b.well x myTomorrows: Building Patient‑Centric Flows Across Care, Research, and Treatment

Table of Contents

• The Challenges of Clinical Trial Recruitment
• Health Data Now Moves with the Patient
• Creating a New Operating Model
• From Patient Identification to Continuous Access
• Unlocking Less Burden, Better Signal, Faster Development
• Improving Patient Access and Trial Success Rates

Healthcare, particularly clinical trials and pre‑approval access, remains fragmented, creating extra burden for patients seeking treatment options.

b.well and myTomorrows are partnering to address this by enabling patient-centric flows across care, research, and treatment. By combining b.well’s connected data infrastructure with our access and navigation capabilities, this creates a new model for identifying, matching, and supporting patients across their journey.

Data rarely travels with the patient, which means decisions are frequently made without understanding the full picture, and critical details fall through the cracks at every transition point.

Within clinical trials, this fragmentation becomes even more visible. Patients are expected to navigate a system that is rarely designed around their journey towards treatment access. At the same time, clinical trial sites and sponsors carry the operational burden of connecting fragmented data, workflows, and systems. Connecting these remains largely manual. The result is poor efficiency, delays in development timelines, and missed opportunities for both patients and sponsors.

the same time, something fundamental in the patient’s access to care journey is beginning to change. For the first time, user‑authorized health data can be securely integrated across systems, allowing information to follow the patient instead of remaining locked within individual institutions. This shift creates the technological and structural foundation for a more connected, patient‑centric model of care, research, and access. And this is where b.well and myTomorrows are coming together.

The Challenges of Clinical Trial Recruitment

Clinical trial recruitment challenges remain one of the most persistent barriers in drug development today. Traditional recruitment and access models are largely site‑centric, reactive, and heavily dependent on manual processes. Patients are typically identified late in their disease progression, often when treatment options have already narrowed, and eligibility determination requires extensive manual review across multiple disconnected systems.

Trials themselves are becoming more complex. They increasingly target earlier disease stages, narrower patient populations, and more precise endpoints. Eligibility criteria now often require a detailed, longitudinal understanding of a patient’s clinical history, including prior treatments, comorbidities, genomic or biomarker data, and sometimes even social or behavioural factors. However, because this information is fragmented across electronic health records (EHRs), claims databases, lab systems, and other data sources, identifying the right patients accurately, and at the right time, becomes extremely difficult.

The result is a cascade of inefficiencies. Recruitment timelines are extended, screen failure rates remain high, and sites are burdened with administrative work that detracts from patient care. Data is often collected retrospectively, limiting its usefulness for real‑time decision‑making and reducing the overall quality of signals available to sponsors and regulators.

Health Data Now Moves with the Patient

Luckily, a system‑level shift is underway. Advances in interoperability and patient‑authorized data sharing are enabling a new model. Through the combined capabilities of b.well and myTomorrows, this model can now be operationalized, allowing health data to move with the patient across data providers, hospitals, institutions, and research environments.

At the same time, patients will be able to connect medical records in a few clicks and find the most relevant options for their care journey faster.

Frameworks such as along with broader interoperability standards, signal a transition away from fragmented data ownership toward more connected ecosystems. At the same time, advancements in digital health experiences and AI‑supported analytics and orchestration are making it increasingly possible to interpret this data in real-time.

As a result, data is no longer just documenting what has already happened. It is becoming an active, dynamic layer that supports earlier patient identification, more informed decision making, and end-to-end coordination across care, research, and treatment pathways.

Creating a New Operating Model

The path forward in this healthcare ecosystem shift is to focus on integrated patient journeys.

Historically, healthcare has responded to complexity by adding layers: new tools, platforms, and processes designed to solve individual problems. While each layer may deliver incremental value, together they often reinforce fragmentation and increase operational burden.

What the system needs instead is a move toward integrated flows, with the patient at the centre. An integrated care ecosystem will connect previously isolated steps: patient identification, eligibility assessment, clinical trial participation, follow‑ups and treatment, into a coordinated lifecycle.

In this model:

• Eligibility assessment shifts to real-time, contextual evaluation
• Access pathways extend beyond clinical trials into expanded and post-trial access
• Long-term follow-up and real-world evidence become continuously enriched

These flows also strengthen collaboration across the broader ecosystem, including patient advocacy groups, providers, payers, and research networks. In a highly complex and fragmented system, enabling the right flows, rather than adding more layers, can dramatically increase the probability that patients, sites, and sponsors successfully connect.

From Patient Identification to Continuous Access

The impact of this model becomes most tangible in how patients are identified and supported over time.

myTomorrows enables earlier identification and pre‑qualification by assessing patient suitability in context. For example, a patient could connect their medical records through b.well and be identified earlier for a relevant clinical trial via myTomorrows, without needing to navigate multiple disconnected systems or repeat data entry.

Together, this approach enables more precise eligibility assessment, reduces unnecessary screening, and lowers screen failure rates. A central question across the industry is how to improve patient identification in clinical trials, particularly as eligibility criteria become more complex and time‑sensitive. This enables clinical and claims data to be surfaced and assessed in context, improving matching accuracy and timing. It also brings referring providers and specialists closer to the process, allowing for earlier signal detection and more informed clinical decisions. At the same time, it creates the foundation for building and enriching patient registries and trial databases as living, longitudinal data assets.

From there, patients can move more seamlessly across their journey, from identification into clinical trials, and onward into registries, observational research, and long‑term follow‑up without needing to repeatedly re-enter or recreate their data. Through myTomorrows, these pathways can be orchestrated and actively navigated, ensuring that patients are guided toward the most relevant opportunities at each stage.

Importantly, continuity extends beyond trials themselves. Ultimately this paves the way to approved therapies. This model also supports more holistic clinical decision‑making, including treatment repurposing within multidisciplinary care settings. Access, in this context, is no longer a single decision point, it becomes an end-to-end, adaptive journey enabled by patient‑mediated data and coordinated access pathways.

Unlocking Less Burden, Better Signal, Faster Development

One of the most immediate benefits of this model is reduced operational burden alongside improved signal quality and decision readiness.

Traditional workflows rely on fragmented systems, manual record collection, and repeated data entry, placing strain on sites and care teams. By surfacing only, the most relevant information at the point of decision, processes become more efficient and less administratively heavy.

At the same time, data becomes more timely, contextual, and actionable. Through the integrated model enabled by b.well and myTomorrows, data environments provide continuous, decision‑ready insight.

Beyond trials, these flows support scalable registries, stronger long‑term follow‑up, and richer real‑world evidence, while enabling better collaboration with patient advocacy groups through transparent, consent‑driven data use. For sponsors and regulators, this translates into faster recruitment, stronger evidence, and more adaptive development aligned with real‑world care.

Improving Patient Access and Trial Success Rates

Continuous, patient-centred flows, delivered by b.well and myTomorrows, increase the likelihood that patients reach the right opportunities at the right time, improving matching, reducing screen failures, and accelerating timelines.

For BioPharma, this enables earlier, more precise patient identification beyond site-based recruitment. Consented, longitudinal data surfaces patients who might otherwise be missed, improving matching, reducing screen failure rates, accelerating timelines, and enabling continuous tracking across the patient journey.

It also helps close the gap between trials and real‑world need. More connected models extend access across trials, expanded access, and post‑approval pathways without compromising development.

Access now has the ability to become a core capability, not a one‑time decision, improving patient reach, strengthening evidence, and increasing the value of therapies.