Dynamic Price Application to Prevent Financial Losses to Hospitals Based on Machine Learning Algorithms.

Hospitals that are considered non-profit take into consideration not to make any losses other than seeking profit. A model that ensures that hospital price policies are variable due to hospital revenues depending on patients with appointments is presented in this study. A dynamic pricing approach is presented to prevent patients who have an appointment but do not show up to the hospital from causing financial loss to the hospital. The research leverages three distinct machine learning (ML) algorithms, namely Random Forest (RF), Gradient Boosting (GB), and AdaBoost (AB), to analyze the appointment status of 1073 patients across nine different departments in a hospital. A mathematical formula has been developed to apply the penalty fee to evaluate the reappointment situations of the same patients in the first 100 days and the gaps in the appointment system, considering the estimated patient appointment statuses. Average penalty cost rates were calculated based on the ML algorithms used to determine the penalty costs patients will face if they do not show up, such as 22.87% for RF, 19.47% for GB, and 14.28% for AB. As a result, this study provides essential criteria that can help hospital management better understand the potential financial impact of patients missing appointments and can be considered when choosing between these algorithms.

Evaluation of no-show rate in outpatient clinics with open access scheduling system: A systematic review.

Background: Patients' missed appointments can cause interference in the functions of the clinics and the visit of other patients. One of the most effective strategies to solve the problem of no-show rate is the use of an open access scheduling system (OA). This systematic review was conducted with the aim of investigating the impact of OA on the rate of no-show of patients in outpatient clinics. Methods: Relevant articles in English were investigated based on the keywords in title and abstract using PubMed, Scopus, and Web of Science databases and Google Scholar search engine (July 23, 2023). The articles using OA and reporting the no-show rate were included. Exclusion criteria were as follows: (1) review articles, opinion, and letters, (2) inpatient scheduling system articles, and (3) modeling or simulating OA articles. Data were extracted from the selected articles about such issues as study design, outcome measures, interventions, results, and quality score. Findings: From a total of 23,403 studies, 16 articles were selected. The specialized fields included family medicine (62.5%, 10), pediatrics (25%, four), ophthalmology, podiatric, geriatrics, internal medicine, and primary care (6.25%, one). Of 16 articles, 10 papers (62.5%) showed a significant decrease in the no-show rate. In four articles (25%), the no-show rate was not significantly reduced. In two papers (12.5%), there were no significant changes. Conclusions: According to this study results, it seems that in most outpatient clinics, the use of OA by considering some conditions such as conducting needs assessment and system design based on the patients' and providers' actual needs, and cooperating of all system stakeholders through consistent training caused a significant decrease in the no-show rate.

Evaluating the Impact of the COVID-19 Pandemic on Late-Canceled and No-Show Appointments at the Department of Neurological Surgery.

The coronavirus disease 2019 (COVID-19) pandemic resulted in unprecedented restrictions on the general public and disturbances to the routines of hospitals worldwide. These restrictions are now being relaxed as the number of vaccinated individuals increases and as the rates of incidence and prevalence decrease; however, they left a lasting impact on healthcare systems that is still being felt today. This retrospective study evaluated the total number of canceled or missed outpatient clinic appointments in a Neurological Surgery department before and after peak COVID-19 restrictions and attempted to assess the impact of these disruptions on neurosurgical clinical attendance. We also attempted to compare our data with the data from another surgical subspecialty department. We evaluated 32,558 scheduled appointments at the Loyola University Medical Center Department of Neurological Surgery, as well as 139,435 scheduled appointments with the Department of Otolaryngology. Appointments before April 2020 were defined as pre-COVID, while appointments during or after April 2020 were defined as post-COVID. Here, we compare no-show and non-attendance rates (no-shows plus late-canceled appointments) within the respective time range. Overall, we observed that before COVID-19 restrictions were put into place, there was an 8.9% no-show rate and a 17.4% non-attendance rate for the Department of Neurological Surgery. After COVID restrictions were implemented, these increased to 10.9% and 18.3%, respectively. Greater no-show and cancellation rates (9.8% in the post-COVID era vs 8.0% in the pre-COVID era) were associated with varying socioeconomic and racial demographics. African-American patients (2.56 times higher), new-visit patients (1.67 times higher), and those with Medicaid/Medicare insurance policies (1.48 times higher) were at the highest risk of no-show in the post-COVID era compared to the pre-COVID era.

Reducing No-Show Appointments in Preanesthesia Screening.

PURPOSE: Preanesthesia screening is critical to evaluate the patient's medical and surgical history before any procedure or surgery to assess for risks and to optimize outcomes during the perioperative period. The purpose of this quality improvement project was to decrease the number of missed appointments in the outpatient preanesthesia and surgical screening clinic and the impact on provider satisfaction. DESIGN: The design of this quality improvement project was pre and post design. Automated and live phone calls reminders were provided for patients scheduled in the outpatient preanesthesia. Data were collected to compare missed appointment rates from a 3-month period before the project implementation and a 3-month period afterward. METHODS: Predata collection included the number of no-shows in the electronic health record system from the previous 3 months. Participants included all adult patients who are scheduled for a preanesthesia surgical screening appointment. Provider satisfaction was assessed using a 5-question survey, pre and postinnovation. FINDINGS: Reminder systems had a statistically significant impact on reducing the number of no-shows in the preanesthesia and surgical screening clinic. No significant impact was shown in provider satisfaction. CONCLUSIONS: Implementation of a reminder system can help to reduce no-show rates in clinics. Patient no-shows overload the health system by reducing the productivity of providers and waste resources including use of clinic staff, longer wait times for other patients, and the timing providers put into chart preparation.

Appointment Factors Contributing to Children with Speech Disorders Missing Speech and Language Pathology Appointments.

This study explores missed pediatric speech and language pathology (SLP) appointments to identify barriers for patients with speech disorders. Data from 839 referrals at Boston Medical Center, including demographics, appointment details, COVID-19 lockdown, and number of items on patient problem lists, were analyzed using chi-square tests and logistic regression. The findings revealed that lockdown status, appointment timing, appointment type (in-person vs telemedicine), referral department (ear, nose, and throat [ENT] vs non-ENT), sex, race, primary language, birthplace, and primary care provider presence had no significant impact on attendance. However, the number of patient-listed problems, prior cancelations, and missed appointments were significant predictors of patients who did not keep appointments. In conclusion, this research emphasizes the patient's problem list and past appointment behavior as critical factors in predicting missed SLP appointments for pediatric speech disorder patients. These insights can guide targeted interventions to improve attendance and enhance SLP engagement.

Cross-sectional analysis of primary care clinics' policies, practices, and availability of patient support services during the COVID-19 pandemic.

BACKGROUND: Healthcare accessibility and utilization are important social determinants of health. Lack of access to healthcare, including missed or no-show appointments, can have negative health effects and be costly to patients and providers. Various office-based approaches and community partnerships can address patient access barriers. OBJECTIVES: (1) To understand provider perceptions of patient barriers; (2) to describe the policies and practices used to address late or missed appointments, and (3) to evaluate access to patient support services, both in-clinic and with community partners. METHODS: Mailed cross-sectional survey with online response option, sent to all Nebraska primary care clinics (n = 577) conducted April 2020 and January through April 2021. Chi-square tests compared rural-urban differences; logistic regression of clinical factors associated with policies and support services computed odds ratios (OR) and 95% confidence intervals (CI). RESULTS: Response rate was 20.3% (n = 117), with 49 returns in 2020. Perceived patient barriers included finances, higher among rural versus urban clinics (81.6% vs. 56.1%, p =.009), and time (overall 52.3%). Welcoming environment (95.5%), telephone appointment reminders (74.8%) and streamlined admissions (69.4%) were the top three clinic practices to reduce missed appointments. Telehealth was the most commonly available patient support service in rural (79.6%) and urban (81.8%, p =.90) clinics. Number of providers was positively associated with having a patient navigator/care coordinator (OR = 1.20, CI = 1.02-1.40). For each percent increase in the number of privately insured patients, the odds of providing legal aid decreased by 4% (OR = 0.96, CI = 0.92-1.00). Urban clinics were less likely than rural clinics to provide social work services (OR = 0.16, CI = 0.04-0.67) or assist with applications for government aid (OR = 0.22, CI = 0.06-0.90). CONCLUSIONS: Practices to reduce missed appointments included a variety of reminders. Although finances and inability to take time off work were the most frequently reported perceived barriers for patients' access to timely healthcare, most clinics did not directly address them. Rural clinics appeared to have more community partnerships to address underlying social determinants of health, such as transportation and assistance applying for government aid. Taking such a wholistic partnership approach is an area for future study to improve patient access.

No-Show Rates in an Academic Otolaryngology Practice Before and During the COVID-19 Pandemic.

OBJECTIVE: Our objectives were to determine the no-show and nonattendance rate for an outpatient academic otolaryngology practice, to identify patient and systemic factors associated with nonattendance, and to evaluate the impact that the COVID-19 pandemic had on the rate of nonattendance. METHODS: This is a retrospective review of the Epic practice management and billing reports from all scheduled outpatient visits at a multi-physician, academic, general, and sub-specialty otolaryngology practice from January 2019 to December 2021. RESULTS: Over three years, 121,347 clinic visits were scheduled in the otolaryngology practice. The overall nonattendance rate was 18.3%. A statistically significant increase in nonattendance was noted during the COVID-19 pandemic (16.8% vs. 19.8%, p < 0.001). The rate of nonattendance in patients of younger age (under 18 years) (p <0.001), female gender (p=0.03), afternoon appointments (p=0.04), and extended time between the day of scheduling and the day of appointment (p <0.001) increased. Head and neck clinics were found to have the lowest nonattendance rates, while pediatric otolaryngology clinics had the highest (12.6% vs. 21.3%). On multivariate regression, younger age (p < 0.001), female gender (p=0.01), afternoon appointments (p< 0.001), and online self-scheduling (p< 0.001) were significantly associated with nonattendance. CONCLUSIONS: Both patient and appointment-related factors were found to impact rates of nonattendance in this academic otolaryngology practice. In this study, young age, female gender, afternoon appointments, and online self-scheduling were associated with increased nonattendance. In addition, the COVID-19 pandemic significantly impacted no-show rates across all otolaryngologic subspecialties.

Analysis of missed clinic visits, preferred languages, and telemedicine in a pediatric gastroenterology practice.

OBJECTIVES: Previous studies have demonstrated a relationship between socioeconomic disparities and missed clinic visits (MCV). However, the relationship between patient-preferred language and MCVs, particularly with respect to telemedicine, remains relatively underexplored. We sought to characterize the associations between MCV and patient-level predictors, including preferred language, in a large single-center pediatric gastroenterology, hepatology, and nutrition practice. METHODS: This retrospective longitudinal cohort study included all missed or completed outpatient visits in the Gastroenterology, Hepatology, and Nutrition Programs at Boston Children's Hospital from January 1, 2016 to May 20, 2022. Univariate and multivariate hierarchical generalized linear mixed models were employed to identify associations between visit- and patient-level predictors and an MCV outcome. RESULTS: A total of 300,201 visits from 70,710 patients residing in Massachusetts were included. Univariate analyses revealed higher MCV odds for Hispanic patients and those from areas with the highest Social Vulnerability Index (SVI), and these odds increased with telemedicine (Hispanic in-person odds ratio [OR] 5.21 [(95% confidence interval) 4.93-5.52] vs. telemedicine OR 8.79 [7.85-9.83]; highest SVI in-person OR 5.28 [4.95-5.64] vs. telemedicine OR 7.82 [6.84-8.96]). Controlled multivariate analyses revealed that among six language groups, only Spanish language preference was associated with higher MCV odds, which increased with telemedicine (Spanish in-person adjusted OR [aOR] 1.35 [1.24-1.48] vs. telemedicine aOR 2.1 [1.83-2.44]). CONCLUSIONS: Patients preferring Spanish experience unique barriers to care beyond those faced by other language preference groups, and telemedicine may exacerbate these barriers.

Is there a prospective association between psychological distress as measured by the CORE-OM and treatment attendance and treatment duration? A follow-up study at a Norwegian Community Mental Health Centre.

BACKGROUND: Feasible and reliable methods for identifying factors associated with treatment duration and treatment attendance in mental health services are needed. This study examined to what degree the Clinical Outcomes in Routine Evaluation-Outcome Measure (CORE-OM) at the start of treatment is associated with treatment attendance and treatment duration. METHODS: Outpatients (N = 124) at a community mental health centre in Norway completed the 34-item CORE-OM questionnaire addressing the domains of subjective well-being, problems and symptoms, functioning and risk at the start of treatment. The CORE-OM subscales and the 'all' items total scale were used as predictor variables in regression models, with treatment duration, number of consultations attended, treatment attendance (number of therapy sessions attended divided by number of sessions offered) and termination of treatment (planned versus unplanned) as outcome variables. RESULTS: Higher CORE-OM subscale scores and the 'all' scale were associated with longer treatment duration. No association was found between CORE-OM scales and number of therapy sessions, treatment attendance (sessions attended/offered) or whether the patients unexpectedly ended treatment. CONCLUSION: Higher patient-reported psychological distress as measured by the CORE-OM at the start of treatment was prospectively associated with treatment duration but not with treatment attendance or drop-out of treatment. The findings imply that patients with higher initial psychological distress need longer treatment but that treatment attendance may be related to factors other than the severity of distress.

Enhancing Health Equity by Predicting Missed Appointments in Health Care: Machine Learning Study.

BACKGROUND: The phenomenon of patients missing booked appointments without canceling them-known as Did Not Show (DNS), Did Not Attend (DNA), or Failed To Attend (FTA)-has a detrimental effect on patients' health and results in massive health care resource wastage. OBJECTIVE: Our objective was to develop machine learning (ML) models and evaluate their performance in predicting the likelihood of DNS for hospital outpatient appointments at the MidCentral District Health Board (MDHB) in New Zealand. METHODS: We sourced 5 years of MDHB outpatient records (a total of 1,080,566 outpatient visits) to build the ML prediction models. We developed 3 ML models using logistic regression, random forest, and Extreme Gradient Boosting (XGBoost). Subsequently, 10-fold cross-validation and hyperparameter tuning were deployed to minimize model bias and boost the algorithms' prediction strength. All models were evaluated against accuracy, sensitivity, specificity, and area under the receiver operating characteristic (AUROC) curve metrics. RESULTS: Based on 5 years of MDHB data, the best prediction classifier was XGBoost, with an area under the curve (AUC) of 0.92, sensitivity of 0.83, and specificity of 0.85. The patients' DNS history, age, ethnicity, and appointment lead time significantly contributed to DNS prediction. An ML system trained on a large data set can produce useful levels of DNS prediction. CONCLUSIONS: This research is one of the very first published studies that use ML technologies to assist with DNS management in New Zealand. It is a proof of concept and could be used to benchmark DNS predictions for the MDHB and other district health boards. We encourage conducting additional qualitative research to investigate the root cause of DNS issues and potential solutions. Addressing DNS using better strategies potentially can result in better utilization of health care resources and improve health equity.

A Data Visualisation Approach in Understanding the Obstetrics Patients Conversion.

As the leading maternal and child hospital in Singapore, it is important to understand the current hospital standing and maintain our competitiveness by monitoring the population movement. Through the use of data visualization techniques, the team processed historical data from 2012 to 2020 and presented new data insights for the hospital management to identify potential areas for improvement to increase the delivery rate in the hospital.

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.

Machine Learning Predictions on Outpatient No-Show Appointments in a Malaysia Major Tertiary Hospital.

Introduction: A no-show appointment occurs when a patient does not attend a previously booked appointment. This situation can cause other problems, such as discontinuity of patient treatments as well as a waste of both human and financial resources. One of the latest approaches to address this issue is predicting no-shows using machine learning techniques. This study aims to propose a predictive analytical approach for developing a patient no-show appointment model in Hospital Kuala Lumpur (HKL) using machine learning algorithms. Methods: This study uses outpatient data from the HKL's Patient Management System (SPP) throughout 2019. The final data set has 246,943 appointment records with 13 attributes used for both descriptive and predictive analyses. The predictive analysis was carried out using seven machine learning algorithms, namely, logistic regression (LR), decision tree (DT), k-near neighbours (k-NN), Naïve Bayes (NB), random forest (RF), gradient boosting (GB) and multilayer perceptron (MLP). Results: The descriptive analysis showed that the no-show rate was 28%, and attributes such as the month of the appointment and the gender of the patient seem to influence the possibility of a patient not showing up. Evaluation of the predictive model found that the GB model had the highest accuracy of 78%, F1 score of 0.76 and area under the curve (AUC) value of 0.65. Conclusion: The predictive model could be used to formulate intervention steps to reduce no-shows, improving patient care quality.

Exploring the appointment factors affecting pediatric patients with swallow disorders: Implications for speech and language pathology attendance.

INTRODUCTION: Feeding and swallowing disorders have become increasingly prevalent among children, necessitating effective management to prevent long-term complications. Speech and language pathology (SLP) services play a crucial role in diagnosing and treating these disorders. The objective of this study was to explore the factors that influence patient attendance to SLP appointments for swallow disorders. METHODS: This study was conducted at Boston Medical Center, involving 359 pediatric patients referred to SLP for swallow-related concerns. De-identified patient and appointment information was obtained from the electronic medical record. Various factors such as age, gender, race/ethnicity, primary language, appointment date/time, and COVID-19 lockdown status were analyzed to determine their impact on patient no-shows. Statistical analyses, including Chi-Square tests and binary logistic regression, were conducted using appropriate methodologies. RESULTS: 355 individual patient records were included in the analysis. Lockdown status and appointment time of day did not significantly affect patient no-shows. However, appointments conducted through telemedicine showed a significant difference in attendance. Patient referral department, gender, race, language, and being born at the medical center did not significantly influence patient attendance. Notably, having a primary care provider (PCP) at the medical center significantly affected patient attendance. Furthermore, previous appointment cancellations made a patient more likely to no-show. CONCLUSION: This study provides valuable insights into the factors influencing patient attendance at SLP appointments for pediatric swallowing disorders. Having a PCP at the medical center and utilizing telemedicine appointments were associated with higher attendance rates. Addressing appointment cancellations and investigating underlying reasons behind missed appointments should be prioritized in future research. Understanding these factors will facilitate the development of interventions to optimize patient attendance and improve the delivery of SLP services in pediatric populations.

Behavioural economic interventions to reduce health care appointment non-attendance: a systematic review and meta-analysis.

BACKGROUND: Appointment non-attendance - often referred to as "missed appointments", "patient no-show", or "did not attend (DNA)" - causes volatility in health systems around the world. Of the different approaches that can be adopted to reduce patient non-attendance, behavioural economics-oriented mechanisms (i.e., psychological, cognitive, emotional, and social factors that may impact individual decisions) are reasoned to be better suited in such contexts - where the need is to persuade, nudge, and/ or incentivize patients to honour their scheduled appointment. The aim of this systematic literature review is to identify and summarize the published evidence on the use and effectiveness of behavioural economic interventions to reduce no-shows for health care appointments. METHODS: We systematically searched four databases (PubMed/Medline, Embase, Scopus, and Web of Science) for published and grey literature on behavioural economic strategies to reduce no-shows for health care appointments. Eligible studies met four criteria for inclusion; they were (1) available in English, Spanish, or French, (2) assessed behavioural economics interventions, (3) objectively measured a behavioural outcome (as opposed to attitudes or preferences), and (4) used a randomized and controlled or quasi-experimental study design. RESULTS: Our initial search of the five databases identified 1,225 articles. After screening studies for inclusion criteria and assessing risk of bias, 61 studies were included in our final analysis. Data was extracted using a predefined 19-item extraction matrix. All studies assessed ambulatory or outpatient care services, although a variety of hospital departments or appointment types. The most common behaviour change intervention assessed was the use of reminders (n = 56). Results were mixed regarding the most effective methods of delivering reminders. There is significant evidence supporting the effectiveness of reminders (either by SMS, telephone, or mail) across various settings. However, there is a lack of evidence regarding alternative interventions and efforts to address other heuristics, leaving a majority of behavioural economic approaches unused and unassessed. CONCLUSION: The studies in our review reflect a lack of diversity in intervention approaches but point to the effectiveness of reminder systems in reducing no-show rates across a variety of medical departments. We recommend future studies to test alternative behavioural economic interventions that have not been used, tested, and/or published before.

Leveraging machine learning to create user-friendly models to mitigate appointment failure at dental school clinics.

PURPOSE/OBJECTIVES: This study had a twofold outcome. The first aim was to develop an efficient, machine learning (ML) model using data from a dental school clinic (DSC) electronic health record (EHR). This model identified patients with a high likelihood of failing an appointment and provided a user-friendly system with a rating score that would alert clinicians and administrators of patients at high risk of no-show appointments. The second aim was to identify key factors with ML modeling that contributed to patient no-show appointments. METHODS: Using de-identified data from a DSC EHR, eight ML algorithms were evaluated: simple decision tree, bagging regressor classifier, random forest classifier, gradient boosted regression, AdaBoost regression, XGBoost regression, neural network, and logistic regression classifier. The performance of each model was assessed using a confusion matrix with different threshold level of probability; precision, recall and predicted accuracy on each threshold; receiver-operating characteristic curve (ROC) and area under curve (AUC); as well as F1 score. RESULTS: The ML models agreed on the threshold of probability score at 0.20-0.25 with Bagging classifier as the model that performed best with a F1 score of 0.41 and AUC of 0.76. Results showed a strong correlation between appointment failure and appointment confirmation, patient's age, number of visits before the appointment, total number of prior failed appointments, appointment lead time, as well as the patient's total number of medical alerts. CONCLUSIONS: Altogether, the implementation of this user-friendly ML model can improve DSC workflow, benefiting dental students learning outcomes and optimizing personalized patient care.

Telemedicine as a path to bridging inequities in patients with epilepsy.

OBJECTIVE: Access to epilepsy specialist care is not uniform in the USA, with prominent gaps in rural areas. Understanding the reasons for nonattendance at epilepsy appointments may help identify access hurdles faced by patients. This study was undertaken to better understand clinic absenteeism in epilepsy and how it may be influenced by telemedicine. METHODS: In this retrospective study, social determinants of health were collected for all adult patients scheduled in epilepsy clinic, as either an in-person or telemedicine appointment, at University of Kentucky between July 2021 and December 2022. The primary outcome measure was attendance or absence at the appointment. Subgroup analyses were done to better understand the drivers of attendance at telemedicine visits and evaluate telemedicine utilization by underserved populations. RESULTS: A total of 3025 patient encounters of in-person and telemedicine visits were included. The no-show rate was significantly higher for in-person visits (32%) compared with telemedicine visits (20%, p < .001). A nominal logistic regression model identified seven factors increasing risk of absenteeism, including in-person visits, prior missed appointments, longer lead times to appointment, Medicaid/Medicare as payors, no significant other, lower mean annual income, and minority race. For each $10 000 increase in mean annual income, the odds of missing the appointment decreased by 8% (odds ratio = .92, 95% confidence interval = .89-.96, p < .001). Forty-one percent of underserved population opted for telemedicine visits, and they had a lower no-show rate (22%) as compared with in-person visits (33%, p < .001). Predictors of no-shows to televisits (1382) included Medicare/Medicaid coverage (as opposed to private insurance), no significant others, and a history of missing appointments. SIGNIFICANCE: Telemedicine is effective at improving attendance, overcoming socioeconomic hurdles, and widening access to epilepsy care, particularly among underserved populations. Access to telecare depends on insurance coverage and emphasizes the need to include telemedicine in insurance plans to ensure uniform access to high-quality epilepsy care, irrespective of socioeconomic status.

Disparities in high risk prenatal care adherence along racial and ethnic lines.

The term "high-risk pregnancy" describes a pregnancy at increased risk for complications due to various maternal or fetal medical, surgical, and/or anatomic issues. In order to best protect the pregnant patient and the fetus, frequent prenatal visits and monitoring are often recommended. Unfortunately, some patients are unable to attend these appointments for various reasons. Moreover, it has been documented that patients from ethnically and racially diverse backgrounds are more likely to miss medical appointments than are Caucasian patients. For instance, a case-control study retrospectively identified the race/ethnicity of patients who no-showed for mammography visits in 2018. Women who no-showed were more likely to be African American than patients who kept their appointments, with an odds ratio of 2.64 (4). Several other studies from several other primary care and specialty disciplines have shown similar results. However, the current research on high-risk obstetric no-shows has focused primarily on why patients miss their appointments rather than which patients are missing appointments. This is an area of opportunity for further research. Given disparities in health outcomes among underrepresented racial/ethnic groups and the importance of prenatal care, especially in high-risk populations, targeted attempts to increase patient participation in prenatal care may improve maternal and infant morbidity/mortality in these populations.