Month: January 2025

Leveraging Next Best Triggers to Transform HCP Engagement in Oncology

Posted on by Abhishek Trigunait

In the dynamic and data-driven field of oncology, effectively engaging healthcare professionals (HCPs) is essential for success. Emerging biopharma companies face unique challenges, including narrow drug labels and complex treatment pathways. To navigate this landscape, Next Best Action (NBA) strategies present a powerful approach, enabling personalized and timely engagement with HCPs. This blog outlines how oncology teams can implement NBA to foster meaningful connections and improve patient outcomes.

Precision Engagement through Contextual Insights

The strength of NBA lies in its ability to deliver tailored interactions based on HCPs’ specific needs, preferences, and clinical contexts. For oncology teams, this means identifying the right oncologists, understanding their treatment priorities, and providing insights that align with their patients’ requirements—all at critical moments in the patient journey.

A Step-by-Step Framework for Implementing NBA in Oncology

  1. Consolidating Data for a Unified View
    The first step in implementing NBA is to gather and integrate data from various sources such as electronic medical records (EMRs), claims data, lab results, and clinical trial databases. By unifying these disparate data sources into a single platform, your team can uncover actionable insights. For instance, identifying oncologists who frequently treat specific cancer types allows you to focus outreach efforts on those most likely to benefit from your therapies.
  2. Identifying High-Impact Triggers
    In the oncology space, triggers often arise from patient journeys or clinical milestones. For example:
    • A newly diagnosed patient may prompt an oncologist to consider first-line therapies.
    • Progression events or biomarker test results could indicate a need for second-line treatments.
      Analyzing these triggers through predictive analytics enables your team to anticipate when an oncologist might require additional information or support, ensuring that your outreach aligns with critical decision-making moments.
  3. Crafting Personalized Content for Oncologists
    With oncologists receiving vast amounts of information daily, your messaging must be relevant and concise:
    • Develop content that addresses specific clinical questions or challenges faced by oncologists treating your target patient population.
    • Use real-world evidence or case studies to demonstrate the efficacy of your therapy in similar clinical scenarios.
    • Tailor delivery channels—such as emails, webinars, or peer-to-peer discussions—based on individual preferences. For instance, an oncologist who frequently attends virtual conferences may respond more positively to webinar invitations than traditional emails.
  4. Leveraging AI-Driven Predictive Analytics
    Artificial intelligence (AI) tools are invaluable for enhancing NBA strategies in oncology. By analyzing historical data and real-time interactions, AI can predict which actions will resonate most with each HCP:
    • Should you prioritize a face-to-face meeting over digital outreach?
    • Is now the right time to share a new study or clinical guideline update?
      AI-driven models continuously refine these recommendations based on feedback loops, ensuring that your engagement strategy evolves alongside HCP needs.
  5. Measuring and Optimizing Engagement
    Oncology marketing teams should adopt a culture of continuous improvement by closely monitoring engagement metrics such as email open rates, content downloads, meeting requests, and shifts in prescribing behavior:
    • Use these insights to identify what’s working and where adjustments are needed.
    • Collaborate with sales and marketing teams to ensure alignment on what constitutes success. For example, if a particular webinar garners high attendance but low follow-up engagement, it may indicate a need for more actionable content or clearer next steps.

Why NBA Matters even more so for Emerging Biopharma

For emerging biopharma companies specializing in oncology therapies, adopting NBA strategies is essential for navigating the complexities of cancer care effectively. The precision required at every interaction with HCPs ensures that resources are focused on high-impact opportunities while building trust through timely and relevant information that supports clinical decisions. This approach not only enhances the likelihood of successful engagements but also ultimately improves patient outcomes by equipping oncologists with the insights they need to make informed treatment choices.

Transforming HCP Engagement through NBA

Implementing NBA in oncology requires commitment but offers significant rewards. By consolidating data into actionable insights, identifying key triggers along the patient journey, crafting personalized content, leveraging AI-driven analytics, and continuously optimizing engagement strategies, your team can transform its interactions with oncologists. In an era where precision medicine is reshaping cancer care, NBA empowers emerging biopharma teams to deliver the right message through the right channel at the right time—creating lasting value for both HCPs and their patients.

Leveraging ‘Predictive  Analytics to support Oncology Patient Journeys’

Posted on by Abhishek Trigunait

The oncology landscape is rapidly changing, with a significant surge in the number of approved therapies. In the past five years alone, 60+ new oncology drugs have been launched, intensifying competition among biopharmaceutical companies to effectively identify and engage specific patient segments that could benefit from these treatments. However, the challenge remains: how can companies accurately pinpoint the right patients at the right time amidst narrow drug labels and small patient populations? To navigate this complexity, it is essential to establish a robust analytics capability that predicts key events in the oncology patient journey. This capability not only assists your sales and marketing teams in targeting oncologists likely to treat eligible patients but also empowers patient support programs to anticipate potential drop-offs and proactively assist those patients. Additionally, it enables medical teams to identify optimal clinical trial sites that align with patient eligibility criteria.

Developing a predictive capability for oncology patient journeys can be structured into four essential steps:

  1. Mapping the Oncology Patient Journey
    The first step involves carefully mapping out the oncology patient journey while prioritizing critical events that impact brand success. For instance, predictive modeling should focus on identifying patients who are transitioning between therapy lines, especially for drugs indicated for first or subsequent lines of treatment. Utilizing established guidelines like those from the National Comprehensive Cancer Network (NCCN) can provide a solid foundation for this mapping process. Engaging internal experts and conducting research within oncology practices will further refine your understanding of patient pathways.
  2. Integrating Real-World Patient Data
    The next step is to acquire and integrate various real-world data sources, such as claims data, electronic medical records (EMR), lab test results, and specialty pharmacy information. A systematic data strategy is crucial for curating and connecting these sources to effectively support predictive modeling capabilities. Your team should evaluate factors such as data richness, granularity, capture rates, refresh rates, acquisition costs, and connectivity with other data sources. Since a single comprehensive patient-level data source may not be available, it may be necessary to make trade-offs across these dimensions while initially developing predictive capabilities using one or two primary data sources.
  3. Developing Predictive Algorithms
    The third step involves creating and testing algorithms designed to predict key events along the oncology journey. Your team can utilize retrospective modeling on real-world data to develop various algorithms while paying close attention to changes in therapy lines across different cancer types. Considerations such as evolving treatment pathways, variability in oncologist practices, and gaps in real-world data must be considered during this process. Collaborating with sales, marketing, and medical teams will enhance algorithm accuracy and foster buy-in for adopting these predictive models. Increasingly, machine learning algorithms are being employed alongside robust feedback mechanisms to create adaptive predictive models that improve over time.
  4. Establishing a Scalable Analytics Platform
    The final step is implementing a scalable analytics platform that facilitates both the development of predictive models and the dissemination of insights to relevant stakeholders. Key features of an effective analytics platform include a comprehensive patient data management module for integrating real-world data sources, an intelligent business rules engine for creating longitudinal patient cohorts with diverse eligibility criteria, an analytics workbench for model development using machine learning techniques, and a business insights consumption module for sharing predictive insights across sales, marketing, patient support, and medical teams.

By strategically combining these four components—mapping the patient journey, integrating real-world data, developing predictive algorithms, and establishing a scalable analytics platform—your biopharma team can significantly enhance its capabilities in predicting oncology patient journeys. Organizations that successfully implement these strategies are likely to receive positive feedback from their field sales teams regarding the timely availability of predictive insights. This capability not only allows sales teams to connect with busy oncologists at critical moments but also ensures that effective therapies reach the right cancer patients when they need them most. In an era where precision medicine is paramount, developing an oncology patient journey prediction capability presents a transformative opportunity for your emerging biopharma team to optimize market strategies and improve patient outcomes.

Planning commercial success for an upcoming specialty drug launch in 2025

Posted on by Sunil Vij

As the pharmaceutical landscape continues to evolve, launching a new specialty drug in 2025 presents both unique opportunities and significant challenges. The rapid growth in specialty medications, particularly in areas like cell and gene therapies, requires commercial technology and analytics leaders to adopt a strategic approach that leverages data-driven insights and innovative engagement tactics. Understanding the key factors that will influence a successful launch is crucial for maximizing market impact and ensuring that new therapies reach the patients who need them most.

Navigating the Complex Landscape

The complexity of launching a specialty drug has intensified in recent years due to increased competition, regulatory scrutiny, and the demand for real-world evidence. As of 2024, specialty medications accounted for nearly 40% of total prescription revenues, underscoring their importance in the pharmaceutical market. However, with this growth comes heightened expectations from stakeholders, including healthcare professionals (HCPs), healthcare insurers payers, and patients.

A successful launch requires not only a robust understanding of the competitive landscape but also an agile approach to market access strategies. Companies must be prepared to navigate payer preferences and health technology assessments (HTAs) while ensuring that their product offers distinct value compared to existing therapies. Furthermore, the COVID-19 pandemic has permanently altered how pharmaceutical companies engage with HCPs. In-person meetings decreased significantly during the pandemic, leading to a surge in digital interactions. In 2025, companies must continue to embrace this digital-first approach while balancing it with traditional engagement methods. Leveraging data analytics to understand HCP preferences for virtual versus in-person interactions will be essential for optimizing outreach efforts.

Key Considerations for Launch Strategy

When planning a new specialty drug launch, commercial technology and analytics leaders should focus on several critical areas:

1. Comprehensive Market Preparation: Begin your launch strategy at least 18-24 months prior to the product’s approval. This preparation should include identifying key opinion leaders (KOLs) and engaging them early in the process to gather insights into their needs and expectations. Establishing relationships with KOLs can facilitate smoother access to HCPs once the drug is launched.

2. Data-Driven Decision Making: Implement advanced analytics tools that integrate multiple data sources, including market research, sales forecasts, and real-world evidence. This data should inform your go-to-market strategy by identifying target segments and tailoring messaging accordingly. A robust Customer Relationship Management (CRM) system will also be vital for tracking interactions with HCPs and managing ongoing relationships post-launch.

3. Digital Engagement Strategy: Develop a comprehensive digital engagement plan that includes virtual meetings, webinars, and online educational content tailored for HCPs. Given that a significant portion of HCP interactions now occur through digital channels, ensuring that your messaging is accessible and engaging is crucial for building awareness and driving adoption.

4. Value Demonstration: Clearly articulate the value proposition of your specialty drug not just from a clinical perspective but also in terms of patient outcomes and cost-effectiveness. Providing real-world evidence demonstrates how the drug improves patient care can help persuade payers and HCPs of its worth.

5. Post-Launch Monitoring and Adaptation: Once your drug is on the market, continuously monitor its performance against key metrics such as sales volume, market share, and HCP engagement levels. Use this data to adapt your strategy as needed—whether that means refining messaging or adjusting promotional tactics based on real-time feedback from the field.

Conclusion

Launching a specialty drug in 2025 requires careful planning and execution across multiple dimensions—from market preparation and data analytics to digital engagement strategies and value demonstration. By embracing these considerations, commercial technology and analytics leaders can position their organizations for success in an increasingly competitive environment. As the pharmaceutical industry continues to transform through technological advancements and changing healthcare dynamics, staying ahead of these trends will be essential for achieving long-term commercial success. Ultimately, a well-executed launch not only drives revenue but also plays a critical role in improving patient access to innovative therapies that can enhance their quality of life.

The Future of Drug Discovery: How Technology is Changing the Game

Posted on by Abhishek Trigunait

Drug discovery is a protracted and resource-intensive endeavor, typically requiring a decade or more and substantial financial investment to bring a single novel therapeutic to market. However, the emergence of innovative technologies presents a transformative potential for accelerating and optimizing this process.

Artificial intelligence (AI) stands as a prominent example, offering powerful capabilities in drug target identification, novel molecule design, and the refinement of drug screening methodologies. Furthermore, AI-driven predictive models are increasingly employed to assess the safety and efficacy of potential drug candidates.

Beyond AI, other cutting-edge technologies significantly impact the drug discovery paradigm. Genomics, the study of the human genome, provides invaluable insights into disease-associated genes, enabling the development of targeted therapies. Similarly, proteomics, the study of proteins, facilitates the identification of disease-relevant protein targets for therapeutic intervention.

Single-cell analysis, a revolutionary technique, allows for the in-depth characterization of individual cells, enabling the identification of rare cell populations implicated in disease pathogenesis and the development of targeted therapies for these elusive subpopulations.

These advancements collectively signify a paradigm shift in drug discovery. By leveraging these emerging technologies, researchers can expedite the discovery process, enhance the efficiency of drug development pipelines, and ultimately expand the therapeutic armamentarium for previously intractable diseases. The future of drug discovery holds immense promise, with these technological innovations poised to revolutionize healthcare and improve the lives of countless individuals.”

The Power of AI and Next-Gen technology in Patient Finding and Clinical recruitment

Posted on by Jane Urban

AI’s capability to analyze vast datasets, including electronic health records, genetic profiles, and even social media content, is proving invaluable in identifying potential rare disease patients. By leveraging machine learning algorithms, pharmaceutical companies can now sift through mountains of data to uncover patterns and indicators that human researchers might miss. These advanced technologies enable more precise patient stratification, matching individuals to clinical trials based on their specific genetic markers and medical histories. This level of precision not only accelerates the recruitment process but also ensures that the right patients are enrolled in the right trials, potentially leading to more successful outcomes.

Benefits of AI and analytics in patient finding
In the world of commercial pharmaceuticals, AI and analytics are revolutionizing patient finding, especially for rare diseases. These technologies are transforming traditional recruitment methods and enhancing the overall efficiency of clinical trials.AI algorithms are now analyzing vast datasets, including electronic health records, genetic profiles, and even social media content, to identify potential rare disease patients with unprecedented precision. This capability allows pharmaceutical companies to match patients with clinical trials based on specific genetic markers and medical histories, significantly accelerating the recruitment process.

The benefits of AI and analytics in rare disease patient finding are substantial:

  1. Recruitment speed has increased dramatically, with AI tools capable of scanning global healthcare databases to identify eligible patients across diverse geographical and demographic boundaries.
  2. Patient selection accuracy has improved through the analysis of complex datasets, enabling the identification of individuals who might have been overlooked by conventional methods.
  3. A more personalized approach to patient engagement has emerged, with advanced analytics facilitating tailored communication strategies that improve participation and retention rates.
  4. Cost-efficiency has been enhanced by streamlining the recruitment process, reducing the time and resources traditionally required for patient finding.
  5. Trial designs have been optimized based on insights gained from AI analysis, potentially leading to more successful studies and faster drug development.
  6. Global reach has expanded, with AI-powered tools capable of identifying rare disease patients across different healthcare systems worldwide.

These advancements are not only accelerating the pace of clinical trials but also opening doors to treatments that might otherwise remain undiscovered, offering new hope for patients with rare diseases.

Challenges and Considerations

Despite its promise, the use of AI and analytics in ‘patient finding’ for rare diseases is not without challenges:

  1. Data Privacy and Ethics: The use of sensitive health data raises important questions about privacy and ethical considerations, requiring careful navigation of regulatory landscapes.
  2. Data Quality and Interoperability: The effectiveness of AI algorithms depends on the quality and compatibility of data from various sources, which can be inconsistent across different healthcare systems.
  3. Algorithmic Bias: There’s a risk of perpetuating or introducing biases if AI models are trained on non-diverse datasets, potentially leading to inequitable patient selection.
  4. Technology Adoption: Implementing AI systems requires significant investment and organizational change, which can be challenging for some pharmaceutical companies.

Conclusion: A New Era in Rare Disease Research

The integration of AI and analytics in patients finding for rare diseases marks a significant leap forward in pharmaceutical research. By harnessing the power of these technologies, we’re not just improving the efficiency of clinical trials; we’re opening doors to treatments that might otherwise remain undiscovered. As we navigate this new terrain, it’s crucial for pharmaceutical leaders to embrace these innovations while addressing the associated challenges head-on. The potential to transform the lives of patients with rare diseases is immense, and with careful implementation and continued development, AI and analytics could be the key to unlocking breakthroughs in some of medicine’s most challenging areas.

The Transformative Power of Data Aggregation in Pharmaceuticals: Driving Innovation and Patient-Centric Care

Posted on by Sunil Vij

The pharmaceutical industry has undergone a significant transformation in its approach to data aggregation, moving from siloed information to interconnected networks that offer unprecedented insights. This evolution has reshaped how companies understand patient needs, optimize drug development, and improve overall healthcare outcomes.

Many pharmaceutical companies have leveraged data aggregation to advance personalized medicine. By combining genomic data with clinical trial results and real-world evidence, these companies have developed targeted therapies for specific patient subgroups. For instance, targeted therapies for breast cancer are prescribed based on biomarkers like HER2 protein expression levels, demonstrating how data-driven approaches can lead to more effective treatments.

Other common real-life examples deployed at pharma involves implementing comprehensive real-world data (RWD) initiatives to complement traditional clinical trials. By aggregating data from electronic health records, claims databases, and wearable devices, companies gain insights into drug effectiveness and safety in diverse populations. This approach helps refine post-market surveillance for drugs, allowing for rapid identification of potential side effects and more informed decision-making.

Key Aspects of Data Aggregation in Pharma

  1. Patient-Centric Approach

Modern data aggregation in pharma focuses on patient-centric insights. This involves collecting and analyzing data from various touchpoints, including:

  • Patient demographics
  • Medication adherence patterns
  • Clinical outcomes
  • Adverse events
  • Cost metrics

By integrating these diverse data sources, pharmaceutical companies can gain a holistic view of patient experiences and needs.

  • Collaborative Ecosystem

Data aggregation fosters collaboration among stakeholders in the healthcare ecosystem. Many companies have partnered with healthcare providers and technology firms to create data-sharing platforms. This collaboration enables seamless communication between healthcare providers, payers, and pharmaceutical manufacturers, leading to more efficient care coordination and improved patient outcomes.

  • Advanced Analytics and AI

The fusion of comprehensive datasets with AI-driven analytics is revolutionizing drug discovery and development. Pharmaceutical companies are using machine learning algorithms to analyze aggregated clinical and genomic data, helping to identify new drug targets and predict patient responses to treatments. This approach accelerates the drug development pipeline and improves the success rate of clinical trials.

Challenges and Considerations

While data aggregation offers immense potential, it also presents challenges:

  1. Data Privacy: Ensuring patient confidentiality while leveraging vast amounts of health data remains a critical concern.
  2. Data Quality: Maintaining the accuracy and reliability of aggregated data from diverse sources is crucial for deriving meaningful insights.
  3. Interoperability: Overcoming the hurdles of integrating data from disparate healthcare systems requires ongoing technological advancements.

Conclusion

The evolution of data aggregation in pharmaceuticals represents a pivotal shift towards more personalized, efficient, and effective healthcare. By harnessing the power of aggregated data, pharmaceutical companies can drive innovation in drug development, optimize patient care, and ultimately improve health outcomes. As the industry continues to navigate this data-driven era, striking a balance between leveraging data’s potential and upholding ethical standards will be crucial for sustained success and patient trust.

Enhancing Your Commercial Model Design via Medical Science Liaisons

Posted on by Sunil Vij

In the pharmaceutical industry, the role of Medical Science Liaisons (MSLs) has evolved significantly since its inception about 50 years ago. Initially focused on promotional activities, MSLs now serve as vital connectors between pharmaceutical companies and the medical community. For commercial technology and analytics leaders, understanding how to effectively integrate MSLs into your commercial model design can enhance engagement with healthcare professionals (HCPs) and ultimately drive better patient outcomes.

Key Challenges Faced by Medical Science Liaisons

Despite their importance, MSLs encounter several challenges that can hinder their effectiveness. One significant challenge is engaging KOLs, who have diverse needs that change over time. MSLs must stay attuned to these evolving preferences but tracking them can be cumbersome without the right tools. Additionally, accessing top KOLs presents a formidable obstacle; busy schedules and gatekeepers often limit MSL availability to these influential figures. This inaccessibility can create barriers to meaningful engagement. Lastly, MSLs face the daunting task of simplifying information consumption amidst an overwhelming amount of data—from clinical trial updates to industry trends. Without streamlined processes for assimilating this information, MSLs may struggle to focus on actionable insights rather than getting lost in data.

Leveraging MSLs for Commercial Model Design

Integrating MSLs into your commercial model design offers several strategic advantages:

  • Enhanced Relationship Management: By equipping MSLs with advanced analytics tools, pharmaceutical companies can empower them to build deeper relationships with KOLs. These tools can help track interactions, preferences, and emerging trends, enabling more personalized engagement strategies.
  • Data-Driven Insights: MSLs are uniquely positioned to gather real-world insights from KOL interactions. By incorporating these insights into your commercial strategy, you can better align your messaging and product offerings with the needs of healthcare providers. This alignment not only enhances engagement but also fosters trust and credibility in your brand.
  • Streamlined Communication Channels: Establishing clear communication channels between MSLs and commercial teams ensures that insights gathered by MSLs are effectively shared across the organization. This collaboration can lead to more informed decision-making regarding marketing strategies, sales approaches, and product development.

Implementing Effective Solutions

To address the challenges faced by MSLs and enhance your commercial model design, pharmaceutical firms consider many viable solutions and strategies such as:

  1. Investing in Technology: Implementing a robust Customer Relationship Management (CRM) system tailored for medical affairs can streamline data collection and analysis for MSLs. This technology should facilitate tracking KOL interactions, preferences, and emerging trends in real time.
  2. Providing effective Training and Support: Continuous training for MSLs on the latest scientific advancements and industry trends is essential. Additionally, equipping them with resources that simplify information consumption will allow them to focus on high-impact activities rather than administrative tasks.
  3. Fostering Cross-Functional Collaboration: Encourage collaboration between MSL teams and other departments such as marketing, sales, and R&D. Regular meetings to share insights from KOL engagements can lead to a more cohesive strategy that aligns with market needs.

The integration of Medical Science Liaisons into your commercial model design is not just an operational enhancement; it is a strategic imperative in today’s competitive pharmaceutical landscape. By recognizing the critical role that MSLs play in bridging the gap between science and commercial success, pharmaceutical companies can leverage their expertise to foster deeper relationships with healthcare professionals, drive informed decision-making, and ultimately improve patient outcomes.

As commercial technology and analytics leaders navigate this evolving landscape, investing in the capabilities of MSLs will be key to achieving long-term success in the pharmaceutical industry. Embracing this approach will not only enhance engagement but also position your organization as a trusted partner in advancing healthcare solutions.

Adopting AI to Reduce the Burden of Cardiovascular Disease (CVD)

Posted on by Michelle Bookholdt

Cardiovascular disease (CVD) is the leading cause of death globally, accounting for an estimated 19.1 million deaths in 2022. However, artificial intelligence (AI) is emerging as a powerful tool for reducing CVD burden.

AI can be used to improve CVD prevention, diagnosis, treatment, and management in several ways. For example:

  • Prevention: Personalized risk prediction models for CVD. These models can be used to identify individuals at high risk of CVD and target them with preventive interventions, such as lifestyle changes or medication.
  • Diagnosis: Develop more accurate and efficient diagnostic tools for CVD. For example, AI-powered algorithms can be used to analyze medical images to identify abnormalities that may be missed by the human eye.
  • Treatment: Personalized treatment plans for CVD patients. These plans can be tailored to the individual patient’s disease profile and lifestyle factors. AI can also be used to develop new and more effective therapies for CVD.
  • Management: Tools to help patients manage their CVD. For example, AI-powered apps can be used to help patients track their symptoms, medication adherence, and lifestyle changes.

AI-enabled MedTech solutions are already being used to reduce CVD burden globally. For example, the following are just a few examples:

  • AI-powered cardiovascular imaging software: This software can be used to analyze medical images such as MRI, CT, and PET scans to identify abnormalities that may be missed by the human eye. This can lead to earlier diagnosis and treatment of CVD.
  • AI-powered risk assessment tools: These tools can be used to assess an individual’s risk of developing CVD based on their medical history, lifestyle factors, and genetic makeup. This information can be used to develop personalized preventive interventions.
  • AI-powered wearable devices: These devices can be used to track vital signs such as heart rate, blood pressure, and activity levels. This data can be used to monitor CVD patients and identify early signs of complications.
  • AI-powered mobile health (mHealth) solutions: These solutions can be used to deliver personalized CVD prevention and management interventions to people in remote and underserved areas. For example, AI-powered mHealth apps can be used to help people track their blood pressure, cholesterol levels, and physical activity levels. They can also be used to provide education and support on CVD risk factors and lifestyle changes.
  • AI-powered cloud-based solutions: These solutions can be used to provide access to advanced CVD diagnostic and treatment technologies to people in LMICs. For example, AI-powered cloud-based services can be used to analyze medical images remotely or to provide telemedicine consultations.

AI-enabled MedTech solutions have the potential to revolutionize the way we prevent, diagnose, treat, and manage CVD. By making these technologies more accessible and affordable to people around the world, we can reduce CVD burden globally and improve the quality of life for millions of people. By leveraging AI-enabled MedTech solutions, we can create a world where CVD is no longer a leading cause of death and disability, regardless of where people live.