ABSTRACT
Artificial intelligence (AI) has the potential to transform care delivery by improving health outcomes, patient safety, and the affordability and accessibility of high-quality care. AI will be critical to building an infrastructure capable of caring for an increasingly aging population, utilizing an ever-increasing knowledge of disease and options for precision treatments, and combatting workforce shortages and burnout of medical professionals. However, we are not currently on track to create this future. This is in part because the health data needed to train, test, use, and surveil these tools are generally neither standardized nor accessible. There is also universal concern about the ability to monitor health AI tools for changes in performance as they are implemented in new places, used with diverse populations, and over time as health data may change. The Future of Health (FOH), an international community of senior health care leaders, collaborated with the Duke-Margolis Institute for Health Policy to conduct a literature review, expert convening, and consensus-building exercise around this topic. This commentary summarizes the four priority action areas and recommendations for health care organizations and policymakers across the globe that FOH members identified as important for fully realizing AI's potential in health care: improving data quality to power AI, building infrastructure to encourage efficient and trustworthy development and evaluations, sharing data for better AI, and providing incentives to accelerate the progress and impact of AI.
ABSTRACT
Primary care is the largest healthcare delivery platform in the US. Facing the Artificial Intelligence and Machine Learning technology (AI/ML) revolution, the primary care community would benefit from a roadmap revealing priority areas and opportunities for developing and integrating AI/ML-driven clinical tools. This article presents a framework that identifies five domains for AI/ML integration in primary care to support care delivery transformation and achieve the Quintuple Aims of the healthcare system. We concluded that primary care plays a critical role in developing, introducing, implementing, and monitoring AI/ML tools in healthcare and must not be overlooked as AI/ML transforms healthcare.
Subject(s)
Artificial Intelligence , Machine Learning , Delivery of Health Care , Health Facilities , Humans , Primary Health CareSubject(s)
COVID-19/epidemiology , School Health Services/organization & administration , Schools/organization & administration , Students/statistics & numerical data , Disease Outbreaks/prevention & control , Humans , Public Health , Residence Characteristics , Retrospective Studies , Risk Assessment , School Teachers/statistics & numerical dataABSTRACT
The complexity and costs associated with traditional randomized, controlled trials have increased exponentially over time, and now threaten to stifle the development of new drugs and devices. Nevertheless, the growing use of electronic health records, mobile applications, and wearable devices offers significant promise for transforming clinical trials, making them more pragmatic and efficient. However, many challenges must be overcome before these innovations can be implemented routinely in randomized, controlled trial operations. In October of 2018, a diverse stakeholder group convened in Washington, DC, to examine how electronic health record, mobile, and wearable technologies could be applied to clinical trials. The group specifically examined how these technologies might streamline the execution of clinical trial components, delineated innovative trial designs facilitated by technological developments, identified barriers to implementation, and determined the optimal frameworks needed for regulatory oversight. The group concluded that the application of novel technologies to clinical trials provided enormous potential, yet these changes needed to be iterative and facilitated by continuous learning and pilot studies.
Subject(s)
Clinical Trials as Topic , Electronic Health Records , Mobile Applications , Wearable Electronic Devices , Humans , Research DesignSubject(s)
Alzheimer Disease/drug therapy , Drugs, Investigational/economics , Nootropic Agents/pharmacology , United States Food and Drug Administration/standards , Clinical Trials as Topic , Drug Evaluation/economics , Drug Evaluation/methods , Drug Evaluation/trends , Government Regulation , Health Policy/legislation & jurisprudence , Health Policy/trends , Humans , Patient Outcome Assessment , United StatesABSTRACT
The need for efficient and controlled delivery is one of the major obstacles to clinical use of gene therapy. In this study, we investigated the use of magnetic resonance imaging-monitored ultrasound (US) to induce expression of luciferase after local injection of the construct Ad-HSP-Luc, an adenoviral vector containing a transgene encoding firefly luciferase under the control of the human hsp70B promoter. The hsp promoter allows induction of the associated transgene only in areas that are subsequently heated after infection. US imaging was used to guide the injection of purified virus into both lobes of the prostates of three beagles. At 48 h after injection, the left lobe of the prostate was heated using a 1.5-MHz US transducer driven by a multichannel radiofrequency system and employing an magnetic resonance imaging guidance system. High levels of luciferase expression were observed only in areas exposed to ultrasonic heating. This study demonstrates the feasibility of using ultrasonic heating to control transgene expression spatially using a minimally-invasive approach.