Person‐centred care in allergen immunotherapy in a digital era
Authors: Chew Lip Ng, De Yun Wang
Journal: Allergy (2024)

In this new digital age, the use of mobile health (mHealth) apps to monitor chronic allergic diseases, including allergic rhinitis (AR) and asthma, has been explored and validated by forward-looking clinicians and researchers.1 Over 2000 mHealth apps have been developed but few have been validated.1 The potential application of mHealth technology ranges from tracking symptoms to monitoring and enhancing treatment compliance, sharing real-time information on environmental allergens and pollutants, and assessing response to treatment such as in allergen immunotherapy (AIT).1 With ubiquitous wearable and mobile devices available, digital connectivity has advanced to a stage where it can enable patients and clinicians to monitor symptoms and compliance to a degree previously unachievable. A central tenet of person-centred care (PCC) is regular monitoring and responsive management with digitalization as a key asset. The data collected in real-time can be analysed to identify digital biomarkers, providing clinicians and researchers with current information on the patient's condition, and facilitating the development of novel interventions. The advent of artificial intelligence (AI) may herald a new age in healthcare of patient data analytics, rendering new insights to facilitate clinical decision-making. Moreover, AI opens future avenues to institute clinical interventions, even without human input. The apps assessed to have adequate validation for use in AIT are AllergyMonitor® and MASK-air® (Mobile Airways Sentinel Network). AllergyMonitor® can be used to assess (i) daily symptoms, (ii) medication intake and (iii) treatment side effects.2, 4 MASK-air® is a Medical Device Regulation-Class IIa registered, well-validated in numerous studies. It has been used, among other functions, for (i) symptom monitoring, (ii) assessment of treatment response and (iii) studying the real-life impact of allergic diseases.5, 6 Specifically, MASK-air® data has demonstrated improved symptom control in patients on AIT vis-a-vis non-AIT patients.7 MASK-air® has also been used as a tool to study the effects of AIT on school performance. Patients reported higher productivity at school8 and work,9 raising the potential for its use in assessing the pharmacoeconomic benefits of AIT and other treatment modalities. The main limitations of these studies are (i) lack of adequately powered studies, (ii) lack of multicentre studies, (iii) heterogeneity of AIT products and application routes and (iv) compliance to regular symptom input. Addressing these limitations will require coordinated multicentre studies that are adequately powered and funded, with rigorous study designs that include streamlining treatment protocols and AIT products used, and the routes of administration. Looking forward, we attempt to forecast what the next phase of digitally-enabled PCC for management of allergic diseases may entail (Figure 1). Integration of data collected from mHealth databases into electronic medical records will enhance the visualization of symptom trends by clinicians and optimise the use of this data within the clinical context. Technical, privacy and access issues need to be addressed but are not insurmountable. Digital biomarkers will come to the fore as useful clinical indicators of patient conditions. The ability to collect long-term patient data via mHealth apps will provide a much-improved clinical profile of individual patients and population health data. Stratification of patients is important in high-prevalence chronic diseases as discussed in the position paper.3 Validated digital biomarkers should be incorporated into clinical practice guidelines and workflows to guide the management and classification of patients. The extensive digital biomarker database and biomarkers from blood, sputum, and tissue samples can be amalgamated into complete patient profiles. This will offer significant advantages in stratification of patients, assessment of patient response to treatment and presents many possibilities in pharmaceutical development, research and clinical care. Digitally-enabled multifaceted, holistic long-term data collection presents extensive data sets for the training and use of AI including large language models, assessment of associations and prediction of therapeutic response, to list but a few amongst the extensive list of potential AI applications. AI-aided clinical management will involve clinician input initially, but it is no longer inconceivable that AI will be integral to patient management in the future, from providing automated advice to prescribing and titrating medications. This will likely be piloted in conditions that are less likely to lead to severe morbidity or mortality such as AR. As an evolving field, ethical and legal considerations are being developed. Many ethical issues in digital health will be debated and guidelines will be required to protect all stakeholders' interests. Terms and conditions for the use of mHealth apps and data collection should be legally and ethically sound. mHealth apps and databases contain confidential health information and are not immune to cyberattacks or misuse. Data security measures must be instituted to prevent data breaches. In conclusion, this timely position paper informs the scientific community that mHealth apps will play an integral role in the management of chronic allergic diseases. It highlighted the clinical use, limitations and unmet needs of digitally-enabled PCC specifically in AIT, and suggested areas for development in this exciting, evolving field that holds promise for personalising the care of allergic diseases. None. We received no funding for this work. Dr CL Ng has received research funding from Jurong Health Fund for his work in digitally-enabled patient care in allergic rhinitis. The authors declare no conflicts of interest in relation to this work. Data sharing not applicable to this article as no datasets were generated or analysed during the current study.