Data integration has been defined as “...the technical and business processes used to (aggregate and) combine data from multiple sources to provide a unified, single view of the data” [1]. In practice, this means building bridges across different sources of data, to create an integrated data set that enables you to ask bigger questions or new questions (ie, integration followed by utilization) [2].

Data integration has been a force for innovation and scaling in many technological fields, helping new products become part of our everyday lives. Why do your new headphones work with your phone? Cross-industry collaboration on standards fueled the growth of Bluetooth from its initial invention at Ericsson in the late 1990s to one of the most prevalent current-day technologies [3], incorporated into an estimated 5 billion new devices in 2021 [4]. Love that your credit card works practically everywhere, including internationally? This would not be possible without the Payment Card Industry Data Security Standard (and associated Security Council), which has been a globally accepted standard since 2004 [5]. Trying to get home the fastest way? The apps and services that help us plan and navigate our journeys rely on the General Transit Feed Specification [6], which began in 2005 as a collaboration between Google and the TriMet transport agency and is now a global standard driving the utilization of transit data [7]. All of these examples underline not only the practical impact of successful data integration, but also the critical role of multisector collaboration in achieving success.

Where is the new field of digital health, and sensor data in particular, in its data integration journey and what needs to be done to move it forward?

The Digital Medicine Society (DiMe), a nonprofit organization dedicated to advancing the safe, effective, ethical, and equitable use of digital medicine to optimize health [8], will examine these questions and more as part of a new Sensor Data Integration Tour of Duty. The tour will convene multistakeholder experts from Amazon Web Services, Evidation Health, the Food and Drug Administration (FDA) Center for Drug Evaluation and Research (CDER) and the Center for Devices and Radiological Health (CDRH), Human First, the Institute of Electrical and Electronics Engineers, Medable, the Moffitt Cancer Center, Oracle, Open mHealth, Savvy Cooperative, Takeda, and the United States Department of Veterans Affairs. The Sensor Data Integration Tour of Duty will build off other initiatives like The Playbook [9] in advancing the adoption and utility of digital medicine tools. In this paper, we will describe the current state of sensor data integration in health care, examine the importance of sensor data integration in achieving the promise of digital health to improve clinical research and patient care at scale, and offer a vision of what overcoming these challenges and continuing progress will enable.

In the world of health care, examples of data integration can be found across the drug development and care delivery continuum:

Data integration is ubiquitous in digital health, but the definition above highlights three critical considerations pertaining to sensor data from digital health products:

Sensor data (ie, data collected by wearable devices, smartphones, and other sensor-equipped connected digital medicine products [20]) is not a silver bullet. We cannot develop new sensor-based measures without combining data from different data sources [21,22], and we cannot deploy those measures without contextualization [23,24].

When we develop a sensor-based digital measure, establishing that it is fit for purpose in a given population involves cross-referencing with clinical and behavioral anchor data to demonstrate accuracy and validity [20,25]. As a therapy moves from clinical development into the hands of patients, it is also critical that clinical outcomes are integrated with real-world data, and that researcher stakeholders and other decision-makers such as payers and clinical policy makers can access and assess those data.

To support utilization and full patient value, health care providers and hospital administrators making treatment decisions all need access to the sensor data and digital measures captured during care and need to be able to digest and infer the same conclusions. Similarly, participants, investigators, statisticians, heads of medical affairs, or regulators need those data to be contextualized and appropriately presented to them, such that they are able to draw insights and value.

Currently, we are far from achieving this potential to improve lives, and it has been claimed that limitations in data integration and utilization are the primary rate limiter [26,27]. Improving lives is always at the front of our minds, making it worthwhile to think about how these tools and applications “look” from the patient’s perspective. Data integration should minimize additional burden caused by management and assessment tools to patients already burdened by their condition(s). Widespread adoption will require interfaces that provide immediate value and are simple, interactive, and intuitive. No longer should patients ask: why does my health care provider not have access to all relevant information? Why should I have to perform the same test in multiple trials, and why do they all have separate apps? I think this data is important; how do I share it with my doctor? Achieving truly integrated continuous care is not possible without effective data integration [28].

There are several specific technical hurdles that get in the way of effective data integration, including the plethora of formats [29,30] and the corresponding lack of consensus around common metadata standards [14,26], although progress is being made [31]. Perhaps more worrisome is the fact that permissioning and consent often exclude integration and exploration [32], and massive gaps in digital health research remain regarding data rights and governance [33,34].

Yet, perhaps the biggest hurdles are cultural adoption, that is, effective incentivization to share data (within and across organizations) and make it easier to integrate [35-37]; recognition that data integration, while perhaps not a glorious pursuit, is central to scaling any digital health tool [38]; and lack of a unified signal from clinical (and other) stakeholders that would enable vendors to address needs around data integration [39,40].

Data integration for digital health, and for sensor data specifically, is in the early phases, and we have yet to see the kind of progress that has been made in other data-heavy health fields (eg, genomics) [41-44]. Currently, no widely adopted data standards or repositories exist, and while progress is being made on the integration of other data types (eg, electronic health records [EHRs], molecular data, patient reported outcomes), we see limited progress around sensor data specifically. This presents an opportunity to provide an early framework for sensor-based data integration and future success of this technology and avoid barriers in integrating other health care data (eg, EHRs). Moreover, understanding the legal and ethical consequences of sharing and integrating sensor data is at a nascent stage [45]. Recent work has shown that “the three most underrepresented areas of research into digital clinical measures were ethics, security, and data rights and governance” [33].

Data integration has been recognized as a key driver of value creation in the private sector. In the past few years, we have seen companies invest to improve their data integration abilities through smaller venture capital deals [46], larger acquisitions [47], or internal initiatives like data42 from Novartis [48].

The public space has also been very active, perhaps helped by the inherently precompetitive nature of such work (ie, progress in data integration helps all parties, without any individual party needing to share proprietary data). Across research, clinical development, and in real-world practice, we are starting to see encouraging progress.

Research has been boosted by a number of initiatives, often focused on improving the awareness and availability of digital health data sets. Examples include meta-reviews of publicly available data sets in specific fields to accelerate integration and use of these data, as well as highlighting potential issues and shortcomings like demographic and ethnic underrepresentation [14]. Similarly, new repositories and portals are helping researchers connect and make their data available, such as Synapse and their Digital Health portal [49] or Zenodo [50]. Zenodo is a robust example as it grew out of OpenAIRE [51], a public project focused on enabling open science, demonstrating the concrete progress that specific projects can drive. The COVID-19 Evidence Accelerator [52] has also contributed to integration across the colossal volumes of data being developed as part of the pandemic response.

Clinical development and care delivery are also benefiting from public initiatives around data standards like Substitutable Medical Applications, Reusable Technologies on Fast Healthcare Interoperability Resources (SMART on FHIR) [53], which focuses on EHRs, or the Clinical Data Interchange Standards Consortium, which has had a substantial impact on the submission of evidence to regulatory bodies [54]. RADAR-base (Remote Assessment of Disease and Relapse) [55] grew out of the Innovative Medicine Initiative’s RADAR-CNS (RADAR in Central Nervous System Disorders) [56] and aims to generalize data integration tools and best practices. These initiatives focus both on the integration of a range of devices and data sources, including mobile apps, and the utilization of the data through standardized visualization and analysis tools. Additional examples are the data integration centers in German medical universities, which have been set up to enable use of data across research and care [57]. Establishing these centers is a “meta-collaboration” of 4 consortia funded by the German Ministry of Education and Research. The recently formed Graphite Health is a nonprofit creating solutions for better health system interoperability [58].

Subsequent data utilization efforts bring data rights to the forefront. Who should be able to use the integrated data sets, and for what purpose? How can consenting and integration be structured such that innovation is not hindered and we remain within the ethical boundaries protecting patient rights? The FDA Sentinel Initiative combines claims data from multiple insurers, data from EHRs and patient reports provided by multiple health systems, and even some sensor technologies to evaluate the safety of medical products in the real world [59]. It combines a common data model with a distributed data architecture. Approved users are given secure, structured access to digital health data while respecting privacy and proprietary concerns. The Sentinel Initiative thus protects the data rights of organizations and individuals by keeping data with the holder while still allowing that data to be used for beneficial (eg, research) purposes. Similarly, the SELFIE Horizon 2020 consortium [60] also puts data rights at the forefront, recognizing that data integration is a key pillar of patient-centric, integrated, continuous care, while attempting to balance “preservation of individual privacy and the need for health data sharing” [28].

What does all this mean for sensor data? To achieve real impact on patient lives, sensor data needs to be integrated and contextualized with many other data types. Concerted progress must therefore be made across digital health, and sensor data must keep up with other sources of data like EHRs and break out of condition-specific efforts.