A Pilot Study of Reasons and Risk Factors for "No-Shows" in a Pediatric Neurology Clinic

Barriers Associated With Missed Palliative Care Telehealth Visits

Background: The COVID-19 pandemic accelerated the adoption of telehealth in palliative care. While this technology showed efficiencies in healthcare delivery, it also unmasked inequalities affecting the socially disadvantaged. Objective: To identify factors associated with missed telehealth visits. Methods: We reviewed telehealth visits between April 1, 2020 and March 31, 2021 at a palliative care clinic. Disease-related and demographic information were recorded, including residency in community outreach zones (COZ)-zip code clusters known for healthcare underutilization. We categorized patients with at least one missed visit as "any miss" (AM), and those with at least three scheduled visits and missed at least 50% as "pattern miss" (PM). Results: Of 1225 scheduled telehealth (i.e., audiovisual) visits, there were 802 completed, 52 missed initial and 371 missed follow-up encounters. Among 505 unique patients, 363 (72%) were receiving cancer treatment, 170 (34%) had multiple insurance, 87 (17%) lived in COZ, 101 (20%) were AM, and 27 (5%) were PM. Patients in COZ had significantly higher risk of PM vs those outside (OR = 2.56, 95% CI: 1.06-5.78, P = .03). Patients with multiple insurance had significantly higher risk of PM vs those with single or no coverage (OR = 3.06, 95% CI: 1.40-6.93, P = .006). Patients on treatment had significantly higher risk of AM vs those not in treatment (OR = 1.75, 95% CI: 1.05-3.06, P = .04). Conclusion: We identified living in areas with healthcare underutilization, active cancer treatment, and multiple insurance coverage as barriers to telehealth visits. Measures are necessary to attenuate disparities in accessing palliative care via telehealth.

Improving Outpatient Follow-Up Rates for New In-Hospital Consults

OBJECTIVE(S)

This investigation aimed to define the rate of outpatient follow-up after in-hospital consultation, identify factors associated with establishing care, and evaluate an alternative scheduling process to improve outpatient adherence.

METHODS

Two-phase, prospective study at an academic, tertiary-care institution from March 2020 to August 2022. First, all patients not previously known to our practice encountered via inpatient consult who warranted outpatient follow-up were prospectively captured. Logistic regression analysis was used to identify demographic, disease, and practice factors predictive of follow-up. Second, a randomized control trial was performed to validate the effects of pre-assigning appointments prior to discharge.

RESULTS

Six hundred subjects were included in the final study cohort; 500 in phase-one, and 100 randomized during phase-two. In the phase-one cohort, 54% (n = 272) were lost to follow-up. Multivariate analysis showed increased odds of outpatient follow-up when appointments were pre-assigned before discharge (odds ratio [OR]: 3.69 [95% confidence interval [CI]: 2.29-5.96], p < 0.001), the primary reason for hospitalization was ENT and consult-related (OR: 3.29 [1.92-5.64], p < 0.001), and the diagnosis was one of Oncology (OR: 1.93 [1.02-3.69], p = 0.045) or Pediatrics (OR: 3.36 [1.41-7.98], p = 0.006) subspecialties. During phase-two, subjects randomized for pre-assigned appointments had higher outpatient follow-up (82%) compared to the control group (20%) (p < 0.001).

CONCLUSION

Hospital-based consultations represent an important referral pathway for new patients. Disease characteristics may identify patients less likely to follow-up upon discharge. Appointment scheduling protocols, including pre-assigning appointments, are modifiable targets for improving adherence to care. Laryngoscope, 133:2540-2545, 2023.

Factors associated with appointment 'no-shows' at two tertiary level outpatient oculoplastic clinics

PURPOSE

Appointment no-shows in clinics can adversely impact patients and physicians alike. This study aimed to determine the rate and potential causes of missed appointments in oculoplastic clinics and compare a private practice and hospital-based academic setting.

METHODS

A retrospective review of patients who booked appointments for oculoplastic consultation, between August 2019 and January 2020 at two oculoplastic clinics was performed. Demographic and patient-specific characteristics of patients who failed to attend their appointment were identified. Data were analysed to determine and compare the no-show rates in both clinics and logistic regression was performed to determine factors associated with them.

RESULTS

The rate of missed appointments was 3% and 17% at the oculoplastic clinics of Lions Eye Institute (LEI, private practice) and Albany Medical Center (AMC, academic hospital-based office), respectively. Patients at the AMC clinic were more likely to be male, younger, have a lower household income, not carry private insurance, and suffer from trauma. Logistic regression analysis showed lower patient age to significantly increase the likelihood of no-shows in both clinics (p = .01 for LEI, p = .003 for AMC), and lead appointment time greater than 90 days to be a significant risk factor for no-shows at LEI (p = .01).

CONCLUSIONS

The no-show rate for oculoplastic appointments is 3% and 17% at LEI and AMC clinics, respectively. Our analysis shows that younger patients are more likely to miss appointments at both clinics, and an appointment lead time greater than 90 days is a significant risk factor for no-shows at LEI.

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.

Predicting no-show appointments in a pediatric hospital in Chile using machine learning

The Chilean public health system serves 74% of the country's population, and 19% of medical appointments are missed on average because of no-shows. The national goal is 15%, which coincides with the average no-show rate reported in the private healthcare system. Our case study, Doctor Luis Calvo Mackenna Hospital, is a public high-complexity pediatric hospital and teaching center in Santiago, Chile. Historically, it has had high no-show rates, up to 29% in certain medical specialties. Using machine learning algorithms to predict no-shows of pediatric patients in terms of demographic, social, and historical variables. To propose and evaluate metrics to assess these models, accounting for the cost-effective impact of possible intervention strategies to reduce no-shows. We analyze the relationship between a no-show and demographic, social, and historical variables, between 2015 and 2018, through the following traditional machine learning algorithms: Random Forest, Logistic Regression, Support Vector Machines, AdaBoost and algorithms to alleviate the problem of class imbalance, such as RUS Boost, Balanced Random Forest, Balanced Bagging and Easy Ensemble. These class imbalances arise from the relatively low number of no-shows to the total number of appointments. Instead of the default thresholds used by each method, we computed alternative ones via the minimization of a weighted average of type I and II errors based on cost-effectiveness criteria. 20.4% of the 395,963 appointments considered presented no-shows, with ophthalmology showing the highest rate among specialties at 29.1%. Patients in the most deprived socioeconomic group according to their insurance type and commune of residence and those in their second infancy had the highest no-show rate. The history of non-attendance is strongly related to future no-shows. An 8-week experimental design measured a decrease in no-shows of 10.3 percentage points when using our reminder strategy compared to a control group. Among the variables analyzed, those related to patients' historical behavior, the reservation delay from the creation of the appointment, and variables that can be associated with the most disadvantaged socioeconomic group, are the most relevant to predict a no-show. Moreover, the introduction of new cost-effective metrics significantly impacts the validity of our prediction models. Using a prototype to call patients with the highest risk of no-shows resulted in a noticeable decrease in the overall no-show rate.

Reduction of No-Show Rate for New Patients in a Pediatric Neurology Clinic

BACKGROUND

The rate of patients not keeping their appointments at our children's hospital outpatient pediatric neurology clinic (no-shows) was high. We conducted a quality improvement project to reduce no-show rates and improve operational efficiency. Specifically, we aimed to decrease the new patient no-show mean rate from 7% to 4% at the main campus and from 17% to 12% at the south campus.

METHODS

After reviewing the previous literature on this topic and institutional data, we used the simplified failure mode and effects analysis (sFMEA) to identify the key drivers. Of the patients at the main campus who failed to keep their appointment, 84% had not confirmed their appointment. Errors in inpatient/family contact information, limited use of the electronic patient portal, and miscommunication were other key drivers identified. Three Plan-Do-Study-Act (PDSA) cycles were completed over seven months. The key interventions we implemented were bidirectional text triage, telephone reminders, and promoting the use of the electronic patient portal. A run chart was used to assess the results of these interventions.

RESULTS

A statistically significant shift was noted in the run chart for the median rate of no-shows, which declined from 7% to 4% at the main campus and 17% to 10% at the south campus.

CONCLUSION

We were able to successfully reduce no-shows among new patients in the neurology clinic. The limitations of our study include unknown external factors, the potential impact of COVID-19, and the brief length of the study.

Non-attendance at outpatient clinic appointments by children with cerebral palsy

AIM

To determine factors that influence non-attendance at outpatient clinics by children with cerebral palsy (CP).

METHOD

This was a retrospective cohort study of 1395 children with CP (59.6% male; born 2005 to 2017) identified from the New South Wales (NSW)/Australian Capital Territory CP Register, who had scheduled appointments at outpatient clinics at two NSW tertiary paediatric hospitals between 2012 and 2019. Associations between sociodemographic, clinical, and process-of-care factors and non-attendance were examined using multivariate logistic regression with generalized estimating equations.

RESULTS

A total of 5773 (12%) of 50 121 scheduled outpatient days were not attended. Non-attendance increased over time (average increase 5.6% per year, 95% confidence interval [CI]: 3.7-7.3). Older children aged 5 to 9 years (adjusted odds ratio [aOR] 1.11; 95% CI: 1.02-1.22) and 10 to 14 years (aOR 1.17; 95% CI: 1.03-1.34), socioeconomic disadvantage (aOR 1.29; 95% CI: 1.11-1.50), previous non-attendance (aOR 1.38; 95% CI: 1.23-1.53), and recent rescheduled or cancelled appointments (aOR 1.08; 95% CI: 1.01-1.16) were associated with increased likelihood of non-attendance.

INTERPRETATION

One in eight outpatient appointments for children with CP were not attended. Non-attendance was associated with increasing age, socioeconomic disadvantage, previous non-attendance, and recent rescheduled or cancelled appointments. Identifying specific barriers and interventions to improve access to outpatient services for these groups is needed.

WHAT THIS PAPER ADDS

Twelve per cent of scheduled appointments for children with cerebral palsy are not attended. Proportions of appointments not attended has increased over the last decade. Increasing age and socioeconomic disadvantage increase the likelihood of non-attendance. Previous non-attendance and recent cancelled or rescheduled appointments increase the likelihood of further non-attendance.

Association of Race and Socioeconomic Disadvantage With Missed Telemedicine Visits for Pediatric Patients During the COVID-19 Pandemic

This comparative effectiveness study examines whether race and ethnicity and socioeconomic disadvantage are factors associated with missing telemedicine visits during the COVID-19 pandemic among pediatric patients in Massachusetts.

Patient Characteristics Associated With Missed Appointments in Pediatric Subspecialty Clinics

OBJECTIVES

Missed appointments negatively affect patients, providers, and health systems. This study aimed to (1) quantify the percentage of missed appointments across 14 pediatric subspecialties in a tertiary-care children's hospital and (2) identify patient characteristics associated with missed appointments in those subspecialties.

METHODS

We extracted patient characteristics from 267,151 outpatient appointments, between January 1, 2013, and December 31, 2018, across 14 subspecialty clinics. Medical complexity was categorized using the Pediatric Medical Complexity Algorithm. The primary outcome was appointment nonattendance. Cancellations, imaging/laboratory visits, patients older than 18 years, and duplicate visits were excluded. Characteristics associated with nonattendance were analyzed with chi-square tests and included in the multivariable model if p < .1. Missing data were addressed using random forest imputation, and assuming data were "missing at random." Variables were considered statistically significant if p < .05.

RESULTS

Of the 128,117 scheduled appointments analyzed, 23,204 (18.1%) were missed. In the multivariable model, clinical nutrition had the greatest subspecialty odds of missed appointments, whereas cardiology had the lowest. Patient characteristics most strongly associated with missed appointments were public insurance, history of >2 missed appointments, appointment lead time, lesser medical complexity, Black race/ethnicity, and fewer medications.

CONCLUSIONS

Clinical characteristics including lesser medical complexity and fewer medications are associated with missed appointments in pediatric subspecialties.

Modelling the no-show of patients to exam appointments of computed tomography

BACKGROUND

Patients' no-shows negatively impact healthcare systems, leading to resources' underutilisation, efficiency loss, and cost increase. Predicting no-shows is key to developing strategies that counteract their effects. In this paper, we propose a model to predict the no-show of ambulatory patients to exam appointments of computed tomography at the Radiology department of a large Brazilian public hospital.

METHODS

We carried out a retrospective study on 8382 appointments to computed tomography (CT) exams between January and December 2017. Penalised logistic regression and multivariate logistic regression were used to model the influence of 15 candidate variables on patients' no-shows. The predictive capabilities of the models were evaluated by analysing the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC).

RESULTS

The no-show rate in computerised tomography exams appointments was 6.65%. The two models performed similarly in terms of AUC. The penalised logistic regression model was selected using the parsimony criterion, with 8 of the 15 variables analysed appearing as significant. One of the variables included in the model (number of exams scheduled in the previous year) had not been previously reported in the related literature.

CONCLUSIONS

Our findings may be used to guide the development of strategies to reduce the no-show of patients to exam appointments.

Predicting Patient No-Shows in an Academic Pediatric Neurology Clinic

Background: No-shows can negatively affect patient care. Efforts to predict high-risk patients are needed. Previously, our epilepsy clinic identified patients with 2 or more no-shows or late cancelations in the past 18 months as being at high risk for no-shows. Our objective was to develop a model to accurately predict the risk of no-shows among patients with epilepsy seen at our neurology clinic. Methods: Using electronic health record data, we developed a least absolute shrinkage and selection operator (LASSO)-regularized logistic regression model to predict no-shows and compared its performance with our neurology clinic's above-mentioned ad hoc rule. Results: The ad hoc rule identified 13% of patients seen at our neurology clinic as high-risk patients for no-shows and resulted in a positive predictive value of 38%. In comparison, our LASSO model resulted in a positive predictive value of 48%. Our LASSO model identified that lack of private insurance, inactive Epic MyChart, greater past no-show rates, fewer appointment changes before the appointment date, and follow-up appointments were more likely to result in no-shows. Conclusions: Our LASSO model outperformed the ad hoc rule used by our neurology clinic in predicting patients at high risk for no-shows. Social workers can use the no-show risk scores generated by our LASSO model to prioritize high-risk patients for targeted intervention to reduce no-shows at our neurology clinic.

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.

Investigating the reasons for missing an outpatient appointment in Royal Hospital, Sultanate of Oman: Perspectives of patients and medical staff in a survey

Introduction

Missed appointments are a major health issue in the healthcare systems globally. They directly impact on the use of hospital resources, patient's health, and can lead to patient's dissatisfaction. This study was conducted to assess the reasons for missing a hospital appointment.

Methods

A survey was conducted with a randomly selected sample of patients who missed their outpatient appointment in Royal hospital, Sultanate of Oman, from March to April 2021 in six clinics. Patients were interviewed via telephone to answer a structured survey. In addition, a self‐administered survey was distributed to medical staff to explore their perspectives.

Results

Two hundred eighty patients and 52 medical staff participated in the study. Frequent patient‐reported reasons for missed appointment were transportation difficulties (11.4%), no longer needing (7.5%), or forgetting the appointment (6.8%); staff‐reported reasons were transportation (23.8%), no SMS received (16.9%), and forgetting the appointment (15.4%). Frequencies of reasons varied substantially between clinics. Family obligations were the main theme in obstetrics (odds ratio [OR] 9.48; 95% confidence interval [CI] 2.66‐33.78) and in diabetes clinic (OR 10.55; 95% CI 2.68‐38.58), where transportation issue was the main theme in Oncology clinic (OR 4.83; 95% CI 1.11‐21.02). The recommendations for improvement were mainly around improving the reminder system, the use of telephone reminders, and developing a flexible appointment scheduling system.

Conclusion

Knowing the reasons for missed appointment from patients and health professionals can help to develop effective interventions. The heterogeneity between clinics in reasons for missed appointment indicates for interventions tailored to clinic and frequent reasons.

Conceptualization and implementation of an interdisciplinary clinic for children with drug-resistant epilepsy during the COVID-19 pandemic

OBJECTIVE

To describe the rapid conceptualization and implementation of an interdisciplinary epilepsy clinic for children with drug-resistant epilepsy (DRE) at Arkansas Children's Hospital (ACH) during the COVID 19 pandemic.

METHODS

Focusing on care design and care coordination for children with DRE, multiple stakeholder groups decided to implement a clinic after the systematic rating of constructs present in a theoretical meta-analytic framework. Based on the projected success, the new interdisciplinary clinic (composed of an epileptologist, a neurosurgeon, and a neuropsychologist and coordinated by a full-time nurse) was established. Clinic operations were further refined through discussions with patients, families, and care providers. We collected data retrospectively (August 2020 to June 2021) to determine referral patterns, clinic scheduling metrics, patient characteristics, clinical recommendations, and epilepsy quality metrics.

RESULTS

Of the 32 Consolidated Framework for Implementation Research constructs assessed, 24 were positively rated to predict a high probability of successful implementation of the clinic. For approximately 100 patient visits, appearance and usage rates were >75%, yielding a clinic utilization rate of approximately 60%. Among 76 unique patients (average age of 12 years, 60% focal epilepsy), 39 patients (51.3%) were deemed eligible for epilepsy surgery evaluation. The majority of the patients (53.9%) were advised for additional diagnostic testing, and 31.6% of patients were scheduled for vagus nerve stimulation. More patients (33%) had changes in their existing anti-seizure medication (ASM) regimen rather than an addition of a new ASM (7.9%). Standardized epilepsy quality measures showed >80% to 90% adherence in 3 (reproductive counseling, depression and anxiety screening, documentation of seizure frequency) out of 4 metrics.

SIGNIFICANCE

This is the first study to show that an interdisciplinary clinic can be a valuable attribute of care models in high-need children with DRE by enabling comprehensive one-stop service for diagnostic evaluation, surgical consideration, and brief assessment of psychiatric comorbidities without compromising consensus-based best practices.

Prevalence and factors associated with missed hospital appointments: a retrospective review of multiple clinics at Royal Hospital, Sultanate of Oman

OBJECTIVES

Missed hospital appointments pose a major challenge for healthcare systems. There is a lack of information about drivers of missed hospital appointments in non-Western countries and extent of variability between different types of clinics. The aim was to evaluate the rate and predictors of missed hospital appointments and variability in drivers between multiple outpatient clinics.

SETTING

Outpatient clinics in the Royal hospital (tertiary referral hospital in Oman) between 2014 and 2018.

PARTICIPANTS

All patients with a scheduled outpatient clinic appointment (N=7 69 118).

STUDY DESIGN

Retrospective cross-sectional analysis.

PRIMARY AND SECONDARY OUTCOME MEASURES

A missed appointment was defined as a patient who did not show up for the scheduled hospital appointment without notifying or asking for the appointment to be cancelled or rescheduled. The outcomes were the rate and predictors of missed hospital appointments overall and variations by clinic. Conditional logistic regression compared patients who attended and those who missed their appointment.

RESULTS

The overall rate of missed hospital appointments was 22.3%, which varied between clinics (14.0% for Oncology and 30.3% for Urology). Important predictors were age, sex, service costs, patient's residence distance from hospital, waiting time and appointment day and season. Substantive variability between clinics in ORs for a missed appointment was present for predictors such as service costs and waiting time. Patients aged 81-90 in the Diabetes and Endocrine clinic had an adjusted OR of 0.53 for missed appointments (95% CI 0.37 to 0.74) while those in Obstetrics and Gynaecology had OR of 1.70 (95% CI 1.11 to 2.59). Adjusted ORs for longer waiting times (>120 days) were 2.22 (95% CI 2.10 to 2.34) in Urology but 1.26 (95% CI 1.18 to 1.36) in Oncology.

CONCLUSION

Predictors of a missed appointment varied between clinics in their effects. Interventions to reduce the rate of missed appointments should consider these factors and be tailored to clinic.

Factors associated with moderate sedation attendance at a university-based pediatric dental clinic

PURPOSE/OBJECTIVES

To evaluate patient attendance for moderate sedation (MS) at a university-based pediatric dental clinic; to identify factors with negative impact on MS being completed and to assess for associations between no-show appointments and patient variables METHODS: The electronic health records of patients scheduled for MS appointments in a 22-month period were assessed by a single investigator. Demographic and clinical data related to appointment attendance and MS procedure performance were collected and statistically analyzed using chi-square, Spearman's rho correlation tests, and logistic regression (p < 0.05) RESULTS: A total of 618 scheduled MS appointments were included. The MS appointment no-show-rate was 17.1 percent. Appointment confirmation (p = 0.001) and dmft score ≥ 9 (p = 0.039) had positive correlation with attendance, while "no-shows" history (p = 0.024) and longer waiting time (p = 0.040) had negative impact on attendance. About 20% of attended MS were not completed, with main reasons of airway risk (32.3%), ongoing illness (28.4%), and violation of NPO guidelines (21.5%). Race, ethnicity, language spoken, child's behavior, and distance traveled had no significant impact on attendance CONCLUSION: Interventions to decrease non-attendance rates should target patients who are unconfirmed, have a history of no-show appointments, and are scheduled well before the sedation appointment.

An Analysis of Disorders Presenting at a Pediatric Neurology Outpatient Clinic: A Report from Turkey

Neurological problems constitute an important part of diseases in children. Studies evaluating neurological diseases in children collectively and reporting their types and rates are very few. We report the clinical and laboratory spectra of children presenting with neurological diseases to our clinic. The charts of patients who presented for the first time to the only pediatric neurology outpatient clinic in the region during a year were evaluated retrospectively. A total of 88,785 patients were seen at the Meram Faculty of Medicine pediatric outpatient clinics in 1 year; 5.5% (4,904) of these patients were seen at the child neurology clinic and 1,807 patients (36.8%) were seen for the initial evaluation. Medical charts of 1,685 (93.2%) patients were reviewed: 952 (56.5%) were male patients and 733 were females. The mean age was 5.77 ± 4.92 years; 30.9% of the patients had a similar disease in the family. The top three presenting complaints that led to hospital seen were seizures (12.2%), paroxysmal events (10%), and headaches (9.2%). The most common diagnoses were epilepsy (18%), headache (8.6%), and developmental delay (7.8%). Our study describes the characteristics of the large number of patients seen for the first time in the child neurology outpatient clinic.

Adherence to laboratory testing in pediatric liver transplant recipients

BACKGROUND

The objectives of this retrospective cohort study are to describe rates of adherence to laboratory testing 6 months to 3 years post-liver transplantation and to examine demographic and clinical factors related to lab non-adherence and the association with medication adherence and clinical outcomes.

METHODS

Medical chart review was conducted for 54 youth (mean age = 5.0 years) transplanted between 2003 and 2014. Lab adherence (≥80%) was measured as the proportion of completed labs out of the number expected. Immunosuppressant drug-level variability was used as a proxy for medication adherence. Clinical outcomes included LAR, viral infection, hospitalization, and non-routine clinic visit ≥12 months after transplant.

RESULTS

Lab adherence decreased substantially over time. Single-parent household (aOR 5.86; 95% CI: 1.38-24.93) and no history of early rejection (aOR 3.96; 95% CI: 1.04-15.24) were independently associated with non-adherence. Lab non-adherence was significantly associated with medication non-adherence (P < .05), LAR (P = .02), and non-routine clinic visits (P = .03).

CONCLUSIONS

Systematic monitoring of lab adherence may help in identifying pediatric LT recipients at increased risk for excessive healthcare use and adverse outcomes possibly due to poor disease management.

Evaluation of Patient No-Shows in a Tertiary Hospital: Focusing on Modes of Appointment-Making and Type of Appointment

No-show appointments waste resources and decrease the sustainability of care. This study is an attempt to evaluate patient no-shows based on modes of appointment-making and types of appointments. We collected hospital information system data and appointment data including characteristics of patients, service providers, and clinical visits over a three-month period (1 September 2018 to 30 November 2018), at a large tertiary hospital in Seoul, Korea. We used multivariate logistic regression analyses to identify the factors associated with no-shows (Model 1). We further assessed no-shows by including the interaction term (“modes of appointment-making” X “type of appointment”) (Model 2). Among 1,252,127 appointments, the no-show rate was 6.12%. Among the modes of appointment-making, follow-up and online/telephone appointment were associated with higher odds of no-show compared to walk-in. Appointments for treatment and surgery had higher odds ratios of no-show compared to consultations. Tests for the interaction between the modes of appointment-making and type of appointment showed that follow-up for examination and online/telephone appointments for treatment and surgery had much higher odds ratios of no-shows. Other significant factors of no-shows include age, type of insurance, time of visit, lead time (time between scheduling and the appointment), type of visits, doctor’s position, and major diagnosis. Our results suggest that future approaches for predicting and addressing no-show should also consider and analyze the impact of modes of appointment-making and type of appointment on the model of prediction.

<p>Who Misses Appointments Made Online? Retrospective Analysis of the Outpatient Department of a General Hospital in Jinan, Shandong Province, China</p>

Purpose

Missed appointments in outpatient registration pose challenges for hospital administrators, especially in the context of China’s shortage of medical resources. Previous studies have identified factors that affect healthcare access via traditional appointment systems. Few studies, however, have specifically investigated Internet appointment systems. Therefore, this study explored the key factors related to missed appointments made on the Internet appointment system of a general hospital in Jinan, Shandong Province.

Methods

Online appointment data were collected from the outpatient department of a general hospital in Jinan from September 2017 to February 2018. Logistic regression was used to analyze the relative importance of eight variables: gender, age, interval between scheduling and appointment, day of the week, physician’s academic rank, appointment fee, previous missed appointments, and clinical department.

Results

A total of 48,777 online appointment records were collected, which included a 15% no-show rate. The key factors associated with no-shows included age, interval between scheduling and appointment, previous missed appointments, and clinical department. No significant relationships were found between no-shows and gender, day of the week, and appointment fee.

Conclusion

No-show rates were influenced by many factors. Based on this study’s findings, targeted measures can be taken to decrease no-show frequency and improve medical efficiency.

Associations Between Congenital Muscular Torticollis Severity and Physical Therapy Episode

PURPOSE

To determine how physical therapy utilization varies with Congenital Muscular Torticollis (CMT) Severity Grading Scale, considering episode of care and clinical practice guidelines.

METHODS

A 3-year retrospective medical record review was conducted. Data were collected for 81 infants receiving physical therapy for CMT. Sample and service characteristics are described; 46 complete records (infants 6 months or younger) were analyzed to determine how physical therapy utilization varied across severity grades.

RESULTS AND CONCLUSIONS

Of the 46 infants with complete care episodes, half had fully resolved all asymmetries. Units billed, episode duration, and total visits each increased across CMT severity grades 1 to 3. Cervical rotation restrictions correlated with total units billed, indicating a positive relationship between CMT severity and service utilization.

WHAT THIS ADDS TO THE EVIDENCE

This study supports that as CMT severity increases, physical therapy utilization increases for grades 1 to 3 of the 2018 CMT Severity Grading Scale.

Using the Six Sigma Methodology to Reduce Missed Appointments at a Pediatric Inner-City Clinic

Pediatric missed appointments impact patient outcomes and the financial well-being of clinics. Our purpose was to implement the Six Sigma methodology at a pediatric clinic to (1) identify significant predictor factors of missed appointments and develop a prediction model and (2) implement interventions to reduce the missed appointment rate. Binary logistic regression identified historical no-show rate, high-risk visit types, lack of insurance, the number of provider visits, and appointment lead time as significant predictor factors. Interventions led to a significant drop in the missed appointment rate and the no-show rate.

Evaluating the Impact of Patient No-Shows on Service Quality

Purpose

Patient no-shows are long-standing issues affecting resource utilization and posing risks to the quality of healthcare services. They also lead to loss of anticipated revenue, particularly in services where resources are expensive and in great demand.

Methods

In order to address common reasons why patients miss appointments, this study reviews the current literature and investigates various tools and methods that have been implemented to mitigate such issues. Further, a case study is conducted to identify the rate of no-shows and underlying causes at a radiology department in one of the leading hospitals in the MENA region.

Results

Our results show that the no-shows are high due to multiple factors, such as patient behavior, patients’ financial situation, environmental factors and scheduling policy.

Conclusion

In conclusion, we generate a list of recommendations that can help in reducing the rate of patient no-shows, such as patient education, application of dynamic scheduling policies and effective appointment reminder systems to patients.

Personal Phone Calls Lead to Decreased Rates of Missed Appointments in an Adolescent/Young Adult Practice

Supplemental Digital Content is available in the text.

Introduction:

Nationally, hospital practice missed appointment rates are high. Our goal was to reduce the rate of missed appointments in an Adolescent/Young Adult Practice through quality improvement methods.

Methods:

During the 12-month intervention period, administrative staff called patients the day before their primary or specialty care appointments to remind them of the date, time, and location, as well as patients who did not attend their appointments to ask about the reason for their missed appointment. We implemented Plan-Do-Study-Act interventions and analyzed data to compare missed appointment rates between the 12 months before and after February 1, 2017, the project intervention date.

Results:

Results showed significant reductions in the missed appointment rate for the Adolescent/Young Adult Practice. A control chart showed a shift in the mean overall percent of completed appointments from 76.7% to 79.2%. The most common reasons for missed appointments included forgetting (39.2%), conflicts with work/school (11.0%), or emailing the provider without contacting administrative staff (7.8%). There were significant reductions in missed appointment rates for both males and females as well as patients who were ≥20 years old, identified English or Spanish as their primary language, had public or private insurance, identified as Black or Hispanic, or did or did not require an interpreter.

Conclusion:

These data show that targeted interventions such as personalized reminder calls can be effective in reducing patient missed appointment rates in Adolescent/Young Adult Practices.

Mothers' Perceived Barriers to and Recommendations for Health Care Appointment Keeping for Children Who Have Cerebral Palsy

Children with cerebral palsy (CP) require ongoing rehabilitation services to address complex health care needs. Attendance at appointments ensures continuity of care and improves health and well-being. The study's aim was to gain insight into mothers' perspectives of the factors associated with nonattendance. A qualitative descriptive design was conducted to identify barriers and recommendations for appointment keeping. Semi-structured interviews were conducted with 15 mothers of children with CP. Data underwent inductive qualitative analysis. Mothers provided rich context regarding barriers confronted for appointment keeping-transportation and travel, competing priorities for the child and family, and health services. Mothers' recommendations for improving the experience of attending appointments included virtual care services, transportation support, multimethod scheduling and appointment reminders, extended service hours, and increased awareness among staff of family barriers to attendance. The results inform services/policy strategies to facilitate appointment keeping, thereby promoting access to ongoing rehabilitation services for children with CP.

Prevalence and risk factors associated with non-attendance in neurodevelopmental follow-up clinic among infants with CHD

BACKGROUND

Neurodevelopmental impairment is increasingly recognised as a potentially disabling outcome of CHD and formal evaluation is recommended for high-risk patients. However, data are lacking regarding the proportion of eligible children who actually receive neurodevelopmental evaluation, and barriers to follow-up are unclear. We examined the prevalence and risk factors associated with failure to attend neurodevelopmental follow-up clinic after infant cardiac surgery.

METHODS

Survivors of infant (<1 year) cardiac surgery at our institution (4/2011-3/2014) were included. Socio-demographic and clinical characteristics were evaluated in neurodevelopmental clinic attendees and non-attendees in univariate and multivariable analyses.

RESULTS

A total of 552 patients were included; median age at surgery was 2.4 months, 15% were premature, and 80% had moderate-severe CHD. Only 17% returned for neurodevelopmental evaluation, with a median age of 12.4 months. In univariate analysis, non-attendees were older at surgery, had lower surgical complexity, fewer non-cardiac anomalies, shorter hospital stay, and lived farther from the surgical center. Non-attendee families had lower income, and fewer were college graduates or had private insurance. In multivariable analysis, lack of private insurance remained independently associated with non-attendance (adjusted odds ratio 1.85, p=0.01), with a trend towards significance for distance from surgical center (adjusted odds ratio 2.86, p=0.054 for ⩾200 miles).

CONCLUSIONS

The majority of infants with CHD at high risk for neurodevelopmental dysfunction evaluated in this study are not receiving important neurodevelopmental evaluation. Efforts to remove financial/insurance barriers, increase access to neurodevelopmental clinics, and better delineate other barriers to receipt of neurodevelopmental evaluation are needed.

Factors associated with patient no-show rates in an academic otolaryngology practice

OBJECTIVES/HYPOTHESIS

Factors affecting access to healthcare is an expanding area of research. This study seeks to identify factors associated with no-show rates in an academic otolaryngology practice to improve clinical efficiency and patient access to care.

STUDY DESIGN

Retrospective review.

METHODS

A retrospective review of scheduled clinical appointments from February 1, 2015 to January 30, 2016 at a single academic otolaryngology department was performed. Statistical analysis was completed to examine the association of no-show rates with the following: otolaryngology subspecialty, clinic location (e.g., main campus vs. satellite), patient demographic factors, attending seniority, temporal factors, insurance types, rurality, and visit type.

RESULTS

There was an overall no-show rate of 20% for 22,759 scheduled clinic visits. Satellite clinics had the highest no-show rates at 25% (P < .001). New patient visits had the highest no-show rate at 24% (P < .001). Among subspecialties, facial plastic surgery had the lowest no-show rate (12.6%), whereas Pediatrics had the highest (23%) (P < .001). No significant association between gender and no-show rates was observed (P = .29), but patients over 60 years old had the lowest no-show rate (12.7%, P < .0001). Patients with Medicaid (28%), Medicare (15.3%), and commercial insurance (12.9%) had significantly different overall no-show rates (P < .0001).

CONCLUSIONS

Increased clinic no-show rates are associated with satellite clinics, new patient visits, younger age, and insurance type. No-show rates varied among subspecialties. Further investigation is warranted to assess barriers to appointment compliance and to develop interventions to improve access to care.

LEVEL OF EVIDENCE

4. Laryngoscope, 128:626-631, 2018.

No-shows in appointment scheduling - a systematic literature review

No-show appointments significantly impact the functioning of healthcare institutions, and much research has been performed to uncover and analyze the factors that influence no-show behavior. In spite of the growing body of literature on this issue, no synthesis of the state-of-the-art is presently available and no systematic literature review (SLR) exists that encompasses all medical specialties. This paper provides a SLR of no-shows in appointment scheduling in which the characteristics of existing studies are analyzed, results regarding which factors have a higher impact on missed appointment rates are synthetized, and comparisons with previous findings are performed. A total of 727 articles and review papers were retrieved from the Scopus database (which includes MEDLINE), 105 of which were selected for identification and analysis. The results indicate that the average no-show rate is of the order of 23%, being highest in the African continent (43.0%) and lowest in Oceania (13.2%). Our analysis also identified patient characteristics that were more frequently associated with no-show behavior: adults of younger age; lower socioeconomic status; place of residence is distant from the clinic; no private insurance. Furthermore, the most commonly reported significant determinants of no-show were high lead time and prior no-show history.

Predicting No-Shows in Radiology Using Regression Modeling of Data Available in the Electronic Medical Record

PURPOSE

To test whether data elements available in the electronic medical record (EMR) can be effectively leveraged to predict failure to attend a scheduled radiology examination.

MATERIALS AND METHODS

Using data from a large academic medical center, we identified all patients with a diagnostic imaging examination scheduled from January 1, 2016, to April 1, 2016, and determined whether the patient successfully attended the examination. Demographic, clinical, and health services utilization variables available in the EMR potentially relevant to examination attendance were recorded for each patient. We used descriptive statistics and logistic regression models to test whether these data elements could predict failure to attend a scheduled radiology examination. The predictive accuracy of the regression models were determined by calculating the area under the receiver operator curve.

RESULTS

Among the 54,652 patient appointments with radiology examinations scheduled during the study period, 6.5% were no-shows. No-show rates were highest for the modalities of mammography and CT and lowest for PET and MRI. Logistic regression indicated that 16 of the 27 demographic, clinical, and health services utilization factors were significantly associated with failure to attend a scheduled radiology examination (P ≤ .05). Stepwise logistic regression analysis demonstrated that previous no-shows, days between scheduling and appointments, modality type, and insurance type were most strongly predictive of no-show. A model considering all 16 data elements had good ability to predict radiology no-shows (area under the receiver operator curve = 0.753). The predictive ability was similar or improved when these models were analyzed by modality.

CONCLUSION

Patient and examination information readily available in the EMR can be successfully used to predict radiology no-shows. Moving forward, this information can be proactively leveraged to identify patients who might benefit from additional patient engagement through appointment reminders or other targeted interventions to avoid no-shows.