CueZen

From IDs to Meaning: The Case for Semantic Embeddings in Recommendation

Recommender systems have become indispensable for navigating the vast digital landscape, helping users sift through large volumes of content, products, and services. In the realm of digital health, they play an increasingly vital role in delivering personalized nudges and guidance, enhancing individual engagement, and supporting better health outcomes.

Traditionally, these systems rely on ID-based embeddings, where each user and item (or in our case, each individual and health nudge) is assigned a unique numerical identifier. These IDs are then converted into dense vector representations using techniques like matrix factorization or collaborative filtering. While effective for individuals with interaction histories and popular nudges, this approach has notable limitations, particularly around generalizability and cold-start scenarios.

Enter ID-free embeddings: a promising shift enabled by advances in Large Language Models (LLMs). Instead of relying on arbitrary IDs, these embeddings are generated directly from the rich, descriptive metadata associated with individuals and health nudges—think nudge content (e.g., text, language, tonality), individual health behavior data (e.g., fitness tracker data, dietary logs), demographic data, and health conditions. By leveraging the advanced semantic understanding capabilities of LLMs, ID-free embeddings capture the inherent meaning and relationships within this textual and behavioral information, offering a more flexible and robust foundation for personalized health recommendations.

Why Move Away from ID-Based Embeddings?

While conventional ID-based embeddings have been the mainstay of recommender systems for decades, it has several limitations:

  1. Cold start: New users or nudges without historical data tend to perform poorly, as the system lacks enough context to generate meaningful recommendations. This leads to a poor initial experience for new users and slower adoption of new nudges.
  2. Overfitting to history: Models often memorize frequent users or nudges, reducing their ability to generalize.
  3. Limited portability: These models are transductive in nature, learning about specific users and nudges, and struggle to generalize to new or unseen ones. For example, user 123 in Deployment A of NudgeStream is a distinct individual from user 123 in Deployment B, so the models learned at each deployment cannot be easily transferred across deployments or use cases.
  4. Operational complexity: ID-based pipelines often require custom retraining and tracking, since user and nudge IDs are unique to each deployment or population.

What is ID-Free Embeddings?

At their core, ID-free embeddings transform the descriptive text and structured data associated with individuals and health nudges into high-dimensional numerical vectors, using a pre-trained LLM encoder. For example, a nudge like “Need a snack? Choose crunchy veggies like carrots to curb hunger without the calories.” Is fed into the LLM, which then outputs a dense vector representing the semantic essence of that nudge. Similarly, an individual’s health profile, including their activity levels from a fitness tracker, dietary preferences, or health goals, can be encoded into an individual embedding. This process allows the recommender system to understand what a nudge is designed to achieve, or who an individual is, based on their attributes and behaviors, rather than just a unique identifier.

In CueZen’s recommender setup:

  • User embeddings represent the individual’s current context, derived from a combination of static attributes (e.g. demographics) and dynamic attributes (e.g. recent health behaviors from tracker data).
  • Item embeddings represent the nudge content itself. For example, a message like “a gentle reminder to take a 10-minute walk after lunch to boost energy” is encoded into a vector that captures its semantic meaning, such that nudges with similar messages or goals have similar embeddings. 

The diagram above illustrates the differences between ID-based and ID-free embeddings. ID-based embedding vectors are created and randomly initialized for every unique individual and nudge. These embeddings are then iteratively updated (along with the model weights) during the training process of the recommender model, such that they optimize some objective or loss function. In the case of ID-free embeddings, user and nudge metadata are transformed into semantic embeddings via a pre-trained LLM, using only semantic content and not requiring any IDs. Model training proceeds as usual, where model weights are iteratively updated to optimize the objective or loss function.

Benefits of ID-Free Embeddings

This semantic approach offers several advantages for building robust and effective digital health recommender systems:

  1. Eliminating cold-start scenarios: Semantic embeddings can be computed for new users or nudges and directly plugged into a recommender model without requiring interaction history [1]. This means relevant recommendations can be made even for brand new users or nudges right out of the gate, leading to a much more engaging initial experience.
  2. Enhanced Generalization and Universal Representations: ID-free embeddings provide universal representations that generalize across different populations and deployments. For example, two individuals from different countries—both 35 years old, prediabetic, overweight, and sedentary—would have similar embeddings. Similarly, two nudges containing the same topic and advice would have similar embeddings even if they were written in different languages. This allows models to learn patterns from the underlying behavior and content, leading to strong performance on previously unseen users or nudges and mitigating the problem of overfitting to historical data [2].
  3. Improved Transferability and Simplified Deployment: Because these embeddings capture intrinsic meaning rather than arbitrary IDs, models trained on one population and use case can transfer effectively to others, delivering good results from day one [3]. Fine-tuning on the target population can further enhance recommendation performance. This also simplifies model deployment, as precomputed embeddings remove the need for ID-specific retraining or the tracking of large ID-based embedding matrices across different deployments.
  4. Reusability of Existing Recommender Architectures: A practical benefit is that many existing recommender architectures can be directly reused, by simply replacing the randomly initialized ID-based embedding matrix with precomputed semantic embeddings. ID-free embeddings can be integrated seamlessly into the vast body of existing recommender models and proven architectures, without needing to reinvent the wheel.
  5. Privacy-Aware Design: By reducing dependence on personal identifiers and focusing on semantic attributes, this approach inherently supports safer handling of sensitive customer health data.

Related Work

Recommender systems using ID-free embeddings are a rapidly evolving area of research. Built on the strong foundations of “traditional” recommender systems, the use of LLMs opens up many new possibilities in addressing previous limitations such as cold start, transferability and generalization across domains. As a result, researchers are increasingly interested in ID-free or modality-based approaches, which leverage semantic embeddings derived from content or behavioral signals.

The foundation for semantic retrieval models was laid by early neural recommender systems like DSSM [4] and the Youtube DNN model [5], which introduced architectures to map users and items into shared embedding spaces using behavioral and content features. Further advancements, such as Two-tower models [6] and attention-based architectures like DIN [7] and DIEN [8], significantly enhanced the ability to model user intent without relying solely on explicit ID representations.

Recent progress has pushed this direction further by demonstrating strong performance using purely semantic inputs. For instance, large language models (LLMs) have achieved state-of-the-art results for collaborative filtering using only textual item descriptions [9]. Similarly, Recformer [10] learns sequential user preferences by applying language modeling techniques directly to item content, thereby bypassing traditional ID lookups entirely.

Zero-shot and transferrable recommendation is another emerging area. ZESRec [11] performs well on cold-start tasks without requiring historical user data, while universal representation learning (UniSRec) [12] and vector-quantized embeddings (VQ-Rec) [13], demonstrate improved generalization across users and domains.

From a broader perspective, [14] present a comparative study between ID-based and modality-based recommenders, concluding that semantic models are not only viable alternatives but, in some cases, superior—especially in sparse or evolving environments. These findings align with our motivation to adopt ID-free recommendation strategies based on behavioral and content embeddings.

What’s Next?

In the next few posts, we’ll go deeper into the following:

  • Comparing our existing ID-based Knowledge Graph Attention Network (KGAT) model with new ID-free approaches.
  • Evaluating performance of the ID-free models across varying training data sizes.
  • Additional ablation studies and evaluations on the robustness of semantic embeddings.

These explorations will help us assess the practical value of ID-free models and guide our next steps in improving our nudge engine. 

References:

[1] S. Sanner and e. al., “Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences,” in RecSys, 2023.
[2] K. Zhang and e. al., “Learning ID-free Item Representation with Token Crossing for Multimodal Recommendation,” arXiv, 2024.
[3] Y. Li and e. al., “A Zero-Shot Generalization Framework for LLM-Driven Cross-Domain Sequential Recommendation,” arXiv, 2025.
[4] P.-S. Huang and e. al., “Learning deep structured semantic models for web search using clickthrough data,” in CIKM, 2013.
[5] P. Covington and e. al., “Deep Neural Networks for YouTube Recommendations,” in RecSys, 2016.
[6] X. Yi and e. al., “Sampling-bias-corrected neural modeling for large corpus item recommendations,” in RecSys, 2019.
[7] G. Zhou and e. al, “Deep Interest Network for Click-Through Rate Prediction,” in KDD, 2018.
[8] G. Zhou and e. al., “Deep interest evolution network for click-through rate prediction,” in AAAI, 2019.
[9] R. Li and e. al., “Exploring the Upper Limits of Text-Based Collaborative Filtering Using Large Language Models: Discoveries and Insights,” arXiv, 2023.
[10] J. Li and e. al., “Text Is All You Need: Learning Language Representations for Sequential Recommendation,” arXiv, 2023.
[11] H. Ding and e. al., “Zero-Shot Recommender Systems,” Amazon Scienc, 2021.
[12] Y. Hou and e. al., “Towards Universal Sequence Representation Learning for Recommender Systems,” in KDD, 2022.
[13] Y. Hou and e. al., “Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders,” in WWW, 2023.
[14] Z. Yuan and e. al., “Where to Go Next for Recommender Systems? ID- vs. Modality-based Recommender Models Revisited,” arXiv, 2023.

CueZen raises $5m Seed Funding to Transform Health Personalization

Point72 Ventures, with participation from Pack VC, Forston VC, and Nextinfinity have backed CueZen with an investment of US$ 5 M. This funding will enable CueZen to accelerate customer acquisition efforts and enhance platform capabilities for health enterprises looking to personalize their digital offerings.
At CueZen, we are committed to making health and wellness truly personal – because that’s where real change begins. This investment is not just fuel for growth – it’s a signal of market readiness for AI that turns everyday lifestyle and clinical data into actions that help millions improve their health. We will leverage this capital to expand our enterprise partnerships, deepen platform capabilities, and unlock measurable ROI — all while delivering personalization in health at unprecedented scale.
Ankur Teredesai
CEO of CueZen and Professor at the University of Washington.
We believe CueZen is addressing a significant gap in the digital health ecosystem. Their platform has the potential to fundamentally change how health data is leveraged across the industry. We're impressed by the early impact they've demonstrated and are excited to support their vision of creating better health outcomes while enabling new revenue streams for technology companies and providers in this space.
Tara Stokes
Partner at Point72 Ventures

CueZen at HIMSS 2025

We're going to be at

Shape the future of personalized health.
Meet. Connect. Partner.

Book a meeting





    Share this:

    The Digital Health divide is growing. Data collected from sensors and wearables by itself is not actionable. Outcomes will not improve until we change behavior.

    CueZen enables sustained and actionable behavior change for your population and your offerings. Connect with us to explore how personalization leads to proactive care.

    Connected solutions:
    2B smart wearables by 2027

    Health outcomes:
    through behavior change at national scale

    CueZen’s AI-powered performance suite is the go to platform for enterprises creating value from personalized health engagement that drives outcomes.
    Explore how you can access curated ecosystems of sensors, biomarkers, engaged communities, and clinically validated programs. Our comprehensive solution seamlessly integrates the CueStream personalized engagement system to drive timely, impactful actions.
    HIMSS is the premier digital health event where innovative solutions and experts share key insights. It brings together healthcare providers, governments, startups, and other health services organizations for engaging discussions and networking. HIMSS is committed to reforming the global health ecosystem through the power of information and technology.

    CueZen's Personalization Suite

    The go-to platform

    for enterprises creating value from personalized health engagement that drives outcomes.

    CueZen is a winner in the Emerge Innovation Experience Contest!

    Catch us at the Emerge Innovation Experience 2025, a HIMSS 2025 debut, high-impact program that brings together health innovators, investors, and executives who will be instrumental in building the future of healthcare.

    Ready to explore the future of personalized health engagement?

    ViVE 2025

    We're going to be at

    Shape the future of personalized health.
    Meet. Connect. Partner.

    Book a meeting





      Share this:

      The Digital Health divide is growing. Data collected from sensors and wearables by itself is not actionable. Outcomes will not improve until we change behavior.

      CueZen enables sustained and actionable behavior change for your population and your offerings. Connect with us to explore how personalization leads to proactive care.

      Connected solutions: 2B smart wearables by 2027

      Health outcomes through behavior change at national scale

      CueZen’s AI-powered performance suite is the go to platform for enterprises creating value from personalized health engagement that drives outcomes.
      Explore how you can access curated ecosystems of sensors, biomarkers, engaged communities, and clinically validated programs. Our comprehensive solution seamlessly integrates the CueStream personalized engagement system to drive timely, impactful actions.
      ViVE is the premier digital health event dedicated to driving transformation in healthcare, bringing together C-suite executives, senior digital health leaders, startups, investors, patient advocates, and solution providers to shape the industry’s future.

      CueZen AI-powered Performance Suite

      The go-to platform

      for enterprises creating value from personalized health engagement that drives outcomes.

      Ready to explore the future of personalized health engagement?

      Revolutionizing Population Health with NudgeRank™: The Future of Personalized AI-Driven Health Nudges

      The promise of artificial intelligence (AI) in healthcare has always been tantalizing. We’ve seen AI make strides in diagnostics, drug discovery, and even in the management of chronic diseases. But one area where AI is poised to make an even more profound impact is in the realm of behavior change—specifically, how we can influence health behaviors at a population level. Enter NudgeRank™—a groundbreaking tool that harnesses the power of AI to deliver personalized health nudges on an unprecedented scale. Let’s dive into how this innovation works, why it matters, and what it could mean for the future of healthcare. 

      The Herculean Task of Changing Health Behaviors

      Changing patient behavior is one of the most formidable challenges in healthcare. Research shows that approximately 70% of premature deaths are linked to behaviors that can be modified, such as smoking, poor diet, and physical inactivity. For instance, smoking cessation alone could prevent 90% of lung cancers, but only about 7% of smokers succeed in quitting on their first attempt. Similarly, adherence to medication regimens is alarmingly low; studies estimate that nearly 50% of patients with chronic diseases in developed countries do not take their medications as prescribed, leading to increased hospitalizations and healthcare costs. These behaviors are difficult to change due to a complex interplay of factors—habits are deeply ingrained, motivation can diminish over time, and everyday life often presents barriers to maintaining healthy choices.

      The World Health Organization (WHO) defines the burden of disease as the impact of a health problem measured by financial cost, mortality, morbidity, or other indicators. It reflects the gap between a population’s current health status and an ideal situation where everyone lives into old age, free of disease and disability. Behavior-related conditions like cardiovascular diseases, diabetes, and respiratory disorders contribute significantly to the global burden of disease. These conditions not only affect individual health but also place a substantial strain on healthcare systems, highlighting the critical need for effective behavior change interventions at a population level.

      Traditional public health approaches—think mass media campaigns, pamphlets, and one-size-fits-all advice—often miss the mark. They fail to account for the unique circumstances of each individual, which is where the need for personalization becomes glaringly evident. What if we could tailor health advice to each person’s specific situation, habits, and needs? That’s the question NudgeRank™ is answering with a resounding “yes.”

      NudgeRank™: Where AI Meets Personalized Health Interventions

      NudgeRank™ is not your typical AI system. It’s a sophisticated digital nudging platform designed to influence health behaviors by delivering personalized, context-aware recommendations to millions of users. At its core, NudgeRank™ combines the predictive prowess of Graph Neural Networks (GNNs) with the dynamic adaptability of a Knowledge Graph, creating a system that learns, evolves, and optimizes its interventions over time.

      Graph Neural Networks (GNNs): Graph Neural Networks are a powerful class of models that operate on graph-structured data, where entities (nodes) and their relationships (edges) can be represented in a more meaningful and flexible way than traditional grid-like data structures such as images or sequences. In NudgeRank™, the entities might be users, health interventions, behavioral goals, or nudges while the edges represent the relationships between these entities—how a user’s past behaviors might influence their future actions, or how certain interventions might be more effective based on demographic factors.

      The essence of a GNN lies in its ability to perform message passing between nodes. Each node aggregates information from its neighbors, allowing the network to learn rich, context-sensitive representations of each entity based on higher-order relationships in the knowledge graph. In the context of NudgeRank™, this means that the system can learn which factors are most predictive of a successful health nudge. For example, a GNN might learn that users who have recently been more active but are starting to lapse respond well to motivational messages tailored to their previous high performance.

      The GNN in NudgeRank™ uses a multi-layer architecture where each layer corresponds to a different level of abstraction. The initial layers might focus on simple features like the user’s age or activity level, while deeper layers capture more complex patterns such as the interaction effects between multiple health interventions. This hierarchical processing allows NudgeRank™ to build a nuanced understanding of each user’s unique circumstances.

      The model’s attentive graph convolution layers are particularly crucial. These layers enable the GNN to weigh the importance of different nodes and edges, dynamically adjusting which information is most relevant in recommending a successful health nudge at any given time. For instance, if a user’s recent behavior suggests a decline in physical activity, the model can prioritize nudges that have historically been effective for similar patterns, ensuring that interventions are timely and contextually appropriate.

      Knowledge Graph Integration: A Knowledge Graph is much more than a static database, it’s a dynamic structure that continuously evolves as new data is introduced. In NudgeRank™, the Knowledge Graph is populated with information from various sources—demographic data, historical health behaviors, interactions with past nudges, and even external data from wearables or health records. Each user’s data forms a personalized subgraph within the larger structure, with nodes representing their attributes and behaviors, and edges denoting relationships such as “increased activity after receiving a motivational nudge.”

      What makes this integration so powerful is the extensibility of the Knowledge Graph. It’s designed to easily incorporate new types of data as they become available, allowing the system to adapt and improve over time. This is particularly important in healthcare, where new information—such as updates to a user’s medical history or the introduction of new health guidelines—can significantly alter the relevance of certain interventions.

      Moreover, the Knowledge Graph is heterogeneous, meaning it includes a variety of node and edge types. For example, a node could represent a user’s demographic group, while another might represent a specific health goal like “increasing daily steps.” The edges between them could capture relationships such as how often the user met their step goals after receiving a certain type of nudge. This richness in representation allows NudgeRank™ to make more informed decisions about which nudges to prioritize, based on a deep, contextual understanding of the user’s situation.

      The combination of GNNs and Knowledge Graphs in NudgeRank™ represents a significant advance in how we can personalize health interventions at scale. By leveraging the structure and relationships within the data, NudgeRank™ doesn’t just push generic health advice—it delivers finely-tuned, data-driven nudges that are optimized for each individual, adapting in real-time to changes in behavior and context.

      Real-World Impact: Scaling Health Interventions in Singapore 

      NudgeRank™ isn’t just a theoretical model; it’s already making a difference in the real world. In Singapore, the Health Promotion Board integrated NudgeRank™ with their Healthy 365 app, a platform used by over a million citizens to track their health activities. The goal? To encourage healthier behaviors across the nation.

      The results have been impressive: users who received personalized nudges experienced a 6.17% increase in daily steps and a 7.61% increase in weekly exercise minutes, compared to those who did not receive nudges.

      NudgeRank™ achieves this through a robust, scalable architecture. The system operates within a Kubernetes cluster, enabling it to process data for millions of users in real time. It’s designed to handle up to 19 billion user-nudge pairs, ensuring that as the user base grows, the system remains responsive and effective.

      What This Means for Healthcare Providers

      For healthcare professionals, the implications of NudgeRank™ are significant. Imagine being able to ensure that your patients are receiving tailored advice and reminders that actually resonate with them—without adding to your workload. NudgeRank™ does this by automating the delivery of personalized nudges, freeing up clinicians to focus on more complex tasks that require human expertise.

      Enhanced Patient Engagement: We’ve long known that engaged patients are healthier patients. NudgeRank™ significantly boosts patient engagement by delivering nudges that are precisely calibrated to each individual’s current state. This isn’t a generic reminder to take more steps—it’s a targeted suggestion based on real-time data that’s been fine-tuned to maximize impact.

      Improved Clinical Outcomes: The relationship between patient engagement and positive health outcomes is well documented. With NudgeRank™, the connection is even more direct. By encouraging small, incremental changes that build over time, the system helps patients achieve better health outcomes, whether it’s managing a chronic condition or simply staying active.

      Operational Efficiency: Healthcare systems are under constant pressure to do more with less. NudgeRank™ helps by automating a process that’s both time-consuming and crucial—keeping patients on track with their health goals. The system’s robust design includes automated feedback loops, ensuring that it learns from each patient interaction to refine future nudges.

      Implications for the Wearables and Digital Health Industry

      The rise of wearable technology and digital health platforms has transformed how we monitor and manage our health. Devices like smartwatches, fitness trackers, and even connected home health devices generate a wealth of data that offers unprecedented insights into our daily lives. But the real value of this data lies in how it’s used to influence behavior and improve health outcomes—a challenge that NudgeRank™ is uniquely positioned to address.

      Bridging the Gap Between Data and Action: Wearables have become ubiquitous, with millions of users tracking everything from their steps and sleep patterns to heart rate and blood oxygen levels. Yet, while these devices collect vast amounts of data, translating that data into actionable health improvements has been a significant challenge. This is where NudgeRank™ comes into play.

      NudgeRank™ bridges the gap between passive data collection and active health management. By integrating data from wearables into its Knowledge Graph, NudgeRank™ can tailor its health nudges based on real-time data from users’ devices. For instance, if a wearable detects that a user’s physical activity has decreased over the past week, NudgeRank™ can immediately respond with personalized recommendations to get back on track—be it a reminder to take a walk or a nudge to join a local fitness challenge.

      Enhanced Personalization Through Continuous Feedback: The combination of wearables and NudgeRank™ offers a feedback loop that continuously refines and enhances the personalization of health interventions. Wearables provide the continuous stream of data needed to understand users’ behaviors in real time, while NudgeRank™ uses this data to generate and adjust nudges dynamically.

      This continuous feedback loop means that the more a user engages with their wearable and the NudgeRank™ system, the more personalized and effective the nudges become. For example, if a user consistently responds well to motivational nudges after a period of inactivity, the system will prioritize similar interventions in the future. This level of personalization can significantly enhance user engagement with their health goals, leading to better long-term outcomes.

      A New Paradigm for Digital Health Platforms: For the digital health industry, the integration of AI-driven systems like NudgeRank™ represents a new paradigm in how health interventions are delivered. No longer are digital health platforms simply repositories of data; they become active participants in the health management process, capable of delivering real-time, personalized interventions that are informed by the continuous flow of data from wearables.

      This shift has profound implications for the digital health industry:

      1. Increased User Engagement: As wearables and digital health platforms offer more personalized and effective interventions, user engagement is likely to increase. This not only benefits users by improving their health outcomes but also strengthens the value proposition of wearable and digital health products.

      2. Data-Driven Health Management: The integration of AI with wearable data enables a shift from reactive to proactive health management. Instead of waiting for users to encounter health issues, platforms can use predictive analytics to anticipate potential problems and intervene early, thereby reducing the risk of serious health events.

      3. Scalability and Reach: NudgeRank™ demonstrates that personalized health interventions can be delivered at scale. This is particularly important for global digital health platforms that cater to diverse populations with varying health needs. The ability to tailor interventions to each user, regardless of their location or specific health challenges, makes these platforms more effective and inclusive.

      4. Collaboration Opportunities: As AI-driven personalization becomes more sophisticated, there will be increasing opportunities for collaboration between wearable manufacturers, digital health platforms, and AI developers. These partnerships could lead to even more advanced health management solutions, combining the strengths of each industry to create integrated, seamless user experiences.

      The Road Ahead: What’s Next for NudgeRank™? 

      NudgeRank™ is already a powerful tool, but its potential is far from fully realized. The next frontier involves integrating Reinforcement Learning, allowing the system to not only suggest actions but to optimize these suggestions based on how patients respond. Imagine a system that doesn’t just learn from what you do today but adapts in real time to guide you toward better health tomorrow.

      There’s also the possibility of expanding NudgeRank™ into new areas of health. While it’s currently focused on physical activity, the underlying technology could be applied to a wide range of behaviors—diet, medication adherence, mental health support, and more. The modular design of NudgeRank™ means it can easily incorporate new data sources and goals, making it a versatile tool for any healthcare setting.

      Ethical Considerations and the Future of AI in Health Nudging

      As we continue to push the boundaries of what AI can achieve in healthcare, it’s crucial to pause and consider the ethical implications of these advancements. NudgeRank™ is a powerful tool, but with great power comes great responsibility. The integration of AI into health nudging raises important questions about privacy, fairness, and the transparency of machine learning models, especially in a field as sensitive as healthcare.

      Interpretable Machine Learning and Transparency: One of the key challenges in deploying AI systems in healthcare is ensuring that they are interpretable. Patients and healthcare providers need to understand how and why certain decisions are made, particularly when these decisions can significantly impact a person’s health. In the context of NudgeRank™, interpretability means that the system’s recommendations—whether it’s a nudge to exercise more or a reminder to take medication—must be explainable.

      Healthcare providers should be able to understand the reasoning behind each nudge, which in turn can help them trust the system and communicate more effectively with their patients. This transparency is not just a technical requirement but an ethical one, ensuring that AI supports informed decision-making rather than obscure or undermine it.

      Interpretable models are essential as they allow clinicians to see the factors that contributed to each recommendation. For instance, if NudgeRank™ suggests a particular health intervention, the model should be able to explain whether this suggestion was based on the patient’s recent activity levels, demographic data, or past responses to similar interventions. This level of clarity helps build trust in the AI system, both for clinicians and patients.

      Fairness in Healthcare AI: Another critical area of concern is fairness. AI systems have the potential to perpetuate or even exacerbate existing biases in healthcare. If not carefully designed, these systems could deliver different quality of care to different populations, reinforcing disparities rather than addressing them. Ensuring fairness in AI involves developing systems that are equitable across diverse patient populations.

      In NudgeRank™, fairness is addressed by ensuring that the system is trained on diverse datasets that represent a wide range of demographic and socioeconomic backgrounds. This diversity helps to prevent the model from becoming biased towards any particular group, ensuring that the health nudges it generates are equally effective for all users.

      However, fairness in AI goes beyond the data. It also involves continuously monitoring the system for any signs of bias and making adjustments as needed. For example, if the system’s nudges are found to be less effective for a particular demographic group, this issue needs to be addressed promptly, whether through retraining the model on more representative data or adjusting the algorithms to better account for the needs of that group.

      Privacy and Data Security: With the vast amounts of personal health data involved, privacy and data security are paramount. NudgeRank™ is built with robust data protection measures, including pseudonymization and encryption, ensuring that patient data is handled with the highest standards of confidentiality. This is particularly important in light of regulations like the General Data Protection Regulation (GDPR) in Europe and the Health Insurance Portability and Accountability Act (HIPAA) in the United States.

      Designing AI systems that respect patient autonomy is also crucial. Patients must be able to control how their data is used and opt-out of certain types of data collection or analysis. This respect for patient autonomy is essential for maintaining trust in AI-driven healthcare solutions.

      The Future of AI in Health Nudging: Looking ahead, the ethical deployment of AI in health nudging will require ongoing vigilance and commitment to principles of fairness, transparency, and privacy. As systems like NudgeRank™ become more integrated into everyday healthcare, it’s crucial that we continue to develop frameworks that ensure these technologies serve all patients equitably.

      The future of AI in health nudging is bright, but it must be guided by a strong ethical compass. By focusing on interpretability, fairness, and privacy, we can create systems that not only enhance health outcomes but do so in a way that is just, transparent, and respectful of patient autonomy.

      References: 

      1. Chiam, J., Lim, A., & Teredesai, A. (2024). NudgeRank: Digital Algorithmic Nudging for Personalized Health. Proceedings of KDD ’24, Barcelona, Spain.
      2. Chiam, J., Lim, A., Nott, C., Mark, N., Teredesai, A., & Shinde, S. (2024). Co-Pilot for Health: Personalized Algorithmic AI Nudging to Improve Health Outcomes. arXiv preprint arXiv:2401.10816.
      3. Topol, E. J. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books.
      4. World Health Organization. (2022). Noncommunicable Diseases Progress Monitor. Retrieved from [https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases](https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases)
      5. Wang, H., Zhang, F., Wang, J., Zhao, M., Li, W., Xie, X., & Guo, M. (2018). Ripplenet: Propagating user preferences on the knowledge graph for recommender systems. In Proceedings of the 27th ACM international conference on information and knowledge management (pp. 417-426).
      6. Wang, H., Zhao, M., Xie, X., Li, W., & Guo, M. (2019). Knowledge graph convolutional networks for recommender systems. In The world wide web conference (pp. 3307-3313).
      7. Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., & Bengio, Y. (2017). Graph Attention Networks. arXiv preprint arXiv:1710.10903.
      8. Wang, X., He, X., Cao, Y., Liu, M., & Chua, T.-S. (2019). KGAT: Knowledge Graph Attention Network for Recommendation. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.

      Cuezen selected to participate in Microsoft for Startups Pegasus Program

      Designed for startups, the Pegasus Program by Microsoft provides crucial support to enhance revenue for selected companies that have already demonstrated product-market fit in industries like AI, healthcare and life sciences, cybersecurity, and retail. 

      CueZen has recently been chosen for the exclusive Microsoft for Startups Pegasus Program, highlighting our dedication to driving innovative advancements in healthcare technology and delivering personalized healthcare solutions.

      CueZen’s AI-powered personalization engine enhances individual health engagement by offering tailored recommendations throughout the care journey, helping people achieve their personal health objectives.  

      The Pegasus program allows us to bring advanced Open AI integration and Azure features to our customers and gives us increased access to Microsoft's enterprise health customer base, thereby expanding our global footprint together
      Ankur Teredesai
      CEO of CueZen and Professor at the University of Washington.

      HIMSS 2024

      Join us at

      Share this:

      We’re excited to be attending HIMSS 2024 in Orlando, Florida, from 11-15 March 2024. This year, HIMSS is all about Creating Tomorrow’s Health. Everyone who matters in healthcare is there to discuss everything that’s shaping the future of healthtech. We look forward to all the conversations and can’t wait for you to discover how Cuezen is helping global Pharma, Provider, Payors, and HealthTech/smart wearables OEMs in Creating Tomorrow’s Health.

      Orthopedic Care

      Personalized Orthopedic Patient pathways with a surgeon-assigned care plan, continuous monitoring, engagement, and AI-based recommendations for patient engagement

      COPD

      Stratifying patients based on their risk for COPD, providing personalized nudges, and collecting data to power AI-driven pathways for diagnosis and treatment

      Clinical Trials

      Clinical Trial setup including segmentation, Adaptive Learning, Continuous Monitoring, Trial Adaptation. Personalization to improve patient engagement and medication adherence

      Come meet us and learn more about our innovative healthcare solutions for Orthopedic Care, COPD, Clinical Trials and more.
      Cuezen’s AI-based personalization engine for health delivers hyper-personalized nudges that drive behavior change, guiding individuals effectively through their unique health journeys toward positive health outcomes. Cuezen is at the forefront of the personalized healthcare revolution and was recently selected to the prestigious Microsoft for Startups Pegasus Program.

      Book a meeting




        Titan Smart Wearables partners with CueZen to revolutionize health and wellness in India

        Going beyond counting steps and measuring BPM 

        Titan Smart Wearables recently announced their strategic partnership with CueZen with a shared vision to empower Indians with a new generation of smart wearables & AI-powered solutions that offer actionable guidance and support for a healthier lifestyle.

        “In light of the health challenges confronting India today, our collaboration is laser-focused on delivering customized solutions that cater to the diverse requirements of the Indian consumers. Leveraging the power of AI and wearable technology, Titan Smart Wearables and CueZen are committed to developing products and services that profoundly influence individuals’ fitness endeavors…” Ravi Kuppuraj. Business Head COO – Titan Smart Wearables.

        CueZen’s AI-driven personalization engine delivers hyper-personalized nudges and actionable recommendations encouraging lifestyle and behavioral change, significantly improving health outcomes and adherence to care protocols.

        “We must accept that most of us are highly irrational when it comes to prioritizing our lifestyle and health goals. A tiny bit of personalized nudging and engagement can go a long way to help our future self be a bit less irrational. Personalization is, therefore, the future of health. Our collaboration with Titan Smart Wearables leverages cutting-edge technology, fundamentals of behavioral economics, and AI-driven hyper-personalization to meet the diverse healthcare needs of India at nation scale,” said Ankur Teredesai, CEO of CueZen

        ViVe 2024: The Next Stop for Digital Health Decision Makers

        We're going to be at

        Share this:

        ViVE is where digital health decision-makers navigating the dynamic technology landscape of healthcare business are convening. The event is expected to bring together 8,000+ attendees, 1,500+ C-level executives, 800+ sponsors, 300+ health leaders, buyers, health startups, investors, policymakers, and the vast patient community. 

        Our CEO, Ankur Teredesai and CueZen’s advisor, Tom Lawry, will speak at the convention’s Sports & Health Tech Summit. The talk will explore the synergy of sports technology and holistic wellness.

        Cuezen is at the forefront of personalized healthcare revolution and was recently selected to the prestigious Microsoft for Startups Pegasus Program. Cuezen’s AI-based personalization engine for health delivers hyper-personalized nudges that drive behavior change, guiding individuals effectively through their unique health journeys toward positive health outcomes.

        Delivering personalized nudges for a healthy Singapore

        Unlike generic notifications that can overwhelm individuals, CueZen’s approach focuses on precision.
        Dr. Keren Priyadarshini
        Healthtech Evangelist, Microsoft
        CueZen integrates data from lifestyle apps, health programs, and wearables and creates highly individualized recommendations. As a result, Singapore is seeing three times higher engagement rates than industry standards
        Ankur Teredesai
        CEO of CueZen and Professor at the University of Washington.

        References:

        [1] Study findings from Global Burden of Disease 2019

        [2] According to the World Health Organization, in Singapore, life expectancy at birth (years) has improved by ▲4.78 years from 78.4 years in 2000 to 83.2 years in 2019

        Book a meeting





          Request a demo