Namaste Yogis. Welcome to the Blockchain & AI Forum, where your blockchain and artificial intelligence technology questions are answered! As a bonus, a proverb is also included. Today’s question, submitted by Doctor Vargas of Manati, PR, and she wants to understand what academic researchers in the field of health care are say about the use of artificial intelligence.
Doctor Vargas, you came to the right place. There has been an explosion in research related to AI in health care. I have an answer to your question based on an article titled, Artificial Intelligence in Healthcare Institutions: A Systematic Literature Review on Influencing Factors by Roppelt, Kanbach, and Kraus, 2024 in a scholarly journal called, Science Direct. Time to examine what the literature says. https://www.sciencedirect.com/science/article/pii/S0160791X23002488
According to the researchers, the global health system is facing serious challenges. They cite the growing number of patients due to population growth, increased prevalence of chronic diseases, increasing healthcare costs, and staff shortages. The good news is AI has the potential to solve some of the challenges ahead, claim the authors. The researchers say, “… AI is a technology that, through rules-based logic, can help to significantly speed up the process of analyzing vast amounts of data and leverage patterns by mimicking human intelligence. AI may result in fast and often better-advised decisions. Thus, as it pertains to the healthcare industry, AI has the potential to overcome staff shortages in developing and developed countries, enhance organizational efficiency, and maximize diagnostic accuracy as well as patient outcomes by providing at least comparable results in terms of quality compared to human based assessments. Consequently, Al may reduce costs due to avoidance of inefficiencies, unnecessary treatments, and late diagnoses.
THE UNIVERSE OF LITERATURE REVIEWED BY THE RESEARCHERS
Researchers discovered approximately 1,100 published articles on the topic since 2023. However, after analyzing the universe of literature in greater detailed soon learned only about 130 were truly on point. The authors also found that most studies were published in journals related to information technology, not medicine, or journals dedicated to the nexus of AI and medicine. Nearly 60 % elaborate generally on the factors influencing AI adoption in the healthcare industry, with the US at the high end of the spectrum. AI seems to be most widely adopted in cardiology and radiology and rather at an early stage in dermatology and psychiatry. Interesting that there are not many studies of AI in medicine in business management journals.
FACTORS THAT INFLUENCE AI ADOPTION IN HEALTHCARE: THE FINDINGS
Macro-economic readiness. The research at Science Direct found that macro-economic readiness is mainly driven by governments. For example, the research say adequate IT infrastructure, e.g., access to smart devices for the population and energy adequacy, especially for remote areas and certain populations. Also of interest is the role played by “AI communities.” The notion is that when there are established partnerships, collaborations, etc. the network effects kicks in and the impact is exponential.
Technological readiness. Researchers are finding that technological readiness depends on three factors:
- The importance of overcoming algorithmic challenges. What type of algorithmic challenges? Many, including lack of data, and/or inferior quality, etc.
- Provide a compelling, multi-faceted value proposition. The most obvious are clearly added value in the form of efficiency, user-friendliness, adherence to data-privacy regulations, interoperability, and the ability to tailor the application to individual needs.
- Center the AI application on real-world evidence. One size fit all is unlikely the optimal solution. The AI system used must be adapted to the health institution’s individual needs, preferences, and contexts of use.
Regulatory readiness. Essentially, the authors are suggesting close collaborations with regulators. The author point to three key issues: political support, clarification of legal questions, and establishment of regulations. As a former government regulator, this is sage advice but not easy to execute because contrary to popular belief the “government” is not a unified organism.
Organizational readiness. A failure to plan is a plan to failure, say the authors hence the reason they strongly recommend to prepare the organization before implementing an AI health care system or be prepared to fail. The researchers break down the steps into four steps: prepare an organizational strategy, develop a supportive organizational culture, clear tasks and assignments, and an adequate IT setup.
User readiness. Recognizing that organizations consists of people, the researchers says users are at the center. Furthermore, researchers conclude four facts are the key to user readiness:
- Awareness: in other words, do users know the technology exist?
- Beliefs: do users believe the technology will work?
- Personal innovativeness: are users willing to try something new?
- The financial situation of the individual:
Doctor Vargas, now you know what the literature says. I hope this helped. I end with a proverb from Mexico: in the absence of love, some tacos al pastor will do.”
Until Next Time,
Yogi Nelson
Blockchain & AI Forum 