OBJECTIVE: This study experimentally evaluated how well lay individuals could interpret and use 4 types of electronic health record (EHR) patient-facing immunization visualizations. MATERIALS AND METHODS: Participants (n = 69) completed the study using a secure online survey platform. Participants viewed the same immunization information in 1 of 4 EHR-based immunization visualizations: 2 different patient portals (Epic MyChart and eClinicWorks), a downloadable EHR record, and a clinic-generated electronic letter (eLetter). Participants completed a common task, created a standard vaccine schedule form, and answered questions about their perceived workload, subjective numeracy and health literacy, demographic variables, and familiarity with the task. RESULTS: The design of the immunization visualization significantly affected both task performance measures (time taken to complete the task and number of correct dates). In particular, those using Epic MyChart took significantly longer to complete the task than those using eLetter or eClinicWorks. Those using Epic MyChart entered fewer correct dates than those using the eLetter or eClinicWorks. There were no systematic statistically significant differences in task performance measures based on the numeracy, health literacy, demographic, and experience-related questions we asked. DISCUSSION: The 4 immunization visualizations had unique design elements that likely contributed to these performance differences. CONCLUSION: Based on our findings, we provide practical guidance for the design of immunization visualizations, and future studies. Future research should focus on understanding the contexts of use and design elements that make tables an effective type of health data visualization.

OBJECTIVE: To construct an exhaustive Complementary and Integrative Health (CIH) Lexicon (CIHLex) to help better represent the often underrepresented physical and psychological CIH approaches in standard terminologies, and to also apply state-of-the-art natural language processing (NLP) techniques to help recognize them in the biomedical literature. MATERIALS AND METHODS: We constructed the CIHLex by integrating various resources, compiling and integrating data from biomedical literature and relevant sources of knowledge. The Lexicon encompasses 724 unique concepts with 885 corresponding unique terms. We matched these concepts to the Unified Medical Language System (UMLS), and we developed and utilized BERT models comparing their efficiency in CIH named entity recognition to well-established models including MetaMap and CLAMP, as well as the large language model GPT3.5-turbo. RESULTS: Of the 724 unique concepts in CIHLex, 27.2% could be matched to at least one term in the UMLS. About 74.9% of the mapped UMLS Concept Unique Identifiers were categorized as "Therapeutic or Preventive Procedure." Among the models applied to CIH named entity recognition, BLUEBERT delivered the highest macro-average F1-score of 0.91, surpassing other models. CONCLUSION: Our CIHLex significantly augments representation of CIH approaches in biomedical literature. Demonstrating the utility of advanced NLP models, BERT notably excelled in CIH entity recognition. These results highlight promising strategies for enhancing standardization and recognition of CIH terminology in biomedical contexts.

OBJECTIVE: Identifying consumer health informatics (CHI) literature is challenging. To recommend strategies to improve discoverability, we aimed to characterize controlled vocabulary and author terminology applied to a subset of CHI literature on wearable technologies. MATERIALS AND METHODS: To retrieve articles from PubMed that addressed patient/consumer engagement with wearables, we developed a search strategy of textwords and Medical Subject Headings (MeSH). To refine our methodology, we used a random sample of 200 articles from 2016 to 2018. A descriptive analysis of articles (N = 2522) from 2019 identified 308 (12.2%) CHI-related articles, for which we characterized their assigned terminology. We visualized the 100 most frequent terms assigned to the articles from MeSH, author keywords, CINAHL, and Engineering Databases (Compendex and Inspec together). We assessed the overlap of CHI terms among sources and evaluated terms related to consumer engagement. RESULTS: The 308 articles were published in 181 journals, more in health journals (82%) than informatics (11%). Only 44% were indexed with the MeSH term "wearable electronic devices." Author keywords were common (91%) but rarely represented consumer engagement with device data, eg, self-monitoring (n = 12, 0.7%) or self-management (n = 9, 0.5%). Only 10 articles (3%) had terminology from all sources (authors, PubMed, CINAHL, Compendex, and Inspec). DISCUSSION: Our main finding was that consumer engagement was not well represented in health and engineering database thesauri. CONCLUSIONS: Authors of CHI studies should indicate consumer/patient engagement and the specific technology investigated in titles, abstracts, and author keywords to facilitate discovery by readers and expand vocabularies and indexing.

OBJECTIVE: Theory-based research of social and behavioral determinants of health (SBDH) found SBDH-related patterns in interventions and outcomes for pregnant/birthing people. The objectives of this study were to replicate the theory-based SBDH study with a new sample, and to compare these findings to a data-driven SBDH study. MATERIALS AND METHODS: Using deidentified public health nurse-generated Omaha System data, 2 SBDH indices were computed separately to create groups based on SBDH (0-5+ signs/symptoms). The data-driven SBDH index used multiple linear regression with backward elimination to identify SBDH factors. Changes in Knowledge, Behavior, and Status (KBS) outcomes, numbers of interventions, and adjusted R-squared statistics were computed for both models. RESULTS: There were 4109 clients ages 13-40 years. Outcome patterns aligned with the original research: KBS increased from admission to discharge with Knowledge improving the most; discharge KBS decreased as SBDH increased; and interventions increased as SBDH increased. Slopes of the data-driven model were steeper, showing clearer KBS trends for data-driven SBDH groups. The theory-based model adjusted R-squared was 0.54 (SE = 0.38) versus 0.61 (SE = 0.35) for the data-driven model with an entirely different set of SBDH factors. CONCLUSIONS: The theory-based approach provided a framework to identity patterns and relationships and may be applied consistently across studies and populations. In contrast, the data-driven approach can provide insights based on novel patterns for a given dataset and reveal insights and relationships not predicted by existing theories. Data-driven methods may be an advantage if there is sufficiently comprehensive SBDH data upon which to create the data-driven models.

Nursing terminologies like the Omaha System are foundational in realizing the vision of formal representation of social determinants of health (SDOH) data and whole-person health across biological, behavioral, social, and environmental domains. This study objective was to examine standardized consumer-generated SDOH data and resilience (strengths) using the MyStrengths+MyHealth (MSMH) app built using Omaha System. Overall, 19 SDOH concepts were analyzed including 19 Strengths, 175 Challenges, and 76 Needs with additional analysis around Income Challenges. Data from 919 participants presented an average of 11(SD = 6.1) Strengths, 21(SD = 15.8) Challenges, and 15(SD = 14.9) Needs. Participants with at least one Income Challenge (n = 573) had significantly (P < .001) less Strengths [9.4(6.4)], more Challenges [27.4(15.5)], and more Needs [15.1(14.9)] compared to without an Income Challenge (n = 337) Strengths [13.4(4.5)], Challenges [10.5(8.9)], and Needs [5.1(10.0)]. This standards-based approach to examining consumer-generated SDOH and resilience data presents a great opportunity in understanding 360-degree whole-person health as a step towards addressing health inequities.

OBJECTIVE: Numerous studies indicate that the social determinants of health (SDOH), conditions in which people work, play, and learn, account for 30%-55% of health outcomes. Many healthcare and social service organizations seek ways to collect, integrate, and address the SDOH. Informatics solutions such as standardized nursing terminologies may facilitate such goals. In this study, we compared one standardized nursing terminology, the Omaha System, in its consumer-facing form, Simplified Omaha System Terms (SOST), to social needs screening tools identified by the Social Interventions Research and Evaluation Network (SIREN). MATERIALS AND METHODS: Using standard mapping techniques, we mapped 286 items from 15 SDOH screening tools to 335 SOST challenges. The SOST assessment includes 42 concepts across 4 domains. We analyzed the mapping using descriptive statistics and data visualization techniques. RESULTS: Of the 286 social needs screening tools items, 282 (98.7%) mapped 429 times to 102 (30.7%) of the 335 SOST challenges from 26 concepts in all domains, most frequently from Income, Home, and Abuse. No single SIREN tool assessed all SDOH items. The 4 items not mapped were related to financial abuse and perceived quality of life. DISCUSSION: SOST taxonomically and comprehensively collects SDOH data compared to SIREN tools. This demonstrates the importance of implementing standardized terminologies to reduce ambiguity and ensure the shared meaning of data. CONCLUSIONS: SOST could be used in clinical informatics solutions for interoperability and health information exchange, including SDOH. Further research is needed to examine consumer perspectives regarding SOST assessment compared to other social needs screening tools.

OBJECTIVE: This study aimed to identify phenotypes of nutritional needs of home-visited clients with low income, and compare overall changes in knowledge, behavior, and status of nutritional needs before and after home visits by identified phenotypes. MATERIALS AND METHODS: Omaha System data collected by public health nurses from 2013 to 2018 were used in this secondary data analysis study. A total of 900 low-income clients were included in the analysis. Latent class analysis (LCA) was used to identify phenotypes of nutrition symptoms or signs. Score changes in knowledge, behavior, and status were compared by phenotype. RESULTS: The five subgroups included Unbalanced Diet, Overweight, Underweight, Hyperglycemia with Adherence, and Hyperglycemia without Adherence. Only the Unbalanced Diet and Underweight groups showed an increase in knowledge. No other changes in behavior and status were observed in any of the phenotypes. DISCUSSION AND CONCLUSIONS: This LCA using standardized Omaha System Public Health Nursing data allowed us to identify phenotypes of nutritional needs among home-visited clients with low income and prioritize nutrition areas that public health nurses may focus on as part of public health nursing interventions. The sub-optimal changes in knowledge, behavior, and status suggest a need to re-examine the intervention details by phenotype and develop strategies to tailor public health nursing interventions to effectively meet the diverse nutritional needs of home-visited clients.

The COVID-19 pandemic has once again highlighted the ubiquity and persistence of health inequities along with our inability to respond to them in a timely and effective manner. There is an opportunity to address the limitations of our current approaches through new models of informatics-enabled research and clinical practice that shift the norm from small- to large-scale patient engagement. We propose augmenting our approach to address health inequities through informatics-enabled citizen science, challenging the types of questions being asked, prioritized, and acted upon. We envision this democratization of informatics that builds upon the inclusive tradition of community-based participatory research (CBPR) as a logical and transformative step toward improving individual, community, and population health in a way that deeply reflects the needs of historically marginalized populations.