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1. A User Interface Informing Medical Staff on Continuous Indoor Environmental Quality to Support Patient Care and Airborne Disease Mitigation

   https://doi.org/10.1109/SIEDS52267.2021.9483774  

   Rantas, Jacob ; Wang, David ; Jarrard, Will ; Sterchi, James ; Wang, Alan ; Varnosfaderani, Mahsa Pahlavikhah ; Heydarian, Arsalan ( April 2021 , 2021 Systems and Information Engineering Design Symposium (SIEDS))

   This project seeks to investigate the under addressed issue of indoor environmental quality (IEQ) and the impacts these factors can have on human health. The recent COVID-19 pandemic has once again brought to the forefront the importance of maintaining a healthy indoor environment. Specifically, the improvement of indoor air flow has shown to reduce the risk of airborne virus exposure. This is extremely important in the context of hospitals, which contain high concentrations of atrisk individuals. Thus, the need to create a healthy indoor space is critical to improve public health and COVID-19 mitigation efforts. To create knowledge and provide insight on environmental qualities in the hospital setting, the authors have designed and built an interface to deploy in the University of Virginia Hospital Emergency Department (ED). The interface will display room-specific light, noise, temperature, CO 2 , humidity, VOC, and PM 2.5 levels measured by the low-cost Awair Omni sensor. These insights will assist ED clinicians in mitigating disease-spread and improving patient health and satisfaction while reducing caregiver burden. The team addressed the problem through agile development involving localized sensor deployment and analysis, discovery interviews with hospital clinicians and data scientists throughout, and the implementation of a human-design centeredmore »Django interface application. Furthermore, a literature survey was conducted to ascertain appropriate thresholds for the different environmental factors. Together, this work demonstrates opportunities to assist and improve patient care with environmental data.« less
2. SARS-CoV-2 detection status associates with bacterial community composition in patients and the hospital environment

   https://doi.org/10.1186/s40168-021-01083-0  

   Marotz, Clarisse ; Belda-Ferre, Pedro ; Ali, Farhana ; Das, Promi ; Huang, Shi ; Cantrell, Kalen ; Jiang, Lingjing ; Martino, Cameron ; Diner, Rachel E. ; Rahman, Gibraan ; et al ( June 2021 , Microbiome)

   SARS-CoV-2 is an RNA virus responsible for the coronavirus disease 2019 (COVID-19) pandemic. Viruses exist in complex microbial environments, and recent studies have revealed both synergistic and antagonistic effects of specific bacterial taxa on viral prevalence and infectivity. We set out to test whether specific bacterial communities predict SARS-CoV-2 occurrence in a hospital setting.

   We collected 972 samples from hospitalized patients with COVID-19, their health care providers, and hospital surfaces before, during, and after admission. We screened for SARS-CoV-2 using RT-qPCR, characterized microbial communities using 16S rRNA gene amplicon sequencing, and used these bacterial profiles to classify SARS-CoV-2 RNA detection with a random forest model.

   Sixteen percent of surfaces from COVID-19 patient rooms had detectable SARS-CoV-2 RNA, although infectivity was not assessed. The highest prevalence was in floor samples next to patient beds (39%) and directly outside their rooms (29%). Although bed rail samples more closely resembled the patient microbiome compared to floor samples, SARS-CoV-2 RNA was detected less often in bed rail samples (11%). SARS-CoV-2 positive samples had higher bacterial phylogenetic diversity in both human and surface samples and higher biomass in floor samples. 16S microbial community profiles enabled high classifier accuracy for SARS-CoV-2 status in not onlymore »nares, but also forehead, stool, and floor samples. Across these distinct microbial profiles, a single amplicon sequence variant from the genusRothiastrongly predicted SARS-CoV-2 presence across sample types, with greater prevalence in positive surface and human samples, even when compared to samples from patients in other intensive care units prior to the COVID-19 pandemic.

   These results contextualize the vast diversity of microbial niches where SARS-CoV-2 RNA is detected and identify specific bacterial taxa that associate with the viral RNA prevalence both in the host and hospital environment.

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3. Evaluating the Effect of a COVID-19 Predictive Model to Facilitate Discharge: A Randomized Controlled Trial

   https://doi.org/10.1055/s-0042-1750416  

   Major, Vincent J. ; Jones, Simon A. ; Razavian, Narges ; Bagheri, Ashley ; Mendoza, Felicia ; Stadelman, Jay ; Horwitz, Leora I. ; Austrian, Jonathan ; Aphinyanaphongs, Yindalon ( May 2022 , Applied Clinical Informatics)

   Abstract Background We previously developed and validated a predictive model to help clinicians identify hospitalized adults with coronavirus disease 2019 (COVID-19) who may be ready for discharge given their low risk of adverse events. Whether this algorithm can prompt more timely discharge for stable patients in practice is unknown. Objectives The aim of the study is to estimate the effect of displaying risk scores on length of stay (LOS). Methods We integrated model output into the electronic health record (EHR) at four hospitals in one health system by displaying a green/orange/red score indicating low/moderate/high-risk in a patient list column and a larger COVID-19 summary report visible for each patient. Display of the score was pseudo-randomized 1:1 into intervention and control arms using a patient identifier passed to the model execution code. Intervention effect was assessed by comparing LOS between intervention and control groups. Adverse safety outcomes of death, hospice, and re-presentation were tested separately and as a composite indicator. We tracked adoption and sustained use through daily counts of score displays. Results Enrolling 1,010 patients from May 15, 2020 to December 7, 2020, the trial found no detectable difference in LOS. The intervention had no impact on safety indicators of death, hospice or re-presentationmore »after discharge. The scores were displayed consistently throughout the study period but the study lacks a causally linked process measure of provider actions based on the score. Secondary analysis revealed complex dynamics in LOS temporally, by primary symptom, and hospital location. Conclusion An AI-based COVID-19 risk score displayed passively to clinicians during routine care of hospitalized adults with COVID-19 was safe but had no detectable impact on LOS. Health technology challenges such as insufficient adoption, nonuniform use, and provider trust compounded with temporal factors of the COVID-19 pandemic may have contributed to the null result. Trial registration ClinicalTrials.gov identifier: NCT04570488.« less
4. Machine Learning Highlights Downtrending of COVID-19 Patients with a Distinct Laboratory Profile

   https://doi.org/10.34133/2021/7574903  

   Yang, He S. ; Hou, Yu ; Zhang, Hao ; Chadburn, Amy ; Westblade, Lars F. ; Fedeli, Richard ; Steel, Peter A. ; Racine-Brzostek, Sabrina E. ; Velu, Priya ; Sepulveda, Jorge L. ; et al ( June 2021 , Health Data Science)

   Background . New York City (NYC) experienced an initial surge and gradual decline in the number of SARS-CoV-2-confirmed cases in 2020. A change in the pattern of laboratory test results in COVID-19 patients over this time has not been reported or correlated with patient outcome. Methods . We performed a retrospective study of routine laboratory and SARS-CoV-2 RT-PCR test results from 5,785 patients evaluated in a NYC hospital emergency department from March to June employing machine learning analysis. Results . A COVID-19 high-risk laboratory test result profile (COVID19-HRP), consisting of 21 routine blood tests, was identified to characterize the SARS-CoV-2 patients. Approximately half of the SARS-CoV-2 positive patients had the distinct COVID19-HRP that separated them from SARS-CoV-2 negative patients. SARS-CoV-2 patients with the COVID19-HRP had higher SARS-CoV-2 viral loads, determined by cycle threshold values from the RT-PCR, and poorer clinical outcome compared to other positive patients without the COVID12-HRP. Furthermore, the percentage of SARS-CoV-2 patients with the COVID19-HRP has significantly decreased from March/April to May/June. Notably, viral load in the SARS-CoV-2 patients declined, and their laboratory profile became less distinguishable from SARS-CoV-2 negative patients in the later phase. Conclusions . Our longitudinal analysis illustrates the temporal change of laboratory testmore »result profile in SARS-CoV-2 patients and the COVID-19 evolvement in a US epicenter. This analysis could become an important tool in COVID-19 population disease severity tracking and prediction. In addition, this analysis may play an important role in prioritizing high-risk patients, assisting in patient triaging and optimizing the usage of resources.« less
5. Recommendations for Health Equity and Virtual Care Arising From the COVID-19 Pandemic: Narrative Review

   https://doi.org/10.2196/23233  

   Shaw, James ; Brewer, LaPrincess C ; Veinot, Tiffany ( January 2021 , JMIR Formative Research)

   Background The COVID-19 health crisis has disproportionately impacted populations who have been historically marginalized in health care and public health, including low-income and racial and ethnic minority groups. Members of marginalized communities experience undue barriers to accessing health care through virtual care technologies, which have become the primary mode of ambulatory health care delivery during the COVID-19 pandemic. Insights generated during the COVID-19 pandemic can inform strategies to promote health equity in virtual care now and in the future. Objective The aim of this study is to generate insights arising from literature that was published in direct response to the widespread use of virtual care during the COVID-19 pandemic, and had a primary focus on providing recommendations for promoting health equity in the delivery of virtual care. Methods We conducted a narrative review of literature on health equity and virtual care during the COVID-19 pandemic published in 2020, describing strategies that have been proposed in the literature at three levels: (1) policy and government, (2) organizations and health systems, and (3) communities and patients. Results We highlight three strategies for promoting health equity through virtual care that have been underaddressed in this literature: (1) simplifying complex interfaces and workflows, (2)more »using supportive intermediaries, and (3) creating mechanisms through which marginalized community members can provide immediate input into the planning and delivery of virtual care. Conclusions We conclude by outlining three areas of work that are required to ensure that virtual care is employed in ways that are equity enhancing in a post–COVID-19 reality.« less