Abstract: Artificial Intelligence (AI) is positioned to transform healthcare services and operations into intelligent systems, delivering innovative solutions to improve the quality of care while containing rising costs. However, AI readiness at all levels of the healthcare ecosystem must be examined in combination with real-world implementations that deliver actual transformation. Readiness is commonly defined as the state of being fully prepared for something. In a healthcare management context, readiness refers to the state of a healthcare organization in determining whether the required capabilities exist for the successful adoption of a new technology or concept. Towards AI integration in healthcare, the concept applies at three levels—technological, data, and skills readiness—and can be assessed using maturity frameworks that provide an indication of progress and direction, as well as stakeholder analysis.

While Technology Adoption Models assess potential uptake of AI solutions by health practitioners, these validations assess preparedness to adopt and/or innovate in AI solutions within the healthcare ecosystem. Emerging AI applications covering clinical needs from radiology to mental health support, or from product development to operational management, present recognition and acceptance challenges requiring stakeholder engagement. Stakeholder impact on the success of AI solutions can be assessed using readiness levels, grouping stakeholders into three categories: IDPC leaders responsible for implementation, users at the point of care who interact directly with patients, and patients, the users of the healthcare system and the ultimate beneficiaries or sufferers from the role out of the AI solution.

Keywords: AI readiness assessment, Digital health infrastructure, Clinical workflow integration, Health data interoperability, Electronic health record (EHR) maturity, Data governance frameworks, Ethical AI in healthcare, Workforce AI competency, Change management in healthcare, Clinical decision support systems, Health information security, Regulatory compliance for AI, Algorithm transparency, Patient data quality, Organizational readiness for AI,AI adoption barriers, Health system innovation capacity, Human–AI collaboration, AI-enabled care delivery, Implementation science for AI in healthcare.

Downloads: | DOI: 10.17148/IJIREEICE.2023.111214