Journal of the American Medical Informatics Association : JAMIA
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J Am Med Inform Assoc · Dec 2020
Patient safety and quality improvement: Ethical principles for a regulatory approach to bias in healthcare machine learning.
Accumulating evidence demonstrates the impact of bias that reflects social inequality on the performance of machine learning (ML) models in health care. Given their intended placement within healthcare decision making more broadly, ML tools require attention to adequately quantify the impact of bias and reduce its potential to exacerbate inequalities. ⋯ Drawing from the ethical principles underpinning these approaches, we argue that patient safety and quality improvement lenses support the quantification of relevant performance metrics, in order to minimize harm while promoting accountability, justice, and transparency. We identify specific methods for operationalizing these principles with the goal of attending to bias to support better decision making in light of controllable and uncontrollable factors.
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J Am Med Inform Assoc · Dec 2020
MINIMAR (MINimum Information for Medical AI Reporting): Developing reporting standards for artificial intelligence in health care.
The rise of digital data and computing power have contributed to significant advancements in artificial intelligence (AI), leading to the use of classification and prediction models in health care to enhance clinical decision-making for diagnosis, treatment and prognosis. However, such advances are limited by the lack of reporting standards for the data used to develop those models, the model architecture, and the model evaluation and validation processes. ⋯ We call for a standard to accurately and responsibly report on AI in health care. This will facilitate the design and implementation of these models and promote the development and use of associated clinical decision support tools, as well as manage concerns regarding accuracy and bias.
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J Am Med Inform Assoc · Dec 2020
Leveraging health system telehealth and informatics infrastructure to create a continuum of services for COVID-19 screening, testing, and treatment.
We describe our approach in using health information technology to provide a continuum of services during the coronavirus disease 2019 (COVID-19) pandemic. COVID-19 challenges and needs required health systems to rapidly redesign the delivery of care. ⋯ There are substantial benefits in utilizing telehealth during the COVID-19, including the ability to rapidly scale the number of patients being screened and providing continuity of care.
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J Am Med Inform Assoc · Dec 2020
Latent bias and the implementation of artificial intelligence in medicine.
Increasing recognition of biases in artificial intelligence (AI) algorithms has motivated the quest to build fair models, free of biases. However, building fair models may be only half the challenge. ⋯ Here we describe 3 major challenges related to bias in AI algorithms and propose several ways of managing them. There is an urgent need to address latent biases before the widespread implementation of AI algorithms in clinical practice.
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J Am Med Inform Assoc · Dec 2020
Characteristics of telehealth users in NYC for COVID-related care during the coronavirus pandemic.
To explore whether racial/ethnic differences in telehealth use existed during the peak pandemic period among NYC patients seeking care for COVID-19 related symptoms. ⋯ The response to COVID-19 has involved an unprecedented expansion in telehealth. While older Americans and minority populations among others are known to be disadvantaged by the digital divide, few studies have examined disparities in telehealth specifically, and none during COVID-19. Additional research into sociodemographic heterogeneity in telehealth use is needed to prevent potentially further exacerbating health disparities overall.