The characteristics of a person's health status are often guided by how they live, grow, learn, their genetics, as well as their access to health care. Yet, all too often, studies examining the relationship between social determinants of health (behavioral, sociocultural, and physical environmental factors), the role of demographics, and health outcomes poorly represent these relationships, leading to misinterpretations, limited study reproducibility, and datasets with limited representativeness and secondary research use capacity. This is a profound hurdle in what questions can or cannot be rigorously studied about COVID‐19. In practice, gene–environment interactions studies have paved the way for including these factors into research. Similarly, our understanding of social determinants of health continues to expand with diverse data collection modalities as health systems, patients, and community health engagement aim to fill the knowledge gaps toward promoting health and wellness. Here, a conceptual framework is proposed, adapted from the population health framework, socioecological model, and causal modeling in gene–environment interaction studies to integrate the core constructs from each domain with practical considerations needed for multidisciplinary science.
As society moves towards a preventative approach to healthcare, there is growing interest in scientific research involving technology that can monitor and prevent adverse health outcomes. The primary objective of this paper is to develop an Internet of Things (IoT) wearable system based on Fried’s phenotype that is capable of detecting frailty. To determine user requirements, the system’s architecture was designed based on the findings of a questionnaire administered to individuals confirmed to be frail. A functional prototype was successfully developed and tested under real-world conditions. This paper introduces the methodology that was used to analyze the data collected from the prototype. It proposes an interdisciplinary approach to interpret wearable sensor data, providing a comprehensive overview through both visual representations and computational analyses facilitated by machine learning models. The findings of these analyses offer insights into the ways in which different types of activities can be classified and quantified as part of an overall physical activity level, which is recognized as an important indicator of frailty. The results provide the foundations for a new generation of affordable and non-intrusive systems able to detect and assess early signs of frailty.
Public Health (PH) applications in County of Los Angeles (LAC), Department of PH have been developed to meet individual PH program’s goals. This resulted in lack of county-wide PH data integration, efficiency, and usefulness. LAC encouraged the development of web-based applications utilizing standards-based PH Information Network interoperable service-oriented architecture (SOA). The goal was to stop the evolution of fragmented health data systems which place limitations on the PH mission of safeguarding and improving the health of the community as well as responding to large-scale threats to PH. PH Nursing case management is one example of LAC’s initiative for promotion of web-based tools which will be utilized within this SOA. This PH architecture is capable of supporting electronic data exchange from PH partners using a HL7 integration hub. It promotes the development of management tools and applications to assist PH response and recovery activities while providing resources to support departmental integration.
Patient Clinical Management tools are necessary for doctor's and support staff to undertake patient care more efficiently. Such tools are also of interest from an administrative perspective because they help reduce costs. We have developed a mobile patient clinical management tool, CALDr + . This tool holds all the vital information for patient's under a clinician or institutional care in a mobile platform. This allows rapid access simultaneously to both patient specific clinical and administrative information within specific disease domains. CALDr + is designed such that it can interact with existing Hospital Information Systems.
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