Digital Medicine: A Primer on Measurement

Abstract

Technology is changing how we practice medicine. Sensors and wearables are getting smaller and cheaper, and algorithms are becoming powerful enough to predict medical outcomes. Yet despite rapid advances, healthcare lags behind other industries in truly putting these technologies to use. A major barrier to entry is the cross-disciplinary approach required to create such tools, requiring knowledge from many people across many fields. We aim to drive the field forward by unpacking that barrier, providing a brief introduction to core concepts and terms that define digital medicine. Specifically, we contrast "clinical research" versus routine "clinical care," outlining the security, ethical, regulatory, and legal issues developers must consider as digital medicine products go to market. We classify types of digital measurements and how to use and validate these measures in different settings. To make this resource engaging and accessible, we have included illustrations and figures throughout that we hope readers will borrow from liberally. This primer is the first in a series that will accelerate the safe and effective advancement of the field of digital medicine.

Keywords: Clinical care; Clinical research; Cybersecurity; Data governance; Data rights; Decentralized clinical trials; Digital biomarkers; Digital health; Digital medicine; Digital technology tools; Ethics; FDA; Mobile technology; Privacy; Regulatory science; Research ethics; Sensors.

Fig. 1

Digital medicine overview. Digital medicine uses software and algorithmically driven products to measure or intervene to improve human health.

Fig. 2

The clinical landscape. The healthcare landscape can be broadly split into premarket clinical research and postmarket clinical care.

Fig. 3

The phases of clinical trial research. Clinical trials pass through a series of phases as the trial sponsor gains more evidence around the investigational drug or biologic. Preclinical studies are often conducted in cell and animal models (e.g., on mice), and then are slowly expanded into humans. First in healthy humans in small numbers to test safety, and then to larger sets of humans who have the condition in question to test both safety and efficacy.

Fig. 4

Screenshot from the Connected and Open Research Ethics (CORE) Q&A Forum.

Fig. 5

Screenshot from Sage Bionetwork's “Elements of Informed Consent” toolkit.

Fig. 6

Digital specimens and social contracts. Our healthcare system has strong protections for a patients' biological specimens, such as a blood sample, but what about our “digital specimens”? With the increase in biometric surveillance from these tools, data rights and governance – who gets access to what data and when – becomes critical.

Fig. 7

The BEST (Biomarkers, EndpointS, and other Tools) Framework. In 2016, the FDA and NIH collaborated to draft “Biomarkers, EndpointS, and other Tools (BEST),” which contains a description of seven types of biomarkers. All of these biomarkers could be measured using digital tools, which results in a digital biomarker.

Fig. 8

Clinical outcome assessment. Clinical outcome assessments (COAs) are the instruments used to measure clinical outcomes. The FDA recognizes four types: ClinRO, ObsRO, PRO, and PerfO. If digitized, they are considered electronic clinical outcome assessments (eCOA).

Fig. 9

Human participation. Connected technologies can collect physiological and behavioral data. Some do so passively via sensors, while others (like sleep tracker surveys) may be more active in nature. Some are a hybrid where they will track some metrics algorithmically and then ask for confirmation.

Fig. 10

Fit-for-purpose digital measure decision tree. Decisions about what to measure in a clinical trial are rooted in the research question being posed. Whether a fit-for-purpose digital measure exists to help to answer that question depends on both clinical and operational considerations that involve many stakeholders.

Fig. 11

Decentralized clinical trials. There are two components to consider to determine the level of decentralization in a clinical trial: whether the data are captured at a site or near the patient (e.g., location), and how the data are captured (e.g., manually or digitally).

Fig. 12

Verification and validation. Verification and validation are two processes that, together, indicate whether a digital measurement tool is fit-for-purpose.