Expanding Personalized, Data-Driven Dermatology: Leveraging Digital Health Technology and Machine Learning to Improve Patient Outcomes

Abstract

The current revolution of digital health technology and machine learning offers enormous potential to improve patient care. Nevertheless, it is essential to recognize that dermatology requires an approach different from those of other specialties. For many dermatological conditions, there is a lack of standardized methodology for quantitatively tracking disease progression and treatment response (clinimetrics). Furthermore, dermatological diseases impact patients in complex ways, some of which can be measured only through patient reports (psychometrics). New tools using digital health technology (e.g., smartphone applications, wearable devices) can aid in capturing both clinimetric and psychometric variables over time. With these data, machine learning can inform efforts to improve health care by, for example, the identification of high-risk patient groups, optimization of treatment strategies, and prediction of disease outcomes. We use the term personalized, data-driven dermatology to refer to the use of comprehensive data to inform individual patient care and improve patient outcomes. In this paper, we provide a framework that includes data from multiple sources, leverages digital health technology, and uses machine learning. Although this framework is applicable broadly to dermatological conditions, we use the example of a serious inflammatory skin condition, chronic cutaneous graft-versus-host disease, to illustrate personalized, data-driven dermatology.

Keywords: AAD, American Academy of Dermatology; NIH, National Institutes of Health; PRO, patient-reported outcome; cGVHD, chronic graft-versus-host disease.

Figure 1

Framework for personalized, data-driven dermatology. The key components of the framework include longitudinal data, analytics, and new knowledge that can be applied to patient care to improve patient outcomes. Note that the data in the framework are composed of not only traditionally gathered clinical data but also data generated by patients (e.g., tracking their symptoms and self-reported outcomes as well as physiological measurements ranging from home blood pressure and glucose levels to step count and sleep duration/quality) and through digital technology such as ones enabling skin measurements of stiffness and wound healing. Labs, laboratory measurements.

Figure 2

The potential of personalized, data-driven dermatology in the care of patients with cGVHD. This framework can allow for the quantification of the severity and extent of GVHD through both clinimetrics and psychometrics. An example of data that can be collected includes measurements of skin stiffness as shown in the plot, where dynamic stiffness measurements are shown over time where each color corresponds to a different patient’s trajectory (gray: clinically stable, gold: disease progression, blue: clinical improvement, green: no skin involvement; modified from Baker et al., 2021b). These data can be collected longitudinally to enable tools in three main categories: (i) risk prediction and early detection, (ii) individualized therapy and longitudinal monitoring of treatment response, and (iii) patient engagement with digital tools allowing for individualized patient education and self-monitoring. Although the figure in this paper focuses on illustrating the digital measurements related to the skin, the same framework can be considered for a much broader set of digital measurements. For example, including physiological measurements such as home blood pressure and glucose levels to step count and sleep duration/quality can help to inform skin disease biology and management as well as underscore the relationship of skin diseases with other systemic conditions. Images: MyotonPRO Device (Baker et al., 2021b); image capturing erythematous lesion severity of GVHD (Tkaczyk et al., 2018; reprinted with permission from Elsevier). BSA, body surface area; cGVHD, chronic graft-versus-host disease; GVHD, graft-versus-host disease.

Figure 3

Key next steps. This figure outlines the key next steps toward achieving personalized, data-driven dermatology.