Successfully implementation of a smartphone-based tracking tool into managed care for CSU patients will require careful consideration of the patient-provider interface. Whether managed by a specialist dermatologist or primary care physician, there are a number of common scoring tools available including the Urticaria Activity Score (UAS), Urticaria Control Test (UCT), and Chronic Urticaria Quality of Life Questionnaire (CU-Q2oL) [19]. These scales can provide key insights to disease history and impact and inform treatment plans. This said, their deployment is not widespread or uniform, as there is a natural tendency for providers to treat presented visual symptoms, initially with antihistamines. Since a high percentage of patients are refractory to antihistamine treatment administered at the standard dose, there is potential for patients to disengage from care, resulting in impact to quality of life. The envisioned app could provide, on demand, a digital log of any visual symptoms, a patient provided log of impact on quality of life, and potentially a log of medication history from which to assess impact of both therapeutics themselves and dosage (Figure 2). Such digital records could also serve to inform insurer and payer claims by offering accurate insight to impact on quality of life, potentially supporting decisions on dose escalations or the need to consider switching to alternate or higher efficacy medications to control symptoms and co-morbidities [20]. Although patient reported outcomes constitute components of existing diagnostic tools [19], there may be potential for higher levels of engagement if provisioned on the patient’s personal device, in an app which also imports personal images and medication logs. There may also be the potential to keep patients engaged in their care in the absence of flares or symptoms, for example, by careful design of features in the app which celebrate ‘days clear of symptoms’ through prompts. Clearly, if approached appropriately, there is potential for such an app to strengthen the interactions between patient and provider, based on longitudinally captured data and where the patient becomes more proactive in care of their condition. More specifically, given the limited time available for typical patient-physician consults, the availability of this digital data could help maximize productivity and the drive to improved outcomes.

The evolution of digital tools for patient self-management continues apace and in the case of CSU, the smartphone offers high potential to become a core component and contributor to patients electronic health records. In order to realize the myriad benefits and opportunities outlined herein, the following are anticipated next steps:

It is anticipated that many of the features and concepts outlined could be offered through patient support networks provisioned by developers of symptomatic and disease modifying therapies, typically through native apps or web portals. However, a key to the approach advocated herein is to more fully engage the patient on their terms, through their lived experiences in a highly personalized manner. There are two potential pathways for introduction of the features described – either as a consumer or patient support tool or through a more refined, clinical grade instrument which falls under the aegis of a regulated SaMD provisions. In either case the relative maturity of the sensors, algorithms, and their clinical validation are key considerations and there is optimism in this regard as the many features proposed are constantly evolving and refining. Although feeds on environmental data are now commonplace globally [21], and colorimetric analysis of dermatologic features is becoming more precise [22,23], many of the desired features will benefit from concerted effort for analytical refinement and clinical validation (Figure 3). Considerable progress has been made in feature dimensioning [24], and topology [8,25,26], and there is every reason to be optimistic that with collaborative efforts these can be refined for CSU applications. Likewise, metrics on ocular light sensitivity continue to evolve through measures of luminance [27,28], as do approaches to assessing pupil dilation using smartphones [29]. Even the more ambitious potential applications such as monitoring intro-ocular pressure can be accomplished with the aid of external tonometers, suggesting that future generations of devices might incorporate such functionality when miniaturized [30]. Regardless of whether such features are available in consumer products of SaMD variants, the route to implementation must also be mindful of the extant need for continual calibration of device sensors. A number of approaches to device calibration have been suggested [22,31], and systematic procedures on design of appropriate clinical trials [32]. Finally, and importantly, learnings from the ground breaking work which went in to development of existing support apps such as CRUSE can help guide adoption of appropriate controls in terms of data privacy, and provide culturally relevant learnings from the different regions where it is deployed globally [9]. In terms of clinical development, integrating learnings from the device through standardized reporting frameworks such as COSORT AI and TRIPOD AI [33,34] will allow predictive models to evolve and may help benefit related dermatological research.

The combination of smartphone sensors coupled with powerful generative AI features, both of which are trained and evolved around the individual patient themselves has a high probability of achieving the goals articulated herein and we encourage the dermatology community to embrace this vision with enthusiasm. Accordingly, we encourage active dialog in the CSU community and reader base of this journal to help realize this opportunity.