Objectives To describe the education, experience, skills, and knowledge required for health informatics jobs in the United States. Methods Health informatics job postings (n = 206) from Indeed.com on April 14, 2020 were analyzed in an empirical analysis, with the abstraction of attributes relating to requirements for average years and types of experience, minimum and desired education, licensure, certification, and informatics skills. Results A large percentage (76.2%) of posts were for clinical informaticians, with 62.1% of posts requiring a minimum of a bachelor's education. Registered nurse (RN) licensure was required for 40.8% of posts, and only 7.3% required formal education in health informatics. The average experience overall was 1.6 years (standard deviation = 2.2), with bachelor's and master's education levels increasing mean experience to 3.5 and 5.8 years, respectively. Electronic health record support, training, and other clinical systems were the most sought-after skills. Conclusion This cross-sectional study revealed the importance of a clinical background as an entree into health informatics positions, with RN licensure and clinical experience as common requirements. The finding that informatics-specific graduate education was rarely required may indicate that there is a lack of alignment between academia and industry, with practical experience preferred over specific curricular components. Clarity and shared understanding of terms across academia and industry are needed for defining and advancing the preparation for and practice of health informatics.

During a pandemic, basic public health precautions must be taken across settings and populations. However, confinement conditions change what can be done in correctional settings. Correctional nursing (CN) care, like all nursing care, needs to be named and encoded to be recognized and used to generate data that will advance the discipline and maintain standards of care. The Omaha System is a standardized interprofessional terminology that has been used since 1992 to guide and document care. In 2019, a collaboration between the newly formed American Correctional Nurses Association and the Omaha System Community of Practice began a joint effort with other stakeholders aimed at encoding evidence-based pandemic response interventions used in CN. The resulting guidelines are included and illustrated with examples from CN practice.

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.