has been right there to provide the key information needed to take advantage of such cutting-edge tools.

Personalized medicine represents the use of a patient’s genetic makeup and profile to determine the prevention, diagnosis, prognosis, and treatment of various medical ailments. Just as healthcare has evolved and advanced through the years, so has the diagnostic tools used in the pursuit of such personalized medicine. Once considered something that was a “luxury” to be able to access, genetic testing has become much more attainable—and useful—to the clinical world.

One exciting use of molecular and genetic diagnostics is in pharmacogenomics. With specific genetic information, the clinician has the ability to provide more targeted therapy/treatment for the patient.

Thanks to the Human Genome project, molecular and genetic testing can provide a wealth of knowledge regarding many perplexing human ailments. Molecular testing provides insight into the key biomarkers and variants on a patient’s genome that may be affecting their health. This type of testing uses sequencing technology to access and analyze a patient’s genomic information. Next-Generation Sequencing (NGS) and Sanger Sequencing are arguably the most used methods in the molecular diagnostic world today, with each type of sequencing having their own pros and cons. The benefit of NGS testing is the ability to review a very large amount of genomic material at once (i.e., millions of DNA fragments simultaneously). Conversely, this type of technology may be found to be quite expensive, with a longer time from test request to result. On the other hand, Sanger sequencing reviews a single DNA fragment at a time, allowing concentration on and prompt resulting of a small number of genes with great accuracy. The disadvantage of this technology is that its low sensitivity leads to a decreased ability to recognize actionable mutations in a larger amount of genes.

Genetic testing uses the chromosomal makeup of a patient in the pursuit of better overall outcomes. Often used interchangeably with genomic testing, genetic testing uses cytogenetic methods such as karyotyping, fluorescent in-situ hybridization (FISH), and microarray to provide insight into a patient’s current (and maybe future) condition. Cytogenetics, one of the earliest diagnostic tests used in personalized medicine, utilizes various technical methods to identify the chromosomal material of the patients cells. This testing is often used to identify significant developmental disorders and congenital disabilities, as well as various types of hematological cancers (i.e., Leukemias). It is also a good tool for prognostics and treatment predictors. On the contrary, genetic testing does not account for single nucleotide variations that could be contributing to the identified condition. As such, it is suggested that it be used in conjunction with other complementary tools—such as molecular sequencing.

Like everything else in science, healthcare, and technology, the use of diagnostics in personalized medicine is continuously evolving. However, a few applications of diagnostics for this purpose has matured over the years of its prescriptive use.

Pharmacogenomics: One exciting use of molecular and genetic diagnostics is in pharmacogenomics. With specific genetic information, the clinician has the ability to provide more targeted therapy/treatment for the patient. In some specific cases, whether a patient will have side effects from a drug may even be determined with such testing.

Diagnosis: Perhaps the most common use of molecular and genetic diagnostics is to diagnose various conditions. As previously stated, a wealth of knowledge may be garnered when analyzing a patient’s genetic makeup. In conjunction with other clues (such as history and physical examination), identifying variants, biomarkers, etc., may paint a very clear picture of the patient’s health status. With this information, a clinician can more quickly begin the process of addressing the identified issues.

Predicting and Monitoring Prognosis: In addition to identifying issues, diagnostic testing can assist with predicting a patient’s prognosis. Specific mutations and variants are known to have a direct correlation to either a good or not-so-good outcome. Clinicians can use the advantage of this predictive value to help guide what they do with their patients. In addition to being used to predict future prognosis, these valuable tools can also be used to monitor the patient’s progress. Once the disease state is diagnosed, the appropriate test to monitor the progress of treatment may be determined. For example, the BCR-ABL1 Quantitative test by PCR may be used to monitor the progression of treatment for chronic myeloid leukemia (CML) by providing a logarithmic depiction of the patient’s status (as demonstrated by the test results). With this information in hand, a provider can determine if anything in the patient’s treatment plan needs to be adjusted.

Prevention: Risk stratification is another important use of diagnostic testing. Specific types of tests may be used as a means of screening for potential future issues. For example, what may be one of the most widely known tests is for the BRCA mutations that are linked to breast cancer. In 2013, a famous actress made it known publicly that she opted for a radical, prophylactic double mastectomy after testing positive for this mutation.

The use of diagnostic testing in personalized medicine has proven to be of great value. The information from such tools has great potential of contributing to the continued advancement of healthcare. As time and technology continue to progress, the power of such information will undoubtedly continue to grow in strength.

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The company’s mission statement underscores its commitment to superior and smarter diagnostics for improved health outcomes. QuidelOrtho was the first to receive approval from the US FDA for an antigen-based test to aid in the detection of the coronavirus!

In 2024 and beyond, QuidelOrtho is exploring opportunities to add new tests to diagnose other health conditions and diseases to its Savanna® Real-Time PCR Testing Platform. This addition would address a critical need and open new avenues for building intelligent solutions. 

QuidelOrtho’s robust and integrated platforms provide comprehensive diagnostic insights beyond simple test results. These platforms enable healthcare providers to deliver more personalized care and make optimal patient-care decisions.

Start of the Innovation Journey

QuidelOrtho was born from the merger of two great industry pioneers, Quidel Corporation and Ortho Clinical Diagnostics, in 2022. Both companies embarked on a shared vision of serving patients fully by transforming diagnostic data into actionable insights. They shared a common legacy of innovation and excellence and combined these strengths to redefine possibilities in diagnostics.

Today, QuidelOrtho offers solutions that help serve the healthcare ecosystem, from small- to medium-volume/high-complexity hospitals to point-of-care settings in clinics and home use. As a unified company, QuidelOrtho boasts stronger innovation capabilities and leverages decades of expertise in the in-vitro diagnostics industry. Since healthcare diagnostics is the cornerstone of adequate healthcare, the company strives to keep up with the demand for patient- and customer-centric solutions. Their leadership and teams of dedicated researchers and scientists, with their unique experiences, build strategies and direction to advance as diagnostics innovators.

Being at the forefront of innovation in diagnostics allows us to contribute to a future where every individual has access to the best possible care.

Brian J. Blaser, the President and CEO of QuidelOrtho, also spoke about his role in driving a meaningful change in healthcare. He says, “Being at the forefront of innovation in diagnostics allows us to contribute to a future where every individual has access to the best possible care.” He believes the journey has been enriching, seeing how QuidelOrtho’s solutions positively impact lives around the globe and across the healthcare continuum.

QuidelOrtho’s innovation strategy is built on three core pillars: Cutting-Edge Technology, Collaborative Research, and Continuous Improvement. The company invests in advanced diagnostic technologies, such as molecular diagnostics and immunoassays, to develop products at the forefront of innovation. Its collaborative research with leading academic institutions and healthcare providers helps address emerging trends and clinical needs with relevant and practical solutions.

Solutions Impacting the Entire Care Continuum

When asked to talk about a flagship solution, Brian added that it is not about having just a single solution. QuidelOrtho’s innovations span centralized to decentralized areas, reference labs, regional reference labs, and up to at-home testing. Brian mentioned that the Sofia® 2 Platform is one of QuidelOrtho’s most recognizable solutions. It is a rapid immunoassay platform that epitomizes their commitment to impact the entire healthcare value chain.

The Sofia 2 platform is designed to provide rapid, accurate diagnostic results at the point of care using advanced fluorescence technology. This enables healthcare providers to make informed, timely decisions across the healthcare continuum – from large hospitals to physician office labs or pharmacies.

QuidelOrtho’s high-volume clinical laboratory solution delivers quality and efficiency when labs need them most. The Vitros® XT 7600 Integrated System and assays lead the industry in Six-Sigma-defined world-class or excellent quality. The Six Sigma classification on Vitros XT 7600 system creates quality assurance, enabling labs to deliver the right result the first time.

The system leverages Vitros MicroSlides, which incorporate dry-slide technology with no requirement for water and drains, making our solution flexible and easy to install.

Hospitals, especially small- to medium-volume ones, are segments where QuidelOrtho has successfully established its robust presence and value-add. This is a strategic position, as hospitals and hospital systems increasingly become the central hubs of healthcare delivery, responsible for a broader spectrum of care. They now own physician groups, urgent cares, and retail pharmacies, serving the entire continuum of care. Their solutions are integral in supporting these expanding needs, providing the right intelligence at the right time.

Beyond Diagnostic Results

Additionally, QuidelOrtho’s expertise is beyond delivering diagnostic results. The company also provides the intelligence that labs and doctors need to support their communities. QuidelOrtho’s solutions for lab professionals are designed to enhance workflow efficiency, reduce errors, and improve overall lab performance. By integrating advanced analytics and automation, these products help lab professionals deliver the highest levels of care with accuracy and speed, as demonstrated by our Vitros Systems, which can increase testing volume and improve turnaround times.

Staffing and labor shortages are significant challenges in the healthcare industry. QuidelOrtho’s products, designed for ease of use, reliability, and efficiency, help address these issues by reducing the need for highly technical labor. This allows existing staff to focus on tasks utilizing their expertise, knowledge, and skills, optimizing workforce productivity and enhancing overall healthcare delivery.

A Dependable Way of Infectious Disease Management

When asked about a notable use case, Brian mentioned an incident where the client needed help enhancing infectious disease management.

In a world still feeling the effects of the pandemic, a prominent healthcare provider network faced ongoing challenges with infectious disease outbreaks. They needed a swift and dependable way to identify and manage these cases to protect their patients and staff.

The Sofia 2 rapid testing platform provided an ideal solution. Its quick and accurate diagnostic results available at the point of care enabled the healthcare provider to swiftly identify and isolate affected individuals, preventing further spread within their facilities.

The Sofia 2 platform delivered the speed the provider needed to make prompt decisions with diagnostic results in minutes, not hours. It also provided accurate and reliable patient management results and offered the precision to effectively contain and treat infections. The platform’s flexibility helped the provider adapt to different settings, from emergency rooms to clinics. Also, the Sofia 2 platform was easy to integrate and use across various environments.

It helped deliver faster results with 80% reduced diagnostic turnaround time, allowing quicker interventions. The healthcare provider experienced improved accuracy rates, which led to fewer false results, facilitating proper patient management. Moreover, rapid, precise diagnostics meant timely treatments, reducing the impact and spread of infections. Overall, the improved efficiency allowed better resource use and eased the burden on the medical staff.

By utilizing QuidelOrtho’s advanced diagnostic tools, the healthcare provider improved its approach to managing infectious diseases, resulting in better patient care and a healthier community overall. This use case highlights how QuidelOrtho’s solutions empower healthcare providers to deliver top-tier care, making a tangible difference in public health.

The Way Forward

For the rest of 2024 and beyond, QuidelOrtho is focused on bringing forward several key assays that it believes can be game-changers in the industry. The company is expecting the launch of a new respiratory assay that could broaden the placement and accessibility of its molecular diagnostic tools. QuidelOrtho is also prioritizing the launch of sexually transmitted infections (STI) and gastrointestinal (GI) panels. The company sees a substantial opportunity to make a difference and drive growth in these areas.

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