A recent article published in a leading international journal specialised in healthcare management describes developments in artificial intelligence (AI) for healthcare in the past ten years. The article identified serious gaps between the priorities defined for healthcare and nursing management and the actual developments developed by AI solutions for healthcare.
The article here briefly discussed outlined the contrast between priorities for nursing management proposed in 2011 and the parallel developments on AI for healthcare and nursing management since then. The article describes a clear gap between the focus of Artificial Intelligence developers and the priorities for healthcare management.
AI research is expected to identify innovative approaches to supporting health professionals’ clinical decision-making and support on tasks not related to patient interaction, namely administrative activities and patient records.
However, the article demonstrates that in 2022 it is still unclear to what extent the priorities of Healthcare and Nursing Management have been adopted by AI solutions.
The article’s reasoning evolved from identifying three priorities for healthcare management identified in 2011 and goes on to identify recent evidence concerning the past ten years of AI applications development.
Overall, the article identified a serious conceptual gap between both worlds. That is to say the real world of healthcare management and the world of conceptual and programming in AI solutions for healthcare., While admitting that there have been positive developments in AI for healthcare, the key finding of this article is that the previously defined priorities for healthcare management, as perceived by health professionals, have been taken into consideration by AI developers.
There seems to be a gap when we set prime concerns of both sides. Healthcare management priorities, as proposed in 2011, focussed on three key ideas. One, would be evolving human resources management from staff satisfaction to staff retention namely through revised workload, enhancing group cohesion and social support. Second priority was developing the support and access to sources of evidence-based-practice (EBP) promoting impacts on the quality of healthcare. Also, adapt research resources for those purposes via more efficient knowledge transfer to improve clinical supervision. A third priority established in 2011 was the expectation of the advancement of competencies on financial responsibility practiced by health professionals and contributing to health organisations sustainability, budgetary control for healthcare added value and accountable care.
The article argues that these priorities have not been taken into consideration by AI developments.
To back up this argument, the authors worked around major scientific evidence available till 2021 and the main areas of development devised by Artificial Intelligence applied to healthcare. Thus, evidence suggests that AI contributions have adopted a clear focus on hospitals and independent living at home complemented with a focus on patient tracking and monitoring, classification of activity and broad care coordination.
Additionally, published research on AI shows that a major topic in the past ten years has been image and signal processing and machine learning (ML) systems.
In this sense, while exploring contributions of AI to patient care, evidence suggests a focus on patient fall detection as well as predicting and classifying pressure ulcers (on a rare emphasis on specific healthcare management issues).
In short, the article puts in contrast the evolution between 2011 and 2021 and argues that the gap between the proposed priorities for the improvement of healthcare management and the actual developments of AI applications is quite clear.
The evidence explored in the article raises important questions for the international debate:
The article does not deny the value and interest of AI applications for healthcare care. It has, however, identified a set of gaps that needs to be discussed internationally.
Additionally, a further critical problem signalled in the article concerns the limitations of the clinical storage systems, noise removal methods and multi-disease prediction models, these being related to innovation system performance.
Overall, the article contributes to the international debate on research priorities for health systems and the application of innovative solutions namely those stemming from technological developments. A closer relation between commercial developers and all professions involved in healthcare management is fundamental.
References:
Chen, Y., Moreira, P., Liu, W., Monachino, M., Nguyen, T. L. H., & Wang, A. (2022). Is there a gap between artificial intelligence applications and priorities in health care and nursing management? Journal of Nursing Management, 1–7. https://doi.org/10.1111/jonm.13851
Jacennik, B. (2022). On Digital Health Research Priorities: From Telemedicine to Telehealth. International Healthcare Review (online), 1(1). https://doi.org/10.56226/ihr.v1i1.13
Lloyd Williams, D. (2022). On Healthcare Research Priorities in the USA : From Long COVID to Precision Health, what else is new?. International Healthcare Review (online), 1 (1). https://doi.org/10.56226/ihr.v1i1.14
Moreira, P. (2022). On New Clinical Research Methods and Technologies: From decentralised designs to Artificial Intelligence. International Healthcare Review (online), 1(1). https://doi.org/10.56226/ihr.v1i1.11
Moreira, P., Monachino, M., Williams, D.L., Dsouza, B., Chen, Y., Antunes, V., Ueyda, M., Nguyen, T & Jacennik B. (2022) Healthcare Research Priorities: an International Agenda for 2024. International Healthcare Review (online), 1:1 , 1-8, DOI: https://doi.org/10.56226/ihr.vi
