Strategy development for anticipating and handling a disruptive technology

DISRUPTIVE INNOVATION IN HEALTHCARE: A REVIEW AND FUTURE RESEARCH AGENDAS

Disruptive innovation (DI) refers to a mode of low cost high ancillary performance innovation that starts at the lower end of the market and gradually moves up to eventually displace its incumbent counterparts. The concept of DI has become more and more popular in the healthcare sector. The paper studies how healthcare research has embraced the concept of DI in various areas within the healthcare sector. Areas of healthcare, where DI has been adopted as a management strategy, were identified in the last two decades, through initially shortlisting 157 papers and the final reviewed set of 117 papers. Through a multi-method approach comprising of co-word analysis, identification of themes, mapping of research work across these themes and geography, the study found that, although from a management perspective there are 13 major themes, the majority of the focus is on four major themes—healthcare delivery and services, healthcare administration, technology and equipment, and nursing and palliative care. Furthermore, the majority of the geography-specific research was conducted in developed countries, especially the USA. It was also observed that organisational strategies for successfully implementing DI in the healthcare sector are still in the nascent stage and the focus is mostly in the area of healthcare delivery and administration from the management perspective. Future research studies could aim at looking at the role that DI plays in the healthcare sector of the developing countries, as well as using DI in other areas of healthcare, rather than a few concentrated areas.

Will machine learning end the viability of radiology as a thriving medical specialty?

There have been tremendous advances in artificial intelligence (AI) and machine learning (ML) within the past decade, especially in the application of deep learning to various challenges. These include advanced competitive games (such as Chess and Go), self-driving cars, speech recognition, and intelligent personal assistants. Rapid advances in computer vision for recognition of objects in pictures have led some individuals, including computer science experts and health care system experts in machine learning, to make predictions that ML algorithms will soon lead to the replacement of the radiologist. However, there are complex technological, regulatory, and medicolegal obstacles facing the implementation of machine learning in radiology that will definitely preclude replacement of the radiologist by these algorithms within the next two decades and beyond. While not a comprehensive review of machine learning, this article is intended to highlight specific features of machine learning which face significant technological and health care systems challenges. Rather than replacing radiologists, machine learning will provide quantitative tools that will increase the value of diagnostic imaging as a biomarker, increase image quality with decreased acquisition times, and improve workflow, communication, and patient safety. In the foreseeable future, we predict that today's generation of radiologists will be replaced not by ML algorithms, but by a new breed of data science-savvy radiologists who have embraced and harnessed the incredible potential that machine learning has to advance our ability to care for our patients. In this way, radiology will remain a viable medical specialty for years to come.

Entrepreneurship in the academic radiology environment

RATIONALE AND OBJECTIVES

Innovation and entrepreneurship in health care can help solve the current health care crisis by creating products and services that improve quality and convenience while reducing costs.

MATERIALS AND METHODS

To effectively drive innovation and entrepreneurship within the current health care delivery environment, academic institutions will need to provide education, promote networking across disciplines, align incentives, and adapt institutional cultures. This article provides a general review of entrepreneurship and commercialization from the perspective of academic radiology departments, drawing on information sources in several disciplines including radiology, medicine, law, and business.

CONCLUSIONS

Our review will discuss the role of universities in supporting academic entrepreneurship, identify drivers of entrepreneurship, detail opportunities for academic radiologists, and outline key strategies that foster greater involvement of radiologists in entrepreneurial efforts and encourage leadership to embrace and support entrepreneurship.

O perfil Brasileiro de propriedade intelectual em radiologia e diagnóstico por imagem em um contexto internacional, nos anos 2000-2009

OBJETIVO: Analisar a inovação tecnológica brasileira na área de radiologia e diagnóstico por imagem, em termos de indicadores de patentes. MATERIAIS E MÉTODOS: Este é um estudo analítico-exploratório de informações recuperadas por meio de consultas cruzadas nas bases de dados dos escritórios de propriedade intelectual do Brasil (Instituto Nacional da Propriedade Industrial - INPI), dos Estados Unidos (United States Patent and Trademark Office - USPTO) e da Europa (European Patent Office - EPO). RESULTADOS: Foram encontradas 277.057 patentes. Desse total, 7.800 foram registradas no INPI (3%), 65.428 (24%) registradas no EPO e 203.829 (73%) emitidas no USPTO. O Brasil é o país signatário em 1.732 patentes publicadas no INPI, 80 no EPO e 26 no escritório USPTO. Globalmente, 219.993 (79%) patentes referiam-se a dispositivos eletrônicos ligados a informática em saúde, ultrassonografia, ressonância magnética, tomografia computadorizada e procedimentos de geração, comunicação e arquivamento de imagens, e 57.064 (21%) das patentes tratavam das tecnologias relacionadas a radioproteção e dosimetria, física nuclear, eletroterapia, terapia magnética e radioterapia. CONCLUSÃO: Os resultados obtidos apontam para a fragilidade da produção nacional de inovação tecnológica registrada em patentes, no campo de radiologia e diagnóstico por Imagem.