The implications of using emerging technologies on trial participant’s privacy and security

DHTs (e.g., smart devices, new wearables, and environmental sensors) are rapidly evolving. Already they play a significant role in enabling trial participants’ experience in modern clinical trials⁶. These technologies facilitate multiple trial-related activities: communication, enrollment, recruitment, consent, and continuous data acquisition. The validated capabilities to record novel digital endpoints have also been key to their use⁷. However, these new technologies may serve to provide new avenues to violate trust. “Zero-trust architectures”^8,9 or networks assume that none of the DHTs used outside an organization’s network (in this case, the DHT network) can be trusted and require mutual authorization from the DHT client and server devices (Table ​(Table11).

Integrating clinical data obtained from DCTs (i.e., real-world settings) may add additional context between remote and in-person clinical interactions. Further, use of federated computing (FC) and zero trust approaches ensure trial participants’ privacy and deployment of complex digital endpoint-based machine learning (ML) algorithms while enabling classic double-blinded trial designs. The following section discusses the opportunities and challenges associated with these technological advances.

Remote capture of high-throughput biomarkers and endpoints

Per U.S. Food and Drug Administration, “a biomarker is a piece of anatomical, physiological, or biochemical data that is used to diagnose or develop a treatment plan for a patient. A digital biomarker is simply a biomarker that is developed from data that are analyzed using advanced analytics and artificial intelligence (AI) to extract previously invisible insights.”¹⁰. An example of a biomarker is blood sugar which if detected with an electronic glucometer is a digital biomarker. In contrast, digital endpoints are measurements that capture an observable physiological “characteristic” of a participant through a DHT (e.g., an image or a signal). A standard or novel digital endpoint measures a biomarker that has clinical relevance. Examples of digital endpoints include the appearance, heat, or pallor of skin. Examples of standard digital endpoints are measurements for blood pressure or heart rate. Examples of novel digital endpoints include measurements for composite endpoints, such as the synergistic combination of heart rate and movement intensity¹¹.

To determine the efficacy and safety of an investigational medical product, novel digital endpoints¹² may complement standard endpoints, both because of their predictive capabilities and their feasibility of inclusion in DCTs¹³.

New techniques from ML may play a critical role in deriving utility from information-dense digital endpoints gathered from DHTs. Complex digital signals such as images that allow for the derivation of new clinical endpoints (such as for diabetic retinopathy¹⁴ or cancerous skin lesions¹⁵) have shown promise, reaching clinician-grade quality in multiple novel trials.

Other DHTs capable of digital endpoint monitoring include low-risk, consumer-grade devices that may measure clinically relevant endpoints, such as blood oxygen saturation, pulse, respiratory rate, and temperature. These technologies may also engage signal processing and ML algorithms that operate either on- or off-device (i.e., in the cloud and separated from the signal-capturing DHT)^11,16.

The environmental and contextual sensory capabilities of DHTs may support novel trial designs

A well-designed clinical trial should characterize the efficacy of an intervention after accounting for environmental and social determinants. However, traditional clinical research methods rarely account for these determinants. In contrast, DHTs often have sensors capable of automatically collecting location-based contextual factors related to participants, giving investigators additional visibility into the underlying determinants of observed endpoints.

Examples of such factors—collectively, the “exposome”—include air pollution (such as ozone or carbon monoxide) or particulate matter (such as pollen or nanometric plastics)¹⁷. Modalities such as satellite imaging may render additional insights, such as characteristics of the built environment, including access to green spaces for physical activity, and quality of food and water sources¹⁸. Digital tracking of the exposome may enhance the generalizability of clinical research findings into real-world settings.

The promise of federated computation and zero-trust platforms for DCTs

Advances in FL (Table ​(Table1)1) may ensure trial participants’ privacy while enabling computation-intensive administration of DCTs, including the need to continuously monitor digital endpoints, perform linkage of remote data, and apply ML on digital endpoints²⁴.

We present a conceptual schematic that demonstrates the process (Fig. ​(Fig.1).1). First, a “master” compute node becomes responsible for administering the trial, encoding the logic behind the trial design (Fig. ​(Fig.1A).1A). This master node transmits a baseline model to trial participant nodes. The intervention is deployed randomly or prospectively (Fig. ​(Fig.1B).1B). Next, individual client processing trial participant data, such as generating a digital endpoint (Fig. ​(Fig.1C)1C) to input into a FL algorithm, which learns an output for a trial participant (Fig. ​(Fig.1D)1D) while optimizing the overall population-level effect of the intervention (Fig. ​(Fig.1E).1E). Specifically, the federated learner is optimizing overall the trial participant nodes securely (no data leaves the device). The learnings are aggregated and sent back to the master compute node. Zero-trust authentication (Table ​(Table1)1) occurs among all respective nodes of the network. One emerging technology to achieve zero-trust architectures in health care is via blockchain-based technologies²⁵ and this has been evaluated in medical image sharing²⁶.

Ensuring trial participant privacy

The most significant advance that FC brings to DCTs is the groundwork to enable computationally intensive encryption for trial participants’ privacy.

Federated edge computing enables medical data portability (Table ​(Table1).1). Designs derived from edge models could then be used for powering key use cases such as improving cost of care models or for proactive risk management for a variety of end users—including payors, pharmaceutical companies, providers, and clinical research organizations. For example, pharmaceutical companies could use horizontal federation to introduce synthetic control arms, while providers could use vertical federation to combine medical data clouds between sources of clinical care to advance biomedical science and improve trial participant care.