Effect of COVID-19 on Alzheimer's and dementia measured through ocular indications

Screening for early detection of Alzheimer's disease (AD) through a comprehensive eye exam appears to be promising and could potentially provide a more sensitive, inexpensive way to visualize early signs of AD for early detection in large populations. Optical coherence tomography (OCT), as well as retinal imaging techniques such as Doppler and fluorescence lifetime imaging ophthalmoscopy (FLIO), can detect signs of early AD such as vascular changes or accumulations of Tau proteins and beta-amyloid proteins. In the age of COVID-19, this screening opportunity is threatened by increased no-show rates leading to decreased early detection of AD. Through the combination of COVID-19 neuroinflammation potentially augmenting AD neurodegeneration, as well as missed opportunity in the use of early ophthalmic detection, the pandemic may have significantly worsened the trajectory of AD. © 2023 Elsevier Inc. All rights reserved.

Predictors of Follow-Up Appointment No-Shows Before and During COVID Among Adults with Type 2 Diabetes.

Background: The coronavirus disease 2019 (COVID-19) pandemic has rapidly transformed health care delivery into telehealth visits. Attending regular medical appointments are critical to prevent or delay diabetes-related complications. Although telehealth visits have addressed some barriers to in-person visits, appointment no-shows are still noted in the telehealth setting. It is not completely clear how the predictors of appointment no-shows differ between in-person and telehealth visits in diabetes care. Objective: This retrospective study examined if predictors of appointment no-shows differ (1) between pre-COVID (January 1, 2019-March 22, 2020) and COVID (March 23, 2020-December 31, 2020) periods and (2) by health care delivery modes (in-person or telehealth visits) during COVID among adults with type 2 diabetes mellitus (T2DM). Methods: We used electronic health records between January 1, 2019 and December 31, 2020 across four diabetes clinics in a tertiary academic hospital in Baltimore, Maryland. Appointments marked as completed or no-show by established adults with T2DM were included in the analyses. Results: Among 7,276 appointments made by 2,235 patients, overall appointment no-show was 14.99%. Being older and White were protective against appointment no-shows in both unadjusted and adjusted models during both time periods. The interaction terms of COVID periods (i.e., pre-COVID vs. COVID) were significant for when glycated hemoglobin drawn before this visit and for missing body mass index. Telehealth visits during COVID decreased more half of the odds of appointment no-shows. Conclusions: In the context of diabetes care, the implementation of telehealth reduced appointment no-shows. Future studies are needed to address social determinants of health, including access to internet access, to further reduce health disparities among adults with T2DM.

Factors Associated With No-Show to Ambulatory Tele-Video Neurology Visits.

Introduction Telehealth visits (TH) have become an important pillar of healthcare delivery during the COVID pandemic. No-shows (NS) may result in delays in clinical care and in lost revenue. Understanding the factors associated with NS may help providers take measures to decrease the frequency and impact of NS in their clinics. We aim to study the demographic and clinical diagnoses associated with NS to ambulatory telehealth neurology visits. Methods We conducted a retrospective chart review of all telehealth video visits (THV) in our healthcare system from 1/1/2021 to 5/1/2021 (cross-sectional study). All patients at or above 18 years of age who either had a completed visit (CV) or had an NS for their neurology ambulatory THV were included. Patients having missing demographic variables and not meeting the ICD-10 primary diagnosis codes were excluded. Demographic factors and ICD-10 primary diagnosis codes were retrieved. NS and CV groups were compared using independent samples t-tests and chi-square tests as appropriate. Multivariate regression, with backward elimination, was conducted to identify pertinent variables. Results Our search resulted in 4,670 unique THV encounters out of which 428 (9.2%) were NS and 4,242 (90.8%) were CV. Multivariate regression with backward elimination showed that the odds of NS were higher with a self-identified non-Caucasian race OR = 1.65 (95%, CI: 1.28-2.14), possessing Medicaid insurance OR = 1.81 (95%, CI: 1.54-2.12) and with primary diagnoses of sleep disorders OR = 10.87 (95%, CI: 5.55-39.84), gait abnormalities (OR = 3.63 (95%, CI: 1.81-7.27), and back/radicular pain OR = 5.62 (95%, CI: 2.84-11.10). Being married was associated with CVs OR = 0.74 (95%, CI: 0.59-0.91) as well as primary diagnoses of multiple sclerosis OR = 0.24 (95%, CI: 0.13-0.44) and movement disorders OR = 0.41 (95%, CI: 0.25-0.68). Conclusion Demographic factors, such as self-identified race, insurance status, and primary neurological diagnosis codes, can be helpful to predict an NS to neurology THs. This data can be used to warn providers regarding the risk of NS.

To confirm your appointment, please press one: Examining demographic and health system interface factors that predict missed appointments in a pediatric outpatient neuropsychology clinic.

Objective: Missed patient appointments have a substantial negative impact on patient care, child health and well-being, and clinic functioning. This study aims to identify health system interface and child/family demographic characteristics as potential predictors of appointment attendance in a pediatric outpatient neuropsychology clinic. Method: Pediatric patients (N = 6,976 across 13,362 scheduled appointments) who attended versus missed scheduled appointments at a large, urban assessment clinic were compared on a broad array of factors extracted from the medical record, and the cumulative impact of significant risk factors was examined. Results: In the final multivariate logistic regression model, health system interface factors that significantly predicted more missed appointments included a higher percentage of previous missed appointments within the broader medical center, missing pre-visit intake paperwork, assessment/testing appointment type, and visit timing relative to the COVID-19 pandemic (i.e. more missed appointments prior to the pandemic). Demographic characteristics that significantly predicted more missed appointments in the final model included Medicaid (medical assistance) insurance and greater neighborhood disadvantage per the Area Deprivation Index (ADI). Waitlist length, referral source, season, format (telehealth vs. in-person), need for interpreter, language, and age were not predictive of appointment attendance. Taken together, 7.75% of patients with zero risk factors missed their appointment, while 22.30% of patients with five risk factors missed their appointment. Conclusions: Pediatric neuropsychology clinics have a unique array of factors that impact successful attendance, and identification of these factors can help inform policies, clinic procedures, and strategies to decrease barriers, and thus increase appointment attendance, in similar settings.

Assessing the Impact of COVID-19 on Retention in HIV Primary Care: A Longitudinal Multisite Analysis.

We compared retention in care outcomes between a pre-COVID-19 (Apr19-Mar20) and an early-COVID-19 (Apr20-Mar21) period to determine whether the pandemic had a significant impact on these outcomes and assessed the role of patient sociodemographics in both periods in individuals enrolled in the Data for Care Alabama project (n = 6461). Using scheduled HIV primary care provider visits, we calculated a kept-visit measure and a missed-visit measure and compared them among the pre-COVID-19 and early-COVID-19 periods. We used logistic regression models to calculated odds ratios (OR) and accompanying 95% confidence intervals (CI). Overall, individuals had lowers odds of high visit constancy [OR (95% CI): 0.85 (0.79, 0.92)] and higher odds of no-shows [OR (95% CI): 1.27 (1.19, 1.35)] during the early-COVID-19 period. Compared to white patients, Black patients were more likely to miss an appointment and transgender people versus cisgender women had lower visit constancy in the early-COVID-19 period.

Telemedicine is associated with reduced socioeconomic disparities in outpatient clinic no-show rates.

INTRODUCTION: The global pandemic caused by coronavirus (COVID-19) sped up the adoption of telemedicine. We aimed to assess whether factors associated with no-show differed between in-person and telemedicine visits. The focus is on understanding how social economic factors affect patient no-show for the two modalities of visits. METHODS: We utilized electronic health records data for outpatient internal medicine visits at a large urban academic medical center, from February 1, 2020 to December 31, 2020. A mixed-effect logistic regression was used. We performed stratified analysis for each modality of visit and a combined analysis with interaction terms between exposure variables and visit modality. RESULTS: A total of 111,725 visits for 72,603 patients were identified. Patient demographics (age, gender, race, income, partner), lead days, and primary insurance were significantly different between the two visit modalities. Our multivariable regression analyses showed that the impact of sociodemographic factors, such as Medicaid insurance (OR 1.23, p < 0.01 for in-person; OR 1.03, p = 0.57 for telemedicine; p < 0.01 for interaction), Medicare insurance (OR 1.11, p = 0.04 for in-person; OR 0.95, p = 0.32 for telemedicine; p = 0.03 for interaction) and Black race (OR 1.36, p < 0.01 for in-person; OR 1.20, p < 0.01 for telemedicine; p = 0.03 for interaction), on increased odds of no-show was less for telemedicine visits than for in-person visits. In addition, inclement weather and younger age had less impact on no-show for telemedicine visits. DISCUSSION: Our findings indicated that if adopted successfully, telemedicine had the potential to reduce no-show rate for vulnerable patient groups and reduce the disparity between patients from different socioeconomic backgrounds.

Improving Specialty Care Access via Telemedicine.

Introduction: Telehealth is a potential solution to persistent disparities in health and health care access by eliminating structural barriers to care. However, its adoption in urban underserved settings has been limited and remains poorly characterized. Methods: This is a prospective cohort study of patients receiving telemedicine (TM) consultation for specialty care of diabetes, hypertension, and/or kidney disease with a Federally Qualified Health Center (FQHC) as the originating site and an academic medical center (AMC) multispecialty group practice as the distant site in an urban setting. Primary data were collected onsite at a local FQHC and an urban AMC between March 2017 and March 2020, before the COVID-19 pandemic. Clinical outcomes of study participants were compared with matched controls (CON) from a sister FQHC site who were referred for traditional in-person specialty visits at the AMC. No-show rates for study participants were calculated and compared to their no-show rates for standard (STD) in-person specialty visits at the AMC during the study period. A patient satisfaction questionnaire was administered at the end of each TM visit. Results: Visit attendance data were analyzed for 104 patients (834 visits). The no-show rate was 15%. The adjusted odds ratio for no-show for TM versus STD visits was 1.03 [0.66-1.63], p = 0.87. There were no significant differences between TM and CON groups in the change from pre- to intervention periods for mean arterial pressure (p = 0.26), serum creatinine (p = 0.90), or estimated glomerular filtration rate (p = 0.56). The reduction in hemoglobin A1c was significant at a trend level (p = 0.053). Patients indicated high overall satisfaction with TM. Discussion: The study demonstrated improved glycemic control and equivalent outcomes in TM management of hypertension and kidney disease with excellent patient satisfaction. This supports ongoing efforts to increase the availability of TM to improve access to care for urban underserved populations.

A Predictive Decision Analytics Approach for Primary Care Operations Management: A Case Study of Double-Booking Strategy Design and Evaluation

Primary care plays a vital role for individuals and families in accessing care, keeping well, and improving quality of life. However, the complexities and uncertainties in the primary care delivery system (e.g., patient no-shows/walk-ins, staffing shortage, COVID-19 pandemic) have brought significant challenges in its operations management, which can potentially lead to poor patient outcomes and negative primary care operations (e.g., loss of productivity, inefficiency). This paper presents a decision analytics approach developed based on predictive analytics and hybrid simulation to better facilitate management of the underlying complexities and uncertainties in primary care operations. A case study was conducted in a local family medicine clinic to demonstrate the use of this approach for patient no-show management. In this case study, a patient no-show prediction model was used in conjunction with an integrated agent-based and discrete-event simulation model to design and evaluate double-booking strategies. Using the predicted patient no-show information, a prediction-based double-booking strategy was created and compared against two other strategies, namely random and designated time. Scenario-based experiments were then conducted to examine the impacts of different double-booking strategies on clinic's operational outcomes, focusing on the trade-offs between the clinic productivity (measured by daily patient throughput) and efficiency (measured by visit cycle and patient wait time for doctor). The results showed that the best productivity-efficiency balance was derived under the prediction-based double-booking strategy. The proposed hybrid decision analytics approach has the potential to better support decision-making in primary care operations management and improve the system's performance. Further, it can be generalized in the context of various healthcare settings for broader applications.

Two-stage robust telemedicine assignment problem with uncertain service duration and no-show behaviours.

The current pandemic of COVID-19 has caused significant strain on medical center resources, which are the main plac healthcare managers to make an effective assignment plan for the patients and telemedical doctors when providing telemedicine services. Motivated by this, we present the first comprehensive study of a two-stage robust telemedicine assignment problem when three different sources of uncertainty are incorporated, including uncertain service duration, no-show behaviours of both patients and telemedical doctors. From an algorithmic viewpoint, we propose an efficient nested column-and-constraint generation (C&CG) solution scheme that decomposes the model into an outer level problem and an inner level problem. Our results show that we can solve the problems of realistic sizes within a reasonable time (e.g., up to 100 patients, 10 telemedical doctors, and 200 scenarios within two hours). On the empirical side, we demonstrate how the hyper-parameters make a balance between cost management and the coverage level of the served patients in the presence of three different sources of uncertainty. Our comparison with a two-stage stochastic programming model implies that our model is not overly conservative and seems to provide a relatively cheaper modeling alternative that requires much less information support when hedging against three different sources of uncertainty under a worst-case situation.

Predictors of No-Show in Neurology Clinics.

In this study, we aim to identify predictors of a no-show in neurology clinics at our institution. We conducted a retrospective review of neurology clinics from July 2013 through September 2018. We compared odds ratio of patients who missed appointments (no-show) to those who were present at appointments (show) in terms of age, lead-time, subspecialty, race, gender, quarter of the year, insurance type, and distance from hospital. There were 60,012 (84%) show and 11,166 (16%) no-show patients. With each day increase in lead time, odds of no-show increased by a factor of 1.0019 (p < 0.0001). Odds of no-show were higher in younger (p ≤ 0.0001, OR = 0.49) compared to older (age ≥ 60) patients and in women (p < 0.001, OR = 1.1352) compared to men. They were higher in Black/African American (p < 0.0001, OR = 1.4712) and lower in Asian (p = 0.03, OR = 0.6871) and American Indian/Alaskan Native (p = 0.055, OR = 0.6318) as compared to White/Caucasian. Patients with Medicare (p < 0.0001, OR = 1.5127) and Medicaid (p < 0.0001, OR = 1.3354) had higher odds of no-show compared to other insurance. Young age, female, Black/African American, long lead time to clinic appointments, Medicaid/Medicare insurance, and certain subspecialties (resident and stroke clinics) are associated with high odds of no show. Possible suggested interventions include better communication and flexible appointments for the high-risk groups as well as utilizing telemedicine.

Impact of the Coronavirus Disease 2019 Pandemic on the Ophthalmology Department.

We aimed to evaluate the effects of the coronavirus disease (COVID-19) pandemic on the Ophthalmology Department. This study was based on data collected between January 2019 and November 2021. We divided patients scheduled for eye care during pre-COVID-19 (January-December 2019), early COVID-19 (January-December 2020), and late COVID-19 (January-November 2021) periods. Changes in the outpatient cancellation rate in each department were analyzed and compared in the pre-, early, and late periods. The basic information of cancellation and reason for not visiting the clinic were also analyzed. Overall, 121,042 patients were scheduled to visit the Sanggye Paik Hospital Ophthalmology Department. The overall cancellation rate was 19.13% during pre-COVID-19, 24.13% during early COVID-19, and 17.34% during late COVID-19. The reasons for not visiting the clinic included hospital, patient, and contact factors; hospitalization in other departments and hospitals; and death. The Strabismus/Pediatric Ophthalmology Department showed the highest cancellation rate of 24.21% over three years. There were no significant differences in the causes of hospital visits by period. The COVID-19 pandemic has caused an overall decrease in the number of ophthalmic outpatients. However, after about a year, the number of outpatients in these departments recovered to the level before the COVID-19 outbreak.

A Stochastic Programming Model for Service Scheduling with Uncertain Demand: an Application in Open-Access Clinic Scheduling

This paper addressed a scheduling problem which handles urgent tasks along with existing schedules. The uncertainties in this problem come from random process of existing schedules and unknown upcoming urgent tasks. To deal with the uncertainties, this paper proposes a stochastic integer programming (SIP) based aggregated online scheduling method. The method is illustrated through a study case from the outpatient clinic block-wise scheduling system which is under a hybrid scheduling policy combining regular far-in-advance policy and the open-access policy. The COVID-19 pandemic brings more challenges for the healthcare system including the fluctuations of service time and increasing urgent requests which this paper is designed for. The schedule framework designed in the method is comprehensive to accommodate various uncertainties in the healthcare service system, such as: no-shows, cancellations and punctuality of patients as well as preference of patients over time slots and physicians. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

Two-Stage Chance-Constrained Telemedicine Assignment Model with No-Show Behavior and Uncertain Service Duration

The current global pandemic of COVID-19 has caused significant strain on the medical resources of the healthcare providers, so more and more hospitals use telemedicine and virtual care for remote treatment (i.e. consulting, remote diagnosis, treatment, monitoring and follow-ups and so on) in response to COVID-19 pandemic, which is expected to deliver timely care while minimizing exposure to protect medical practitioners and patients. In this study, we study the telemedicine assignment between the patients and telemedical specialists by considering different sources of uncertainty, i.e. uncertain service duration and the no-show behavior of the doctors that is caused by the unexpected situations (i.e. emergency events). We propose a two-stage chance-constrained model with the assignment decisions in the recourse problem and employ an uncertainty set to capture the behavior of telemedical doctors, which finally gives rise to a two-stage binary integer program with binary variables in the recourse problem. We propose an enumeration-based column-and-constraint generation solution method to solve the resulting problem. A simple numerical study is done to illustrate our proposed framework. To the best of our knowledge, this is the first attempt to incorporate the behavior of doctors and uncertain service duration for the telemedicine assignment problem in the literature. We expect that this work could open an avenue for the research of telemedicine by incorporating different sources of uncertainty from an operations management viewpoint, especially in the context of a data-driven optimization framework. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Reduced No-Show Rates and Sustained Patient Satisfaction of Telehealth During the COVID-19 Pandemic.

Introduction: The unprecedented COVID-19 pandemic has thrust telehealth into the center stage of health care, leading to a dramatic increase in utilization of telehealth services. The impact of telehealth on patient satisfaction during the current pandemic is yet to be fully understood. Objective: This study aimed to identify patient perspectives and behaviors toward virtual primary care appointments at a telehealth-naïve institution during the COVID-19 pandemic and establish the rate of missed appointments to help guide future implementation of telehealth services. Methods: Patients at a primary and specialty care clinic, seen between March and May 2020, completed a survey analyzing nine commonly used satisfaction metrics. The rate of missed appointments was recorded and compared with analogous cohorts of in-person office visits. Results: The no-show rate of telehealth visits during the COVID-19 pandemic was 7.5% (14/186), lower than both the no-show rate of 36.1% for in-office visits (56/155) (p < 0.0001) and a pre-pandemic in-office no-show rate of 29.8% (129/433) (p < 0.0001). Surveyed patients who experienced telehealth visits (n = 65) had similar satisfaction compared with those surveyed who attended in-office visits (n = 36) in seven of nine metrics. No statistically significant differences were identified in the satisfaction metrics with telehealth visits performed on video (n = 26) versus the phone-only format (n = 38). Patients aged 65 years or over were less likely to have a video component to their virtual visit (1/12, 8.3%) than those under age 65 (25/44, 56.8%) (p = 0.0031). Discussion/Conclusions: Telehealth offers significant benefits for both patients and providers, strongly supporting its widespread utilization both during and following the COVID-19 pandemic.

Impact of COVID-19 social distancing regulations on outpatient diagnostic imaging volumes and no-show rates.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has significantly impacted outpatient radiology practices, necessitating change in practice infrastructure and workflow. OBJECTIVE: The purpose of this study was to assess the consequences of social distancing regulations on 1) outpatient imaging volume and 2) no-show rates per imaging modality. METHODS: Volume and no-show rates of a large, multicenter metropolitan healthcare system outpatient practice were retrospectively stratified by modality including radiography, CT, MRI, ultrasonography, PET, DEXA, and mammography from January 2 to July 21, 2020. Trends were assessed relative to timepoints of significant state and local social distancing regulatory changes. RESULTS: The decline in imaging volume and rise in no-show rates was first noted on March 10, 2020 following the declaration of a state of emergency in New York State (NYS). Total outpatient imaging volume declined 85% from baseline over the following 5 days. Decreases varied by modality: 88% for radiography, 75% for CT, 73% for MR, 61% for PET, 80% for ultrasonography, 90% for DEXA, and 85% for mammography. Imaging volume and no-show rate recovery preceded the mask mandate of April 15, 2020, and further trended along with New York City's reopening phases. No-show rates recovered within 2 months of the height of the pandemic, however, outpatient imaging volume has yet to recover to baseline after 3 months. CONCLUSION: The total outpatient imaging volume declined alongside an increase in the no-show rate following the declaration of a state of emergency in NYS. No-show rates recovered within 2 months of the height of the pandemic with imaging volume yet to recover after 3 months. CLINICAL IMPACT: Understanding the impact of social distancing regulations on outpatient imaging volume and no-show rates can potentially aid other outpatient radiology practices and healthcare systems in anticipating upcoming changes as the COVID-19 pandemic evolves.

Documented Reasons of Cancellation and Rescheduling of Telehealth Appointments During the Pandemic.

Background and Objective: The COVID-19 pandemic increased the use of telehealth around the world. The aim is to minimize health care service disruption as well as reducing COVID-19 exposure. However, one of the major operational concerns is cancellations and rescheduling (C/Rs). C/Rs may create additional burden and cost to the patient, provider, and the health system. Our aim is to understand the reasons for C/Rs of the telehealth session after the scheduled start time. Materials and Methods: We reviewed electronic health records (EHRs) to identify the C/R reasons for behavioral health and speech language pathology departments. Documented C/Rs in the medical charts were identified from EHR by using a keyword-based and Natural Language Processing (NLP)-supported EHR search engine. From the search results, we randomly selected 200 notes and conducted a thematic analysis. Results: We identified four themes explaining C/R reasons. Most frequent theme was "technicality" (47, 36%), followed by "engagement" (34, 25%), "scheduling" (31, 24%), and "unspecified" (20, 15%). The findings showed that technical reasons are the leading cause of C/Rs, constituting 36% of the cases (95% confidence interval [CI]: 29-43%). Notably, "engagement" constituted a sizeable 25% (95% CI: 19-31%) of C/Rs, as a result of the inability to engage a patient to complete the telehealth session. Conclusions: The study shows that engagement is one of the new challenges to the pediatric telehealth visits. Future studies of new engagement models are needed for the success of telehealth. Our findings will help fill the literature gaps and may help with enhancing the digital experience for both caregivers and providers, reducing wasted time and resources due to preventable C/Rs, improving clinical operation efficiency, and treatment adherence.

Changing Patterns of Medical Visits and Factors Associated with No-show in Patients with Rheumatoid Arthritis during COVID-19 Pandemic.

BACKGROUND: The main barrier to the effective rheumatoid arthritis (RA) therapy is poor adherence. Coronavirus disease 2019 (COVID-19) pandemic have led to a significant change in the pattern and the number of medical visits. We assessed changing patterns of medical visits and no-show, and identified factors associated with no-show in patients with RA during COVID-19 pandemic. METHODS: RA patients treated with disease-modifying antirheumatic drugs at least 6 months who had been in remission or those with mild disease activity were observed for 6 months from February to July 2020. No-show was defined as a missed appointment that was not previously cancelled by the patient and several variables that might affect no-show were examined. RESULTS: A total of 376 patients and 1,189 appointments were evaluated. Among 376 patients, 164 patients (43.6%) missed appointment more than one time and no-show rate was 17.2% during COVID-19 pandemic. During the observation, face-to-face visits gradually increased and no-show gradually decreased. The logistic regression analysis identified previous history of no-show (adjusted odds ratio [OR], 2.225; 95% confidence interval [CI], 1.422-3.479; P < 0.001) and fewer numbers of comorbidities (adjusted OR, 0.749; 95% CI, 0.584-0.961; P = 0.023) as the independent factors associated with no-show. CONCLUSION: Monthly analysis showed that the no-show rate and the pattern of medical visits gradually changed in patients with RA during COVID-19 pandemic. Moreover, we found that previous history of no-show and fewer numbers of comorbidities as the independent factors associated with no-show.

Improving Attendance and Patient Experiences During the Expansion of a Telehealth-Based Pediatric Otolaryngology Practice.

OBJECTIVE: To determine the rates and primary causes of missed appointments (MAs) for telehealth visits and present remedies for improvement. METHODS: This cross-sectional survey was conducted at a tertiary care pediatric otolaryngology practice during expansion of telehealth-based visits. A review of questionnaire responses was performed for 103 consecutive patients with MAs over 50 business days from March 20, 2020, to May 29, 2020. Families were asked a brief survey regarding the cause of the MA and assisted with technical support and rescheduling. MA rates and causes were analyzed. RESULTS: The overall MA rate during the initiation of telehealth services was significantly increased at 12.4% as compared with clinic-based visits of a similar duration before COVID of 5.2% (P < .001). Technical issues were the most common causes of MAs (51.3%). Of the caregivers, 23.8% forgot or reported cancellation of the appointment. Five percent of patients were non-English speaking and scheduled without translator support. Minorities and patients with public insurance represented 53.6% and 61.9% of MAs, respectively. DISCUSSION: Technical difficulties were the most commonly reported cause of missed telehealth appointments. Optimization of applications by providing patient reminders, determining need for translator assistance, and reducing required upload/download speeds may significantly reduce rates of MAs and conversions to other communication. IMPLICATIONS FOR PRACTICE: Clear, concise education materials on the technical aspects of telehealth, platform optimization, and robust technical and administrative support may be necessary to reduced missed telehealth appointments and support large-scale telehealth operations. An assessment of institutional capacity is critical when considering telehealth expansion.