Department of Cardiovascular Diseases, John Ochsner Heart and Vascular Institute, Ochsner Clinical School - University of Queensland School of Medicine, and the Center for Healthcare Innovation, Ochsner Health System, New Orleans, Louisiana.
Published: May 2017
AI Article Synopsis
Article Abstract
Hypertension (HTN) is the most common chronic disease in the U.S., and the standard model of office-based care delivery has yielded suboptimal outcomes, with approximately 50% of affected patients not achieving blood pressure (BP) control. Poor population-level BP control has been primarily attributed to therapeutic inertia and low patient engagement. New models of care delivery utilizing patient-generated health data, comprehensive assessment of social health determinants, computerized algorithms generating tailored interventions, frequent communication and reporting, and non-physician providers organized as an integrated practice unit, have the potential to transform population-based HTN control. This review will highlight the importance of these elements and construct the rationale for a reengineered model of care delivery for populations with HTN.
Institute of History and Ethics in Medicine, Department of Preclinical Medicine, TUM School of Medicine and Health, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany.
Integrating artificial intelligence (AI) into critical domains such as healthcare holds immense promise. Nevertheless, significant challenges must be addressed to avoid harm, promote the well-being of individuals and societies, and ensure ethically sound and socially just technology development. Innovative approaches like Embedded Ethics, which refers to integrating ethics and social science into technology development based on interdisciplinary collaboration, are emerging to address issues of bias, transparency, misrepresentation, and more.
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