Predictors of No-Show in Neurology Clinics

The impact of headache disorders: a prospective analysis of headache referrals to outpatient and inpatient neurology and emergency services in an Irish University teaching hospital

BACKGROUND

Headache represents a significant proportion of disability globally in general practice, neurology outpatient settings, and emergency departments. There is scant literature regarding the impact of headache on healthcare services in Ireland.

AIMS

We aimed to investigate headache burden across the emergency department, inpatient stays, and neurology outpatient department referrals in an Irish University teaching hospital.

METHODS

We prospectively collected data regarding emergency department presentations, inpatient neurology consultations, and neurology outpatient referrals for patients with headache between 13th January and 8th March 2020. Data were analyzed using descriptive statistics.

RESULTS

There were 180 emergency department attendances, 50 inpatient consultations, and 76 outpatient referrals with headache. Neurological examinations were often incomplete; neuroimaging was commonly employed. Migraine was the most frequent headache diagnosis at discharge in the emergency department and among inpatients after neurology review. Diagnostic uncertainty was identified-33% of patients left the emergency department with no diagnosis, and "unknown/unspecified headache" was recorded on 49% of outpatient referrals and 30% of inpatient consult requests. Medication overuse headache coexisted with migraine in nine patients in the inpatient group. Prophylaxis had been trialed in 56% of patients with migraine referred to outpatients.

CONCLUSIONS

Primary headache disorders have a large impact on hospital services. Diagnostic uncertainty is common; neuroimaging is relied upon. Appropriate care pathways, education, and resource allocation should be prioritized.

Veteran Caretaker Perspectives of the Need for Childcare Assistance During Health Care Appointments

PURPOSE

In 2020, Congress passed legislation to establish the national Veterans Child Care Assistance Program (VCAP) targeting eligible veterans receiving care through the Veterans Health Administration (VA). This needs assessment describes the childcare needs of veteran caretakers of young children and explores the implications of inadequate childcare on health care engagement.

METHODS

Survey data were collected from 2,000 VA users with dependent children; data were analyzed using standard descriptive statistics. Qualitative data were collected from 19 veterans through focus groups and analyzed using rapid thematic analysis.

FINDINGS

More than 75% of veterans surveyed indicated that they required childcare assistance during health care appointments and 73% reported barriers to finding childcare. Prominent barriers included the high cost of childcare and not having a trusted source of childcare. Nearly 58% of survey respondents reported missed or canceled VA health care appointments due to childcare challenges. Furthermore, 35% of surveyed veterans reported that their children had accompanied them to an appointment in the past year. Among these veterans, 59% brought their children into the exam room. Focus group participants discussed how having children present during their health care appointments hampered communication with health care providers.

CONCLUSIONS

Veterans report that lack of childcare keeps them from attending and remaining focused on the provider during their health care visits, which could compromise quality of care. As one of the only health systems in the United States that will offer childcare assistance, VCAP presents an opportunity to improve health care access and quality by reducing missed appointments and suboptimal care.

Application of a Machine Learning Algorithm to Develop and Validate a Prediction Model for Ambulatory Non-Arrivals

BACKGROUND

Non-arrivals to scheduled ambulatory visits are common and lead to a discontinuity of care, poor health outcomes, and increased subsequent healthcare utilization. Reducing non-arrivals is important given their association with poorer health outcomes and cost to health systems.

OBJECTIVE

To develop and validate a prediction model for ambulatory non-arrivals.

DESIGN

Retrospective cohort study.

PATIENTS OR SUBJECTS

Patients at an integrated health system who had an outpatient visit scheduled from January 1, 2020, to February 28, 2022.

MAIN MEASURES

Non-arrivals to scheduled appointments.

KEY RESULTS

There were over 4.3 million ambulatory appointments from 1.2 million adult patients. Patients with appointment non-arrivals were more likely to be single, racial/ethnic minorities, and not having an established primary care provider compared to those who arrived at their appointments. A prediction model using the XGBoost machine learning algorithm had the highest AUC value (0.768 [0.767-0.770]). Using SHAP values, the most impactful features in the model include rescheduled appointments, lead time (number of days from scheduled to appointment date), appointment provider, number of days since last appointment with the same department, and a patient's prior appointment status within the same department. Scheduling visits close to an appointment date is predicted to be less likely to result in a non-arrival. Overall, the prediction model calibrated well for each department, especially over the operationally relevant probability range of 0 to 40%. Departments with fewer observations and lower non-arrival rates generally had a worse calibration.

CONCLUSIONS

Using a machine learning algorithm, we developed a prediction model for non-arrivals to scheduled ambulatory appointments usable for all medical specialties. The proposed prediction model can be deployed within an electronic health system or integrated into other dashboards to reduce non-arrivals. Future work will focus on the implementation and application of the model to reduce non-arrivals.

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.

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.

Audio or Video? Access to Pediatric Neurology Outpatient Services Varies by the Type of Telehealth, Especially for Black Children

BACKGROUND

Children of minority race/ethnicity face barriers to accessing specialty services. During the COVID pandemic, health insurance companies reimbursed telehealth services. Our objective was to evaluate the effect of audio versus video visits on children's access to outpatient neurology services, particularly for Black children.

METHODS

Using Electronic Health Record data, we collected information about children who had outpatient neurology appointments in a tertiary care children's hospital in North Carolina from March 10, 2020, to March 9, 2021. We used multivariable models to compare appointment outcomes (canceled vs completed, and missed vs completed) by visit type. We then conducted similar evaluation for the subgroup of Black children.

RESULTS

A total of 1250 children accounted for 3829 scheduled appointments. Audio users were more likely to be Black and Hispanic, and to have public health insurance than video users. Adjusted odds ratio (aOR) for appointments completed versus canceled was 10 for audio and 6 for video, compared to in-person appointments. Audio visits were twice as likely as in-person visits to be completed versus missed; video visits were not different. For the subgroup of Black children, aOR for appointments completed versus canceled for audio was 9 and video was 5, compared to in-person appointments. For Black children, audio visits were 3 times as likely as in-person visits to be completed versus missed; video visits were not different.

CONCLUSIONS

Audio visits improved access to pediatric neurology services, especially for Black children. Reversal of policies to reimburse audio visits could deepen the socioeconomic divide for children's access to neurology services.

Appointment Non-attendance for Telehealth Versus In-Person Primary Care Visits at a Large Public Healthcare System

BACKGROUND

Appointment non-attendance has clinical, operational, and financial implications for patients and health systems. How telehealth services are associated with non-attendance in primary care is not well-described, nor are patient characteristics associated with telehealth non-attendance.

OBJECTIVE

We sought to compare primary care non-attendance for telehealth versus in-person visits and describe patient characteristics associated with telehealth non-attendance.

DESIGN

An observational study of electronic health record data.

PARTICIPANTS

Patients with primary care encounters at 23 adult primary care clinics at a large, urban public healthcare system from November 1, 2019, to August 31, 2021.

MAIN MEASURES

We analyzed non-attendance by modality (telephone, video, in-person) during three time periods representing different availability of telehealth using hierarchal multiple logistic regression to control for patient demographics and variation within patients and clinics. We stratified by modality and used hierarchal multiple logistic regression to assess for associations between patient characteristics and non-attendance in each modality.

KEY RESULTS

There were 1,219,781 scheduled adult primary care visits by 329,461 unique patients: 754,149 (61.8%) in-person, 439,295 (36.0%) telephonic, and 26,337 (2.2%) video visits. Non-attendance for telephone visits was initially higher than that for in-person visits (adjusted odds ratio 1.04 [95% CI 1.02, 1.07]) during the early telehealth availability period, but decreased later (0.82 [0.81, 0.83]). Non-attendance for video visits was higher than for in-person visits during the early (4.37 [2.74, 6.97]) and later (2.02 [1.95, 2.08]) periods. Telephone visits had fewer differences in non-attendance by demographics; video visits were associated with increased non-attendance for patients who were older, male, had a primary language other than English or Spanish, and had public or no insurance.

CONCLUSIONS

Telephonic visits may improve access to care and be more easily adoptable among diverse populations. Further attention to implementation may be needed to avoid impeding access to care for certain populations using video visits.