Observing patients at a single point in time often fails to capture the full context around their health status, yet that is the view available via traditional data sources such as claims, electronic health records (EHRs), and cross-sectional research. This data set also excludes people outside the clinic walls — those who are not accessing healthcare delivery systems and resources regularly, if at all.

A more holistic view of individuals and their lived experiences can be achieved by using longitudinal real-world data (RWD), like wearable sensors and patient-reported outcomes (PROs), to reach the right people at the right time in clinically meaningful ways. This insight can help lead to:

• Better health outcomes by effectively meeting patients’ needs and
• Improved resource optimization for healthcare and life science companies by enhancing activation efforts around new and innovative therapies and support

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To discuss the opportunities that these types of insights provide across the industry, our recent panel discussion presented varied yet complementary viewpoints from the following representatives from health tech, healthcare, and biopharma:

• Ernesto Ramirez, PhD; Senior Manager of Consumer Research at Evidation Health
• Arash Harzand, MD, MBA; Co-Director and Chief Health Advisor for Digital Health at the VA Office of Healthcare Innovation and Learning
• Kalahn Taylor-Clark, MPH, PhD; VP and Head of Strategic Partnerships and Innovation at Myovant Sciences

The panel discussed how life science companies and healthcare organizations can effectively:

• Find hard-to-reach or underserved individuals to engage them in their health and with health systems
• Address gaps in the understanding of people’s everyday health experiences missed by episodic clinical data and contextualize a more holistic view of the whole person’s journey
• Balance the demands on healthcare providers with the benefits gained by having greater insights into healthcare consumers

Access the on-demand recording here to listen to the full discussion.

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Meeting patients where they are

A large gap experienced with today’s traditional data sources is the coverage of only people who are visiting the healthcare delivery system, while individuals who are likely to benefit most from engagement strategies are those outside of that system and therefore missed. It’s this part of the population that can be the most challenging to understand and engage when companies and healthcare providers attempt to develop solutions for issues surrounding access to care and patient involvement in their health.

Even within a healthcare system that provides services for a very specific portion of the population, Veterans, this challenge exists, as Dr. Arash Harzand of the Department of Veteran Affairs (VA), discussed, “We have a very interesting conundrum in the sense that, as an agency, we are responsible for the care of all Veterans, not just the Veterans who are enrolled in VA benefits and see us in the Veterans Health Administration. Because it’s the latter who we have the most contact with, we have this very large blind spot for the rest of the Veteran population, and I've been trying to find novel ways of connecting with some of those Veterans outside the traditional channels that we use to engage with them — through social media and digital health measurement and engagement platforms like Evidation to meet Veterans where they are.”

Through a collaborative effort, the digital health ecosystem has provided Dr. Harzand and the VA a better sense of the characteristics and the barriers to care of the entire Veteran population, not just the individuals they see regularly:

_{Case study: Reaching and engaging Veterans both inside and outside of the healthcare system}

_Challenge:

• _{Veterans aren’t aware of their eligibility for Veterans Affairs healthcare or are aware but not interacting with the VA system.}
• _{The VA wasn’t sure of the best approach to reach Veterans who would benefit from care.}

_Solution:

• _{Veterans were recruited to the Heart Health program using social media, email, and Evidation’s digital health measurement and engagement platform.}
• _{VA branding was used for the portion of the program targeted at veterans.}

_Outcomes:

• _{1,078 Veterans were enrolled in 38 days (693 new and 385 existing Evidation users).}
• _{46% of those surveyed were not already enrolled in VA healthcare.}
• _{89% of those surveyed reported owning a smartwatch or fitness tracker. Most use apps to track activities, nutrition, and heart rate.} 
• _{Reported reasons for not accessing VA services included a lack of childcare and adult caregiver duties.}

_{More information can be found in the abstract presented at ACC.23.}

Providing context to see the whole person, not just the disease

Important details about health can also be overlooked for patients who are regular visitors to the healthcare delivery system, exacerbated by the episodic, and often short, visits to healthcare providers. In most cases, only a snapshot of a person’s life and the effects of a disease or condition is captured during healthcare visits. Reimbursement as the driving force behind EHR and claims data documentation contributes to this narrow vision and a persistent focus on the disease rather than placing the disease within the context of the whole person. 

During the webinar, Dr. Kalahn Taylor-Clark highlighted the intersection of mental health with diseases such as endometriosis that have a significant impact on individuals’ health-related quality of life: “When you look at the data, women with endometriosis, because of the pain that they experience, are two times more likely to commit self-harm, including suicide, than other women. If we were able to collect data through wearables, for example, we might be able to find signals around anxiety and depression for women with endometriosis, whereby we could potentially develop a solution that would more holistically treat them beyond just the medicine for the condition itself. Then, we're thinking about how we actually bring these data in to understand an entire lived experience and support whole person care.”

When we’re able to broaden our view, we can gain insights into the endpoints of importance to patients and their families and how to improve overall health, such as by developing wraparound services or treatments that address concerns beyond the target disease.

_{Case study: The important role of social determinants of health in overall health}

_{In a research study of people with diabetes conducted by Evidation and a biopharma partner, one objective was determining the reason for non-adherence to diabetes treatment. Contrary to expectations, an inability to afford the medication was not a primary factor. Instead, depression played a big role, and the study team was able to detect signals of declining mental health in the longitudinal data months before the participants stopped their medication.} 

_{In this case, healthcare providers might not be enquiring about mental health status because it is not part of the diabetes care workflow. Therefore, being able to capture this information in other ways can be a very powerful method to allow people to voice their concerns and experience and to notify the healthcare system of other factors that could be addressed to ensure both mental and physical health.}

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Integrating RWD in ways that consider the entire health ecosystem

Long before healthcare systems were contending with a rapidly shrinking workforce, the ability for healthcare providers to absorb new sources of patient-generated data was already limited within their current work schedules and data infrastructure. After all, to effectively use this type of data for earlier interventions and enhanced patient care, a person must review the data and react accordingly. Therefore, for many healthcare providers, although the idea of using additional data to improve patient care is promising and exciting, the initial, achievable level of RWD integration is the wearable equivalent of what is already being collected and used in daily patient care (heart rate variability, weight, exercise levels).

“What we’re saying around the power of being able to track things remotely to identify early changes of clinical decompensation and intervening early is appropriate. However, as the physician here, from the health system perspective, digital health is moving much faster than most health systems can actually adapt, ingest, and work with. The challenges with staff are a big part of it, but it's important to point out that we're still trying to figure out how to give patients a way of getting this data back to us. From an infrastructure standpoint, we're still not equipped in the ways we need to be, and healthcare systems are very slow to adopt new technologies,” commented Dr. Harzand.

That does not mean, however, that RWD cannot inform the level and timing of clinical care for individuals. This is where partnerships with healthtech companies can help, by providing patients with signals about when to seek care or consolidating their relevant information in formats that enhance their healthcare interactions, rather than placing the onus on the providers.

_{Case study: Guiding individuals to appropriate actions for influenza-like illness (ILI)}

_Background:

• _{ILI-associated morbidity that does not require hospitalization has been poorly characterized.}
• _{It was unknown if data from wearables could help detect ILI, including COVID-19.}

_Approach:

• _{Wearable data were combined with self-reported ILI-related symptoms and health care–seeking behaviors to develop a model.}
• _{The model uses the wearable data to determine patterns that identify someone who is exhibiting symptoms of ILI and is likely infected.}
• _{The model is also able to discriminate between individuals who do and don’t seek care.}

_{Applications of the model:}

• _{Running on the Evidation platform, the model detects signals for ILI in wearable data.}
• _{When it identifies a person with signals indicative of ILI, the platform interacts with the affected person to determine if they are experiencing symptoms or feel ill and provides literature about the best actions to take depending on the severity of their illness.}
• _{This model has also been used to identify people with influenza to recruit their participation in a clinical trial. Once identified, they were able to enter into the clinical trial recruitment flow, which allowed them to integrate their wearable data and self-reported symptoms from the Evidation platform.} 

_{More information about the methodology to characterize ILI using wearable data can be found in the article published in the Journal of Medical Internet Research.}

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What does the future hold?

Meeting both patients and providers where they are will remain key to widespread adoption of longitudinal, patient-provided RWD in healthcare. Programs and tools must be appropriate for the current situation while also looking to the future of healthcare 10.0, which encompasses fueling clinical trials with data to enhance trial designs and reach a broader population. In this future state, data statistics and analytics on the combination of large continuous streams of longitudinal data with self-reported questionnaire data, clinically validated measures, and more could surface the information and outcomes that matter and can actually drive meaningful conversations. 

Ernesto Ramirez summarized the group’s hopes well: “My hope would be that we continue as a field, as a community that is interested in improving the health and wellness of individuals, to remember that data is from people. It's not just ones and zeros going through the cloud. It's not just a report that ends up in your inbox. We need to be better at having conversations with people to develop that relationship, by asking: What actually matters? If we're going to design a program to help you manage your health, what does that program look like to you? What is going to be effective in your day-to-day life? That's the magic.” 

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