The digital health landscape has been developing since the 1990s and gaining momentum since the early 2000s to the modern-day AI revolution. Various modalities of digital health such as telemedicine, bots, m-health, Software as a Medical Device (SaMD), and virtual reality have been catering to the growing health and care demands. The pandemic also propelled this development trajectory with rapid policy support for telemedicine, along with the development of practice guidelines and implementation of innovations for disease management. The research on digital health has revolved around success stories, scaling up digital health interventions, successful implementation of digital health interventions (DHI), and trials of DHI being effective. While digital health has seldom faced criticism and emerged to be a standalone silver bullet in the healthcare domain (although not), a growing research and knowledge gap is spiraling in this domain. Through this narrative, I try to highlight the areas that are seldom explored but need attention:

1. Pilotitis in DHI🚨

An area that needs to be explored at the earliest is the phenomenon of pilotitis of DHI. It is defined as “a term used to express the frustration of many of those in the health sector at the continuing emphasis on demonstrating successful outcomes from narrowly focused interventions targeting relatively small populations”(3). Common reasons often attributed to pilotitis are limited funding bandwidth, rapidly changing needs of the community and funder, evolving technological and regulatory landscape, and multiple partners working in silos. Various applications, interventions, and modalities have entered the healthcare domain. Adding to this is the start-up ecosystem which is vibrant in terms of design capabilities but often lacks the fuel to conduct trials and demonstrate its utility in terms of effectiveness, efficiency, and value. Various applications enter and exit the market while a few remain successful only as pilots and do not scale up. Although pilotitis has gained some importance in identifying best practices in scaling up digital health, it remains unquantified owing to the limited case studies and methods(4). However, this phenomenon has motivated me to emphasize the quote “An app for an app will make the user blind”.

2. Documenting the de-implementation of DHIs 📑❌

Synonymous with drugs, the DHIs are also modified or replaced with another effective one. While scientific scholarship has been highlighting the success of implementing DHIs, it has become imperative to document the case studies of the de-implementation of DHI. Such documentation offers a takeaway to those involved in the application design and deployment of DHIs, thereby reducing futile efforts and budget. The case of not documenting the de-implementation is not related to DHI alone but to broader health and social domains. This could be attributed to limited funding opportunities for such endeavors.  Walsh-Baily et al., (2021) have curated a list of de-implementation frameworks that may be used to inform the documentation of case studies related to de-implementation(5).

3. Documenting the trial timelines from conceptualization to implementation for digital health trials ⏳💡📊

Evidence suggests that the average time from initiation to bringing the drug to market is 10- 15 years. In addition, huge investments are made in human resources, adherence to various regulatory requirements, volunteer management, and data(8). Further expenditures are being incurred as the debates have shifted from efficacy and effectiveness to demonstrating the value of the intervention using economic principles, conducting trials among vulnerable populations, and testing treatments for rare diseases.

While the above debate is knit around drug trials, a decade for DHIs may be sufficient to make a novel technology obsolete, replaced, and the generation not remembering the technology. It is a need of the hour to understand the timelines and resources involved from the initiation of trials until the product/intervention reaches the market. In summary, while both drug development and digital health market access involve rigorous evidence generation, regulatory compliance, and resource allocation, the key difference lies in the pace of innovation and the risk of obsolescence.

4. Understanding the reasons for terminated trials related to digital health 🚫📉

While the drug trials could be terminated for an array of reasons, it is important to enumerate the terminated trials of DHI and document the reasons for the same. A good repository to begin this inquiry would be the International Clinical Trials Registry Platform (ICTRP), which hosts several other registries. Another repository that has been recently launched is the World Health Organization’s (WHO) Digital Health Atlas (DHA) which tries to address the stages of development, scale of deployment, and adoption. Although data aggregation would remain a challenge to this exercise, it is a step in the right direction.

5. Holistic evaluation of digital health interventions 🔍🌍📈

Evaluations of DHIs have been a growing debate. While most evaluations focus on clinical and cost-effectiveness, there is a dire need for holistic evaluations using established Health Technology Assessment (HTA) approaches. Evidence highlights that there are limited HTAs of DHI among developing economies(6). Despite the rapid growth of DHIs, their holistic evaluations need to gain momentum to inform decision-makers regarding their actual value proposition in addressing health problems. Parallelly, frameworks and approaches to measure ever-changing applications need to be explored, despite a few proposed recently(7). From a practical perspective, these frameworks and approaches should be easy to use, consume less time, capture updates, and provide insights to different stakeholders involved.

Going forward, tracing the evolution, understanding the maturity of technologies, and bridging the gaps in the evaluation of DHIs will be a cornerstone to the development of this field. I pen this article in pursuit of making efforts to find answers to the under-explored research frontiers in digital health.

References

1 Gudi N, Konapur R, John O, Sarbadhikari S, Landry M. Telemedicine supported strengthening of primary care in WHO South East Asia region: lessons from the COVID-19 pandemic experiences. BMJ Innov 2021; 7: 580–5.

2 Murthy S, Kamath P, Godinho MA, Gudi N, Jacob A, John O. Digital health innovations for non-communicable disease management during the COVID-19 pandemic: a rapid scoping review. BMJ Innov 2023; 9: 3–18.

3 Huang F, Blaschke S, Lucas H. Beyond pilotitis: taking digital health interventions to the national level in China and Uganda. Globalization and Health 2017; 13: 49.

4 Labrique AB, Wadhwani C, Williams KA, et al. Best practices in scaling digital health in low and middle income countries. Global Health 2018; 14: 103.

5 Walsh-Bailey C, Tsai E, Tabak RG, et al. A scoping review of de-implementation frameworks and models. Implementation Science 2021; 16: 100.

6 Gudi N, Raj EA, Jahn B, Siebert U, Brand A. Evaluations of digital public health interventions in the WHO Southeast Asia Region: a systematic literature review. International journal of technology assessment in health care. 2024 Jan;40(1):e78.

7 Moshi MR, Tooher R, Merlin T. Suitability of current evaluation frameworks for use in the health technology assessment of mobile medical applications: a systematic review. International Journal of Technology Assessment in Health Care. 2018 Jan;34(5):464-75.

8 What’s the average time to bring a drug to market in 2022? https://lifesciences.n-side.com/blog/what-is-the-average-time-to-bring-a-drug-to-market-in-2022 (accessed July 22, 2023).

Corresponding author email: ngudi@path.org