Standardization of Emergency Department Clinical Note Templates: A Retrospective Analysis Across an Integrated Health System

Background / Objective: Clinical documentation is essential for conveying medical decision-making, communication between providers and patients, and capturing quality, billing, and regulatory measures during emergency department (ED) visits. Growing evidence suggests the benefits of note template standardization, however, variations in documentation practices are common. The primary objective of this study is to measure the utilization and coding performance of a standardized ED note template implemented across a nine-hospital health system.

METHODS

This was a retrospective study before and after the implementation of a standardized ED note template. A multi-disciplinary group consensus was built around standardized note elements, provider note workflows within the electronic health record (EHR), and how to incorporate newly required medical decision-making elements. The primary outcomes measured included the proportion of ED visits using standardized note templates, and the distribution of billing codes in the six months before and after implementation.

RESULTS

In the pre-implementation period, a total of six legacy ED note templates were being used across nine emergency departments, with the most used template accounting for approximately 36% of ED visits. Marked variations in documentation elements were noted across six legacy templates. After the implementation, 82% of ED visits system-wide used a single standardized note template. Following implementation, we observed a 1% increase in the proportion of ED visits coded as highest acuity and an unchanged proportion coded as second highest acuity.

CONCLUSIONS

We observed a greater than two-fold increase in the use of a standardized ED note template across a 9-hospital health system in anticipation of the new 2023 coding guidelines. The development and utilization of a standardized note template format relied heavily on multi-disciplinary stakeholder engagement to inform design that worked for varied documentation practices within the EHR. After the implementation of a standardized note template, we observed better-than-anticipated coding performance.

Identifying the Optimal Look-back Period for Prior Antimicrobial Resistance Clinical Decision Support

BACKGROUND

Lack of consensus on the appropriate look-back period for multi-drug resistance (MDR) complicates antimicrobial clinical decision support. We compared the predictive performance of different MDR look-back periods for five common MDR mechanisms (MRSA, VRE, ESBL, AmpC, CRE).

METHODS

We mapped microbiological cultures to MDR mechanisms and labeled them at different look-back periods. We compared predictive performance for each look-back period-MDR combination using precision, recall, F1 scores, and odds ratios.

RESULTS

Longer look-back periods resulted in lower odds ratios, lower precisions, higher recalls, and lower delta changes in precision and recall compared to shorter periods. We observed higher precision with more information available to clinicians.

CONCLUSION

A previously positive MDR culture may have significant enough precision depending on the mechanism of resistance and varying information available. One year is a clinically relevant and statistically sound look-back period for empiric antimicrobial decision-making at varying points of care for the studied population.

Structure and Funding of Clinical Informatics Fellowships: A National Survey of Program Directors

BACKGROUND

In 2011, the American Board of Medical Specialties established clinical informatics (CI) as a subspecialty in medicine, jointly administered by the American Board of Pathology and the American Board of Preventive Medicine. Subsequently, many institutions created CI fellowship training programs to meet the growing need for informaticists. Although many programs share similar features, there is considerable variation in program funding and administrative structures.

OBJECTIVES

The aim of our study was to characterize CI fellowship program features, including governance structures, funding sources, and expenses.

METHODS

We created a cross-sectional online REDCap survey with 44 items requesting information on program administration, fellows, administrative support, funding sources, and expenses. We surveyed program directors of programs accredited by the Accreditation Council for Graduate Medical Education between 2014 and 2021.

RESULTS

We invited 54 program directors, of which 41 (76%) completed the survey. The average administrative support received was $27,732/year. Most programs (85.4%) were accredited to have two or more fellows per year. Programs were administratively housed under six departments: Internal Medicine (17; 41.5%), Pediatrics (7; 17.1%), Pathology (6; 14.6%), Family Medicine (6; 14.6%), Emergency Medicine (4; 9.8%), and Anesthesiology (1; 2.4%). Funding sources for CI fellowship program directors included: hospital or health systems (28.3%), clinical departments (28.3%), graduate medical education office (13.2%), biomedical informatics department (9.4%), hospital information technology (9.4%), research and grants (7.5%), and other sources (3.8%) that included philanthropy and external entities.

CONCLUSION

CI fellowships have been established in leading academic and community health care systems across the country. Due to their unique training requirements, these programs require significant resources for education, administration, and recruitment. There continues to be considerable heterogeneity in funding models between programs. Our survey findings reinforce the need for reformed federal funding models for informatics practice and training.

Twitter discussions on breastfeeding during the COVID-19 pandemic

BACKGROUND

Breastfeeding is a critical health intervention in infants. Recent literature reported that the COVID-19 pandemic resulted in significant mental health issues in pregnant and breastfeeding women due to social isolation and lack of direct professional support. These maternal mental health issues affected infant nutrition and decreased breastfeeding rates during COVID-19. Twitter, a popular social media platform, can provide insight into public perceptions and sentiment about various health-related topics. With evidence of significant mental health issues among women during the COVID-19 pandemic, the perception of infant nutrition, specifically breastfeeding, remains unknown.

METHODS

We aimed to understand public perceptions and sentiment regarding breastfeeding during the COVID-19 pandemic through Twitter analysis using natural language processing techniques. We collected and analyzed tweets related to breastfeeding and COVID-19 during the pandemic from January 2020 to May 2022. We used Python software (v3.9.0) for all data processing and analyses. We performed sentiment and emotion analysis of the tweets using natural language processing libraries and topic modeling using an unsupervised machine-learning algorithm.

RESULTS

We analyzed 40,628 tweets related to breastfeeding and COVID-19 generated by 28,216 users. Emotion analysis revealed predominantly "Positive emotions" regarding breastfeeding, comprising 72% of tweets. The overall tweet sentiment was positive, with a mean weekly sentiment of 0.25 throughout, and was affected by external events. Topic modeling revealed six significant themes related to breastfeeding and COVID-19. Passive immunity through breastfeeding after maternal vaccination had the highest mean positive sentiment score of 0.32.

CONCLUSIONS

Our study provides insight into public perceptions and sentiment regarding breastfeeding during the COVID-19 pandemic. Contrary to other topics we explored in the context of COVID (e.g., ivermectin, disinformation), we found that breastfeeding had an overall positive sentiment during the pandemic despite the documented rise in mental health challenges in pregnant and breastfeeding mothers. The wide range of topics on Twitter related to breastfeeding provides an opportunity for active engagement by the medical community and timely dissemination of advice, support, and guidance. Future studies should leverage social media analysis to gain real-time insight into public health topics of importance in child health and apply targeted interventions.

Is there an association between peri-diagnostic vaccination and clinical outcomes in COVID-19 patients?

Background

Peri-diagnostic vaccination contemporaneous with SARS-CoV-2 infection might boost antiviral immunity and improve patient outcomes. We investigated, among previously unvaccinated patients, whether vaccination (with the Pfizer, Moderna, or J&J vaccines) during the week before or after a positive COVID-19 test was associated with altered 30-day patient outcomes.

Methods

Using a deidentified longitudinal EHR repository, we selected all previously unvaccinated adults who initially tested positive for SARS-CoV-2 between December 11, 2020 (the date of vaccine emergency use approval) and December 19, 2021. We assessed whether vaccination between days -7 and +7 of a positive test affected outcomes. The primary measure was progression to a more severe disease outcome within 30 days of diagnosis using the following hierarchy: hospitalization, intensive care, or death.

Results

Among 60,031 hospitalized patients, 543 (0.91%) were initially vaccinated at the time of diagnosis and 59,488 (99.09%) remained unvaccinated during the period of interest. Among 316,337 nonhospitalized patients, 2,844 (0.90%) were initially vaccinated and 313,493 (99.1%) remained unvaccinated. In both analyses, individuals receiving vaccines were older, more often located in the northeast, more commonly insured by Medicare, and more burdened by comorbidities. Among previously unvaccinated patients, there was no association between receiving an initial vaccine dose between days -7 and +7 of diagnosis and progression to more severe disease within 30 days compared to patients who did not receive vaccines.

Conclusions

Immunization during acute SARS-CoV-2 infection does not appear associated with clinical progression during the acute infectious period.

Assessing Racial and Ethnic Bias in Text Generation for Healthcare-Related Tasks by ChatGPT1

Large Language Models (LLM) are AI tools that can respond human-like to voice or free-text commands without training on specific tasks. However, concerns have been raised about their potential racial bias in healthcare tasks. In this study, ChatGPT was used to generate healthcare-related text for patients with HIV, analyzing data from 100 deidentified electronic health record encounters. Each patient's data were fed four times with all information remaining the same except for race/ethnicity (African American, Asian, Hispanic White, Non-Hispanic White). The text output was analyzed for sentiment, subjectivity, reading ease, and most used words by race/ethnicity and insurance type. Results showed that instructions for African American, Asian, Hispanic White, and Non-Hispanic White patients had an average polarity of 0.14, 0.14, 0.15, and 0.14, respectively, with an average subjectivity of 0.46 for all races/ethnicities. The differences in polarity and subjectivity across races/ethnicities were not statistically significant. However, there was a statistically significant difference in word frequency across races/ethnicities and a statistically significant difference in subjectivity across insurance types with commercial insurance eliciting the most subjective responses and Medicare and other payer types the lowest. The study suggests that ChatGPT is relatively invariant to race/ethnicity and insurance type in terms of linguistic and readability measures. Further studies are needed to validate these results and assess their implications.

Risk Factors for SARS-CoV-2 Infection and Severe Outcomes Among People With Human Immunodeficiency Virus: Cohort Study

Background

Studies on COVID-19 in people with HIV (PWH) have had limitations. Further investigations on risk factors and outcomes of SARS-CoV-2 infection among PWH are needed.

Methods

This retrospective cohort study leveraged the national OPTUM COVID-19 data set to investigate factors associated with SARS-CoV-2 positivity among PWH and risk factors for severe outcomes, including hospitalization, intensive care unit stays, and death. A subset analysis was conducted to examine HIV-specific variables. Multiple variable logistic regression was used to adjust for covariates.

Results

Of 43 173 PWH included in this study, 6472 had a positive SARS-CoV-2 result based on a polymerase chain reaction test or antigen test. For PWH with SARS-CoV-2 positivity, higher odds were found for those who were younger (18-49 years), Hispanic White, African American, from the US South, uninsured, and a noncurrent smoker and had a higher body mass index and higher Charlson Comorbidity Index. For PWH with severe outcomes, higher odds were identified for those who were SARS-CoV-2 positive, older, from the US South, receiving Medicaid/Medicare or uninsured, a current smoker, and underweight and had a higher Charlson Comorbidity Index. In a subset analysis including PWH with HIV care variables (n = 5098), those with unsuppressed HIV viral load, a low CD4 count, and no antiretroviral therapy had higher odds of severe outcomes.

Conclusions

This large US study found significant ethnic, racial, and geographic differences in SARS-CoV-2 infection among PWH. Chronic comorbidities, older age, lower body mass index, and smoking were associated with severe outcomes among PWH during the COVID-19 pandemic. SARS-CoV-2 infection was associated with severe outcomes, but once we adjusted for HIV care variables, SARS-CoV-2 was no longer significant; however, low CD4 count, high viral load, and lack of antiretroviral therapy had higher odds of severe outcomes.

Public perception of COVID-19 vaccines through analysis of Twitter content and users

BACKGROUND

With the global continuation of the COVID-19 pandemic, the large-scale administration of a SARS-CoV-2 vaccine is crucial to achieve herd immunity and curtail further spread of the virus, but success is contingent on public understanding and vaccine uptake. We aim to understand public perception about vaccines for COVID-19 through the wide-scale, organic discussion on Twitter.

METHODS

This cross-sectional observational study included Twitter posts matching the search criteria (('covid*' OR 'coronavirus') AND 'vaccine') posted during vaccine development from February 1st through December 11th, 2020. These COVID-19 vaccine related posts were analyzed with topic modeling, sentiment and emotion analysis, and demographic inference of users to provide insight into the evolution of public attitudes throughout the study period.

FINDINGS

We evaluated 2,287,344 English tweets from 948,666 user accounts. Individuals represented 87.9 % (n = 834,224) of user accounts. Of individuals, men (n = 560,824) outnumbered women (n = 273,400) by 2:1 and 39.5 % (n = 329,776) of individuals were ≥40 years old. Daily mean sentiment fluctuated congruent with news events, but overall trended positively. Trust, anticipation, and fear were the three most predominant emotions; while fear was the most predominant emotion early in the study period, trust outpaced fear from April 2020 onward. Fear was more prevalent in tweets by individuals (26.3 % vs. organizations 19.4 %; p < 0.001), specifically among women (28.4 % vs. males 25.4 %; p < 0.001). Multiple topics had a monthly trend towards more positive sentiment. Tweets comparing COVID-19 to the influenza vaccine had strongly negative early sentiment but improved over time.

INTERPRETATION

This study successfully explores sentiment, emotion, topics, and user demographics to elucidate important trends in public perception about COVID-19 vaccines. While public perception trended positively over the study period, some trends, especially within certain topic and demographic clusters, are concerning for COVID-19 vaccine hesitancy. These insights can provide targets for educational interventions and opportunity for continued real-time monitoring.

Correction: The Association of a Geographically Wide Social Media Network on Depression: County-Level Ecological Analysis

[This corrects the article DOI: 10.2196/43623.].

Incidence and Risk Factors for Severe Outcomes in Pediatric Patients With COVID-19

OBJECTIVES

Throughout the pandemic, children with COVID-19 have experienced hospitalization, ICU admission, invasive respiratory support, and death. Using a multisite, national dataset, we investigate risk factors associated with these outcomes in children with COVID-19.

METHODS

Our data source (Optum deidentified COVID-19 Electronic Health Record Dataset) included children aged 0 to 18 years testing positive for COVID-19 between January 1, 2020, and January 20, 2022. Using ordinal logistic regression, we identified factors associated with an ordinal outcome scale: nonhospitalization, hospitalization, or a severe composite outcome (ICU, intensive respiratory support, death). To contrast hospitalization for COVID-19 and incidental positivity on hospitalization, we secondarily identified patient factors associated with hospitalizations with a primary diagnosis of COVID-19.

RESULTS

In 165 437 children with COVID-19, 3087 (1.8%) were hospitalized without complication, 2954 (1.8%) experienced ICU admission and/or intensive respiratory support, and 31 (0.02%) died. We grouped patients by age: 0 to 4 years old (35 088), and 5 to 11 years old (75 574), 12 to 18 years old (54 775). Factors positively associated with worse outcomes were preexisting comorbidities and residency in the Southern United States. In 0- to 4-year-old children, there was a nonlinear association between age and worse outcomes, with worse outcomes in 0- to 2-year-old children. In 5- to 18-year-old patients, vaccination was protective. Findings were similar in our secondary analysis of hospitalizations with a primary diagnosis of COVID-19, though region effects were no longer observed.

CONCLUSIONS

Among children with COVID-19, preexisting comorbidities and residency in the Southern United States were positively associated with worse outcomes, whereas vaccination was negatively associated. Our study population was highly insured; future studies should evaluate underinsured populations to confirm generalizability.

Challenges in Forecasting Antimicrobial Resistance

Antimicrobial resistance is a major threat to human health. Since the 2000s, computational tools for predicting infectious diseases have been greatly advanced; however, efforts to develop real-time forecasting models for antimicrobial-resistant organisms (AMROs) have been absent. In this perspective, we discuss the utility of AMRO forecasting at different scales, highlight the challenges in this field, and suggest future research priorities. We also discuss challenges in scientific understanding, access to high-quality data, model calibration, and implementation and evaluation of forecasting models. We further highlight the need to initiate research on AMRO forecasting using currently available data and resources to galvanize the research community and address initial practical questions.

Analyzing an Emerging Pandemic on Twitter: Monkeypox

Background

Social media platforms like Twitter provide important insights into the public's perceptions of global outbreaks like monkeypox. By analyzing tweets, we aimed to identify public knowledge and opinions on the monkeypox virus and related public health issues.

Methods

We analyzed English-language tweets using the keyword "monkeypox" from 1 May to 23 July 2022. We reported gender, ethnicity, and race of Twitter users and analyzed tweets to identify predominant sentiment and emotions. We performed topic modeling and compared cohorts of users who self-identify as LGBTQ+ (an abreviation for lesbian, gay, bisexual, transgender, queer, and/or questioning) allies versus users who do not, and cohorts identified as "bots" versus humans.

Results

A total of 48 330 tweets were written by LGBTQ+ self-identified advocates or allies. The mean sentiment score for all tweets was -0.413 on a -4 to +4 scale. Negative tweets comprised 39% of tweets. The most common emotions expressed were fear and sadness. Topic modeling identified unique topics among the 4 cohorts analyzed.

Conclusions

The spread of mis- and disinformation about monkeypox was common in our tweet library. Various conspiracy theories about the origins of monkeypox, its relationship to global economic concerns, and homophobic and racial comments were common. Conversely, many other tweets helped to provide information about monkeypox vaccines, disease symptoms, and prevention methods. Discussion of rising monkeypox case numbers globally was also a large aspect of the conversation.

Conclusions

We demonstrated that Twitter is an effective means of tracking sentiment about public healthcare issues. We gained insight into a subset of people, self-identified LGBTQ+ allies, who were more affected by monkeypox.

The Association of a Geographically Wide Social Media Network on Depression: County-Level Ecological Analysis

BACKGROUND

Social connectedness decreases human mortality, improves cancer survival, cardiovascular health, and body mass, results in better-controlled glucose levels, and strengthens mental health. However, few public health studies have leveraged large social media data sets to classify user network structure and geographic reach rather than the sole use of social media platforms.

OBJECTIVE

The objective of this study was to determine the association between population-level digital social connectedness and reach and depression in the population across geographies of the United States.

METHODS

Our study used an ecological assessment of aggregated, cross-sectional population measures of social connectedness, and self-reported depression across all counties in the United States. This study included all 3142 counties in the contiguous United States. We used measures obtained between 2018 and 2020 for adult residents in the study area. The study's main exposure of interest is the Social Connectedness Index (SCI), a pair-wise composite index describing the "strength of connectedness between 2 geographic areas as represented by Facebook friendship ties." This measure describes the density and geographical reach of average county residents' social network using Facebook friendships and can differentiate between local and long-distance Facebook connections. The study's outcome of interest is self-reported depressive disorder as published by the Centers for Disease Control and Prevention.

RESULTS

On average, 21% (21/100) of all adult residents in the United States reported a depressive disorder. Depression frequency was the lowest for counties in the Northeast (18.6%) and was highest for southern counties (22.4%). Social networks in northeastern counties involved moderately local connections (SCI 5-10 the 20th percentile for n=70, 36% of counties), whereas social networks in Midwest, southern, and western counties contained mostly local connections (SCI 1-2 the 20th percentile for n=598, 56.7%, n=401, 28.2%, and n=159, 38.4%, respectively). As the quantity and distance that social connections span (ie, SCI) increased, the prevalence of depressive disorders decreased by 0.3% (SE 0.1%) per rank.

CONCLUSIONS

Social connectedness and depression showed, after adjusting for confounding factors such as income, education, cohabitation, natural resources, employment categories, accessibility, and urbanicity, that a greater social connectedness score is associated with a decreased prevalence of depression.

Reaching Populations at Risk for HIV Through Targeted Facebook Advertisements: Cost-Consequence Analysis

BACKGROUND

An undiagnosed HIV infection remains a public health challenge. In the digital era, social media and digital health communication have been widely used to accelerate research, improve consumer health, and facilitate public health interventions including HIV prevention.

OBJECTIVE

We aimed to evaluate and compare the projected cost and efficacy of different simulated Facebook (FB) advertisement (ad) approaches targeting at-risk populations for HIV based on new HIV diagnosis rates by age group and geographic region in the United States.

METHODS

We used the FB ad platform to simulate (without actually launching) an automatically placed video ad for a 10-day duration targeting at-risk populations for HIV. We compared the estimated total ad audience, daily reach, daily clicks, and cost. We tested ads for the age group of 13 to 24 years (in which undiagnosed HIV is most prevalent), other age groups, US geographic regions and states, and different campaign budgets. We then estimated the ad cost per new HIV diagnosis based on HIV positivity rates and the average health care industry conversion rate.

RESULTS

On April 20, 2021, the potential reach of targeted ads to at-risk populations for HIV in the United States was approximately 16 million for all age groups and 3.3 million for age group 13 to 24 years, with the highest potential reach in California, Texas, Florida, and New York. When using different FB ad budgets, the daily reach and daily clicks per US dollar followed a cumulative distribution curve of an exponential function. Using multiple US $10 ten-day ads, the cost per every new HIV diagnosis ranged from US $13.09 to US $37.82, with an average cost of US $19.45. In contrast, a 1-time national ad had a cost of US $72.76 to US $452.25 per new HIV diagnosis (mean US $166.79). The estimated cost per new HIV diagnosis ranged from US $13.96 to US $55.10 for all age groups (highest potential reach and lowest cost in the age groups 20-29 and 30-39 years) and from US $12.55 to US $24.67 for all US regions (with the highest potential reach of 6.2 million and the lowest cost per new HIV diagnosis at US $12.55 in the US South).

CONCLUSIONS

Targeted personalized FB ads are a potential means to encourage at-risk populations for HIV to be tested, especially those aged 20 to 39 years in the US South, where the disease burden and potential reach on FB are high and the ad cost per new HIV diagnosis is low. Considering the cost efficiency of ads, the combined cost of multiple low-cost ads may be more economical than a single high-cost ad, suggesting that local FB ads could be more cost-effective than a single large-budget national FB ad.

The lived experience of people with disabilities during the COVID-19 pandemic on Twitter: Content analysis

Objective

People with disabilities (PWDs) are at greater risk of COVID-19 infection, complications, and death, and experience more difficulty accessing care. We analyzed Twitter tweets to identify important topics and investigate health policies' effects on PWDs.

Methods

Twitter's application programming interface was used to access its public COVID-19 stream. English-language tweets from January 2020 to January 2022 containing a combination of keywords related to COVID-19, disability, discrimination, and inequity were collected and refined to exclude duplicates, replies, and retweets. The remaining tweets were analyzed for user demographics, content, and long-term availability.

Results

The collection yielded 94,814 tweets from 43,296 accounts. During the observation period, 1068 (2.5%) accounts were suspended and 1088 (2.5%) accounts were deleted. Account suspension and deletion among verified users tweeting about COVID-19 and disability were 0.13% and 0.3%, respectively. Emotions were similar among active, suspended, and deleted users, with general negative and positive emotions most common followed by sadness, trust, anticipation, and anger. The overall average sentiment for the tweets was negative. Ten of the 12 topics identified (96.8%) related to pandemic effects on PWDs; "politics that rejects and leaves the disabled, elderly, and children behind" (48.3%) and "efforts to support PWDs in the COVID crisis" (31.8%) were most common. The sample of tweets by organizations (43.9%) was higher for this topic than for other COVID-19-related topics the authors have investigated.

Conclusions

The primary discussion addressed how pandemic politics and policies disadvantage PWDs, older adults, and children, and secondarily expressed support for these populations. The increased level of Twitter use by organizations suggests a higher level of organization and advocacy within the disability community than in other groups. Twitter may facilitate recognition of increased harm to or discrimination against specific populations such as people living with disability during national health events.

2357. Risk Factors for COVID-19 Infection and Outcomes in People Living with HIV

Background

As the risk for concomitant COVID-19 infection in people living with HIV (PLHIV) remains largely unknown, we explored a large national database to identify risk factors for COVID-19 infection among PLHIV.

Methods

Using the COVID-19 OPTUM de-identified national multicenter database, we identified 29,393 PLHIV with either a positive HIV test or documented HIV ICD9/10 codes. Using a multiple logistic regression model, we compared risk factors among PLHIV, who tested positive for COVID-19 (5,134) and those who tested negative (24,259) from January 20, 2020, to January 20, 2022. We then compared secondary outcomes including hospitalization, Intensive Care Unit (ICU) stay, and death within 30 days of test among the 2 cohorts, adjusting for COVID-19 positivity and covariates. We adjusted all models for the following covariates: age, gender, race, ethnicity, U.S. region, insurance type, adjusted Charlson Comorbidity Index (CCI), Body Mass Index (BMI), and smoking status.

Results

Among PLHIV, factors associated with higher odds for acquiring COVID-19 (Figure 1) included lower age (compared to age group 18–49, age groups 50–64 and >65 were associated with odds ratios (OR) of 0.8 and 0.75, P= 0.001), female gender (compared to males, OR 1.06, P= 0.07), Hispanic White ethnicity/race (OR 2.75, P= 0.001), Asian (OR 1.35, P= 0.04), and African American (OR 1.23, P= 0.001) [compared to non-Hispanic White], living in the U.S. South (compared to the Northeast, OR 2.18, P= 0.001), being uninsured (compared to commercial insurance, OR 1.46, P= 0.001), higher CCI (OR 1.025, P= 0.001), higher BMI category (compared to having BMI< 30, Obesity category 1 or 2, OR 1.2 and obesity category 3, OR 1.34, P= 0.001), and noncurrent smoking status (compared to current smoker, OR 1.46, P= 0.001). Compared to PLHIV who tested negative for COVID-19, PLHIV who tested positive, had an OR 1.01 for hospitalization (P = 0.79), 1.03 for ICU stay (P=0.73), and 1.47 for death (P=0.001).

Conclusion

Our study found that among PLHIV, being Hispanic, living in the South, lacking insurance, having higher BMI, and higher CCI scores were associated with increased odds of testing positive for COVID-19. PLHIV who tested positive for COVID-19 had higher odds of death.

Disclosures

Christoph U. Lehmann, MD, Celanese: Stocks/Bonds|Markel: Stocks/Bonds|Springer: Honoraria|UTSW: Employee Jeremy Y. Chow, M.D., M.S., Gilead Sciences: Grant/Research Support.

2298. Clinical and Demographic Characteristics of COVID-19 in Pediatric Patients in the United States

Background

The percentage of children infected with COVID-19 has outpaced that of adults. As children &gt;5 years are now eligible to receive vaccines, it is necessary to understand the effect of vaccination in the context of demographic characteristics, clinical factors, and variants on pediatric COVID-19 illness severity.

Methods

We conducted a descriptive study of patients ≤18 years from the Optum® COVID-19 electronic health record dataset. Patients were included if positive for COVID-19 by polymerase chain reaction or antigen testing for the first time from 3/12/2020 to 1/20/2022. We compare race and ethnicity, age, gender, US region of residence, vaccination status, body mass index (BMI), pediatric comorbidity index (PCI) (Sun, Am. J. Epidemiol. 2021), and predominant variant (by time and region) with 2-tailed t-test, multi-category chi-square test, and odds ratios (R version 4.1.2; α = 0.05). PCI is a validated comorbidity index predicting hospitalization in pediatric patients.

Results

Of all pediatric patients in our dataset, 165,468 (13.2%) tested positive for COVID-19. 3,087 (1.9%) were hospitalized, 1,417 (0.9%) were admitted to the ICU, 1545 (0.9%) received respiratory support, and 31 (0.02%) died, comparable to AAP-reported hospitalization and mortality rates in US children. Patients with severe outcomes were more likely to be younger, non-Caucasian, from the US South, unvaccinated, and have a higher PCI (Figure 1). Excluding non-severe outcomes, rates of death and ICU admission were higher in 0–4-year-olds compared to 5–11 or 12–18-year-olds (Figure 2). All patients receiving at least one dose of the vaccine survived. The odds ratio of a severe outcome is 0.11 (95% CI 0.07–0.16) in fully vaccinated patients compared to unvaccinated patients. The odds ratio of a severe outcome is 0.55 (95% CI 0.49–0.63) in partially vaccinated patients compared to unvaccinated patients.

Demographic and clinical characteristics of pediatric patients with COVID-19

Relative proportion of clinically severe outcomes within age groups, excluding non-severe outcomes

Conclusion

In this large population, incidence rate of severe outcomes from COVID-19 in pediatric patients was higher among non-Caucasian patients, living in the South, with underlying comorbid illness, and those not yet eligible for vaccination. These findings reinforce the need for a vaccine for younger patients and targeted vaccine outreach to racial and ethnic minorities and children with chronic conditions.

Disclosures

Christoph U. Lehmann, MD, Celanese: Stocks/Bonds|Markel: Stocks/Bonds|Springer: Honoraria|UTSW: Employee.

1369. Ten Year Trends of Typhus Fever in North Texas: Epidemiologic Characteristics and Clinical Manifestations

Background

Murine Typhus remains endemic in southern California and in southern Texas where it is transmitted by fleas, with opossums serving as the amplifying host. In Texas, the disease is increasingly recognized in municipalities outside its historic rural range and is spreading in a northward distribution. Since its expansion, we have observed increased cases in the Dallas-Fort Worth (DFW) area and aim to describe murine typhus in North Texas from 2011-2021.

Methods

Leveraging the electronic health record, we retrospectively identified 482 individuals tested for murine typhus by Rickettsia typhi (R. typhi) serology in 2 Dallas hospitals. We subsequently collected epidemiologic characteristics, clinical features, and outcomes of 58 patients with positive R. typhi serologies ( &gt;1:64).

Results

Of the 58 patients with positive R. typhi serology, 39 (67%) were male, 45 (78%) were White, and 23 (40%) were Hispanic. Seventy-nine percent had symptom onset between May and November, and 36/58 (62%) were diagnosed in 2020 and 2021. Twenty-six (45%) had exposure to dogs, 18 (31%) to cats, and 13 (22%) to opossums. Twelve (21%) patients were immunocompromised. Fifty-two (90%) had fever, 35 (60%) headache, 26 (45%) nausea and vomiting, 26 (45%) rash, 25 (43%) myalgia, 20 (34%) cough, and 17 (29%) abdominal pain. In 2020 and 2021, 35/36 (97%) patients were additionally tested for COVID-19, and 29/35 (83%) patients had more than one negative SARS-CoV-2 test prior to R. typhi serologies being sent. Twenty-one out of fifty (42%) had an abnormal chest x-ray (CXR) and 28/30 (93%) had an abnormal chest computed tomography (CT). Nine (16%) had hypoxia, 9 (16%) required an intensive care unit, and 3 (5%) required mechanical ventilation. No patients died within 30 days of diagnosis.

Conclusion

Our study highlights the expansion of murine typhus in North Texas (Figure 1) and demonstrates the heightened need for clinicians to be aware of this disease in the appropriate epidemiologic and clinical settings. We also describe increasing rates of respiratory findings, demonstrated through over half of patients having at least one respiratory symptom, and 93% having an abnormal chest CT (findings traditionally associated with severe disease). Figure 1:Heatmap Distribution of Murine Typhus Cases in the Dallas-Fort Worth Metroplex (2011-2021)

Disclosures

Christoph U. Lehmann, MD, FAAP, FACMI, FIAHSI, Celanese: Stocks/Bonds|Markel: Stocks/Bonds|Springer: Honoraria Helen L. King, MD, Gilead Sciences: Grant/Research Support.

1161. Vaccination During Acute COVID-19 Infection Protects Against 30 Day Adverse Outcomes

Background

Therapeutic vaccination following SARS-CoV-2 infection might stimulate anti-viral immunity and improve patient outcomes. We investigated, amongst previously unvaccinated patients, whether vaccination with the Pfizer, Moderna, or Johnson & Johnson vaccines within 14 days of a positive SARS-CoV-2 test affected 30-day patient outcomes.

Methods

Using a deidentified national electronic health record dataset (Optum, Inc.), we identified previously unvaccinated patients who tested positive for COVID-19 between 12/11/2020 and 12/19/2021. Among this cohort, 1,909 patients received a first vaccine dose within 14 days (vaccinated) while 446,309 did not receive a first dose of vaccine within 30 days of their first positive test (unvaccinated). We performed 1:1 propensity score matching based on age, gender, race, ethnicity, region, BMI, insurance, and comorbidities from the Charlson Comorbidity Index. Next, we compared odds of severe outcomes within 30 days between vaccinated and unvaccinated groups using a partial proportional odds model with the following ordinal severity outcome: no hospitalization, hospitalization, ICU stay, or death (Stata version 17.0, α = 0.05).

Results

1,909 vaccinated patients were propensity score-matched to 1,909 unvaccinated patients. The final matched cohort was statistically indistinguishable (p &gt; 0.05) for all metrics used in propensity score calculation. This matched cohort (n = 3,818) was 58.6% female, 67.7% white, 12.6% Hispanic, and 56.4% commercially insured, with a mean age of 50.6 years and a similar comorbidity profile. A partial proportional odds model showed that unvaccinated patients were at increased risk for hospitalization and higher ordered outcomes (OR = 1.19, 95% CI: 1.02-1.39), ICU stay and higher ordered outcomes (OR 1.63, 95% CI: 1.21-2.20), and death (OR 4.57, 95% CI: 2.50-8.37).

Conclusion

Among previously unvaccinated patients, those who received a first dose vaccine within 14 days of a positive COVID-19 test were less likely to experience hospitalization, ICU stay, or death compared to matched peers who did not receive a first dose in the acute phase of infection. The sample size of patients vaccinated during the acute phase is limited, so further studies are indicated to evaluate the safety and efficacy of this practice.

Disclosures

Christoph U. Lehmann, MD, FAAP, FACMI, FIAHSI, Celanese: Stocks/Bonds|Markel: Stocks/Bonds|Springer: Honoraria.

Clinically undetected polyclonal heteroresistance among Pseudomonas aeruginosa isolated from cystic fibrosis respiratory specimens

BACKGROUND

Pseudomonas aeruginosa infection is the leading cause of death among patients with cystic fibrosis (CF) and a common cause of difficult-to-treat hospital-acquired infections. P. aeruginosa uses several mechanisms to resist different antibiotic classes and an individual CF patient can harbour multiple resistance phenotypes.

OBJECTIVES

To determine the rates and distribution of polyclonal heteroresistance (PHR) in P. aeruginosa by random, prospective evaluation of respiratory cultures from CF patients at a large referral centre over a 1 year period.

METHODS

We obtained 28 unique sputum samples from 19 CF patients and took multiple isolates from each, even when morphologically similar, yielding 280 unique isolates. We performed antimicrobial susceptibility testing (AST) on all isolates and calculated PHR on the basis of variability in AST in a given sample. We then performed whole-genome sequencing on 134 isolates and used a machine-learning association model to interrogate phenotypic PHR from genomic data.

RESULTS

PHR was identified in most sampled patients (n = 15/19; 79%). Importantly, resistant phenotypes were not detected by routine AST in 26% of patients (n = 5/19). The machine-learning model, using the extended sampling, identified at least one genetic variant associated with phenotypic resistance in 94.3% of isolates (n = 1392/1476).

CONCLUSION

PHR is common among P. aeruginosa in the CF lung. While traditional microbiological methods often fail to detect resistant subpopulations, extended sampling of isolates and conventional AST identified PHR in most patients. A machine-learning tool successfully identified at least one resistance variant in almost all resistant isolates by leveraging this extended sampling and conventional AST.

The Texas Health Resources Clinical Scholars Program: Learning healthcare system workforce development through embedded translational research

Introduction

Texas Health Resources (THR), a large, nonprofit health care system based in the Dallas-Fort Worth area, has collaborated with the University of Texas Southwestern Medical Center (UTSW) to develop and operate a unique, integrated approach for Learning Health System (LHS) workforce development. This training model centers on academic health system faculty members conducting later-stage translational research within a partnering regional care delivery organization.

Methods

The THR Clinical Scholars Program engages early career UTSW faculty members to conduct studies that are likely to have an impact on care delivery at the health system level. Interested candidates submit formal applications to the program. A joint committee comprised of senior research faculty from UTSW and THR clinical leadership reviews proposals with a focus on the shared LHS needs of both institutions-developing high quality research output that can be applied to enhance care delivery. A key prioritization criterion for funding is the degree to which the research addresses a question relevant to THR as a high-volume network with multiple channels for consumers to access care. The program emphasis is on supporting embedded research initiatives using health system data to generate knowledge that will improve the quality and efficiency of care for the patient populations served by the participant organizations.

Results

We discuss specific strategic and tactical components of the THR Clinical Scholars Program including an overview of the academic affiliation agreement between the collaborating organizations, criteria for successful program applications, data sharing, and funding. We also share project summaries from selected clinical scholars as examples of the LHS research done in the program to date.

Conclusion

This experience report provides an implementation framework for other academic health systems interested in adopting similar LHS workforce training models with community partners.

Implementation of Outpatient Infectious Diseases E-Consults at a Safety Net Healthcare System

Background

Safety net healthcare systems have high patient volumes and significant demands for specialty care including infectious diseases (ID) consultations. Electronic ID consults (E-consults) can lessen this burden by providing an alternative to face-to-face ID referrals and decreasing financial, time, and travel constraints on patients. This system could increase access to ID care for patients in limited-resource settings.

Methods

We described characteristics of all outpatient ID E-consults at Parkland Health in Dallas, Texas, from March 2018 to February 2021. We used modeling to determine which characteristics influenced conversion of E-consults to clinic visits and integrated these data into a predictive model for face-to-face conversion.

Results

For 725 E-consults, common E-consult topics included 118 (16%) latent tuberculosis, 116 (16%) syphilis, and 76 (10%) gastrointestinal infections. Nearly two-thirds of E-consults (456 [63%]) were requested by primary care providers. The majority (78%) were resolved without a face-to-face ID visit. Osteomyelitis, nontuberculous mycobacterial, and gastrointestinal questions frequently required face-to-face visits at rates of 65%, 49%, and 32%, respectively. Our logistic regression model predicted the need for a face-to-face visit with 80% accuracy and an area under the receiver operating characteristic curve of 0.72.

Conclusions

An outpatient ID E-consult program at a safety net healthcare system was an effective tool to provide timely input on common ID topics. E-consults were requested by a range of providers, and most were completed without a face-to-face visit. Predictive modeling identified important characteristics of E-consults and predicted conversion to face-to-face visits with reasonable accuracy.

The Politicization of Ivermectin Tweets During the COVID-19 Pandemic

Background

We explore the ivermectin discourse and sentiment in the United States with a special focus on political leaning through the social media blogging site Twitter.

Methods

We used sentiment analysis and topic modeling to geospatially explore ivermectin Twitter discourse in the United States and compared it to the political leaning of a state based on the 2020 presidential election.

Results

All modeled topics were associated with a negative sentiment. Tweets originating from democratic leaning states were more likely to be negative.

Conclusions

Real-time analysis of social media content can identify public health concerns and guide timely public health interventions tackling disinformation.

Use of virtual care by infectious disease specialists in Canada: A national survey

OBJECTIVE

The aim of this study was to characterize the type and extent of virtual care use among infectious disease specialists in Canada, with a focus on the clinical factors that influence the decision to provide virtual versus in-person care.

METHODS

Infectious disease physicians practicing in Canada were invited to complete a survey regarding their experiences with virtual care. The survey included 14 vignettes depicting new outpatient and post-hospital-discharge referrals. Participants were asked to select which (if any) virtual care modalities they would feel comfortable using and to specify a reason if they did not feel comfortable providing care virtually. Machine learning and natural language processing techniques were used to identify themes.

RESULTS

In total, 57 infectious disease physicians completed the survey. Respondents reported devoting 36.5% (SD, 18.4%) of their infectious disease practice to outpatient care, with 44.2% (SD, 23.2%) of it being delivered virtually. Respondents were more comfortable providing virtual care to post-hospital-discharge referrals who had been seen by an infectious disease physician compared to new outpatient referrals. When respondents were not comfortable with using any virtual care modality, the following common themes emerged: the need for physical examination, the importance of establishing a therapeutic relationship, the need for additional in-person tests or diagnostics, and patient counselling.

CONCLUSION

This study provides a glimpse into the current state of virtual care use in Canada and some of the major themes that affect decision making for virtual versus in-person care.

Analyzing COVID-19 disinformation on Twitter using the hashtags #scamdemic and #plandemic: Retrospective study

INTRODUCTION

The use of social media during the COVID-19 pandemic has led to an "infodemic" of mis- and disinformation with potentially grave consequences. To explore means of counteracting disinformation, we analyzed tweets containing the hashtags #Scamdemic and #Plandemic.

METHODS

Using a Twitter scraping tool called twint, we collected 419,269 English-language tweets that contained "#Scamdemic" or "#Plandemic" posted in 2020. Using the Twitter application-programming interface, we extracted the same tweets (by tweet ID) with additional user metadata. We explored descriptive statistics of tweets including their content and user profiles, analyzed sentiments and emotions, performed topic modeling, and determined tweet availability in both datasets.

RESULTS

After removal of retweets, replies, non-English tweets, or duplicate tweets, 40,081 users tweeted 227,067 times using our selected hashtags. The mean weekly sentiment was overall negative for both hashtags. One in five users who used these hashtags were suspended by Twitter by January 2021. Suspended accounts had an average of 610 followers and an average of 6.7 tweets per user, while active users had an average of 472 followers and an average of 5.4 tweets per user. The most frequent tweet topic was "Complaints against mandates introduced during the pandemic" (79,670 tweets), which included complaints against masks, social distancing, and closures.

DISCUSSION

While social media has democratized speech, it also permits users to disseminate potentially unverified or misleading information that endangers people's lives and public health interventions. Characterizing tweets and users that use hashtags associated with COVID-19 pandemic denial allowed us to understand the extent of misinformation. With the preponderance of inaccessible original tweets, we concluded that posters were in denial of the COVID-19 pandemic and sought to disperse related mis- or disinformation resulting in suspension.

CONCLUSION

Leveraging 227,067 tweets with the hashtags #scamdemic and #plandemic in 2020, we were able to elucidate important trends in public disinformation about the COVID-19 vaccine.

Developing a COVID-19 WHO Clinical Progression Scale inpatient database from electronic health record data

OBJECTIVE

There is a need for a systematic method to implement the World Health Organization's Clinical Progression Scale (WHO-CPS), an ordinal clinical severity score for coronavirus disease 2019 patients, to electronic health record (EHR) data. We discuss our process of developing guiding principles mapping EHR data to WHO-CPS scores across multiple institutions.

MATERIALS AND METHODS

Using WHO-CPS as a guideline, we developed the technical blueprint to map EHR data to ordinal clinical severity scores. We applied our approach to data from 2 medical centers.

RESULTS

Our method was able to classify clinical severity for 100% of patient days for 2756 patient encounters across 2 institutions.

DISCUSSION

Implementing new clinical scales can be challenging; strong understanding of health system data architecture was integral to meet the clinical intentions of the WHO-CPS.

CONCLUSION

We describe a detailed blueprint for how to apply the WHO-CPS scale to patient data from the EHR.

District-Level Universal Masking Policies and COVID-19 Incidence During the First 8 Weeks of School in Texas

Texas discontinued state-sponsored business restrictions and mask mandates on March 10, 2021, and mandated that no government officials, including public school officials, may implement mask requirements even in areas where COVID-19 hospitalizations comprised more than 15% of hospitalizations. Nonetheless, some public school districts began the 2021-2022 school year with mask mandates in place. We used quasi-experimental methods to analyze the impact of school mask mandates, which appear to have resulted in approximately 40 fewer student cases per week in the first eight weeks of school. (Am J Public Health. Published online ahead of print May 2, 2022: e1-e5. https://doi.org/10.2105/AJPH.2022.306769).

NetworkSIR and EnvironmentalSIR: Effective, Open-Source Epidemic Modeling in the Absence of Data

The rapidly changing situation characterized by the COVID-19 pandemic highlighted a need for new epidemic modeling strategies. Due to an absence of computationally efficient models robust to paucity of reliable data, we developed NetworkSIR, a model capable of making predictions when only the approximate population density is known. We then extend NetworkSIR to capture the effect of indirect disease spread on the progression of an epidemic (EnvironmentalSIR).

The 21st Century Cures Act and Multiuser Electronic Health Record Access: Potential Pitfalls of Information Release

Although the Office of The National Coordinator for Health Information Technology's (ONC) Information Blocking Provision in the Cures Act Final Rule is an important step forward in providing patients free and unfettered access to their electronic health information (EHI), in the contexts of multiuser electronic health record (EHR) access and proxy access, concerns on the potential for harm in adolescent care contexts exist. We describe how the provision could erode patients' (both adolescent and older patients alike) trust and willingness to seek care. The rule's preventing harm exception does not apply to situations where the patient is a minor and the health care provider wishes to restrict a parent's or guardian's access to the minor's EHI to avoid violating the minor's confidentiality and potentially harming patient-clinician trust. This may violate previously developed government principles in the design and implementation of EHRs for pediatric care. Creating legally acceptable workarounds by means such as duplicate "shadow charting" will be burdensome (and prohibitive) for health care providers. Under the privacy exception, patients have the opportunity to request information to not be shared; however, depending on institutional practices, providers and patients may have limited awareness of this exception. Notably, the privacy exception states that providers cannot "improperly encourage or induce a patient's request to block information." Fearing being found in violation of the information blocking provisions, providers may feel that they are unable to guide patients navigating the release of their EHI in the multiuser or proxy access setting. ONC should provide more detailed guidance on their website and targeted outreach to providers and their specialty organizations that care for adolescents and other individuals affected by the Cures Act, and researchers should carefully monitor charting habits in these multiuser or proxy access situations.

Early identification of patients admitted to hospital for covid-19 at risk of clinical deterioration: model development and multisite external validation study

OBJECTIVE

To create and validate a simple and transferable machine learning model from electronic health record data to accurately predict clinical deterioration in patients with covid-19 across institutions, through use of a novel paradigm for model development and code sharing.

DESIGN

Retrospective cohort study.

SETTING

One US hospital during 2015-21 was used for model training and internal validation. External validation was conducted on patients admitted to hospital with covid-19 at 12 other US medical centers during 2020-21.

PARTICIPANTS

33 119 adults (≥18 years) admitted to hospital with respiratory distress or covid-19.

MAIN OUTCOME MEASURES

An ensemble of linear models was trained on the development cohort to predict a composite outcome of clinical deterioration within the first five days of hospital admission, defined as in-hospital mortality or any of three treatments indicating severe illness: mechanical ventilation, heated high flow nasal cannula, or intravenous vasopressors. The model was based on nine clinical and personal characteristic variables selected from 2686 variables available in the electronic health record. Internal and external validation performance was measured using the area under the receiver operating characteristic curve (AUROC) and the expected calibration error-the difference between predicted risk and actual risk. Potential bed day savings were estimated by calculating how many bed days hospitals could save per patient if low risk patients identified by the model were discharged early.

RESULTS

9291 covid-19 related hospital admissions at 13 medical centers were used for model validation, of which 1510 (16.3%) were related to the primary outcome. When the model was applied to the internal validation cohort, it achieved an AUROC of 0.80 (95% confidence interval 0.77 to 0.84) and an expected calibration error of 0.01 (95% confidence interval 0.00 to 0.02). Performance was consistent when validated in the 12 external medical centers (AUROC range 0.77-0.84), across subgroups of sex, age, race, and ethnicity (AUROC range 0.78-0.84), and across quarters (AUROC range 0.73-0.83). Using the model to triage low risk patients could potentially save up to 7.8 bed days per patient resulting from early discharge.

CONCLUSION

A model to predict clinical deterioration was developed rapidly in response to the covid-19 pandemic at a single hospital, was applied externally without the sharing of data, and performed well across multiple medical centers, patient subgroups, and time periods, showing its potential as a tool for use in optimizing healthcare resources.

Characteristics and Outcomes of Infectious Diseases Electronic COVID-19 Consultations at a Multisite Academic Health System

Objective The need for clinicians to access Infectious Diseases (ID) consultants for clinical decision-making support increased during the Coronavirus Disease 2019 (COVID-19) pandemic. Traditional ID consultations with face-to-face (FTF) patient assessments are not always possible or practical during a pandemic and involve added exposure risk and personal protective equipment (PPE) use. Electronic consultations (e-consults) may provide an alternative and improve access to ID specialists during the pandemic. Methods We implemented ID e-consult platforms designed to answer clinical questions related to COVID-19 at three academic clinical institutions in Dallas, Texas. We conducted a retrospective review of all COVID-19 ID e-consults between March 16, 2020 and May 15, 2020 evaluating characteristics and outcomes of e-consults among the clinical sites. Results We completed 198 COVID-19 ID e-consults at participating institutions. The most common e-consult indications were for 63 (32%) repeat testing, 61 (31%) initial testing, 65 (33%) treatment options, and 61 (31%) Infection Prevention (IP). Based on the e-consult recommendation, 53 (27%) of patients were initially tested for COVID-19, 45 (23%) were re-tested, 44 (22%) of patients had PPE precautions initiated, and 37 (19%) had PPE precautions removed. The median time to consult completion was four hours and 8 (4%) consults were converted to standard FTF consults. Conclusion E-consult services can provide safe and timely access to ID specialists during the COVID-19 pandemic, minimizing the risk of infection to the patient and health care workers, while preserving PPE and testing supplies.

618. Characteristics and Outcomes of an Outpatient Infectious Diseases E-consult Program at a County Safety-Net Healthcare System

Background

Safety-net healthcare systems often have significant demands for specialty care due to large patient volumes. Infectious Disease (ID) e-consults have the capability to relieve some of this burden by presenting providers with an alternative to face-to-face ID referrals that also lessens financial, travel, and time constraints on patients. Such a system offers the prospect of increasing access to ID care for patients in limited resource settings.

Methods

We performed a retrospective review describing characteristics and outcomes of all outpatient ID e-consults at Parkland Health and Hospital System in Dallas, Texas from March 2018 – February 2021.

Results

In the study period, 725 e-consults were completed. All e-consults were answered within 72 hours per hospital policy. The most common e-consult topics were 135 (19%) tuberculosis (TB), 116 (16%) syphilis, 97 (13%) respiratory and 79 (11%) musculoskeletal (Figure 1). Nearly two-thirds of the e-consults 456 (63%) came from primary care providers (PCPs). The remainder came from specialists with the most common referring specialties being GI 55 (8%), Hematology/Oncology 36 (5%), Rheumatology 28 (4%) Neurology 27 (4%), and Dermatology 22 (3%) (Figure 2). The majority of e-consults 569 (78%) were resolved without a face-to-face visit.

Figure 1. Number of E-consults over Time, by Topic

Figure 2. E-consult Topics by Referring Specialty

Conclusion

Implementation of an outpatient ID e-consult program at a large safety-net healthcare system was an effective means of providing timely input on common ID topics, such as latent TB and interpretation of syphilis serologies, without formal clinic visits. E-consults were able to service a range of providers including PCPs and a variety of specialties, and most e-consults were completed without a clinic visit.

Disclosures

All Authors: No reported disclosures

Rolling Up the Sleeve: Equitable, Efficient, and Safe COVID-19 Mass Immunization for Academic Medical Center Employees

BACKGROUND

Novel coronavirus disease 2019 (COVID-19) vaccine administration has faced distribution barriers across the United States. We sought to delineate our vaccine delivery experience in the first week of vaccine availability, and our effort to prioritize employees based on risk with a goal of providing an efficient infrastructure to optimize speed and efficiency of vaccine delivery while minimizing risk of infection during the immunization process.

OBJECTIVE

This article aims to evaluate an employee prioritization/invitation/scheduling system, leveraging an integrated electronic health record patient portal framework for employee COVID-19 immunizations at an academic medical center.

METHODS

We conducted an observational cross-sectional study during January 2021 at a single urban academic center. All employees who met COVID-19 allocation vaccine criteria for phase 1a.1 to 1a.4 were included. We implemented a prioritization/invitation/scheduling framework and evaluated time from invitation to scheduling as a proxy for vaccine interest and arrival to vaccine administration to measure operational throughput.

RESULTS

We allotted vaccines for 13,753 employees but only 10,662 employees with an active patient portal account received an invitation. Of those with an active account, 6,483 (61%) scheduled an appointment and 6,251 (59%) were immunized in the first 7 days. About 66% of invited providers were vaccinated in the first 7 days. In contrast, only 41% of invited facility/food service employees received the first dose of the vaccine in the first 7 days (p < 0.001). At the vaccination site, employees waited 5.6 minutes (interquartile range [IQR]: 3.9-8.3) from arrival to vaccination.

CONCLUSION

We developed a system of early COVID-19 vaccine prioritization and administration in our health care system. We saw strong early acceptance in those with proximal exposure to COVID-19 but noticed significant difference in the willingness of different employee groups to receive the vaccine.

Managing Pandemics with Health Informatics: Successes and Challenges

INTRODUCTION

The novel COVID-19 pandemic struck the world unprepared. This keynote outlines challenges and successes using data to inform providers, government officials, hospitals, and patients in a pandemic.

METHODS

The authors outline the data required to manage a novel pandemic including their potential uses by governments, public health organizations, and individuals.

RESULTS

An extensive discussion on data quality and on obstacles to collecting data is followed by examples of successes in clinical care, contact tracing, and forecasting. Generic local forecast model development is reviewed followed by ethical consideration around pandemic data. We leave the reader with thoughts on the next inevitable outbreak and lessons learned from the COVID-19 pandemic.

CONCLUSION

COVID-19 must be a lesson for the future to direct us to better planning and preparing to manage the next pandemic with health informatics.

What the Coronavirus Disease 2019 (COVID-19) Pandemic Has Reinforced: The Need for Accurate Data

The COVID-19 pandemic has challenged the United States’ existing national public health informatics infrastructure. This report details the factors that have contributed to COVID-19 data inaccuracies and reporting delays and their effect on the modeling and monitoring of the COVID-19 pandemic.

Early Crowdfunding Response to the COVID-19 Pandemic: Cross-sectional Study

Background

As the number of COVID-19 cases increased precipitously in the United States, policy makers and health officials marshalled their pandemic responses. As the economic impacts multiplied, anecdotal reports noted the increased use of web-based crowdfunding to defray these costs.

Objective

We examined the web-based crowdfunding response in the early stage of the COVID-19 pandemic in the United States to understand the incidence of initiation of COVID-19–related campaigns and compare them to non–COVID-19–related campaigns.

Methods

On May 16, 2020, we extracted all available data available on US campaigns that contained narratives and were created between January 1 and May 10, 2020, on GoFundMe. We identified the subset of COVID-19–related campaigns using keywords relevant to the COVID-19 pandemic. We explored the incidence of COVID-19–related campaigns by geography, by category, and over time, and we compared the characteristics of the campaigns to those of non–COVID-19–related campaigns after March 11, when the pandemic was declared. We then used a natural language processing algorithm to cluster campaigns by narrative content using overlapping keywords.

Results

We found that there was a substantial increase in overall GoFundMe web-based crowdfunding campaigns in March, largely attributable to COVID-19–related campaigns. However, as the COVID-19 pandemic persisted and progressed, the number of campaigns per COVID-19 case declined more than tenfold across all states. The states with the earliest disease burden had the fewest campaigns per case, indicating a lack of a case-dependent response. COVID-19–related campaigns raised more money, had a longer narrative description, and were more likely to be shared on Facebook than other campaigns in the study period.

Conclusions

Web-based crowdfunding appears to be a stopgap for only a minority of campaigners. The novelty of an emergency likely impacts both campaign initiation and crowdfunding success, as it reflects the affective response of a community. Crowdfunding activity likely serves as an early signal for emerging needs and societal sentiment for communities in acute distress that could be used by governments and aid organizations to guide disaster relief and policy.

472. The Utility of Infectious Diseases E-consults in the Era of COVID-19

Background

During the COVID-19 pandemic, rapid Infectious Diseases (ID) consultation has been required to answer novel questions regarding SARS-CoV-2 testing and infection prevention. We sought to evaluate the utility of e-consults to triage and provide rapid ID recommendations to providers.

Methods

We performed a retrospective study reviewing ID e-consults in three institutions in the North Texas region: Clements University Hospital (CUH), Parkland Hospital and Health System (PHHS), and the VA North Texas Health Care System (VA) from March 1, 2020 to May 15, 2020. Variables collected include age, sex, ethnicity, comorbidities, time to completion, reason for consult and outcome of consult (initiation or removal of personal protective equipment (PPE) and recommendation to test or retest for COVID-19).

Results

We performed all analysis using R studio (Version 1.3.959). Characteristics of 198 patients included: 112(57%) male, 86(43%) female, 86(43%) Caucasian, 71(36%) Hispanic, 42(21%) African American, 6(3%) Asian and mean(sd) age of 55.1(15.9). Patient comorbidities included: 89(45%) with a heart condition, 77(39%) diabetes, 30(15%) asthma and 14(7%) liver disease.

Median time to completion for all hospitals was 4 hours(h); ((CUH (4h) vs PHHS (2h), p< 0.05; VA (5.5h) vs PHHS (2h) p< 0.05)). Most common reasons for e-consult included: (63)32% regarding re-testing ((CUH 14(21%) vs PHHS 43(50%), p< 0.05; CUH vs VA 14(27%), p< 0.05; PHHS vs VA, p< 0.05)), (61)31% testing ((CUH 25(37%) vs PHHS 39(45%), p< 0.05; CUH vs VA 7(16%), p< 0.05; PHHS vs VA, p< 0.05)) and 61(31%) infection prevention (IP). Based on the e-consult recommendation, 53(27%) of patients were tested ((CUH 31(45%) vs PHHS 11(13%), p< 0.05, CUH vs VA 11(25%), PHHS vs VA, p< 0.05)), 45(23%) were re-tested, 44(22%) of patients had PPE started on and 19% had PPE removed ((CUH 0(0%) vs PHHS 16(19%), p< 0.05; CUH vs VA 21(48%), p< 0.05; PHHS vs VA, p< 0.05)).

Reason for Consult

Conclusion

E-consult services can provide prompt ID input during the COVID-19 pandemic, minimizing the risk of infection to the patient and health care workers while preserving PPE and testing supplies.

Disclosures

Roger Bedimo, MD, MS, Gilead Sciences (Consultant)Merck & Co. (Advisor or Review Panel member)ViiV Healthcare (Advisor or Review Panel member, Research Grant or Support)

486. Understanding Public Perception of COVID-19 Social Distancing on Twitter

Background

Managing and changing public opinion and behavior are vital for social distancing to successfully slow transmission of COVID-19, preserve hospital resources, and prevent overwhelming the healthcare system’s resources. We sought to leveraging organic, large-scale discussion on Twitter about social distancing to understand public’s beliefs and opinions on this policy.

Methods

Between March 27 and April 10, 2020, we sampled 574,903 English tweets that matched the two most trending social distancing hashtags at the time, #socialdistancing and #stayathome. We used natural language processing techniques to conduct a sentiment analysis that identifies tweet polarity and emotions. We also evaluated the subjectivity of tweets and estimated the frequency of discussion of social distancing rules. We then identified clusters of discussion using topic modeling and compared the sentiment by topic.

Results

There was net positive sentiment toward both #socialdistancing and #stayathome with mean sentiment scores of 0.150 (standard deviation [SD], 0.292) and 0.144 (SD, 0.287) respectively. Tweets were also more likely to be objective (median, 0.40; IQR, 0 to 0.6) with approximately 30% of all tweets labeled as completely objective. Approximately half (50.4%) of all tweets primarily expressed joy and one-fifth expressed fear and surprise each (Figure 1). These trends correlated well with topic clusters identified by frequency including leisure activities and community support (i.e., joy), concerns about food insecurity and effects of the quarantine (i.e., fear), and unpredictability of COVID and its unforeseen implications (i.e., surprise) (Table 1).

Table 1. Topic clusters identified by topic modeling. Words contributing to the model are shown in decreasing order of weighting. The topics are labeled manually based on these words. The number of tweets primarily with that topic, mean sentiment, mean subjectivity, and sample tweets are also included.

Figure 1. Emotion analysis for all tweets and stratified by tweets with the hashtag #socialdistancing and #stayathome. Comparison between the two hashtags is done using Chi-squared testing. Bonferroni correction was used to define statistical significance at a threshold of p = 0.008 (0.05/n, where n = 6 since 6 comparisons were completed).

Conclusion

The positive sentiment, preponderance of objective tweets, and topics supporting coping mechanisms led us to believe that Twitter users generally supported social distancing measures in the early stages of their implementation.

Disclosures

All Authors: No reported disclosures

What Twitter Can Tell Us About #IDWeek2020

We used topic modeling, subjectivity analysis, and social graph theory to analyze 11 944 tweets relating to IDWeek 2020. Twitter is a rich medium that can successfully disseminate knowledge and allow users to engage in social networks during a medical conference, despite a virtual format.

Derivation With Internal Validation of a Multivariable Predictive Model to Predict COVID-19 Test Results in Emergency Department Patients

Objectives

The COVID‐19 pandemic has placed acute care providers in demanding situations in predicting disease given the clinical variability, desire to cohort patients, and high variance in testing availability. An approach to stratifying patients by likelihood of disease based on rapidly available emergency department (ED) clinical data would offer significant operational and clinical value. The purpose of this study was to develop and internally validate a predictive model to aid in the discrimination of patients undergoing investigation for COVID‐19.

Methods

All patients greater than 18 years presenting to a single academic ED who were tested for COVID‐19 during this index ED evaluation were included. Outcome was defined as the result of COVID‐19 polymerase chain reaction (PCR) testing during the index visit or any positive result within the following 7 days. Variables included chest radiograph interpretation, disease‐specific screening questions, and laboratory data. Three models were developed with a split‐sample approach to predict outcome of the PCR test utilizing logistic regression, random forest, and gradient‐boosted decision tree methods. Model discrimination was evaluated comparing area under the receiver operator curve (AUC) and point statistics at a predefined threshold.

Results

A total of 1,026 patients were included in the study collected between March and April 2020. Overall, there was disease prevalence of 9.6% in the population under study during this time frame. The logistic regression model was found to have an AUC of 0.89 (95% confidence interval [CI] = 0.84 to 0.94) when including four features: exposure history, temperature, white blood cell count (WBC), and chest radiograph result. Random forest method resulted in AUC of 0.86 (95% CI = 0.79 to 0.92) and gradient boosting had an AUC of 0.85 (95% CI = 0.79 to 0.91). With a consistently held negative predictive value, the logistic regression model had a positive predictive value of 0.29 (0.2–0.39) compared to 0.2 (0.14–0.28) for random forest and 0.22 (0.15–0.3) for the gradient‐boosted method.

Conclusion

The derived predictive models offer good discriminating capacity for COVID‐19 disease and provide interpretable and usable methods for those providers caring for these patients at the important crossroads of the community and the health system. We found utilization of the logistic regression model utilizing exposure history, temperature, WBC, and chest X‐ray result had the greatest discriminatory capacity with the most interpretable model. Integrating a predictive model‐based approach to COVID‐19 testing decisions and patient care pathways and locations could add efficiency and accuracy to decrease uncertainty.

A Comparison of Online Medical Crowdfunding in Canada, the UK, and the US

IMPORTANCE

Despite major differences in their health care systems, medical crowdfunding is increasingly used to finance personal health care costs in Canada, the UK, and the US. However, little is known about the campaigns designed to raise monetary donations for medical expenses, the individuals who turn to crowdfunding, and their fundraising intent.

OBJECTIVE

To examine the demographic characteristics of medical crowdfunding beneficiaries, campaign characteristics, and their association with funding success in Canada, the UK, and the US.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study extracted and manually reviewed data from GoFundMe campaigns discoverable between February 2018 and March 2019. All available campaigns on each country domain's GoFundMe medical discovery webpage that benefitted a unique patient(s) were included from Canada, the UK, and the US. Data analysis was performed from March to December 2019.

EXPOSURES

Campaign and beneficiary characteristics.

MAIN OUTCOMES AND MEASURES

Log-transformed amount raised in US dollars.

RESULTS

This study examined 3396 campaigns including 1091 in Canada, 1082 in the UK, and 1223 in the US. Campaigns in the US (median [IQR], $38 204 [$31 200 to $52 123]) raised more funds than campaigns in Canada ($12 662 [$9377 to $19 251]) and the UK ($6285 [$4028 to $12 348]). In the overall cohort per campaign, Black individuals raised 11.5% less (95% CI, -19.0% to -3.2%; P = .006) than non-Black individuals, and male individuals raised 5.9% more (95% CI, 2.2% to 9.7%; P = .002) than female individuals. Female (39.4% of campaigns vs 50.8% of US population; difference, 11.3%; 95% CI, 8.6% to 14.1%; P < .001) and Black (5.3% of campaigns vs 13.4% of US population; difference, 8.1%; 95% CI, 6.8% to 9.3%; P < .001) beneficiaries were underrepresented among US campaigns. Campaigns primarily for routine treatment expenses were approximately 3 times more common in the US (77.9% [272 of 349 campaigns]) than in Canada (21.9% [55 of 251 campaigns]; difference, 56.0%; 95% CI, 49.3-62.7%; P < .001) or the UK (26.6% [127 of 478 campaigns]; difference, 51.4%; 95% CI, 45.5%-57.3%; P < .001). However, campaigns for routine care were less successful overall. Approved, inaccessible care and experimental care raised 35.7% (95% CI, 25.6% to 46.7%; P < .001) and 20.9% (95% CI, 13.3% to 29.1%; P < .001), respectively, more per campaign than routine care. Campaigns primarily for alternative treatment expenses (16.1% [174 of 1079 campaigns]) were nearly 4-fold more common for cancer (23.5% [144 of 614 campaigns]) vs noncancer (6.5% [30 of 465 campaigns]) diagnoses.

CONCLUSIONS AND RELEVANCE

Important differences were observed in the reasons individuals turn to medical crowdfunding in the 3 countries examined that suggest racial and gender disparities in fundraising success. More work is needed to understand the underpinnings of these findings and their implications on health care provision in the countries examined.

Non-typeable Haemophilus influenzae and purpura fulminans

Haemophilus influenzae typically causes illness and infection in the paediatric population. We report a case of a 53-year-old man who developed invasive non-typeable H. influenzae infection associated with purpura fulminans and multiorgan failure. On review of the literature, this is the first reported case of non-typeable H. influenzae causing purpura fulminans. The patient was treated with intravenous ceftriaxone 2 g/day and was eventually discharged from the hospital almost 2 months after admission. We discuss the role that infection/sepsis plays in disturbances to the coagulation cascade leading to purpura fulminans and the virulence factors that make non-typeable H. influenzae unique. Finally, we review other cases of H. influenzae associated with purpura fulminans and discuss the similarities with our case.

An "Infodemic": Leveraging High-Volume Twitter Data to Understand Early Public Sentiment for the Coronavirus Disease 2019 Outbreak

BACKGROUND

Twitter has been used to track trends and disseminate health information during viral epidemics. On January 21, 2020, the Centers for Disease Control and Prevention activated its Emergency Operations Center and the World Health Organization released its first situation report about coronavirus disease 2019 (COVID-19), sparking significant media attention. How Twitter content and sentiment evolved in the early stages of the COVID-19 pandemic has not been described.

METHODS

We extracted tweets matching hashtags related to COVID-19 from January 14 to 28, 2020 using Twitter's application programming interface. We measured themes and frequency of keywords related to infection prevention practices. We performed a sentiment analysis to identify the sentiment polarity and predominant emotions in tweets and conducted topic modeling to identify and explore discussion topics over time. We compared sentiment, emotion, and topics among the most popular tweets, defined by the number of retweets.

RESULTS

We evaluated 126 049 tweets from 53 196 unique users. The hourly number of COVID-19-related tweets starkly increased from January 21, 2020 onward. Approximately half (49.5%) of all tweets expressed fear and approximately 30% expressed surprise. In the full cohort, the economic and political impact of COVID-19 was the most commonly discussed topic. When focusing on the most retweeted tweets, the incidence of fear decreased and topics focused on quarantine efforts, the outbreak and its transmission, as well as prevention.

CONCLUSIONS

Twitter is a rich medium that can be leveraged to understand public sentiment in real-time and potentially target individualized public health messages based on user interest and emotion.

An "Infodemic": Leveraging High-Volume Twitter Data to Understand Early Public Sentiment for the Coronavirus Disease 2019 Outbreak

Background

Twitter has been used to track trends and disseminate health information during viral epidemics. On January 21, 2020, the Centers for Disease Control and Prevention activated its Emergency Operations Center and the World Health Organization released its first situation report about coronavirus disease 2019 (COVID-19), sparking significant media attention. How Twitter content and sentiment evolved in the early stages of the COVID-19 pandemic has not been described.

Methods

We extracted tweets matching hashtags related to COVID-19 from January 14 to 28, 2020 using Twitter’s application programming interface. We measured themes and frequency of keywords related to infection prevention practices. We performed a sentiment analysis to identify the sentiment polarity and predominant emotions in tweets and conducted topic modeling to identify and explore discussion topics over time. We compared sentiment, emotion, and topics among the most popular tweets, defined by the number of retweets.

Results

We evaluated 126 049 tweets from 53 196 unique users. The hourly number of COVID-19-related tweets starkly increased from January 21, 2020 onward. Approximately half (49.5%) of all tweets expressed fear and approximately 30% expressed surprise. In the full cohort, the economic and political impact of COVID-19 was the most commonly discussed topic. When focusing on the most retweeted tweets, the incidence of fear decreased and topics focused on quarantine efforts, the outbreak and its transmission, as well as prevention.

Conclusions

Twitter is a rich medium that can be leveraged to understand public sentiment in real-time and potentially target individualized public health messages based on user interest and emotion.

Personalized Antibiograms: Machine Learning for Precision Selection of Empiric Antibiotics

Up to 50% of antibiotic use in hospital settings is suboptimal. We build machine learning models trained on electronic health record data to minimize wasteful use of antibiotics. Our classifiers flag no growth blood and urine microbial cultures with high precision. Further, we build models that predict the likelihood of bacterial susceptibility to sets of antibiotics. These models contain decision thresholds that separate subgroups of patients whose susceptibility rates to narrow-spectrum antibiotics equal overall susceptibility rates to broader-spectrum drugs. Retroactively analyzing these thresholds on our one year test set, we find that 14% of patients infected with Escherichia coli and empirically treated with piperacillin/tazobactam could have been treated with ceftriaxone with coverage equal to the overall susceptibility rate ofpiperacillin/tazobactam. Similarly, 13% of the same cohort could have been treated with cefazolin - a first generation cephalosporin.