Background Despite widespread interest in the use of virtual (ie, telephone and video) visits for ambulatory patient care during the COVID-19 pandemic, studies examining their adoption during the pandemic by race, sex, age, or insurance are lacking. Moreover, there have been limited evaluations to date of the impact of these sociodemographic factors on the use of telephone versus video visits. Such assessments are crucial to identify, understand, and address differences in care delivery across patient populations, particularly those that could affect access to or quality of care. Objective The aim of this study was to examine changes in ambulatory visit volume and type (ie, in-person vs virtual and telephone vs video visits) by patient sociodemographics during the COVID-19 pandemic at one urban academic medical center. Methods We compared volumes and patient sociodemographics (age, sex, race, insurance) for visits during the first 11 weeks following the COVID-19 national emergency declaration (March 15 to May 31, 2020) to visits in the corresponding weeks in 2019. Additionally, for visits during the COVID-19 study period, we examined differences in visit type (ie, in-person versus virtual, and telephone versus video visits) by sociodemographics using multivariate logistic regression. Results Total visit volumes in the COVID-19 study period comprised 51.4% of the corresponding weeks in 2019 (n=80,081 vs n=155,884 visits). Although patient sociodemographics between the COVID-19 study period in 2020 and the corresponding weeks in 2019 were similar, 60.5% (n=48,475) of the visits were virtual, compared to 0% in 2019. Of the virtual visits, 61.2% (n=29,661) were video based, and 38.8% (n=18,814) were telephone based. In the COVID-19 study period, virtual (vs in-person) visits were more likely among patients with race categorized as other (vs White) and patients with Medicare (vs commercial) insurance and less likely for men, patients aged 0-17 years, 65-74 years, or ≥75 years (compared to patients aged 18-45 years), and patients with Medicaid insurance or insurance categorized as other. Among virtual visits, compared to telephone visits, video visits were more likely to be adopted by patients aged 0-17 years (vs 18-45 years), but less likely for all other age groups, men, Black (vs White) patients, and patients with Medicare or Medicaid (vs commercial) insurance. Conclusions Virtual visits comprised the majority of ambulatory visits during the COVID-19 study period, of which a majority were by video. Sociodemographic differences existed in the use of virtual versus in-person and video versus telephone visits. To ensure equitable care delivery, we present five policy recommendations to inform the further development of virtual visit programs and their reimbursement.

The COVID-19 pandemic resulted in widespread telehealth expansion. To determine telehealth uptake and potential sociodemographic differences in utilization among people with HIV (PwH), we examined HIV care appointments at the University of Chicago Medicine, an urban tertiary hospital. Visits between March 15th and September 9th for 2019 and 2020 were categorized as in-person, telehealth, and within telehealth, video, and phone. Differences in visit types were modeled using logistic regression to examine associations with demographics, insurance type, and HIV risk transmission category. Telehealth appointments were more likely for those aged 46-60 versus those [31][32][33][34][35][36][37][38][39][40][41][42][43][44][45] AOR 1.89 95% CI (1.14, 3.15)]. Black race and participants of other races were less likely to use telehealth compared to whites [Black: AOR 0.33 95% CI (0.16, 0.64), other: AOR 0.10 95% CI (0.02, 0.34)]. Future studies should continue to examine potential disparities in telehealth use among PwH, including age and racial differences.

BACKGROUND Despite widespread interest in the use of virtual (ie, telephone and video) visits for ambulatory patient care during the COVID-19 pandemic, studies examining their adoption during the pandemic by race, sex, age, or insurance are lacking. Moreover, there have been limited evaluations to date of the impact of these sociodemographic factors on the use of telephone versus video visits. Such assessments are crucial to identify, understand, and address differences in care delivery across patient populations, particularly those that could affect access to or quality of care. OBJECTIVE The aim of this study was to examine changes in ambulatory visit volume and type (ie, in-person vs virtual and telephone vs video visits) by patient sociodemographics during the COVID-19 pandemic at one urban academic medical center. METHODS We compared volumes and patient sociodemographics (age, sex, race, insurance) for visits during the first 11 weeks following the COVID-19 national emergency declaration (March 15 to May 31, 2020) to visits in the corresponding weeks in 2019. Additionally, for visits during the COVID-19 study period, we examined differences in visit type (ie, in-person versus virtual, and telephone versus video visits) by sociodemographics using multivariate logistic regression. RESULTS Total visit volumes in the COVID-19 study period comprised 51.4% of the corresponding weeks in 2019 (n=80,081 vs n=155,884 visits). Although patient sociodemographics between the COVID-19 study period in 2020 and the corresponding weeks in 2019 were similar, 60.5% (n=48,475) of the visits were virtual, compared to 0% in 2019. Of the virtual visits, 61.2% (n=29,661) were video based, and 38.8% (n=18,814) were telephone based. In the COVID-19 study period, virtual (vs in-person) visits were more likely among patients with race categorized as other (vs White) and patients with Medicare (vs commercial) insurance and less likely for men, patients aged 0-17 years, 65-74 years, or ≥75 years (compared to patients aged 18-45 years), and patients with Medicaid insurance or insurance categorized as other. Among virtual visits, compared to telephone visits, video visits were more likely to be adopted by patients aged 0-17 years (vs 18-45 years), but less likely for all other age groups, men, Black (vs White) patients, and patients with Medicare or Medicaid (vs commercial) insurance. CONCLUSIONS Virtual visits comprised the majority of ambulatory visits during the COVID-19 study period, of which a majority were by video. Sociodemographic differences existed in the use of virtual versus in-person and video versus telephone visits. To ensure equitable care delivery, we present five policy recommendations to inform the further development of virtual visit programs and their reimbursement.

BACKGROUND Prediction models are increasingly utilized in clinical practice but the optimal approach to collecting the needed inputs is unknown. OBJECTIVE Our objective was to compare a mortality prediction model inputs and scores based on chart abstraction versus patient survey. METHODS Older patients with type 2 diabetes at an urban, Chicago primary care practice were recruited and the Lee Mortality Index was calculated from retrospective chart review and patient surveys. RESULTS We saw non-significant differences in mortality scores (p=0.61) and subsequent diabetes in older adult health status class recommendations (p=0.70) comparing chart abstraction to survey data. However, there were large differences in certain domains such as functional status and presence of disease. Differences in chart review and survey resulted in 20% having discordant diabetes recommendations. CONCLUSIONS Healthcare organizations should work to systematically and routinely collect PROs that are inputs for widely used prediction models.

Long COVID, or post-COVID syndrome, is a constellation of symptoms observed in patients at least four weeks after COVID-19 infection. We analyzed the effect of COVID-19 vaccination status on risk of either developing Long COVID symptoms or being diagnosed with Long COVID. In separate analyses we compared the effect of vaccination status at time of COVID-19 infection and the effect of vaccination status as a time-dependent covariate where vaccination could occur at any point with respect to COVID-19 infection. To address this question, we identified a subset of adult patients from Truveta Data who experienced a COVID-19 infection as indicated by a positive laboratory test between 2021-10-01 and 2022-11-31. We considered two distinct ways of modeling the effect of vaccination status (time-independent and time-dependent) and two distinct outcomes of interest (Long COVID symptoms or diagnosis with Long COVID), representing four distinct analyses. The presence of Long COVID symptoms was defined as the presence of one or more new symptoms consistent with COVID-19/Long COVID at least four weeks post COVID-19 infection. Diagnosis of Long COVID was determined by the presence of one or more ICD-10-CM or SNOMED-CT codes explicitly identifying a patient as having been diagnosed with Long COVID. Our analysis focusing on the effect of COVID-19 vaccination status at time of COVID-19 infection found that patients who had completed a primary COVID-19 vaccination sequence or had completed a primary vaccination sequence and received a booster dose at time of COVID-19 infection were on average at lower risk of either developing Long COVID symptoms or being diagnosed with Long COVID than unvaccinated patients (vaccinated versus unvaccinated HR of symptoms 0.9 [0.87-0.94], HR of diagnosis 0.86 [0.74-0.99]; vaccinated and boosted versus unvaccinated HR of symptoms 0.87 [0.83-0.91], HR of diagnosis 0.81 [0.69-0.95]). We do not find evidence that having received a booster dose in addition to having completed a primary vaccination sequence offers additional protection over having completed the primary sequence alone (vaccinated and boosted versus vaccinated HR of symptoms 0.96 [0.91-1.01], HR of diagnosis 0.94-1.13) . Our analysis of COVID-19 vaccination status modeled as a time-dependent covariate yielded similar results for patients who had completed a primary COVID-19 vaccination sequence or had completed a primary vaccination sequence and a booster dose. Both groups were on average at lower risk of developing Long COVID symptoms or being diagnosed with Long COVID than patients who where never vaccinated (vaccinated versus unvaccinated HR of symptoms 0.91 [0.88-0.95], HR of diagnosis 0.86 [0.75-0.99]; vaccinated and boosted versus unvaccinated HR of symptoms 0.88 [0.85-0.91], HR of diagnosis 0.77 [0.67-0.9]). As with the time-independent analysis, we also find that having completed a booster dose in addition to a primary COVID-19 vaccination sequence does not provide additional protection from developing Long COVID symptoms or being diagnosed with Long COVID over having completed the primary sequence alone (vaccinated and boosted versus vaccinated HR of symptoms 0.96 [0.92-1.01], HR of diagnosis 0.89 [0.76-1.06]) . We find that completing a primary vaccination sequence is associated with a decreased risk of developing Long COVID symptoms or being diagnosed with Long COVID compared with no vaccination regardless of whether vaccination status is modeled as a time-independent or time-dependent covariate. We find a similar protective effect in patients who have completed a primary vaccination sequence and a booster dose when compared to the those who are unvaccinated. However, we do not find evidence for a difference in protective effect between patients who have completed a primary vaccination sequence and a booster dose and those patients who have only completed a primary vaccination sequence. Our results support the growing evidence that having complete a primary vaccination sequence is protective against the development of Long COVID symptoms or the diagnosis of Long COVID.