Overcoming Pilotitis in Digital Medicine at the Intersection of Data, Clinical Evidence, and Adoption

Abstract

The number of digital medicine technologies, a category of tools that use software and algorithmically driven products to measure or intervene to improve human health, [1] has expanded enormously in recent years, further accelerated by the need for remote care delivery during the COVID-19 pandemic, and now ranges from telemedicine to serious gaming, wearable devices, AI-supported diagnostics, and clinical decision support systems (CDSSs), among others. Building on the experiences during the pandemic, there is a real potential for the digital transformation of health care to the benefit of patients worldwide. However, the promising developments to date are at risk of stalling due to inherent limitations in the way digital medicine and especially medical artificial intelligence (AI) technologies are being developed and implemented today. [2] Already in 2017, the number of digital medicine technologies on the worldwide market exceeded 318 000, with the mobile application segment growing by over 200 daily. [3] There are widespread expectations that the surge will lead to improvement of health care globally, [4] not least evidenced by the WHO declaration on digital health, [5] and by the rapid growth of private and public investment in this field. [6,7] The advent of digital medicine bears special promise for low-and middleincome countries (LMIC), where applications such as telemedicine and CDSS may enable these countries to "leapfrog" from today's often insufficient healthcare systems into more efficient, accessible, and patient-centric models, [8] similar to what has been seen in Africa's finance sector. [9] The level of improvement possible would correspond to the digital transformation seen in other sectors of society, such as media and retail industries, with the digitalization of processes and service models, beyond the mere digitization of traditional healthcare practices seen so

The Effect of the COVID-19 Pandemic on Digital Health–Seeking Behavior: Big Data Interrupted Time-Series Analysis of Google Trends

“…Implementing digital health solutions is complex and relies on many institutional factors, cultural and behavioral traits, and health system characteristics [ 9 , 10 ]. For instance, the design process of digital health solutions is indicative of which populations it will be able to reach and, equally importantly, what population groups will experience difficulties in accessing and using the tool [ 3 , 11 , 12 ]. Policy environments are vital to laying the foundation of how conducive a health system is to adopting a digital health solution and how health professionals are trained in the field of digital health [ 3 , 11 , 13 ].…”

Has the pandemic enhanced and sustained digital health-seeking behaviour? A big data interrupted time-series analysis of Google Trends

“…So long as these factors remain underdeveloped, digital health will face substantial barriers to being implemented and assimilated into national healthcare settings. In fact, the absence of robust market access regulatory frameworks for digital health may also contribute to the current environment where novel tools are stuck in the proof-of-concept or pilot phase and cannot pivot to clinical adoption (3).…”

“…Evidence from a systematic review shows a median 42% decrease in healthcare visits, 28% decrease in admissions, 31% reduction in diagnostics, and 29% reduction in therapeutics, resulting in an overall 37% decrease of utilisation of healthcare services (1). Digital health usage has exploded during the early stages of the COVID-19 pandemic as traditional health services were disrupted (2)(3)(4). Digital health generally refers to the use of internet solutions, big data, and communications technologies to collect, share and manage health information to improve both individual and public health, as well as identify symptoms, plan treatment, monitor key health parameters, and monitor progress and treatment effects (5,6).…”

“…Implementing digital health solutions is complex and relies on many institutional factors, cultural and behavioural traits and health system characteristics (7,8). For instance, the design process of digital health solutions is indicative of which populations it will be able to reach and, equally importantly, what population groups will experience difficulties in accessing and using the tool (3,9). Policy environments are also vital to laying the foundation of how conducive a health system is to adopting a digital health solution and how health professionals are trained in the field of digital health (3,9,10).…”

“…For instance, the design process of digital health solutions is indicative of which populations it will be able to reach and, equally importantly, what population groups will experience difficulties in accessing and using the tool (3,9). Policy environments are also vital to laying the foundation of how conducive a health system is to adopting a digital health solution and how health professionals are trained in the field of digital health (3,9,10). Finally, the readiness and willingness of digital health users are crucial elements in adopting digital health solutions (2,8).…”