Three Epochs of Artificial Intelligence in Health Care.

Importance: Interest in artificial intelligence (AI) has reached an all-time high, and health care leaders across the ecosystem are faced with questions about where, when, and how to deploy AI and how to understand its risks, problems, and possibilities. Observations: While AI as a concept has existed since the 1950s, all AI is not the same. Capabilities and risks of various kinds of AI differ markedly, and on examination 3 epochs of AI emerge. AI 1.0 includes symbolic AI, which attempts to encode human knowledge into computational rules, as well as probabilistic models. The era of AI 2.0 began with deep learning, in which models learn from examples labeled with ground truth. This era brought about many advances both in people's daily lives and in health care. Deep learning models are task-specific, meaning they do one thing at a time, and they primarily focus on classification and prediction. AI 3.0 is the era of foundation models and generative AI. Models in AI 3.0 have fundamentally new (and potentially transformative) capabilities, as well as new kinds of risks, such as hallucinations. These models can do many different kinds of tasks without being retrained on a new dataset. For example, a simple text instruction will change the model's behavior. Prompts such as "Write this note for a specialist consultant" and "Write this note for the patient's mother" will produce markedly different content. Conclusions and Relevance: Foundation models and generative AI represent a major revolution in AI's capabilities, ffering tremendous potential to improve care. Health care leaders are making decisions about AI today. While any heuristic omits details and loses nuance, the framework of AI 1.0, 2.0, and 3.0 may be helpful to decision-makers because each epoch has fundamentally different capabilities and risks.

Transforming Public Health Data Systems to Advance the Population's Health.

Policy Points Accurate and reliable data systems are critical for delivering the essential services and foundational capabilities of public health for a 21st -century public health infrastructure. Chronic underfunding, workforce shortages, and operational silos limit the effectiveness of America's public health data systems, with the country's anemic response to COVID-19 highlighting the results of long-standing infrastructure gaps. As the public health sector begins an unprecedented data modernization effort, scholars and policymakers should ensure ongoing reforms are aligned with the five components of an ideal public health data system: outcomes and equity oriented, actionable, interoperable, collaborative, and grounded in a robust public health system.

Public Health 3.0: A Call to Action for Public Health to Meet the Challenges of the 21st Century.

Public health is what we do together as a society to ensure the conditions in which everyone can be healthy. Although many sectors play key roles, governmental public health is an essential component. Recent stressors on public health are driving many local governments to pioneer a new Public Health 3.0 model in which leaders serve as Chief Health Strategists, partnering across multiple sectors and leveraging data and resources to address social, environmental, and economic conditions that affect health and health equity. In 2016, the US Department of Health and Human Services launched the Public Health 3.0 initiative and hosted listening sessions across the country. Local leaders and community members shared successes and provided insight on actions that would ensure a more supportive policy and resource environment to spread and scale this model. This article summarizes the key findings from those listening sessions and recommendations to achieve Public Health 3.0.

The US Office of the National Coordinator for Health Information Technology: Progress and Promise for the Future at the 10-Year Mark.

In April 2004, President Bush signed Executive Order 13335, which called for the establishment of the Office of the National Coordinator for Health Information Technology (ONC) within the US Department of Health and Human Services. The President charged ONC with the critical responsibility of ensuring that every American had access to his or her electronic health information and establishing connectivity of health information technology.

Transforming Public Health Practice With Generative Artificial Intelligence.

Public health practice appears poised to undergo a transformative shift as a result of the latest advancements in artificial intelligence (AI). These changes will usher in a new era of public health, charged with responding to deficiencies identified during the COVID-19 pandemic and managing investments required to meet the health needs of the twenty-first century. In this Commentary, we explore how AI is being used in public health, and we describe the advanced capabilities of generative AI models capable of producing synthetic content such as images, videos, audio, text, and other digital content. Viewing the use of AI from the perspective of health departments in the United States, we examine how this new technology can support core public health functions with a focus on near-term opportunities to improve communication, optimize organizational performance, and generate novel insights to drive decision making. Finally, we review the challenges and risks associated with these technologies, offering suggestions for health officials to harness the new tools to accomplish public health goals.

Validation of the Kidney Disease Quality of Life Short Form 36 (KDQOL-36) US Spanish and English versions in a cohort of Hispanics with chronic kidney disease.

OBJECTIVE: Evaluate the reliability and validity of the Kidney Disease Quality of Life Short Form 36 (KDQOL-36) in Hispanics with mild-to-moderate chronic kidney disease (CKD). DESIGN: Cross-sectional SETTING: Chronic Renal Insufficiency Cohort Study PARTICIPANTS: 420 Hispanic (150 English- and 270 Spanish-speakers), and 409 non-Hispanic White individuals, matched by age (mean 57 years), sex (60% male), kidney function (mean estimated glomerular filtration rate 36ml/min/1.73m2), and diabetes (70%). METHODS: To measure construct validity, we selected instruments, comorbidities, and laboratory tests related to at least one KDQOL-36 subscale. Reliability was determined by calculating Cronbach's alpha. RESULTS: Reliability of each KDQOL-36 subscale [SF-12 Physical Component Summary (PCS) and Mental Component Summary (MCS), Symptoms/Problems, Burden of Kidney Disease and Effects of Kidney Disease] was very good (Cronbach's alpha >0.8). Construct validity was supported by expected negative correlation between MCS scores and the Beck Depression Inventory in all three subgroups (r=-0.56 to -0.61, P<.0001). There was inverse correlation between the Symptoms/ Problems subscale and the Patient Symptom Form (r= -0.70 to -0.77, P<.0001). We also found significant, positive correlation between the PCS score and a physical activity survey (r=+0.29 to +0.38, P< or =.003); and between the PCS and MCS scores and the Kansas City Questionnaire (r= +0.31 to +0.64, P<.0001). Reliability and validity were similar across all racial/ethnic groups analyzed separately. CONCLUSION: Our findings support the use of the KDQOL-36 as a measure of HRQOL in this cohort of US Hispanics with CKD.