Measuring the Coverage of the HL7® FHIR® Standard in Supporting Data Acquisition for 3 Public Health Registries.

With the increasing need for timely submission of data to state and national public health registries, current manual approaches to data acquisition and submission are insufficient. In clinical practice, federal regulations are now mandating the use of data messaging standards, i.e., the Health Level Seven (HL7®) Fast Healthcare Interoperability Resources (FHIR®) standard, to facilitate the electronic exchange of clinical (patient) data. In both research and public health practice, we can also leverage FHIR® ‒ and the infrastructure already in place for supporting exchange of clinical practice data ‒ to enable seamless exchange between the electronic medical record and public health registries. That said, in order to understand the current utility of FHIR® for supporting the public health use case, we must first measure the extent to which the standard resources map to the required registry data elements. Thus, using a systematic mapping approach, we evaluated the level of completeness of the FHIR® standard to support data collection for three public health registries (Trauma, Stroke, and National Surgical Quality Improvement Program). On average, approximately 80% of data elements were available in FHIR® (71%, 77%, and 92%, respectively; inter-annotator agreement rates: 82%, 78%, and 72%, respectively). This tells us that there is the potential for significant automation to support EHR-to-Registry data exchange, which will reduce the amount of manual, error-prone processes and ensure higher data quality. Further, identification of the remaining 20% of data elements that are "not mapped" will enable us to improve the standard and develop profiles that will better fit the registry data model.

Severe Acute Respiratory Syndrome Coronavirus-2 Lambda Variant Collected from a Child from Arkansas and Sequenced.

An eleven-year-old tested positive for SARS-CoV-2 Lambda variant. Sequencing was performed on the Oxford Nanopore and the Illumina NextSeq 500. Both platforms identified all 7 of the synonymous mutations in the sample, while all 28 nonsynonymous mutations were identified from Oxford Nanopore and 20 nonsynonymous mutations were identified from Illumina.

Elective operations delay and emergency department visits and inpatient admissions during COVID-19.

Introduction: At the beginning of the COVID-19 pandemic, many hospitals postponed elective operations for a 12-week period in early 2020. During this time, there was concern that the delay would lead to worse health outcomes. The objective of this study is to analyze the effect of delaying operations during this period on ED (Emergency Department) visits and/or urgent IP (Inpatient) admissions. Methods: Electronic Health Record (EHR) data on canceled elective operations between 3/17/20 to 6/8/20 was extracted and a descriptive analysis was performed looking at patient demographics, delay time (days), procedure type, and procedure on rescheduled, completed elective operations with and without a related ED visit and/or IP admission during the delay period. Results: Only 4 out of 197 (2.0%) operations among 4 patients out of 186 patients (2.0%) had an ED visit or IP admission diagnosis related to the postponed operation. When comparing the two groups, the 4 patients were older and had a longer median delay time compared to the 186 patients without an ED visit or IP admission. Conclusion: Postponement of certain elective operations may be done with minimal risk to the patient during times of crisis. However, this minimal risk may be due to the study site's selection of elective operations to postpone. For example, none of the elective operations canceled or postponed were cardiovascular operations, which have worse health outcomes when delayed.

Covid-19 Positivity Differences Among Patients of a Rural, Southern US State Hospital System Based on Population Density, Rural-Urban Classification, and Area Deprivation Index.

In this study we examined the correlation of COVID-19 positivity with area deprivation index (ADI), social determinants of health (SDOH) factors based on a consumer and electronic medical record (EMR) data and population density in a patient population from a tertiary healthcare system in Arkansas. COVID-19 positivity was significantly associated with population density, age, race, and household size. Understanding health disparities and SDOH data can add value to health and the creation of trustable AI.