JMIR Publications

ABSTRACT

The SARS-CoV-2 pandemic has marked a significant turning point in global public health, coinciding with the rise of digital health in the era of smartphones. While traditional measures such as lockdowns and mask mandates were widely debated and implemented, the pandemic also witnessed the introduction of smartphone-based applications for pandemic mitigation, particularly digital proximity tracing (DPT). Leveraging Bluetooth technology, these apps aimed to trace proximity contacts and notify individuals of potential exposure to the virus. The decentralized, privacy-preserving proximity tracing (DP-3T) protocol, integrated into Android and iOS smartphones, gained widespread adoption. To sustain the momentum of digital public health, extending the digital transformation to non-communicable diseases (NCDs) is crucial. NCDs account for a significant portion of healthcare expenditures and disability-adjusted life years. The challenges and opportunities of NCD Digital Public Health differ from those of infectious diseases like SARS-CoV-2. This perspective paper aims to define digital public health and explore key features and requirements for its implementation. By identifying potential use cases, we will discuss existing digitally transformed public health measures for NCD prevention and management. Furthermore, we will address the challenges and prerequisites for advancing the digital transformation of NCD Public Health. Digital health, with a specific focus on artificial intelligence (AI), has immense potential to enhance the efficiency, effectiveness, user-friendliness, and equity of public health and healthcare for NCDs. The integration of AI into public health practices aligns with the quadruple aim, seeking to improve healthcare outcomes, patient experiences, population health, and healthcare costs. Digital changes in the essential public health functions should be purposeful, ensuring improvements in at least one dimension of the quadruple aim without significantly compromising others. However, the success of digital public health and AI-supported measures necessitates clear criteria, monitoring, and benchmarks to ensure benefits for the majority without disadvantaging vulnerable and minority groups. Explainable AI and the eradication of discriminatory decision-making are particularly vital for population-level decisions, such as resource allocation in public health. Transparent algorithms and interpretability mechanisms should be established to foster trust, accountability, and fairness. Moreover, the involvement of stakeholders, including patients, citizens, and vulnerable populations, in the development, implementation, and monitoring of AI-powered digital public health is crucial. This participatory approach can help address the specific needs and concerns of diverse communities, ensuring equitable access to and benefits from digital health solutions. It also facilitates the unlearning of biased decision-making and promotes the inclusion of underrepresented groups in public health interventions. In conclusion, the SARS-CoV-2 pandemic has demonstrated the potential of digital health solutions, particularly DPT apps, in mitigating infectious diseases. To advance public health further, the digital transformation needs to extend its focus to NCDs. By leveraging AI and other digital technologies, public health can become more efficient, effective, user-friendly, and equitable. However, achieving these goals requires careful consideration of ethical and societal implications, including the elimination of biases and the involvement of diverse stakeholders. The future of digital public health lies in striking a balance between technological advancements and the well-being of all individuals and communities.