Abstract
Low-income populations are at higher risk of missing appointments, resulting in fragmented care and worsening disparities. Compared to face-to-face encounters, telehealth visits are more convenient and could improve access for low-income populations. All outpatient encounters at the Parkland Health between March 2020 and June 2022 were included. No-show rates were compared across encounter types (face-to-face vs telehealth). Generalized estimating equations were used to evaluate the association of encounter type and no-show encounters, clustering by individual patient and adjusting for demographics, comorbidities, and social vulnerability. Interaction analyses were performed. There were 355,976 unique patients with 2,639,284 scheduled outpatient encounters included in this dataset. 59.9% of patients were of Hispanic ethnicity, while 27.0% were of Black race. In a fully adjusted model, telehealth visits were associated with a 29% reduction in odds of no-show (aOR 0.71, 95% CI: 0.70–0.72). Telehealth visits were associated with significantly greater reductions in probability of no-show among patients of Black race and among those who resided in the most socially vulnerable areas. Telehealth encounters were more effective in reducing no-shows in primary care and internal medicine subspecialties than surgical specialties or other non-surgical specialties. These data suggest that telehealth may serve as a tool to improve access to care in socially complex patient populations.
Supplementary Information
The online version contains supplementary material available at 10.1007/s11524-023-00721-2.
Keywords: Telehealth, Low Income, Health Disparities, Access to Care
Introduction
The rapid rise of telehealth during the COVID-19 pandemic has changed the practice of medicine [1, 2]. Within the first 3 months of 2020, telehealth encounters increased by over 154% [3]. More recent data from middle to late 2021 suggest that approximately 20% of adults have used telehealth services within the last 4 weeks [4]. Due to their increased convenience and shorter waiting times, prior studies have reported high patient satisfaction with telemedicine [5, 6].
Missed appointments burden health systems and result in fragmented care for patients. Previous studies have reported that a single no-show appointment costs the health system approximately $260 (adjusted for inflation) and can total millions of dollars in lost revenue annually [7]. Additionally, studies have demonstrated that missed appointments are associated with worsened glycemic and lipid control, lower rates of preventative cancer screening, and higher utilization of the emergency room [8–10]. As such, a wide spectrum of interventions aimed at reducing no-shows have been studied, including randomized trials with phone call confirmations, text message reminder systems, rideshare transportation services, and financial incentives [11–14]. Furthermore, prior studies have shown that in the early parts of the pandemic, telehealth encounters were associated with a reduction in no-show visits, thereby bridging potential gaps in care associated with lockdowns [5, 15–17].
Low-income populations experience unique challenges that increase their risk of missing an appointment. The most commonly cited reasons for missed visits include forgetting the appointment, conflicting child or family care obligations, and a lack of transportation [18, 19]. An analysis of a large nationally representative survey found that patients below the poverty limit disproportionately experience transportation barriers and are 67% more likely to report transportation-related delays in care [20]. Other studies in low-income populations have reported that lack of access to a car, unreliable public transportation, and transportation-related costs such as gas, parking, and bus/taxi fees all contribute to missed appointments [21]. One observational study found that additional public transportation options reduced overall no-show rate modestly but had the greatest effect on low-income patients [22].
Given that telehealth appointments do not require transportation, parking, or gas fees and are lower time commitment, telehealth may offer the opportunity to alleviate many of the barriers low-income populations experience in accessing healthcare. However, most of the evidence surrounding the use of telehealth has focused primarily on the immediate timeframe associated with the initial waves of COVID and do not include data beyond early 2021, when COVID vaccinations were more widespread and lower lethality COVID variants were dominant. Whether telehealth is viable option to reduce no-show rate in low-income and racial/ethnic minoritized populations is not known. As such, we performed a retrospective cohort analysis to study whether virtual encounters were associated with decreased no-show rates in one of the largest safety-net health systems in the USA.
Methods
We obtained data of all scheduled outpatient encounters at the Parkland Health between March 1, 2020, and June 30, 2022. The Parkland Health predominantly cares for low-income and uninsured patient populations in Dallas County, Texas, providing approximately $1.2 billion in uncompensated care in fiscal year 2021 [23]. Starting in March 2020, the Parkland Health implemented audio-only telehealth encounters in response to the COVID-19 pandemic. These phone visits required patients to answer their phone and did not require any video capacity. Starting in September 2020, Parkland began transitioning from audio-only virtual encounters to video virtual encounters, depending on the patient’s access to a robust cellular data plan or fixed internet connectivity. Prior to being scheduled an appointment, patients were asked their preference between video, audio, and in-person visit. Patients who agreed to a video telehealth visit were sent a link through their Epic MyChart and received a telephone call confirmation. Prior to the scheduled telehealth appointment time, patients were sent a reminder text message with a link that helps them immediately join the video appointment. If a patient lacked capabilities for a video encounter or had connectivity issues, the encounter would be switched to an audio-only encounter. Due to limitations in the dataset, telehealth encounters in this study were defined as either video or audio-only encounters. We hypothesized that telehealth visits in a socially complex patient population would be associated with reduced probability of a no-show encounter.
We extracted encounter data including date of visit, visit type (telehealth [included both audio-only and video] or face-to-face), appointment outcome (completed or no-show), and department specialty. The complete list of department specialties is included in the Supplement. We also obtained sociodemographic and comorbidity data, including age, gender, race/ethnicity, payer type, preferred language (English, Spanish, other), zip code, and all comorbidities associated with the Elixhauser index. Payer type was divided into five categories: private insurance, Medicare, Medicaid, uninsured, and unknown. The Parkland Health provides financial assistance to eligible residents of Dallas County but does not qualify as insurance. Thus, the uninsured patient population in our cohort is comprised of uninsured patients who receive Parkland Financial Assistance (PFA) and uninsured patients who are ineligible for Parkland’s financial assistance (e.g., out of county). Patients with unknown insurance did not provide their insurance information prior to or during the scheduled encounter.
Encounters with missing age, sex, or race/ethnicity data were excluded. Patient zip code was linked to the social deprivation index, a publicly available and validated measure of the socioeconomic status of residents within a zip code within the USA [24]. Social deprivation index was categorized into quintiles, with higher quintiles representing greater social deprivation.
The primary exposure was visit type (telehealth vs face-to-face). The primary outcome was encounter outcome (no-show vs completed).
Statistical Analysis
Summary statistics were used to calculate means and proportions of patient and encounter characteristics. We visualized quarterly rates of no-show by encounter type. Crude no-show rates were calculated for all patient subgroups and by visit type. We evaluated the univariate association between exposures and encounter outcome using simple logistic regression models. To evaluate the association of visit type and encounter outcome, we used generalized estimating equations with an independent correlation structure, accounting for clustering within patients in the Parkland population and adjusted for sociodemographic variables, comorbidities, clinical specialty category, and calendar quarter of appointment. This model provides population level estimates for each variable in the model and handles the correlation from multiple encounters for each unique patient [25].
We performed additional sensitivity analyses evaluating the interaction between visit type and (1) calendar quarter, (2) race/ethnicity, (3) primary language, (4) social deprivation index, and (5) clinical specialty category. To further explore the relationship between visit type, timeframe, and clinical specialty, we performed a three-way interaction analysis of all three variables. Stratified analyses were performed across variables with statistically significant interaction terms.
We considered a P-value of < 0.05 statistically significant. Data were analyzed in September 2021. This study was approved by the UT Southwestern Institutional Review Board. Data were analyzed using SAS v9.4 and figures made in GraphPad Prism.
Results
Patient Population Characteristics
Between March 1, 2020, and June 30, 2022, there were 355,976 patients scheduled in the Parkland Health, totaling 2,639,284 outpatient encounters. Over half (59.9%) of patients were of Hispanic ethnicity, and over a quarter were of Black race (27.0%). 42.1% of patients had a non-English preferred language.
Overall Encounter Characteristics
The encounter characteristics stratified by encounter type are shown in Table 1. Nearly 40% of scheduled encounters were for uninsured patients. There was a comparable distribution of social deprivation index quintile among all scheduled encounters. Of the scheduled encounters, 37.2% were scheduled as a telehealth visit (715,376 encounters). Compared to face-to-face encounters, telehealth encounters were scheduled more often for patients of White and Black race and patients who preferred English and less often for patients of younger age and with Medicaid insurance. In contrast to surgical specialties, telehealth visits were more common than face-to-face visits across primary care, internal medicine subspecialties, and other non-surgical specialties.
Table 1.
Encounter characteristics
Overall | Telehealth encounters | Face-to-face encounters | |
---|---|---|---|
Number of encounters | 2,639,284 | 715,376 | 1,923,908 |
Completed encounters | 2,146,508 (81.3%) | 598,634 (83.7%) | 1,547,874 (80.5%) |
No-show encounters | 492,776 (18.7%) | 116,742 (16.3%) | 376,034 (19.6%) |
Encounter characteristics | |||
Age | 42.4 (20.9) | 48.3 (17.7) | 40.2 (21.5) |
Female | 1,748,576 (66.2%) | 470,531 (65.8%) | 1,278,045 (66.4%) |
Race/ethnicity | |||
Non-Hispanic White | 280,645 (10.6%) | 91,359 (12.8%) | 189,286 (9.8%) |
Non-Hispanic Black | 767,033 (29.0%) | 225,380 (31.5%) | 541,653 (28.2%) |
Hispanic | 1,524,895 (57.8%) | 377,640 (52.8%) | 1,147,255 (59.6%) |
Asian/other | 66,711 (2.5%) | 20,997 (2.9%) | 45,714 (2.4%) |
Primary language | |||
English | 1,532,551 (58.1%) | 435,204 (60.8%) | 1,097,347 (57.0%) |
Spanish | 1,038,335 (39.3%) | 260,253 (36.4%) | 778,082 (40.4%) |
Other | 68,398 (2.6%) | 19,919 (2.8%) | 48,479 (2.5%) |
Payer | |||
Private insurance | 180,953 (6.9%) | 48,309 (6.8%) | 132,644 (6.9%) |
Medicare | 333,883 (12.7%) | 120,685 (16.9%) | 213,198 (11.0%) |
Medicaid | 799,421 (30.3%) | 149,936 (21.0%) | 649,485 (33.8%) |
Uninsured/charity/self-pay/Parkland Financial Assistance | 1,042,991 (39.5%) | 314,516 (44.0%) | 728,475 (37.9%) |
Unknown | 279,290 (10.5%) | 81,559 (11.4%) | 197,731 (10.3%) |
Social deprivation index quintile | |||
1st (least deprived) | 512,248 (19.4%) | 371,013 (19.2%) | 141,235 (19.7%) |
2nd | 562,481 (21.3%) | 405,258 (21.1%) | 157,223 (21.2%) |
3rd | 420,577 (15.9%) | 306,795 (15.9%) | 113,782 (15.9%) |
4th | 616,977 (23.4%) | 457,686 (23.8%) | 159,291 (22.2%) |
5th quintile (most deprived) | 519,444 (19.7%) | 377,658 (19.6%) | 141,786 (19.8%) |
Encounter specialty | |||
Primary care | 1,061,764 (40.2%) | 317,520 (44.4%) | 744,244 (38.7%) |
Internal medicine subspecialty | 494,686 (18.7%) | 182,737 (25.5%) | 311,949 (16.2%) |
Other non-surgical specialty | 160,951 (6.1%) | 83,656 (11.7%) | 77,295 (4.0%) |
Surgical specialty | 907,122 (34.4%) | 130,247 (18.2%) | 776,875 (40.4%) |
Crude Association of Telehealth and No-Show Rate
The overall encounter no-show rate for the cohort was 18.7% (492,776 encounters). Face-to-face encounters had a higher no-show rate compared to telehealth visits (19.6% vs 16.3%). The crude quarterly no-show rates by encounter type are shown in Fig. 1. The crude no-show rates by patient characteristics are shown in Supplemental Table 1. No-show rates were greatest among male patients, Black patients, English-speaking patients, patients with unknown insurance, and patients who lived in the most deprived zip codes. When stratified by encounter type, nearly every subgroup in the telehealth group had statistically significantly lower no-show rates, as compared to the face-to-face encounter. The subgroups with lower no-show rates in the face-to-face group were encounters with patients of Asian/other race/ethnicity, other language, and uninsured/charity/self-pay/PFA insurance status. In univariate analysis, compared to face-to-face encounters, telehealth encounters were associated 20% reduced odds of an encounter resulting in a no-show (95% CI: 0.793, 0.804). The complete univariate analysis is shown in Supplemental Table 2.
Fig. 1.
No-show rates by encounter type between March 2020 and June 2022
Adjusted Association of Telehealth and No-Show Rate
In a fully adjusted model accounting for patient-level clustering, telehealth encounters were associated with a 29% reduction in odds of a no-show encounter (aOR 0.71; 95% CI 0.70, 0.72; P < 0.001), compared to face-to-face encounters (Table 2). Odds of no-show were significantly decreased for patients of female sex (aOR 0.96, 95% CI: 0.95, 0.97), Asian race (aOR 0.79, 95% CI 0.76, 0.83), Spanish (aOR 0.77, 95% CI 0.75, 0.78) or other language preference (aOR 0.81, 95% CI 0.78, 0.84), and Medicare insurance (aOR 0.96, 95% CI 0.94, 0.99). Encounters with patients of Black race or Medicaid insurance had greater odds of no-show (all P < 0.001). Unknown health insurance was associated with the greatest odds of a no-show encounter (aOR 24.86, 95% CI 24.32, 25.41, P < 0.001). Compared to primary care, the odds of a no-show were higher among encounters from internal medicine subspecialties or other non-surgical specialties, but lower among surgical specialty encounters. Social deprivation index quintile was associated with a monotonic increase in odds of no-show.
Table 2.
Hierarchical logistic regression model evaluating association of encounter type and no-show encounter
Adjusted odds ratio | P value | |
---|---|---|
Telehealth encounter (Ref = face-to-face) | 0.71 (0.70, 0.72) | < 0.001 |
Age (per year) | 0.99 (0.99, 0.99) | < 0.001 |
Female sex (Ref = male) | 0.96 (0.95, 0.97) | < 0.001 |
Race/ethnicity (Ref = non-Hispanic White) | ||
Non-Hispanic Black | 1.20 (1.18, 1.23) | < 0.001 |
Hispanic | 1.01 (0.99, 1.04) | 0.28 |
Asian | 0.79 (0.76, 0.83) | < 0.001 |
Primary language (Ref = English) | ||
Spanish | 0.77 (0.75, 0.78) | < 0.001 |
Other language | 0.81 (0.78, 0.84) | < 0.001 |
Insurance (Ref = private) | ||
Medicare | 0.96 (0.94, 0.99) | < 0.001 |
Medicaid | 1.23 (1.21, 1.27) | < 0.001 |
Uninsured/Charity/Self-Pay/Parkland Financial Assistance | − 0.85 (0.83, 0.87) | < 0.001 |
Unknown | 24.86 (24.32, 25.41) | < 0.001 |
Number of comorbidities | 0.99 (0.98, 0.99) | < 0.001 |
Encounter specialty (Ref = primary care) | ||
Internal medicine subspecialty | 1.30 (1.38, 1.42) | < 0.001 |
Other non-surgical specialty | 1.11 (1.09, 1.14) | < 0.001 |
Surgical specialty | 0.83 (0.82, 0.84) | < 0.001 |
Social deprivation index quintile (Reference = Q, least deprived1) | ||
Q2 | 1.02 (1.01, 1.04) | < 0.001 |
Q3 | 1.05 (1.03, 1.07) | < 0.001 |
Q4 | 1.07 (1.05, 1.09) | < 0.001 |
Q5 (most deprived) | 1.11 (1.09, 1.12) | < 0.001 |
Calendar quarter (per quarter) | 1.07 (1.06, 1.07) | < 0.001 |
Model is clustered by individual patient and accounts for the correlation of multiple encounters for an individual patient
Sensitivity Analysis: Interaction Testing
Interaction testing for effect modification was positive across race/ethnicity, primary language, calendar quarter of appointment, social deprivation index, and specialty groups (Pinteraction < 0.001 for all). The fully adjusted odds ratios for each subgroup are displayed in Fig. 2. When stratified by race/ethnic subgroup, we note that the association of encounter type and encounter outcome was most pronounced among encounters for non-Hispanic Black patients (aOR 0.56, 95% CI 0.55, 0.57), followed by non-Hispanic White patients (aOR 0.71, 95% CI 0.69, 0.74), and Hispanic patients (aOR 0.80, 95% CI 0.79, 0.82). Telehealth encounters were not associated with improved no-show rates in Asian patients (aOR 0.94, 95% CI 0.87, 1.01). When stratified by primary language, we found that telehealth visits increased the risk of no-show among patients who primarily spoke non-English or Spanish (aOR 1.13, 95% CI 1.05, 1.22, P < 0.001). When stratified by SDI quintile, we note that telehealth visits were associated with the greatest reduction in no-show risk for patients in the most deprived quintile (Q5 aOR 0.65 vs Q4 aOR 0.74 vs Q3 aOR 0.70 vs Q2 aOR 0.72 vs Q1 aOR 0.72). When stratified by time period, we observe that the risk reduction in no-shows associated with telehealth was substantially attenuated in the later stages of the pandemic compared with the earlier stages (aOR 0.97 from April 2021-June 2022 vs. aOR 0.59 from March 2020 - March 2021). The benefit of telehealth in reducing no-show was most pronounced in primary care (aOR 0.58) and internal medicine subspecialty (aOR 0.51) encounters but paradoxically led to an increased risk of no-show in other non-surgical specialty (aOR 1.36) and surgical specialty encounters (aOR 1.10).
Fig. 2.
Stratified subgroup analysis evaluating association of encounter type and encounter outcome
A three-way interaction between visit type, time period (March 2020–March 2021 vs April 2021–June 2022), and specialty category was significantly positive (Pinteraction < 0.001). The stratified results are shown in Fig. 3. In the early time period, telehealth was associated with reductions in no-shows in nearly all groups, with the exception of the other non-surgical specialty encounters. However, in the later time period, the association is attenuated for primary care (early aOR 0.50 vs later aOR 0.76) and internal medicine subspecialties (early aOR 0.43 vs later aOR 0.65), reverses for surgical specialties (early aOR 0.84 vs later aOR 1.43), and is magnified for encounters with other non-surgical specialties (early aOR 1.02 vs later aOR 1.59).
Fig. 3.
Subgroups stratified by time period and clinical specialty
Discussion
In this study of a large safety-net health system, we find that the telehealth visit type was associated with reduced risk of no-show among a low-income population, after accounting for patient level characteristics and adjusting for sociodemographic factors. We note that patients of Black race, with English preferred language, and patients who lived in the most disadvantaged locations of residence were more likely to complete a telehealth encounter than a face-to-face encounter. We observe that the reduction in no-show rates associated with telehealth visits was most pronounced in the first 12 months after the COVID pandemic and was attenuated in the second year of the COVID pandemic. Lastly, we demonstrate a persistent and clinically meaningful reduction in no-show rates associated with telehealth visits in primary care and internal medicine subspecialty clinics.
The results showed that nearly 1 in every 5 appointments scheduled in one of the nation’s largest safety net health systems is missed. These numbers are similar to other low-income populations previously published in the literature [16, 26]. But with over 2.5 million encounters scheduled and nearly 500,000 no-show appointments over the course of the study, identifying small improvements could have substantial financial and clinical impacts on the health system [7].
We noted that telehealth visits had a lower overall rate of no-shows compared to face-to-face visits (16.3% telehealth vs 19.6% face-to-face). Indeed, when fully adjusted for demographics factors, socioeconomic factors, and comorbid factors, we find that telehealth visits were associated with a 30% reduction in no-show risk. This association is of much greater magnitude than a 2020 study by Adepoju et al. in Federally Qualified Health Centers, which found a 13% reduction in no-show risk associated with telehealth visits [15]. Compared to the Adepoju study, our study population was older (42.4 years vs 27.1 years), had a greater diversity of care services including all internal medicine subspecialties and surgical specialties (compared to only primary care specialties), and captured more than 2 years of scheduled encounters (compared to less than 1 year of data). It is possible that our older patient population may experience barriers to care not seen in younger populations.
Furthermore, we observed that the degree of social deprivation was associated with higher no-show rates, with the odds of missing an appointment increasing in a graded fashion with each quintile of social deprivation. Similar reports have reported that patients with significant unmet social need burden had the highest risk of no-show appointments [27]. This is unsurprising, as completing a medical appointment is likely secondary to more acute priorities such as housing, food, and childcare. Furthermore, we noted effect modification of social deprivation index on the association of encounter type and outcome. However, we observe that the greatest improvement in no-show risk associated with telehealth encounters is among those in the highest quintile of social deprivation. This suggests that using telehealth among the most disadvantaged populations may yield fruitful dividends and improve disparities in care and provide evidence for the promotion and incentivization of telehealth for health systems serving populations with high socioeconomic disparity.
Notably, telehealth visits may benefit racial/ethnic subgroups differently. Consistent with prior studies, we found that the highest no-show rates are those of Black race [18, 27]. These gaps in care have previously been associated with poorer control of chronic medical conditions and likely contribute to racial disparities in health outcomes [8–10]. Thus, it is encouraging that we find that Black patients receive the greatest reduction in no-show risk when scheduled a telehealth visit. This trend is consistent with a previous study by Eberly et al., which showed that Black patients were more likely to complete a telehealth visit than White patients [28]. Interestingly we also note that patients of Hispanic ethnicity were equally likely to complete appointments as non-Hispanic White patients. This differs from previously published studies and may reflect Parkland’s reputation among the Hispanic community in Dallas, its robust multidisciplinary support services, and its accessibility for those who speak Spanish [27, 29].
We also find that the benefit of telehealth was modified by its relationship to the COVID pandemic. Multiple other studies have demonstrated the rise of telehealth during the COVID pandemic and its effect on no-show rates in the short-term period after the COVID pandemic [5, 16, 17, 30]. However, our study adds to the literature by showing longitudinal trends of telehealth, including data through 2022. In the first two quarters of 2020, no-show rates were much higher in face-to-face appointments than telehealth appointments. This gap converged slightly by the fourth quarter of 2020 and remained relatively parallel going forward. Interestingly, when the model was fully adjusted and stratified by time, telehealth encounters appeared to reduce the risk of no-show by only 3.4% between April 2021 and June 2022. However, stratification by specialty reveals a more nuanced finding. In this later period, we observe that telehealth encounters significantly reduced the odds of a no-show in primary care and internal medicine subspecialty encounters but increased the odds in other non-surgical specialties and surgical specialties. As such, these data suggest that telehealth resources should be directed toward primary care and internal medicine subspecialty clinics and in-person resources should be focused on the other non-surgical and surgical specialties.
This study has important health system and equity implications. Given that missed appointments cause significant loss of revenue and productivity, health systems may consider leveraging telehealth encounters as the default type of healthcare encounter in patients at high risk for missing appointments, particularly in primary care and internal medicine subspecialty settings. Health systems may also consider reflexing to telehealth encounters when patients miss a face-to-face appointment to provide remote care and increase revenue. With the large clinical impact of missed appointments and the social challenges disadvantaged populations must overcome to access healthcare, telehealth offers the potential to reduce effectively healthcare disparities and extend opportunities to extend with the health system. The effectiveness of telehealth was evaluated in prior work done at Parkland, which found that audio-only telehealth encounters were non-inferior to face-to-face encounters with respect to glycemic control and are consistent with other studies [6, 31–33]. By reducing barriers to care, health systems may better manage chronic medical conditions and reduce preventable morbidity and mortality.
This study has several strengths. First, we use data from one of the largest safety-net health systems in the USA, with over 2.5 million encounters from over 350,000 unique patients within a 27-month period. Additionally, the low-income population of the Parkland Health is unique, diverse, and under-represented in the literature. Moreover, the data analyzed captures data from 2020 to 2022, allowing us to visualize and account for the acute temporal confounding associated with the COVID pandemic and shutdowns.
Our study has important limitations as well. Due to limitations in the dataset, we were unable to differentiate audio-telehealth visits versus video-telehealth visits and thus are unable to provide greater granularity on the association of medium-specific telehealth visits on no-show rates. Furthermore, as insurance details are often obtained immediately prior to an appointment, a disproportionate share of patients who missed their appointment had unknown insurance. The Parkland Health primarily cares for patients in the Dallas area; thus, our results may only be generalizable to an urban, low-income population. Though we accounted for the Charlson comorbidity index, we were unable to adjust for residual confounding by indication, whereby patients who were scheduled face-to-face appointments had more acute needs than patients who were scheduled telehealth appointments.
In conclusion, we find that in a low-income safety-net setting, telehealth encounters were associated with 30% reduced odds of a missed appointment. Telehealth may represent a viable way to improve revenue for healthcare systems and reduce healthcare disparities in low-income populations with high social burdens, with the greatest benefit in primary care and internal medicine subspecialty settings. Future implementation efforts should consider decision support to suggest or default patients at high-risk for no-show to a telehealth encounter or converting missed face-to-face appointments to a same-day telehealth encounter.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
This study did not receive internal or external funding.
Data Availability
The data from this study are not publicly available due to the high prevalence of vulnerable patients in the Parkland population.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data from this study are not publicly available due to the high prevalence of vulnerable patients in the Parkland population.