What is Digital Health? Unpacking the box
By Dr Sundresan Naicker, AusHSI Research Fellow – Implementation Science
“Digital Health” has become a popular term that can conjure up images of code, touchscreens and futuristic hospitals in the minds of many. While there are certainly truisms to these tropes, it is really a vast umbrella label encompassing almost all aspects of contemporary healthcare, from sophisticated risk modelling to booking systems. The World Health Organization defines the term as “the field of knowledge and practice associated with the development and use of digital technologies to improve health”.
With this in mind, digital health researchers encompass a broad church of disciplines. This ranges from clinical practitioners exploring technology-driven approaches to enhance patient care, to social scientists using ethnographic approaches to examine how people interact with and experience digital health technologies in real-world settings, to the software engineers and computer programmers who work on the algorithms and applications that power these tools. These researchers and scientists often traverse more than one area wearing multiple hats, as this work involves a grounded understanding of health systems across a range of contexts.
The rate of technological advancement can outstrip the system’s ability to seamlessly integrate digital innovations to benefit service providers or patients. This can lead to a whole range of issues, including (but certainly not limited to) inappropriate use of a digital innovation or solution, abandonment of a technology over time despite a costly adoption process, interoperability problems across different platforms resulting in siloed data and workflow interruptions, and gaps in translating evidence to practice which can profoundly undermine the integrity of information provided to clinicians, caregivers and patients.
A hypothetical (though very realistic) scenario could be a radiology department within a busy public hospital adopting an AI-powered diagnostic tool for radiology. Although the tool may have been shown, under testing environments, to detect lesions from high-resolution images more accurately than human counterparts, in practise the hospitals imaging equipment produces lower resolution images that undermine the AI model’s ability to detect lesions with precision. This leads to clinician mistrust of the system entirely, even if the problem is resolved.
Another scenario may involve a case where a natural language processing (NLP) tool is used to transcribe and analyse clinician-patient conversations in real time, so that there is transparent and accurate record keeping. However, the tool frequently misinterprets medical terminology, requiring clinicians to manually correct errors. This increases the workload for clinicians, disrupting their workflows and affecting productivity.
Lastly, a scenario involving the implementation of a remote monitoring system to manage chronic heart failure (CHF) patients. The system analyses data from wearable devices to predict and prevent hospital readmissions by identifying early signs of deterioration. However, despite heavy upfront investment and ongoing operational costs, the system generates false positives in 30% of cases, leading to inappropriate and costly follow up interventions which may outweigh any real benefit.
These scenarios highlight the varying levels of complexity involved in integrating digital innovations within a health system. At AusHSI, our digital health team work closely with our health system counterparts to study the nature of these problems. In doing so, we conduct diverse digital health research across two overlapping disciplines, implementation science and health economics.
The field of implementation science integrates cutting-edge systems thinking with pragmatic know-how to address these challenges in adopting, optimising and sustaining appropriate digital health innovations. Through the application of evidence-based theory, we identify a range of influencing factors across the health system and co-design a range of strategies, solutions and innovations to ensure effective application of digital health tools.
Health economics provides a structured approach to evaluate the costs, benefits, and overall value of implementing digital health technologies, such as AI. It emphasizes not only financial implications, but also systemic outcomes, ensuring technologies improve efficiency, equity, and health outcomes without unintended consequences.
Collectively, we also apply these methods to work with all health system stakeholders to ensure ongoing and future digital health innovations improve health care for all, in ways that are safe, effective and equitable. This work is ongoing and requires the involvement of all voices, as our lives get longer and our needs more complex. Ensuring our society reaps the greatest benefit from our rapid technological progress is in all our interest.
Explore AusHSI’s digital health research
Dr Naicker will further unpack AusHSI’s work in digital health across several talks at the 13th Health Services Research (HSRAANZ) Conference running from 4–6 December 2024 in Brisbane. Please see the conference program for more details.
To read more about AusHSI’s work in this space, please click on the following links to peer reviewed publications:
https://doi.org/10.1016/j.hlpt.2024.100905
https://medinform.jmir.org/2024/1/e60402/
https://link.springer.com/article/10.1186/s13012-023-01287-y
https://formative.jmir.org/2024/1/e54022/
https://link.springer.com/article/10.1186/s12911-024-02647-4
