Responsible AI & Personalization: How Data-Driven Insights Are Transforming Patient Care

Written by Rachel Jiang, SVP Product & Technology at TailorCare

Delivering high-quality, personalized care at scale is one of the greatest challenges in healthcare, especially in musculoskeletal (MSK) care. As organizations grow and patient populations become more complex, maintaining meaningful, trust-based relationships becomes harder.

I’ve spent my career working across product, engineering, and data science to solve problems like this. At Amazon, I helped build some of the earliest healthcare-focused language models for Alexa, designing systems that could understand medication names and clinical terms without losing sight of the patient experience. That work shaped how I think about artificial intelligence (AI).

AI offers a path forward by supporting clinicians engaged in personalized, value-based care. With responsible integration, AI can relieve information overload, surface relevant information at the right moment, and enable clinicians to do what only humans can: connect, guide, and care.

At TailorCare, AI is a tool to scale with intention. Our approach puts humans at the center while equipping care teams with the needed insight and support, always keeping responsible practices at the forefront. The result is a model of value-based care that remains personal, focused on relationships, and strengthened by the thoughtful use of machine learning.

We’re already seeing the results: more than 70% of patients adhere to their recommended treatment plans, 90% report improvements in pain or function, and our Net Promoter Score consistently exceeds +90.

The Challenge of Scaling Personalized, Value-Based MSK Care

MSK patients often begin their journey at many different entry points. PCPs commonly have limited training in musculoskeletal conditions and face significant time constraints. As a result, patients are frequently referred to specialists—most often orthopedists—whose care models tend to prioritize surgical interventions.

This creates a cycle where no single provider manages the entire care journey. Patients might be referred, evaluated, and redirected multiple times, often feeling disconnected and uncertain about their options. Without a dedicated care guide, they may delay treatment, pursue unnecessary procedures, or disengage altogether.

At TailorCare, we’ve seen that effective MSK care depends on more than clinical accuracy. It also requires understanding how a patient lives: their goals, preferences, and limitations must inform the path forward. Achieving that level of understanding requires time to listen, build trust, and respond with care that fits the individual.

As healthcare organizations grow, preserving this level of personalization becomes increasingly difficult. Care navigators are often burdened by fragmented systems, spending valuable time locating information, documenting interactions, and managing routine tasks.

To sustain trust-based care at scale, we must equip our teams with tools that let them focus on what matters most. For us, AI is part of that solution. By using AI to augment and support the care navigation process, we aim to give patients more time with people who can provide personalized, value-based care, even as we grow.

AI Misconceptions in Healthcare

Still, artificial intelligence is often misunderstood. A common concern is that AI will replace physicians, taking over clinical decision-making and weakening the human relationships patients rely on. This is based on a misconception that organizations will use AI to operate independently, without human oversight or nuance.

When I was leading a health and wellness group at Alexa, people often assumed we were building “Dr. Alexa”—an AI that could replace doctors. That was never the goal. We were developing language models to help systems understand medication names and clinical terms, making it easier for clinicians to do their jobs well.

AI’s best role is as a co-pilot. I often think about AI as offering care teams something humans don’t have: infinite memory and infinite compute capability. It’s a way to relieve cognitive burden, remind them of context, and highlight insights that might otherwise be missed. In short: less time spent searching for answers and more time focused on patient engagement. But it needs to be done responsibly. Human oversight is essential for safety, quality, and trust.

How Data-Driven AI Enables Personalized and Scalable Care

This is where data-driven insights come in. AI is only as effective as the data behind it. General-purpose systems don’t perform well when applied to clinical settings. They need training on domain-specific data to deliver relevant, accurate results.

By learning from real-world patient data—both clinical and self-reported—AI can help scale care without losing its personal touch. Here’s how:

• Efficiency and time optimization: AI can automate routine tasks and surface relevant information quickly by pulling from multiple data sources like claims, call notes, intake forms, and more. This reduces time spent on preparation and navigation, allowing care teams to focus on patients.
• Enhanced personalization: AI uses a broad range of data, including symptoms, behaviors, preferences, and context, to build a full picture of each patient. That insight allows care pathways to be dynamic and adjust as new information appears.
• Reducing human limits: AI supports care teams with timely suggestions and evidence-based prompts, drawing on a broader dataset than any one clinician can access. With responsible guidance, it can reduce cognitive burden and mitigate bias without overriding human judgment.
• Continuous improvement: Because AI can integrate feedback in real time, care pathways don’t remain static. As patients share updates or respond differently to interventions, that data helps the care pathway evolve, making it more adaptive, precise, and aligned with outcomes over time.
• Predictive capabilities: Machine learning models trained on real-world data can detect subtle patterns that signal emerging health risks, sometimes before symptoms surface. These insights help care teams intervene earlier, engage patients sooner, and route resources more effectively, reducing the likelihood of unnecessary procedures or emergency escalations.

Responsible AI requires human oversight. The goal is not automation for its own sake but to give care teams better tools to make better decisions. Judgment, empathy, and relationship-building will always be human strengths. AI simply ensures those strengths are never undermined by missing information, overload, or delay.

TailorCare’s Responsible AI Approach to MSK Personalization

At TailorCare, we’ve already translated some of these possibilities into practices that help our care teams work more effectively while keeping patient relationships at the center, particularly through predictive modeling and machine learning.

Using this data, we’re able to identify patients who may be at risk for significant health events, even before they or their providers recognize a need. These models help the care team prioritize outreach, and early intervention can create more opportunities for conservative care pathways.

We validate our AI and machine learning models by simulating real-world data as it flows in. A great deal of intention goes into ensuring that what we’re modeling reflects the actual population we serve, supporting accuracy, relevance, and fairness. All AI-generated insights are reviewed by clinical staff before they inform care decisions, and built-in feedback loops allow for ongoing refinement. At every stage, a human remains at the top of that loop.

The results are meaningful. Among patients we have identified as at risk for a potentially unnecessary surgery, 84% of those who were navigated to conservative care pathways, like physical therapy, reported reduced intention to proceed with surgery. Of those who initially said they were extremely likely to have surgery, 94% shifted toward lower surgical intention, and 7% reversed course entirely, reporting they were now extremely unlikely to undergo surgery in the next six months.

These outcomes show the power of timely, informed engagement guided by predictive models and delivered through trusted human relationships.

The Future of Care Is Human-Centered, AI-Powered

Artificial intelligence is only as powerful as its application. In healthcare, that means keeping people at the center and using AI to support the human relationships that drive trust and outcomes.

At TailorCare, we’ve built our approach around that belief. By applying predictive models responsibly and ensuring clinical oversight at every stage, we’re creating a system that supports earlier intervention, stronger alignment with patient needs, and measurable impact without sacrificing quality of care.

This is a scalable model built with integrity and designed with intention. Our philosophy is that AI can be thoughtfully integrated into complex systems without compromising safety or nuance. When done right, it becomes a partner in better decisions, deeper connection, and more consistent outcomes.

As healthcare organizations navigate how to grow without losing what matters, I believe this kind of responsible, human-centered AI is the path forward, and I’m proud to be part of TailorCare’s goals to build it.