Long COVID is a chronic condition characterized by symptoms such as fatigue, dyspnea, and cognitive impairment that persist or relapse months after an acute infection with the SARS-CoV-2 virus. Many distinct symptoms have been attributed to Long COVID; however, little is known about the potential clustering of these symptoms and risk factors that may predispose patients to certain clusters. In this study, an electronic survey was sent to patients in the UC San Diego Health (UCSDH) system who tested positive for COVID-19, querying if patients were experiencing symptoms consistent with Long COVID. Based on survey results, along with patient demographics reported in the electronic health record (EHR), linear and logistic regression models were used to examine putative risk factors, and exploratory factor analysis was performed to determine symptom clusters. Among 999 survey respondents, increased odds of Long COVID (n = 421; 42%) and greater Long COVID symptom burden were associated with female sex (OR = 1.73, 99% CI: 1.16–2.58; β = 0.48, 0.22–0.75), COVID-19 hospitalization (OR = 4.51, 2.50–8.43; β = 0.48, 0.17–0.78), and poorer pre-COVID self-rated health (OR = 0.75, 0.57–0.97; β = −0.19, −0.32–−0.07). Over one-fifth of Long COVID patients screened positive for depression and/or anxiety, the latter of which was associated with younger age (OR = 0.96, 0.94–0.99). Factor analysis of 16 self-reported symptoms suggested five symptom clusters—gastrointestinal (GI), musculoskeletal (MSK), neurocognitive (NC), airway (AW), and cardiopulmonary (CP), with older age (β = 0.21, 0.11–0.30) and mixed race (β = 0.27, 0.04–0.51) being associated with greater MSK symptom burden. Greater NC symptom burden was associated with increased odds of depression (OR = 5.86, 2.71–13.8) and anxiety (OR = 2.83, 1.36–6.14). These results can inform clinicians in identifying patients at increased risk for Long COVID-related medical issues, particularly neurocognitive symptoms and symptom clusters, as well as informing health systems to manage operational expectations on a population-health level.

Objective: Occupational health (OH) documentation has traditionally been separate from health system electronic health records (EHRs), but this can create patient safety and care continuity challenges. Herein, we describe outcomes and challenges of such integration including how one health system managed compliance with laws, regulations, and ethical principles concerning digital privacy. Methods: Occupational health integration with the enterprise EHR at the University of California San Diego Health was started in June 2021 and completed in December 2021. Results: Integrating with the enterprise EHR allowed for a secure telehealth system, faster visit times, digitization of questionnaires medical clearance forms, and improved reporting capabilities. Conclusions: Integration and interoperability are fundamental building blocks to any OH EHR solution and will allow for evaluation of worker population trends, and targeted interventions to improve worker health status.

Background/Objective: Traditional therapeutic drug monitoring is limited by manual interpretation and specific constraints like sampling at steady-state and requiring a minimum of two drug concentrations. The integration of model-informed precision dosing (MIPD) into population health informatics represents a promising approach to address drug safety and efficacy. This article explored the integration of MIPD within population health informatics and evaluated its potential to enhance precision dosing using artificial intelligence (AI), machine learning (ML), and electronic health records (EHRs). Methods: PubMed and Embase searches were conducted, and all relevant peer-reviewed studies in English published between 1958 and December 2024 were included if they pertained to MIPD and population-level health, with the use of AI/ML algorithms to predict individualized drug dosing requirements. Emphasis was placed on vulnerable populations such as critically-ill, geriatric, and pediatric groups. Results: MIPD with the Bayesian method represents a scalable innovation in precision medicine, with significant implications for population health informatics. By combining AI/ML with comprehensive electronic health records (EHRs), MIPD can offer real-time, precise dosing adjustments. This integration has the potential to improve patient safety, optimize therapeutic outcomes, and reduce healthcare costs, especially for vulnerable populations where evidence is limited. Successful implementation requires collaboration among clinicians, pharmacists, and health informatics professionals, alongside secure data management and interoperability solutions. Conclusions: Further research is needed to define, implement, and evaluate practical applications of AI/ML. This insight may help develop standards and identify drugs for MIPD to advance personalized medicine within population health informatics.

Despite extensive efforts to standardize definitions of obesity, clinical practices of diagnosing obesity vary widely. This study examined (1) discrepancies between biometric body mass index (BMI) measures of obesity and documented diagnoses of obesity in patient electronic health records (EHRs) and (2) how these discrepancies vary by patient gender and race and ethnicity from an intersectional lens. Observational study of 383,380 participants in the National Institutes of Health All of Us Research Program dataset. Over half (60 %) of participants with a BMI indicating obesity had no clinical diagnosis of obesity in their EHRs. Adjusting for BMI, comorbidities, and other covariates, women's adjusted odds of diagnosis were far higher than men's (95 % confidence interval 1.66–1.75). However, the gender gap between women's and men's likelihood of diagnosis varied widely across racial groups. Overall, Non-Hispanic (NH) Black women and Hispanic women were the most likely to be diagnosed and NH-Asian men were the least likely to be diagnosed. Men, and particularly NH-Asian men, may be at heightened risk of underdiagnosis of obesity. Women, and especially Hispanic and NH-Black women, may be at heightened risk of unanticipated harms of obesity diagnosis, including stigma and competing demand with other health concerns. Leveraging diagnosis and biometric data from this unique public domain dataset from the All of Us project, this study revealed pervasive disparities in diagnostic attribution by gender, race, and ethnicity.

Background: Vaccine hesitancy and logistical barriers continue to limit influenza vaccination uptake among healthcare personnel. The STRENGTH initiative evaluated employee drivers of influenza vaccine acceptance in healthcare workgroups with lower vaccination compliance. Methods: This mixed-methods program evaluation used a custom Epic electronic medical record dashboard to identify workgroups with lower influenza vaccination rates, followed by focus groups, targeted worksite vaccination clinics, and an anonymous survey. Survey data from 73 respondents were analyzed using descriptive statistics, Mann-Whitney U tests, Kruskal-Wallis tests, chi-square or Fisher exact tests, and Spearman rank correlations. Results: Respondents had a mean age of 44.8 years (SD 11.3); 36 of 70 respondents reporting binary gender were male. Convenience of worksite vaccination received the highest importance ratings (mean 4.72/5), followed by protection from influenza (mean 4.38/5). Both were rated significantly above the neutral midpoint of 3 (Wilcoxon p<0.001). The strongest observed Spearman correlations were between peer pressure and leadership participation (rho=0.47) and between protection from influenza and willingness to receive a future combined influenza/COVID-19 vaccine (rho=0.42). Conclusions: Convenience and perceived protection were the most important vaccination drivers. Employer-sponsored workplace vaccination programs can reduce logistical barriers while providing opportunities for targeted education and trust-building.