Editorial on the Research Topic
Digital health quality, acceptability, and cost: steps to effective continuity of
cancer care
Over 19 million people were diagnosed with cancer globally in 2020 (1). Recently,
digital health interventions, including electronic medical/health records, telemonitoring,
online patient portals, artificial intelligence (e.g., machine learning) and web-,
mobile-, and text message-based interventions have become commonplace across the cancer
care continuum, from cancer screening to post-treatment follow-up (2, 3). Benefits
of digital health interventions include improved access to and delivery of cancer
screening, decision aids, health information, management and tracking tools (e.g.,
smart watches, apps, websites), including psychosocial and physical health, for people
living with and beyond cancer and their caregivers (2–8). Moreover, digital health
interventions are scalable, adaptable and can be co-designed with multidisciplinary
teams, including end-users, researchers, and clinicians, to address unmet healthcare
needs (9). However, many barriers to access and use of digital health interventions
exist, especially in low-resource areas and low-income countries (10, 11). Limited
access to digital health technologies and limited technological skills or abilities
to seek and understand health information from digital health sources (i.e., low digital
health literacy) can lead to inequities in care delivery (12, 13). Moreover, receiving
too much information from digital health sources can result in negative experiences,
including fear (14). Sustainability of digital health interventions can also be problematic
due to lack of resources (e.g., funding, workforce capacity) (15). The contributions
in this Research Topic highlight the importance of inclusive co-design and equitable
delivery of digital health interventions in cancer care.
Co-designing health innovations with people with diverse expertise (e.g., lived-experience,
clinical, research) has been found to improve the quality of the innovation and users'
perceived acceptability and utility (16). In this Research Topic, Morton et al. highlight
how the design of a surgery decision aid for people with genetic predisposition of
cancer was improved by involving multidisciplinary expertise in co-design. The original
decision aid included detailed descriptions of the decision options (e.g., have surgery
now or decide later), pros and cons of each decision and a quiz to indicate the most
suitable decision. Using an iterative mixed-methods approach, participants made important
alterations to the original decision aid, including a desire for concise descriptions
of each decision option, including “do nothing”, and up-front implications of the
decisions to set “realistic expectations”, with option to read additional information
if desired. They also suggested a list of frequently asked questions, and a personally
tailored summary of quiz results to facilitate clinician communications. Morton et
al. emphasized the importance of including diverse co-designers from various jobs
and ethnicities to facilitate development of future decision aids.
Once digital health interventions are designed, end-user testing is important for
understanding acceptability, utility, and potential adaptations. Virtual patient platforms,
including patient portals, are often used to support health self-management (2). In
this Research Topic, Lamarche et al. describe how a fear of cancer recurrence program
for patients was adapted into a program to support caregivers using a mixed methods,
multidisciplinary approach, which was successfully user-tested by caregivers and therapists
(17). Further, results from O’Connor et al. mixed methods evaluation (service use
data, survey, interviews) of a patient portal to support follow-up care for 627 men
with prostate cancer with low risk of recurrence revealed that within the portal,
participants were most likely to access their test results and the communication systems
(e.g., secured messaging, email) to contact their clinical team. Most participants
felt the portal was quick, easy, convenient and time-saving compared to traveling
to the hospital and reduced stress and facilitated communication with clinicians.
However, people who declined to participate reported that digital equity was an issue,
due to a lack of computer, internet, or technical skills. Participants suggested provision
of technical support and technology could reduce barriers.
In low-income countries and low-resource areas, digital health inequities are exacerbated.
For example, although there is some evidence of patients' acceptability and utility
of virtual patient platforms (e.g., electronic personal health records; ePHRs), successful
implementation is limited (17, 18). Wubante et al. conducted a cross sectional questionnaire
of 402 health professionals in Ethiopia to evaluate their knowledge and attitudes
regarding electronic personal health records. Most (93.5%) had never used ePHRs before
but 64.4% perceived them to be useful for managing health and 55.5% had favorable
attitudes, especially those with to the required technology, high digital health literacy
and skills and access to computer training. Wubante et al. suggest providing training
about technical aspects of ePHRs and their usefulness for health professionals could
improve knowledge, attitudes, and use.
Digital health literacy plays a critical role in people's ability to use digital health
innovations effectively. Nguyen et al. suggest that a validated measure of digital
health literacy is key to understanding if a person is proficient enough to adopt
a novel digital health intervention or require additional education or training. Nguyen
et al. conducted an exploratory and confirmatory factor analysis to validate a novel
digital health literacy tool for Vietnamese adolescents in Vietnam (N = 236). Results
revealed that the tool was valid across gender, education, marital status, age, location,
and household economy, which may facilitate collection of future high-quality digital
health literacy data.
Throughout the cancer care continuum, routinely collected clinical data can also be
evaluated using artificial intelligence, such as machine learning, to predict patient
health outcomes. In a population-based retrospective cohort study (N = 52,199), Zhang
et al. used machine learning to predict lymph node metastases in patients with renal
cell carcinoma, based on their age, sex, tumour laterality, T and M stages, tumour
size, histology, and grade. Their model showed high internal and external validity
(AUC of 0.930 and 0.958, respectively) and good clinical applicability. As a result,
Zhang et al. released a freely available online risk calculator.
This Research Topic Digital Health Quality, Acceptability, and Cost: Steps to Effective
Continuity of Cancer Care provides readers with emerging evidence that furthers understanding
of how digital health interventions can be harnessed to support patients, caregivers
and healthcare professionals throughout the cancer care continuum and improve health
outcomes. Equity, digital health literacy and sustainability are central considerations
for successful digital health integration and usage in cancer care, which can be achieved
through multidisciplinary codesign and testing.