AI Governance: A Challenge for Public Health.

The rapid evolution of artificial intelligence (AI) is structuralizing social, political, and economic determinants of health into the invisible algorithms that shape all facets of modern life. Nevertheless, AI holds immense potential as a public health tool, enabling beneficial objectives such as precision public health and medicine. Developing an AI governance framework that can maximize the benefits and minimize the risks of AI is a significant challenge.

Navigating New Legal Guardrails and Emerging AI Challenges.

Here, we analyze the public health implications of recent legal developments - including privacy legislation, intergovernmental data exchange, and artificial intelligence governance - with a view toward the future of public health informatics and the potential of diverse data to inform public health actions and drive population health outcomes.

Changing US Support for Public Health Data Use Through Pandemic and Political Turmoil.

Unlabelled: Policy Points This study examines the impact of several world-changing events in 2020, such as the pandemic and widespread racism protests, on the US population's comfort with the use of identifiable data for public health. Before the 2020 election, there was no significant difference between Democrats and Republicans. However, African Americans exhibited a decrease in comfort that was different from other subgroups.

Data Sharing in a Decentralized Public Health System: Lessons From COVID-19 Syndromic Surveillance.

The COVID-19 pandemic revealed that data sharing challenges persist across public health information systems. We examine the specific challenges in sharing syndromic surveillance data between state, local, and federal partners. These challenges are complicated by US federalism, which decentralizes public health response and creates friction between different government units.

Case study on communicating with research ethics committees about minimizing risk through software: an application for record linkage in secondary data analysis.

Objective: In retrospective secondary data analysis studies, researchers often seek waiver of consent from institutional Review Boards (IRB) and minimize risk by utilizing complex software. Yet, little is known about the perspectives of IRB experts on these approaches. To facilitate effective communication about risk mitigation strategies using software, we conducted two studies with IRB experts to co-create appropriate language when describing a software to IRBs.

A target-cell limited model can reproduce influenza infection dynamics in hosts with differing immune responses.

We consider a hierarchy of ordinary differential equation models that describe the within-host viral kinetics of influenza infections: the IR model explicitly accounts for an immune response to the virus, while the simpler, target-cell limited TEIV and TV models do not. We show that when the IR model is fitted to pooled experimental murine data of the viral load, fraction of dead cells, and immune response levels, its parameters values can be determined. However, if, as is common, only viral load data are available, we can estimate parameters of the TEIV and TV models but not the IR model.

Views on increased federal access to state and local National Syndromic Surveillance Program data: a nominal group technique study with state and local epidemiologists.

Background: US public health authorities use syndromic surveillance to monitor and detect public health threats, conditions, and trends in near real-time. Nearly all US jurisdictions that conduct syndromic surveillance send their data to the National Syndromic Surveillance Program (NSSP), operated by the US. Centers for Disease Control and Prevention.

Leveraging IP for AI governance.

Copyleft AI with Trusted Enforcement (CAITE) can support an adaptable so ft law approach for ethics in AI.

Positive Impacts of a Four-Week Neuro-Meditation Program on Cognitive Function in Post-Acute Sequelae of COVID-19 Patients: A Randomized Controlled Trial.

Study objective: Long COVID patients can experience high levels of impairment in their cognitive function and mental health. Using a parallel randomized control trial, we evaluated the effectiveness of a neuro-meditation program to reduce cognitive impairment in patients with long COVID. Methods: A total of 34 patients with long COVID were randomized to an intervention group (G-Int; n = 17) or a control group (G-Con; n = 17) and 15 healthy participants were constitutive of a normative group (G-Nor).

A Study of Publicly Available Resources Addressing Legal Data-Sharing Barriers: Systematic Assessment.

Background: United States data protection laws vary depending on the data type and its context. Data projects involving social determinants of health often concern different data protection laws, making them difficult to navigate.

Objective: We systematically aggregated and assessed useful online resources to help navigate the data-sharing landscape.

The Changing Nature of Telehealth Use by Primary Care Physicians in the United States.

Introduction: Prior to the COVID-19 pandemic, telehealth utilization was growing slowly and steadily, although differentially across medical specialties in the United States. The pandemic dramatically expanded physician use of telehealth, but our understanding of how much telehealth use has changed in primary care in the United States, the correlates of physician telehealth uptake, and the frequency with which primary care physicians intend to use telehealth after the pandemic are unknown. This paper is designed to assess these important questions.

Community health worker roles and their evolving interprofessional relationships in the United States.

In the United States, growing attention to the cost of care, the social determinants of health, prevention, and population health, signals a refocusing of efforts on value-based care. Just as Accountable Care Organizations and alternative payment models exemplify this shift in attention, so does the increasing integration of Community Health Workers (CHWs) into the US health care system. CHWs are often referred to as "bridge figures," helping clients to navigate what are oftentimes complicated pathways to access a variety of needed services.

US Privacy Laws Go Against Public Preferences and Impede Public Health and Research: Survey Study.

Background: Reaping the benefits from massive volumes of data collected in all sectors to improve population health, inform personalized medicine, and transform biomedical research requires the delicate balance between the benefits and risks of using individual-level data. There is a patchwork of US data protection laws that vary depending on the type of data, who is using it, and their intended purpose. Differences in these laws challenge big data projects using data from different sources.

Identifying and prioritizing benefits and risks of using privacy-enhancing software through participatory design: a nominal group technique study with patients living with chronic conditions.

Objective: While patients often contribute data for research, they want researchers to protect their data. As part of a participatory design of privacy-enhancing software, this study explored patients' perceptions of privacy protection in research using their healthcare data.

Materials And Methods: We conducted 4 focus groups with 27 patients on privacy-enhancing software using the nominal group technique.

Changes to Timeliness and Completeness of Infectious Disease Reporting in Texas After Implementation of an Epidemiologic Capacity Program.

Objectives: The objective of this study was to characterize the changes in timeliness and completeness of disease case reporting in Texas in response to an increasing number of foodborne illnesses and high-consequence infectious disease investigations and the Texas Department of State Health Services' new state-funded epidemiologist (SFE) program.

Methods: We extracted electronic disease case reporting data on 42 conditions from 2012 through 2016 in all local health department (LHD) jurisdictions. We analyzed data on median time for processing reports and percentage of complete reports across time and between SFE and non-SFE jurisdictions using Mann-Whitney tests and scores.