Cardiovascular Imaging, Climate Change, and Environmental Sustainability.

Environmental exposures including poor air quality and extreme temperatures are exacerbated by climate change and are associated with adverse cardiovascular outcomes. Concomitantly, the delivery of health care generates substantial atmospheric greenhouse gas (GHG) emissions contributing to the climate crisis. Therefore, cardiac imaging teams must be aware not only of the adverse cardiovascular health effects of climate change, but also the downstream environmental ramifications of cardiovascular imaging. The purpose of this review is to highlight the impact of climate change on cardiovascular health, discuss the environmental impact of cardiovascular imaging, and describe opportunities to improve environmental sustainability of cardiac MRI, cardiac CT, echocardiography, cardiac nuclear imaging, and invasive cardiovascular imaging. Overarching strategies to improve environmental sustainability in cardiovascular imaging include prioritizing imaging tests with lower GHG emissions when more than one test is appropriate, reducing low-value imaging, and turning equipment off when not in use. Modality-specific opportunities include focused MRI protocols and low-field-strength applications, iodine contrast media recycling programs in cardiac CT, judicious use of US-enhancing agents in echocardiography, improved radiopharmaceutical procurement and waste management in nuclear cardiology, and use of reusable supplies in interventional suites. Finally, future directions and research are highlighted, including life cycle assessments over the lifespan of cardiac imaging equipment and the impact of artificial intelligence tools. Keywords: Heart, Safety, Sustainability, Cardiovascular Imaging Supplemental material is available for this article. © RSNA, 2024.

Radiology in the era of value-based healthcare: A multi-society expert statement from the ACR, CAR, ESR, IS3R, RANZCR and RSNA.

BACKGROUND: The value-based healthcare (VBH) concept is designed to improve individual healthcare outcomes without increasing expenditure and is increasingly being used to determine resourcing of and reimbursement for medical services. Radiology is a major contributor to patient and societal healthcare at many levels. Despite this, some VBH models do not acknowledge radiology's central role; this may have future negative consequences for resource allocation. METHODS, FINDINGS AND INTERPRETATION: This multi-society paper, representing the views of Radiology Societies in Europe, the USA, Canada, Australia and New Zealand, describes the place of radiology in VBH models and the healthcare value contributions of radiology. Potential steps to objectify and quantify the value contributed by radiology to healthcare are outlined.

Radiology in the Era of Value-Based Healthcare: A Multi-Society Expert Statement From the ACR, CAR, ESR, IS3R, RANZCR, and RSNA.

BACKGROUND: The Value-Based Healthcare (VBH) concept is designed to improve individual healthcare outcomes without increasing expenditure, and is increasingly being used to determine resourcing of and reimbursement for medical services. Radiology is a major contributor to patient and societal healthcare at many levels. Despite this, some VBH models do not acknowledge radiology's central role; this may have future negative consequences for resource allocation. METHODS, FINDINGS AND INTERPRETATION: This multi-society paper, representing the views of Radiology Societies in Europe, the USA, Canada, Australia, and New Zealand, describes the place of radiology in VBH models and the health-care value contributions of radiology. Potential steps to objectify and quantify the value contributed by radiology to healthcare are outlined.

Radiology in the Era of Value-based Healthcare: A Multi-Society Expert Statement from the ACR, CAR, ESR, IS3R, RANZCR, and RSNA.

Background The Value-Based Healthcare (VBH) concept is designed to improve individual healthcare outcomes without increasing expenditure, and is increasingly being used to determine resourcing of and reimbursement for medical services. Radiology is a major contributor to patient and societal healthcare at many levels. Despite this, some VBH models do not acknowledge radiology's central role; this may have future negative consequences for resource allocation. Methods, findings and interpretation This multi-society paper, representing the views of Radiology Societies in Europe, the USA, Canada, Australia, and New Zealand, describes the place of radiology in VBH models and the health-care value contributions of radiology. Potential steps to objectify and quantify the value contributed by radiology to healthcare are outlined. Published under a CC BY 4.0 license.

Radiology in the Era of Value-Based Healthcare: A Multi Society Expert Statement From the ACR, CAR, ESR, IS3R, RANZCR, and RSNA.

BACKGROUND: The Value-Based Healthcare (VBH) concept is designed to improve individual healthcare outcomes without increasing expenditure, and is increasingly being used to determine resourcing of and reimbursement for medical services. Radiology is a major contributor to patient and societal healthcare at many levels. Despite this, some VBH models do not acknowledge radiology's central role; this may have future negative consequences for resource allocation. METHODS, FINDINGS AND INTERPRETATION: This multi-society paper, representing the views of Radiology Societies in Europe, the USA, Canada, Australia, and New Zealand, describes the place of radiology in VBH models and the health-care value contributions of radiology. Potential steps to objectify and quantify the value contributed by radiology to healthcare are outlined.

Canadian Association of Radiologists White Paper on De-Identification of Medical Imaging: Part 1, General Principles.

The application of big data, radiomics, machine learning, and artificial intelligence (AI) algorithms in radiology requires access to large data sets containing personal health information. Because machine learning projects often require collaboration between different sites or data transfer to a third party, precautions are required to safeguard patient privacy. Safety measures are required to prevent inadvertent access to and transfer of identifiable information. The Canadian Association of Radiologists (CAR) is the national voice of radiology committed to promoting the highest standards in patient-centered imaging, lifelong learning, and research. The CAR has created an AI Ethical and Legal standing committee with the mandate to guide the medical imaging community in terms of best practices in data management, access to health care data, de-identification, and accountability practices. Part 1 of this article will inform CAR members on principles of de-identification, pseudonymization, encryption, direct and indirect identifiers, k-anonymization, risks of reidentification, implementations, data set release models, and validation of AI algorithms, with a view to developing appropriate standards to safeguard patient information effectively.

Canadian Association of Radiologists White Paper on De-identification of Medical Imaging: Part 2, Practical Considerations.

The application of big data, radiomics, machine learning, and artificial intelligence (AI) algorithms in radiology requires access to large data sets containing personal health information. Because machine learning projects often require collaboration between different sites or data transfer to a third party, precautions are required to safeguard patient privacy. Safety measures are required to prevent inadvertent access to and transfer of identifiable information. The Canadian Association of Radiologists (CAR) is the national voice of radiology committed to promoting the highest standards in patient-centered imaging, lifelong learning, and research. The CAR has created an AI Ethical and Legal standing committee with the mandate to guide the medical imaging community in terms of best practices in data management, access to health care data, de-identification, and accountability practices. Part 2 of this article will inform CAR members on the practical aspects of medical imaging de-identification, strengths and limitations of de-identification approaches, list of de-identification software and tools available, and perspectives on future directions.

Radiology in the era of value-based healthcare: a multi-society expert statement from the ACR, CAR, ESR, IS3R, RANZCR, and RSNA.

BACKGROUND: The Value-Based Healthcare (VBH) concept is designed to improve individual healthcare outcomes without increasing expenditure, and is increasingly being used to determine resourcing of and reimbursement for medical services. Radiology is a major contributor to patient and societal healthcare at many levels. Despite this, some VBH models do not acknowledge radiology's central role; this may have future negative consequences for resource allocation. METHODS, FINDINGS AND INTERPRETATION: This multi-society paper, representing the views of Radiology Societies in Europe, the USA, Canada, Australia, and New Zealand, describes the place of radiology in VBH models and the healthcare value contributions of radiology. Potential steps to objectify and quantify the value contributed by radiology to healthcare are outlined.

Canadian Association of Radiologists White Paper on Ethical and Legal Issues Related to Artificial Intelligence in Radiology.

Artificial intelligence (AI) software that analyzes medical images is becoming increasingly prevalent. Unlike earlier generations of AI software, which relied on expert knowledge to identify imaging features, machine learning approaches automatically learn to recognize these features. However, the promise of accurate personalized medicine can only be fulfilled with access to large quantities of medical data from patients. This data could be used for purposes such as predicting disease, diagnosis, treatment optimization, and prognostication. Radiology is positioned to lead development and implementation of AI algorithms and to manage the associated ethical and legal challenges. This white paper from the Canadian Association of Radiologists provides a framework for study of the legal and ethical issues related to AI in medical imaging, related to patient data (privacy, confidentiality, ownership, and sharing); algorithms (levels of autonomy, liability, and jurisprudence); practice (best practices and current legal framework); and finally, opportunities in AI from the perspective of a universal health care system.