Digital Endpoints: Definition, Benefits, and Current Barriers in Accelerating Development and Adoption

Abstract

The assessment of health and disease requires a set of criteria to define health status and progression. These health measures are referred to as "endpoints." A "digital endpoint" is defined by its use of sensor-generated data often collected outside of a clinical setting such as in a patient's free-living environment. Applicable sensors exist in an array of devices and can be applied in a diverse set of contexts. For example, a smartphone's microphone might be used to diagnose or predict mild cognitive impairment due to Alzheimer's disease or a wrist-worn activity monitor (such as those found in smartwatches) may be used to measure a drug's effect on the nocturnal activity of patients with sickle cell disease. Digital endpoints are generating considerable excitement because they permit a more authentic assessment of the patient's experience, reveal formerly untold realities of disease burden, and can cut drug discovery costs in half. However, before these benefits can be realized, effort must be applied not only to the technical creation of digital endpoints but also to the environment that allows for their development and application. The future of digital endpoints rests on meaningful interdisciplinary collaboration, sufficient evidence that digital endpoints can realize their promise, and the development of an ecosystem in which the vast quantities of data that digital endpoints generate can be analyzed. The fundamental nature of health care is changing. With coronavirus disease 2019 serving as a catalyst, there has been a rapid expansion of home care models, telehealth, and remote patient monitoring. The increasing adoption of these health-care innovations will expedite the requirement for a digital characterization of clinical status as current assessment tools often rely upon direct interaction with patients and thus are not fit for purpose to be administered remotely. With the ubiquity of relatively inexpensive sensors, digital endpoints are positioned to drive this consequential change. It is therefore not surprising that regulators, physicians, researchers, and consultants have each offered their assessment of these novel tools. However, as we further describe later, the broad adoption of digital endpoints will require a cooperative effort. In this article, we present an analysis of the current state of digital endpoints. We also attempt to unify the perspectives of the parties involved in the development and deployment of these tools. We conclude with an interdependent list of challenges that must be collaboratively addressed before these endpoints are widely adopted.

Keywords: Digital endpoints; Digital evidence; Digital medicine; Machine learning.