Fatores que influenciam a aceitação de Tecnologias de Inteligência Artificial na Saúde

Since 2010, the use of Artificial Intelligence technologies in health and promotion of quality of life presents significant progress in medicine. However, there are many barriers and resistance to its implementation, whether by hospital management, patient, healthcare professional, council and society in general. The purpose of this paper is to summarize the factors that influence the acceptance of Artificial Intelligence in the health area through a systematic review of the studies that evaluated empirically the use of this technology. For the composition of the literary framework, a systematic review of the literature was carried out on the basis of of journals Web of Science with a final sample of 50 papers. The study identified 11 factors: clinical aspects, human aspects, organizational aspects, regulatory requirements, user experience, knowledge level for technology development, knowledge level for use of technology, technological infrastructure, technological implementation, perception of potential and resistance to innovation.Desde 2010, el uso de tecnologías de Inteligencia Artificial en salud y promoción de la calidad de vida ha mostrado un avance significativo en el cuidado de la salud. Sin embargo, existen muchas barreras y resistencias para su implementación, ya sea por parte de la dirección hospitalaria, de los pacientes, de los profesionales de la salud, de los colegios profesionales y de la sociedad en general. El objetivo de esta investigación es identificar los factores que influyen en la aceptación de la Inteligencia Artificial en el cuidado de la salud a través de una revisión sistemática de los estudios que evaluaron empíricamente el uso de esta tecnología. Para componer el marco literario se realizó una revisión sistemática de la literatura basada en revistas Web of Science, con una muestra final de 50 artículos. Como principales resultados se identificaron 11 factores: aspectos clínicos, aspectos humanos, aspectos organizacionales, aspectos regulatorios, experiencia del usuario, nivel de educación para el desarrollo tecnológico, nivel de educación para el uso de la tecnología, infraestructura tecnológica, implementación tecnológica, percepción de potencial y resistencia a la innovación.Desde 2010, a utilização de tecnologias de Inteligência Artificial na saúde e promoção da qualidade de vida apresenta um progresso significativo na medicina. Entretanto, há muitas barreiras e resistência quanto a sua implementação seja por parte da gestão do hospital, paciente, profissional de saúde, conselho e sociedade de forma geral. O objetivo desta pesquisa é identificar os fatores que influenciam a aceitação da Inteligência Artificial na área da saúde por meio de uma revisão sistemática dos estudos que avaliaram empiricamente o uso dessa tecnologia. Para composição do arcabouço literário, foi realizada uma revisão sistemática da literatura na base de periódicos Web of Science com amostra final de 50 artigos. Como principais resultados, foram identificados 11 fatores: aspectos clínicos, aspectos humanos, aspectos organizacionais, aspectos regulatórios, experiência do usuário, grau de instrução para desenvolvimento de tecnologia, grau de instrução para uso da tecnologia, infraestrutura tecnológica, implantação tecnológica, percepção de potencial e resistência à inovação

On designing an algorithmically enhanced NHS: towards a conceptual model for the successful implementation of algorithmic clinical decision support software in the National Health Service

Established in 1948, the National Health Service (NHS) has lasted 75 years. It is, however, under considerable strain: facing chronic staff shortages; record numbers of emergency attendances; an ambulance wait-time crisis; and more. Increasingly, policymakers are of the view that the solution to these problems is to rely more heavily on one of the NHS’s greatest resources: its data. It is hoped that by combining the NHS’s data riches with the latest techniques in artificial intelligence (AI), that the means to make the NHS more effective, more efficient, and more consistent, can be identified and acted upon via the implementation of Algorithmic Clinical Decision Support Software (ACDSS). Yet, getting this implementation right will be both technically and ethically difficult. It will require a careful re-design of the NHS’s information infrastructure to ensure the implementation of ACDSS results in intended positive emergence (benefits), and not unintended negative emergence (harms and risks). This then is the purpose of my thesis. I seek to help policymakers with this re-design process by answering the research question ‘What are the information infrastructure requirements for the successful implementation of ACDSS in the NHS?’. I adopt a mixed-methods, theory-informed, and interpretive approach, and weave the results into a narrative policy synthesis. I start with an analysis of why current attempts to implement ACDSS into the NHS’s information infrastructure are failing and what needs to change to increase the chances of success; anticipate what might happen if these changes are not made; identify the exact requirements for bringing forth the changes; explain why the likelihood of these requirements being met by current policy is limited; and conclude by explaining how the likelihood of policy meeting the identified requirements can be increased by designing the ACDSS’s supporting information infrastructure around the core concepts of ‘utility, usability, efficacy, and trustworthiness’