OBJECTIVE: Modernizing and strengthening the US public health data and information infrastructure requires a strong public health informatics (PHI) workforce. The study objectives were to characterize existing PHI specialists and assess informatics-related training needs. MATERIALS AND METHODS: To examine the PHI workforce, the 2021 Public Health Workforce Interests and Needs Survey (PH WINS), a nationally representative survey with 44 732 governmental public health (PH) respondents was analyzed. The survey included data from 47 state health agencies-central office, 29 large local health departments (Big Cities Health Coalition members), and 259 other local/regional health departments. Analysis focused on "public health informatics specialist" (PHI), "information system manager/information technology specialist" (IT/IS), "public health science" (PHS), and "clinical and laboratory" (CL) roles. RESULTS: PHI specialists account for less than 2% of the governmental PH workforce. A majority were female (68%), White (55%), and close to half in 31-50 age category (49%). Most (74%) were in non-supervisory roles and <1% in managerial/executive roles, with less than one-third (29%) earning >$75 000 salary. Skill gaps on informatics-related tasks included: identify appropriate data/information sources; collect valid data for decision making; participate in quality improvement processes; identify evidence-based approaches. The PHI specialists reported lower skill gaps in data/informatics areas when compared to other public health roles (PHS and CL), and this was consistent across state/local settings. DISCUSSION: Given the scale of work needed for modernization of information systems, PH agencies need more individuals in informatics roles. To attract PHI specialists, better salaries, clear PHI job classifications and permanent PHI workers are needed, which requires sustained investments from federal and state governments. CONCLUSION: Efforts to train PHI specialists, recruit and retain them in the governmental public health workforce, and address hiring issues in public health agencies are essential next steps to transform the US public health enterprise.

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.

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.

Electronic case reporting (eCR) is the automated generation and transmission of case reports from electronic health records to public health for review and action. These reports (electronic initial case reports: eICRs) adhere to recommended exchange and terminology standards. eCR is a partnership of the Centers for Disease Control and Prevention (CDC), Association of Public Health Laboratories (APHL) and Council of State and Territorial Epidemiologists (CSTE). The Minnesota Department of Health (MDH) received eICRs for COVID-19 from April 2020 (3 sites, manual process), automated eCR implementation in August 2020 (7 sites), and on-boarded ∼1780 clinical units in 460 sites across 6 integrated healthcare systems (through March 2022). Approximately 20 000 eICRs/month were reported to MDH during high-volume timeframes. With increasing provider/health system implementation, the proportion of COVID-19 cases with an eICR increased to 30% (March 2022). Evaluation of data quality for select demographic variables (gender, race, ethnicity, email, phone, language) across the 6 reporting health systems revealed a high proportion of completeness (>80%) for half of variables and less complete data for rest (ethnicity, email, language) along with low ethnicity data (<50%) for one health system. Presently eCR implementation at MDH includes only one EHR vendor. Next steps will focus on onboarding other EHRs, additional eICR data extraction/utilization, detailed analysis, outreach to address data quality issues, and expanding to other reportable conditions.

Developing a diverse informatics workforce broadens the research agenda and ensures the growth of innovative solutions that enable equity-centered care. The American Medical Informatics Association (AMIA) established the AMIA First Look Program in 2017 to address workforce disparities among women, including those from marginalized communities. The program exposes women to informatics, furnishes mentors, and provides career resources. In 4 years, the program has introduced 87 undergraduate women, 41% members of marginalized communities, to informatics. Participants from the 2019 and 2020 cohorts reported interest in pursuing a career in informatics increased from 57% to 86% after participation, and 86% of both years' attendees responded that they would recommend the program to others. A June 2021 LinkedIn profile review found 50% of participants working in computer science or informatics, 4% pursuing informatics graduate degrees, and 32% having completed informatics internships, suggesting AMIA First Look has the potential to increase informatics diversity.

Reports by the National Academy of Medicine and leading public health organizations advocate including occupational information as part of an individual's social context. Given recent National Academy of Medicine recommendations on occupation-related data in the electronic health record, there is a critical need for improved representation. The National Institute for Occupational Safety and Health has developed an Occupational Data for Health (ODH) model, currently in draft format. This study aimed to validate the ODH model by mapping occupation-related elements from resources representing recommendations, standards, public health reports and surveys, and research measures, along with preliminary evaluation of associated value sets. All 247 occupation-related items across 20 resources mapped to the ODH model. Recommended value sets had high variability across the evaluated resources. This study demonstrates the ODH model's value, the multifaceted nature of occupation information, and the critical need for occupation value sets to support clinical care, population health, and research.

The healthcare landscape is changing, driven by innovative care models and the emergence of new roles that are inter-professional in nature. Currently, the HL7/LOINC Document Ontology (DO) aids the use and exchange of clinical documents using a multi-axis structure of document attributes for Kind of Document, Setting, Role, Subject Matter Domain, and Type of Service. In this study, the adequacy of the Role axis for representing the type of author documenting care was assessed. Experts used a master list of 220 values created from seven resources and established mapping guidelines. Baseline certification, licensure, and didactic training were identified as key parameters that define roles and hence often need to be pre-coordinated. DO was inadequate in representing 82% of roles, and this gap was primarily due to lack of granularity in DO. Next steps include refinement of the proposed schema for the Role axis and dissemination within the larger standards community.