As the efficacy of artificial intelligence (AI) in improving aspects of healthcare delivery is increasingly becoming evident, it becomes likely that AI will be incorporated in routine clinical care in the near future. This promise has led to growing focus and investment in AI medical applications both from governmental organizations and technological companies. However, concern has been expressed about the ethical and regulatory aspects of the application of AI in health care. These concerns include the possibility of biases, lack of transparency with certain AI algorithms, privacy concerns with the data used for training AI models, and safety and liability issues with AI application in clinical environments. While there has been extensive discussion about the ethics of AI in health care, there has been little dialogue or recommendations as to how to practically address these concerns in health care. In this article, we propose a governance model that aims to not only address the ethical and regulatory issues that arise out of the application of AI in health care, but also stimulate further discussion about governance of AI in health care.
In recent years, there has been massive progress in artificial intelligence (AI) with the development of deep neural networks, natural language processing, computer vision and robotics. These techniques are now actively being applied in healthcare with many of the health service activities currently being delivered by clinicians and administrators predicted to be taken over by AI in the coming years. However, there has also been exceptional hype about the abilities of AI with a mistaken notion that AI will replace human clinicians altogether. These perspectives are inaccurate, and if a balanced perspective of the limitations and promise of AI is taken, one can gauge which parts of the health system AI can be integrated to make a meaningful impact. The four main areas where AI would have the most influence would be: patient administration, clinical decision support, patient monitoring and healthcare interventions. This health system where AI plays a central role could be termed an AI-enabled or AI-augmented health system. In this article, we discuss how this system can be developed based on a realistic assessment of current AI technologies and predicted developments.
ObjectivesTo date, many artificial intelligence (AI) systems have been developed in healthcare, but adoption has been limited. This may be due to inappropriate or incomplete evaluation and a lack of internationally recognised AI standards on evaluation. To have confidence in the generalisability of AI systems in healthcare and to enable their integration into workflows, there is a need for a practical yet comprehensive instrument to assess the translational aspects of the available AI systems. Currently available evaluation frameworks for AI in healthcare focus on the reporting and regulatory aspects but have little guidance regarding assessment of the translational aspects of the AI systems like the functional, utility and ethical components.MethodsTo address this gap and create a framework that assesses real-world systems, an international team has developed a translationally focused evaluation framework termed ‘Translational Evaluation of Healthcare AI (TEHAI)’. A critical review of literature assessed existing evaluation and reporting frameworks and gaps. Next, using health technology evaluation and translational principles, reporting components were identified for consideration. These were independently reviewed for consensus inclusion in a final framework by an international panel of eight expert.ResultsTEHAI includes three main components: capability, utility and adoption. The emphasis on translational and ethical features of the model development and deployment distinguishes TEHAI from other evaluation instruments. In specific, the evaluation components can be applied at any stage of the development and deployment of the AI system.DiscussionOne major limitation of existing reporting or evaluation frameworks is their narrow focus. TEHAI, because of its strong foundation in translation research models and an emphasis on safety, translational value and generalisability, not only has a theoretical basis but also practical application to assessing real-world systems.ConclusionThe translational research theoretic approach used to develop TEHAI should see it having application not just for evaluation of clinical AI in research settings, but more broadly to guide evaluation of working clinical systems.
Implementing a realist evaluation framework to generate robust findings in this context has required innovation in the evaluation design and adaptation by researchers. This article captures the RPHCM team's experience in designing this evaluation.
In recent years, there has been an amplified focus on the use of artificial intelligence (AI) in various domains to resolve complex issues. Likewise, the adoption of artificial intelligence (AI) in healthcare is growing while radically changing the face of healthcare delivery. AI is being employed in a myriad of settings including hospitals, clinical laboratories, and research facilities. AI approaches employing machines to sense and comprehend data like humans has opened up previously unavailable or unrecognised opportunities for clinical practitioners and health service organisations. Some examples include utilising AI approaches to analyse unstructured data such as photos, videos, physician notes to enable clinical decision making; use of intelligence interfaces to enhance patient engagement and compliance with treatment; and predictive modelling to manage patient flow and hospital capacity/resource allocation. Yet, there is an incomplete understanding of AI and even confusion as to what it is? Also, it is not completely clear what the implications are in using AI generally and in particular for clinicians? This chapter aims to cover these topics and also introduce the reader to the concept of AI, the theories behind AI programming and the various applications of AI in the medical domain.
This systematic review sought to identify whether health care reforms led to improvement in the emergency department (ED) length of stay (LOS) and elective surgery (ES) access in Australia, Canada, New Zealand, and the United Kingdom. The review was registered in the PROSPERO database (CRD42015016343), and nine databases were searched for peer-reviewed, English-language reports published between 1994 and 2014. We also searched relevant "grey" literature and websites. Included studies were checked for cited and citing papers. Primary studies corresponding to national and provincial ED and ES reforms in the four countries were considered. Only studies from Australia and the United Kingdom were eventually included, as no studies from the other two countries met the inclusion criteria. The reviewers involved in the study extracted the data independently using standardized forms. Studies were assessed for quality, and a narrative synthesis approach was taken to analyze the extracted data. The introduction of health care reforms in the form of time-based ED and ES targets led to improvement in ED LOS and ES access. However, the introduction of targets resulted in unintended consequences, such as increased pressure on clinicians and, in certain instances, manipulation of performance data.
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