David M. Hartley

Situational awareness is the accurate and timely perception of factors in the environment, comprehension of their meanings, and projection of their future states. We aimed to develop a cloud-based Situational Awareness and Learning Tool (SALT) that generates near-real-time analytic content from multimodal health care, government, community, and environmental data, enabling public health and hospital professionals to make informed decisions during complex population health challenges. Several focus groups were conducted with representatives from local health departments, hospitals, and emergency agencies. The first round identified data needs and requirements to inform SALT's design. SALT was developed as a secure, cloud-based platform featuring automated deployment, role-based access, and version-controlled content publishing. The second round of focus groups evaluated the SALT prototype's utility and gathered feedback for improvements. Participants highlighted the need for integrated data from multiple sources, tailored dashboards for specific audiences, and legal frameworks to guide timely data sharing. SALT met these requirements by providing interactive visuals, secure access levels, and a collaborative content management system. The second focus groups affirmed SALT's effectiveness in enhancing decision-making and strategic planning, suggesting enhancements such as clearer data labeling, expanded data coverage, and forecasting capabilities. SALT addresses limitations exposed by the COVID-19 pandemic in public health data systems by offering a scalable platform for data sharing, rapid analysis, and situational awareness. It fulfills user needs for integrated, timely data, and customized analytic products. SALT represents a viable solution for enhancing public health data systems in preparation for future pandemics and other complex, multisector population health challenges.

It is not uncommon for basic scientists to switch into public health research. Such career transitions present a variety of challenges and opportunities and can reinvigorate a career, lead to new skills, and provide the chance to contribute to individual and community health and social justice. Based on our respective experiences switching from applied physics to infectious disease modelling and from evolutionary physiology to cancer prevention and control, we propose ten simple rules intended to help researchers from other disciplines think about a transition to public health research. Together, these rules are largely about navigating between pairs of extremes related to why you want to move in a new direction, how to balance old and new expertise, and balancing humility with the confidence that you are bringing something important to the table. A career transition can also fulfill some of the basic motivators for a research career, including curiosity and a passion to try to solve important problems. Our career transitions proved deeply satisfying. We hope yours will as well.

Ongoing experience and emerging evidence suggest that Collaborative Learning Health Systems (CLHSs) are a promising approach to transforming population outcomes and organizational care processes. As implied in their name, this type of Learning Health System both promotes and relies upon collaboration to achieve its aims. However, to realize the promise of the Collaborative Learning Health System, a better understanding of engagement as a catalyst for collaboration is necessary. In this commentary, we elaborate the phenomenon of engagement. We define engagement in the context of CLHSs, consider dimensions of engagement, and further explore the role of engagement as a catalyst of network functioning. We conclude by offering an agenda for research and practice intended to develop an understanding of engagement so as to further advance the theory and practice of CLHS efforts and ultimately promote the uptake of the CLHS model.

As the COVID-19 pandemic progressed, reliable, accessible, and equitable community-based testing strategies were sought that did not flood already overburdened hospitals and emergency departments. In Hamilton County, Ohio, home to ~800 000 people across urban, suburban, and rural areas, we sought to develop and optimize an accessible, equitable county-wide COVID-19 testing program. Using Coronavirus Aid, Relief, and Economic Security Act funding, multidisciplinary, multiorganization partners created the test and protect program to deliver safe, reliable testing in neighborhoods and organizations needing it most. Our approach involved: (1) use of geospatial analytics to identify testing locations positioned to optimize access; (2) community engagement to ensure sites were in trusted places; and (3) tracking of data over time to facilitate ongoing improvement. Between August 2020 and December 2021, more than 65 000 tests were completed for nearly 46 000 individuals at community-based testing sites. These methods could have application beyond COVID-19 and our region.

Introduction: The COVID‐19 pandemic disproportionately affected congregate care (CC) facilities due to communal living, presence of vulnerable populations, inadequate preventive resources, and limited ability to respond to the pandemic's rapidly evolving phases. Most facilities function independently and are not organized for collaborative learning and operations.Methods: We formed a learning health system of CC facilities in our 14‐county metropolitan region, coordinated with public health and health care sectors, to address challenges driven by COVID‐19. A CC steering committee (SC) was formed that represented diverse institutions and viewpoints, including skilled nursing facilities, transitional care facilities, residential facilities, prisons, and shelters. The SC met regularly and was guided by situational awareness and systems thinking. A regional CC COVID‐19 dashboard was developed based on publicly available data and weekly data submitted by participating facilities. Those experiencing outbreaks or supply shortages were quickly identified. As the pandemic progressed, the role of the SC shifted to address new and forecasted needs.Results: Over 60 facilities participated in data sharing. The SC shared new guidelines, regulations, educational material, and best practices with the participating facilities. Information about testing sites, supplies, vaccination rollout, and facilities that had the capacity to accept COVID‐19 patients was regularly disseminated. The SC was able to direct resources to those facilities experiencing outbreaks or supply shortages.Conclusions: A novel learning health system of regional CC facilities enabled preparedness, situational awareness, collaboration, and rapid dissemination of best practices across pandemic phases. Such collaborative efforts can play an important role in addressing other public and preventive health challenges.

Introduction: The COVID‐19 pandemic revealed numerous barriers to effectively managing public health crises, including difficulties in using publicly available, community‐level data to create learning systems in support of local public health decision responses. Early in the COVID‐19 pandemic, a group of health care partners began meeting to learn from their collective experiences. We identified key tools and processes for using data and learning system structures to drive equitable public health decision making throughout different phases of the pandemic.Methods: In fall of 2021, the team developed an initial theory of change directed at achieving herd immunity for COVID‐19. The theoretical drivers were explored qualitatively through a series of nine 45‐min telephonic interviews conducted with 16 public health and community leaders across the United States. Interview responses were analyzed into key themes to inform potential future practices, tools, and systems. In addition to the interviews, partners in Dallas and Cincinnati reflected on their own COVID‐19 experiences.Results: Interview responses fell broadly into four themes that contribute to effective, community driven responses to COVID‐19: real‐time, accessible data that are mindful of the tension between community transparency and individual privacy; a continued fostering of public trust; adaptable infrastructures and systems; and creating cohesive community coalitions with shared alignment and goals. These themes and partner experiences helped us revise our preliminary theory of change around the importance of community collaboration and trust building and also helped refine the development of the Community Protection Dashboard tool.Conclusions: There was broad agreement amongst public health and community leaders about the key elements of the data and learning systems required to manage public health responses to COVID‐19. These findings may be informative for guiding the use of data and learning in the management of future public health crises or population health initiatives.