Relationships among interventions and health literacy outcomes for sub-populations: A data-driven approach

Abstract

The goals of this study were to examine relationships among health literacy and outcomes for sub-populations identified within a large, multi-dimensional Omaha System dataset. Specific aims were to extract sub-populations from the data using Latent Class Analysis (LCA); and quantify the change in knowledge score from pre-to post-intervention for common sub-populations. Design: Data-driven retrospective study using statistical modeling methods. Sample: A set of admission and discharge cases, captured in the Omaha System, representing 65,468 cases from various health care providers. Measures: Demographic information and the Omaha System terms including problems, signs/symptoms, and interventions were used as the features describing cases used for this study. Development of a mapping of demographics across health care systems enabled the integration of data from these different systems. Results: Knowledge scores increased for all five sub-populations identified by latent class analysis. Effect sizes of interventions related to health literacy outcomes varied from low to high, with the greatest effect size in populations of young at-risk adults. The most significant knowledge gains were seen for problems including Pregnancy, Postpartum, Family planning, Mental health, and Substance use. Conclusions: This is the first study to demonstrate positive relationships between interventions and health literacy outcomes for a very large sample. A deeper analysis of the results, focusing on specific problems and relevant interventions and their impact on health literacy is required to guide resource allocation in communitybased care. As such, future work will focus on determining correlations between interventions for specific problems and knowledge change post-intervention.

Exploring Large Community‐ and Clinically‐Generated Datasets to Understand Resilience Before and During the COVID‐19 Pandemic

“…The terms and definitions of standardized nursing terminologies such as the Omaha System are a granular set of concepts that together describe a comprehensive, holistic conceptual framework for health (Martin, 2005). These terms and definitions may be used to operationally define and represent more complex concepts such as the social determinants of health, health literacy, and wellbeing (Monsen, 2018; Monsen et al, 2015; Monsen et al, 2017; Michalowski et al, 2018). Conducting advanced exploratory analyses using data generated by standardized nursing terminologies is possible because this rigorous definitional work was completed a priori (Martin, 2005; Monsen, 2018).…”

Algorithm development to improve intervention effectiveness for parents with mental health signs and symptoms

“…Additional methods, such as algorithm development and AI, have potential to identify high‐risk clients who would benefit from interventions at the time of admission would be beneficial for PHN home visiting services. Latent class analysis using the Omaha System have proven useful in identifying clinically relevant groups and corresponding interventions from PHN documentation data (Michalowski et al., 2018; Monsen et al., 2010; Van Laarhoven et al., 2020). A latent class analysis of clients with mental health signs/symptoms and PHN interventions demonstrated that having more mental health signs or symptoms was positively associated with the number of interventions administered by the PHNs, indicating that nurses are identifying those at risk and tailoring interventions accordingly (Van Laarhoven et al., 2020).…”

“…Informatics methods, such as clinical decisions support, have produced useful insight for PHN care (Lu et al, 2022) (Michalowski et al, 2018;Monsen et al, 2010;Van Laarhoven et al, 2020)…”

Digital Personal Health Coaching Platform for Promoting Human Papillomavirus Infection Vaccinations and Cancer Prevention: Knowledge Graph-Based Recommendation System

“…Accordingly, it is pertinent that novel techniques that support precision health promotion are implemented to increase vaccination uptake behaviors. The application of digital educational interventions for personalized health coaching can generate customized recommendations in a case-by-case manner, improve health literacy and knowledge acquisition, optimize information-seeking behavior, promote healthy behavior (eg, HPV vaccine uptake; sexually transmitted infection [STI], HPV, or genital warts testing; and cervical cancer screening), and lead to better population health outcomes (eg, HPV-associated cancer prevention) [ 13 , 14 ]. Advances in artificial intelligence (AI) and digital technologies have revolutionized several applications in health care [ 15 ], promoting and enhancing personalized patient care.…”