Electronic case reporting (eCR) of COVID-19 to public health: implementation perspectives from the Minnesota Department of Health.

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.

Evaluation of Data Exchange Process for Interoperability and Impact on Electronic Laboratory Reporting Quality to a State Public Health Agency.

BACKGROUND: Past and present national initiatives advocate for electronic exchange of health data and emphasize interoperability. The critical role of public health in the context of disease surveillance was recognized with recommendations for electronic laboratory reporting (ELR). Many public health agencies have seen a trend towards centralization of information technology services which adds another layer of complexity to interoperability efforts. OBJECTIVES: The study objective was to understand the process of data exchange and its impact on the quality of data being transmitted in the context of electronic laboratory reporting to public health. This was conducted in context of Minnesota Electronic Disease Surveillance System (MEDSS), the public health information system for supporting infectious disease surveillance in Minnesota. Data Quality (DQ) dimensions by Strong et al., was chosen as the guiding framework for evaluation. METHODS: The process of assessing data exchange for electronic lab reporting and its impact was a mixed methods approach with qualitative data obtained through expert discussions and quantitative data obtained from queries of the MEDSS system. Interviews were conducted in an open-ended format from November 2017 through February 2018. Based on these discussions, two high level categories of data exchange process which could impact data quality were identified: onboarding for electronic lab reporting and internal data exchange routing. This in turn comprised of ten critical steps and its impact on quality of data was identified through expert input. This was followed by analysis of data in MEDSS by various criteria identified by the informatics team. RESULTS: All DQ metrics (Intrinsic DQ, Contextual DQ, Representational DQ, and Accessibility DQ) were impacted in the data exchange process with varying influence on DQ dimensions. Some errors such as improper mapping in electronic health records (EHRs) and laboratory information systems had a cascading effect and can pass through technical filters and go undetected till use of data by epidemiologists. Some DQ dimensions such as accuracy, relevancy, value-added data and interpretability are more dependent on users at either end of the data exchange spectrum, the relevant clinical groups and the public health program professionals. The study revealed that data quality is dynamic and on-going oversight is a combined effort by MEDSS Informatics team and review by technical and public health program professionals. CONCLUSION: With increasing electronic reporting to public health, there is a need to understand the current processes for electronic exchange and their impact on quality of data. This study focused on electronic laboratory reporting to public health and analyzed both onboarding and internal data exchange processes. Insights gathered from this research can be applied to other public health reporting currently (e.g. immunizations) and will be valuable in planning for electronic case reporting in near future.

Realistic Test-Meal Protocols Lead to Blunted Postprandial Lipemia but Similar Inflammatory Responses Compared with a Standard High-Fat Meal.

Background: A substantial increase in triglycerides (TGs) after a meal is associated with an increased risk of cardiovascular disease. Most studies investigating the effects of a meal on TGs have not used meals that reflect typical consumption. Objective: The objective of this study was to compare the TG and inflammatory responses of true-to-life meals, containing moderate fat and energy contents, with a high-fat, high-energy, low-carbohydrate meal (HFM) typically used to test TG responses. Methods: Nine healthy, insufficiently active men [mean ± SD age: 25.1 ± 6.7 y; body mass index (in kg/m2): 25.8 ± 7.0; <150 min moderate- to vigorous-intensity physical activity/wk] completed 3 meal trials in random order: an HFM (17 kcal/kg, 60% fat), a moderate-fat meal (MFM; 8.5 kcal/kg, 30% fat), and a biphasic meal (BPM), in which participants consumed the full MFM at baseline and 3 h postmeal. Blood samples were collected via an indwelling catheter at baseline and hourly for 6 h. Results: Peak blood TGs were significantly greater (P = 0.003) after the HFM (285.2 ± 169.7 mg/dL) than after the MFM (156.0 ± 98.7 mg/dL), but the BPM (198.3 ± 182.8 mg/dL) was not significantly different from the HFM (P = 0.06) or the MFM (P = 0.99). Total area under the curve for TGs was greater after the HFM (1348.8 ± 783.7 mg/dL × 6 h) than after the MFM (765.8 ± 486.8 mg/dL × 6 h; P = 0.0005) and the BPM (951.8 ± 787.7 mg/dL × 6 h; P = 0.03), although the MFM and BPM were not significantly different (P = 0.72). There was a significant time-by-meal interaction for interferon γ, but not for interleukins 6, 8, or 10. Conclusion: These findings in insufficiently active, healthy young men suggest that the large TG response after HFMs in previous studies may not reflect the metabolic state of many individuals in daily life.