Digital Measures That Matter to Patients: A Framework to Guide the Selection and Development of Digital Measures of Health

Abstract

Background: With the rise of connected sensor technologies, there are seemingly endless possibilities for new ways to measure health. These technologies offer researchers and clinicians opportunities to go beyond brief snapshots of data captured by traditional in-clinic assessments, to redefine health and disease. Given the myriad opportunities for measurement, how do research or clinical teams know what they should be measuring? Patient engagement, early and often, is paramount to thoughtfully selecting what is most important. Regulators encourage stakeholders to have a patient focus but actionable steps for continuous engagement are not well defined. Without patient-focused measurement, stakeholders risk entrenching digital versions of poor traditional assessments and proliferating low-value tools that are ineffective, burdensome, and reduce both quality and efficiency in clinical care and research.

Summary: This article synthesizes and defines a sequential framework of core principles for selecting and developing measurements in research and clinical care that are meaningful for patients. We propose next steps to drive forward the science of high-quality patient engagement in support of measures of health that matter in the era of digital medicine.

Key messages: All measures of health should be meaningful, regardless of the product's regulatory classification, type of measure, or context of use. To evaluate meaningfulness of signals derived from digital sensors, the following four-level framework is useful: Meaningful Aspect of Health, Concept of Interest, Outcome to be measured, and Endpoint (exclusive to research). Incorporating patient input is a dynamic process that requires more than a single, transactional touch point but rather should be conducted continuously throughout the measurement selection process. We recommend that developers, clinicians, and researchers reevaluate processes for more continuous patient engagement in the development, deployment, and interpretation of digital measures of health.

Keywords: Digital measures; Digital medicine; Patient engagement.

Fig. 1

Examples of how a variety of concepts of interest cascade from a single meaningful aspect of health across select conditions.

Fig. 2

Examples of how a variety of outcomes cascade from a single concept of interest across select conditions.

Fig. 3

Example of how a variety of clinical endpoints can be used to evaluate a measure and determine whether the intervention being studied is beneficial [22].

Fig. 4

Developing measures that matter to patients. This figure was adapted from original work by Evidation Health, with permission. This figure illustrates patient considerations that should drive digital measure selection and development. These should precede technical considerations [8]. Additional information on subsequent technical considerations are available at [35, 36, 37].