The development of innovation in healthcare depends on a number of key factors: including the available infrastructure, the knowledge base, the available strength of evidence; and the flexibility and creativity of systems where innovations are composed, tested, and eventually adopted and incorporated into routine practice. Especially in fields such as medical research where creativity is a key feature, the role of the knowledge base is further enhanced as a critical precondition for the development of highly adopted innovations.[1] However, accumulating a critical mass of innovations such that the result is to widen a transformation of the healthcare system is a rather rare event and is often comparable to the systemic transformations of economic systems. One of the reasons that such innovation-driven, systemic transformations are rare is because they also necessitate social alignment for their success.[2]

For example, the technological advances in digital communication from the 1990s onward have impacted the quality of healthcare positively by the quick collection and sharing of available information among clinical teams through centralized electronic health records. In turn, this allowed for the establishment and improved functioning of multidisciplinary clinical teams (sometimes brought together only in the digital sphere) for the treatment of complicated patient cases.[3] However, it also required the (re-)training of staff to be able to fully exploit the advances offered by those innovations. The coronavirus disease 2019 (COVID-19) pandemic constitutes perhaps the defining moment at which healthcare information is not simply available at high volumes and speed but expected to be interpretable as an integral part of any innovation. The real-time dynamic and tiered e-consent tool published in this issue of Innovations in Digital Health, Diagnotstics, and Biomarkers (IDDB) provides such an example.[4]

One reason systems transform in long waves is because prior frameworks of operation establish firmly committed procedures that are not easily disrupted. Thus, it takes the exhaustion of existing systems before innovative approaches are considered and even adopted. This might explain why innovations that have been available for years or decades, but not widely implemented, have suddenly become the norm during this pandemic (one such example is the virtual follow-up of patients with cancer posttreatment).[5] The COVID-19 pandemic, as such, has exposed the deficiencies within different clinical care systems and highlighted the need for innovative approaches. The urgency of the response has also mitigated or completely removed any potential financial and social barriers.

However, it is important to note that although the pandemic has heightened interest and accelerated development in medical technologies, as well as provided the ground for the rise in technological innovation within medical research, it does not as yet constitute a systemic transformation. To do so, introduced innovations need to become widely adopted and integrated across a range of settings, from affluent to resource restricted. This aspect is highlighted in the excellent manuscript by Logan et al,[6] in which the untapped social impact of artificial intelligence for breast cancer screening in developing countries is critically appraised.

Therefore, there remains a very real risk that the innovations introduced in the last few years—and the knowledge base that they embody—will not be effectively institutionalized once the crisis subsides, and there will be a return to the pre–COVID-19 status quo. Perhaps the most transformative legacy of the pandemic in healthcare would be the acceleration of the innovation pipeline, allowing new approaches to emerge and be tested more rapidly than ever before. The next critical step would be to focus on applying these innovative technologies where they add value, while widening their reach and eventual adoption globally.

1.
Barlow
J.
Managing Innovation in Healthcare
.
World Scientific Publishing Company;
2016
.
2.
Iveroth
E,
Fryk
P,
Rapp
B.
Information technology strategy and alignment issues in health care organizations
.
Health Care Manage Rev
.
2013
;
38
:
188
200
.
3.
Lasiter
S,
Boustani
MA
.
Critical care recovery center: making the case for an innovative collaborative care model for ICU survivors
.
Am J Nurs
.
2015
;
115
:
24
.
4.
Ivanova
D,
Katsaounis
P.
Real-time dynamic tiered e-consent: a novel tool for patients' engagement and common ontology system for the management of medical data
.
Innov Dig Health Diagn Bio
.
2021
;
1
:
45
49
.
5.
Jazieh
AR,
Al Hadab
A,
Al Olayan
A,
et al
Managing oncology services during a major coronavirus outbreak: lessons from the Saudi Arabia experience
.
JCO Glob Oncol
.
2020
;
6
:
518
524
.
6.
Logan
J,
Kennedy
PJ,
Catchpoole
D.
The untapped social impact of artificial intelligence for breast cancer screening in developing countries: a critical commentary of deepmind
.
Innov Dig Health Diagn Bio
.
2021
;
1
:
29
32
.

Competing Interests

Sources of Support: None. Conflicts of Interest: None.

Disclaimer: Where authors are identified as personnel of the International Agency for Research on Cancer/World Health Organization (IARC/WHO), the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy, or views of the IARC/WHO.