Personal, health system, and geosocial disparities in appointment nonadherence at family medicine clinics in southcentral Pennsylvania, United States

Background

To assess the relationship between patients' demographic, health system-related, and geosocial characteristics and the risk of missed appointments among patients in family medicine practice.

Methods

The study was based on a retrospective cross-sectional design using electronic health records and neighborhood-level social determents of health metrics linked by geocoded patients' home address. The study population consisted of patients who had a primary care provider and at least one appointment at 14 family medicine clinics in rural and suburban areas in January-December 2022. Negative binomial regression was utilized to examine the impact of personal, health system, and geosocial effects on the risk of no-shows and same-day cancellations.

Results

A total of 258,614 appointments were made from 75,182 patients during the study period, including 7.8% no-show appointments from 20,256 patients. The analysis revealed that individuals in the ethnic minority groups were 1.24-1.65 times more likely to miss their appointments than their White counterpart. Females and English speakers had 14% lower risk for no-show. A significant increase (32%-64%) in the odds of no-shows was found among individuals on Medicaid and uninsured. Persons with prior history of no-shows or same day cancellations were 6%-27% more likely to miss their appointments. The no-show risk was also higher among people living in areas experiencing socioeconomic disadvantage.

Conclusion

The risk of missed appointments is affected by personal, health system, and geosocial contexts. Future efforts aiming to reduce no-shows could develop personalized interventions targeting the at-risk populations identified in the analysis.

Understanding the causes of missingness in primary care: a realist review

BACKGROUND

Although missed appointments in healthcare have been an area of concern for policy, practice and research, the primary focus has been on reducing single 'situational' missed appointments to the benefit of services. Little attention has been paid to the causes and consequences of more 'enduring' multiple missed appointments in primary care and the role this has in producing health inequalities.

METHODS

We conducted a realist review of the literature on multiple missed appointments to identify the causes of 'missingness.' We searched multiple databases, carried out iterative citation-tracking on key papers on the topic of missed appointments and identified papers through searches of grey literature. We synthesised evidence from 197 papers, drawing on the theoretical frameworks of candidacy and fundamental causation.

RESULTS

Missingness is caused by an overlapping set of complex factors, including patients not identifying a need for an appointment or feeling it is 'for them'; appointments as sites of poor communication, power imbalance and relational threat; patients being exposed to competing demands, priorities and urgencies; issues of travel and mobility; and an absence of choice or flexibility in when, where and with whom appointments take place.

CONCLUSIONS

Interventions to address missingness at policy and practice levels should be theoretically informed, tailored to patients experiencing missingness and their identified needs and barriers; be cognisant of causal domains at multiple levels and address as many as practical; and be designed to increase safety for those seeking care.

Missed Screening Mammography Appointments: Patient Sociodemographic Characteristics and Mammography Completion After 1 Year

OBJECTIVE

Patients who miss screening mammogram appointments without notifying the health care system (no-show) risk care delays. We investigate sociodemographic characteristics of patients who experience screening mammogram no-shows at a community health center and whether and when the missed examinations are completed.

METHODS

We included patients with screening mammogram appointments at a community health center between January 1, 2021, and December 31, 2021. Language, race, ethnicity, insurance type, residential ZIP code tabulation area (ZCTA) poverty, appointment outcome (no-show, same-day cancelation, completed), and dates of completed screening mammograms after no-show appointments with ≥1-year follow-up were collected. Multivariable analyses were used to assess associations between patient characteristics and appointment outcomes.

RESULTS

Of 6,159 patients, 12.1% (743 of 6,159) experienced no-shows. The no-show group differed from the completed group by language, race and ethnicity, insurance type, and poverty level (all P < .05). Patients with no-shows more often had: primary language other than English (32.0% [238 of 743] versus 26.7% [1,265 of 4,741]), race and ethnicity other than White non-Hispanic (42.3% [314 of 743] versus 33.6% [1,595 of 4,742]), Medicaid or means-tested insurance (62.0% [461 of 743] versus 34.4% [1,629 of 4,742]), and residential ZCTAs with ≥20% poverty (19.5% [145 of 743] versus 14.1% [670 of 4,742]). Independent predictors of no-shows were Black non-Hispanic race and ethnicity (adjusted odds ratio [aOR], 1.52; 95% confidence interval [CI], 1.12-2.07; P = .007), Medicaid or other means-tested insurance (aOR, 2.75; 95% CI, 2.29-3.30; P < .001), and ZCTAs with ≥20% poverty (aOR, 1.76; 95% CI, 1.14-2.72; P = .011). At 1-year follow-up, 40.6% (302 of 743) of patients with no-shows had not completed screening mammogram.

DISCUSSION

Screening mammogram no-shows is a health equity issue in which socio-economically disadvantaged and racially and ethnically minoritized patients are more likely to experience missed appointments and continued delays in screening mammogram completion.

Risk Factors for Missed Appointments at a Multisite Academic Urban Urogynecology Practice

IMPORTANCE

Missed appointments lead to decreased clinical productivity and poor health outcomes.

OBJECTIVES

The objectives of this study were to describe sociodemographic and clinical characteristics of patients who miss urogynecology appointments and identify risk factors for missed appointments.

STUDY DESIGN

We conducted an institutional review board-approved case-control study of women 18 years or older scheduled for a urogynecology appointment at 1 of 4 sites associated with an urban academic tertiary care center over 4 months. Patients were included in the missed appointment group if they canceled their appointments the same day or did not show up for them. For comparison, we included a control group consisting of patients immediately preceding or following the ones who missed their appointments with the same visit type. Logistic regression was used to identify risk factors for missed appointments.

RESULTS

Four hundred twenty-six women were included: 213 in the missed appointment group and 213 in the control group. Women who missed appointments were younger (60 years [interquartile range (IQR), 47-72 years] vs 69 years [IQR, 59-78 years], P < 0.0001). More women in the missed appointment group were Hispanic (24.4% vs 13.1%) and non-Hispanic Black (7.5% vs 3.8%, P = 0.009), had Medicaid (17.4% vs 6.57%, P = 0.0006), missed previous appointments (24.9% vs 11.7% P = 0.0005), waited longer for appointments (39 days [IQR, 23.5-55.5 days] vs 30.5 days [IQR, 12.8-47.0 days], P = 0.002), and made appointments for urinary incontinence (44.1% vs 26.8%, P = 0.0002). On multivariate logistic regression, women with Medicaid had significantly higher odds of missing appointments (adjusted OR, 2.11 [1.04-4.48], P = 0.044).

CONCLUSIONS

Women with Medicaid were more likely to miss urogynecology appointments. Further research is needed to address barriers this group faces when accessing care.

Exploring factors influencing outpatient radiology attendance based on Health Belief Model (HBM): A qualitative study

INTRODUCTION

Non-attendance for radiology outpatient appointments is a global issue and is associated with adverse clinical outcomes and operational inefficiencies. This paper aims to understand the underlying factors influencing outpatient radiology attendances based on the Health Belief Model (HBM).

METHODS

Purposive sampling was used to recruit patients (n=30) for in-depth semi-structured telephone interviews. Inclusion criteria comprised participants who were above 21 years old and fluent in English, while participants reliant on third-party assistance (e.g., nursing homes and prison services), to attend the appointment were excluded. The interviews were recorded and transcribed verbatim. The COREQ (Consolidated Criteria for Reporting Qualitative Research) was utilised to guide the reporting of this study. The data analysis involved a hybrid thematic analysis approach using NVivo.

RESULTS

Six key themes associated with appointment adherence in radiology were identified. These themes included: 1) prioritising health and acceptance of current medical conditions; 2) the impact of perceived severity on non-attendance; 3) perceived benefits of attending appointments; 4) perceived barriers to attendance; 5) influential prompts; and 6) confidence in attendance.

CONCLUSION

This is the first study of its kind to utilise the HBM to examine factors influencing attendance adherence among radiology outpatients in Singapore. Costs, prompts, and the perceived severity of the condition play pivotal roles in shaping the health-seeking behaviours of these outpatients while professionalism of healthcare staff and barriers to attendance present opportunities for providers to address patients' lack of interest towards their appointments.

IMPLICATIONS FOR PRACTICE

Findings of this study will contribute to the development of personalised, targeted interventions for improving patient engagement and attendance in radiology settings.

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.

Missed Healthcare Visits During the COVID-19 Pandemic: A Longitudinal Study

INTRODUCTION

Missed visits have been estimated to cost the U.S. healthcare system $50 billion annually and have been linked to healthcare inefficiency, higher rates of emergency department visits, and worse outcomes. COVID-19 disrupted existing outpatient healthcare utilization patterns. In our study, we sought to examine the frequency of missed outpatient visits over the course of the COVID-19 pandemic and to examine patient-level characteristics associated with non-attendance.

METHODS

This study utilized data from a longitudinal cohort study (the Chicago COVID-19 Comorbidities (C3) study). C3 participants were enrollees in 1 of 4 active, "parent" studies; they were rapidly enrolled in C3 at the onset of the pandemic. Multiple waves of telephone-based interviews were conducted to collect experiences with the pandemic, as well as socio-demographic and health characteristics, health literacy, patient activation, and depressive and anxiety symptoms. For the current analysis, data from waves 3 to 8 (05/01/20-05/19/22) were analyzed. Participants included 845 English or Spanish-speaking adults with 1 or more chronic conditions.

RESULTS

The percentage of participants reporting missed visits due to COVID-19 across study waves ranged from 3.1 to 22.4%. Overall, there was a decline in missed visits over time. No participant sociodemographic or health characteristic was consistently associated with missed visits across the study waves. In bivariate and multivariate analysis, only patient-reported anxiety was significantly associated with missed visits across all study waves.

CONCLUSION

Findings reveal that anxiety was consistently associated with missed visits during the COVID-19 pandemic, but not sociodemographic or health characteristics. Results can inform future public health initiatives to reduce absenteeism by considering patients' emotional state during times of uncertainty.

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.

Demographic and socioeconomic disparities in the hybrid ophthalmology telemedicine model

IMPORTANCE

As telemedicine use expands, it is important to evaluate demographic and socioeconomic disparities among patients receiving ophthalmic care through new hybrid telemedicine models.

OBJECTIVE

To evaluate whether there are demographic and socioeconomic disparities in the delivery of the hybrid telemedicine model.

DESIGN

Retrospective, cross-sectional, case-control analysis of patient encounters from April to December 2020.

SETTING

A single, academic, hospital-based eye clinic in Boston, Massachusetts.

METHODS

Electronic medical records of all patient encounters from April to December 2020 were reviewed and categorized into hybrid, virtual-only, and standard in-person visits. Patient-level data for all visits were extracted including age, sex, race/ethnicity, primary language, Area Deprivation Index (ADI), insurance type, and marital status. Visit-level data for all hybrid visits were also extracted from the medical record including the visit dates and patient adherence. Demographics for the cohort of patients with at least one no-show visit were compared with demographics for the cohort of patients who only had completed visits. The primary study outcomes were the differences in demographic characteristics between the hybrid visit show and no-show groups. The secondary outcomes included demographic characteristics of patients who did not complete their hybrid visit versus a time-matched cohort of patients who did not complete their standard in-person visit. Continuous variables for patient characteristics were compared with independent samples t-tests and categorical variables were compared using Pearson chi-square tests. Multivariate logistic regression was used to examine the differences between the cohorts. Variables with missing values other than suppressed ADI values were imputed using multiple imputations by chained equations.

RESULTS

Of a total of 1025 patients who were scheduled for a hybrid visit, 145 (14.1%) patients failed to complete their visit. Primary language and insurance were found to be statistically different between patients who completed and did not complete their hybrid visits. More English speakers and fewer Haitian Creole speakers completed their hybrid visits (p = 0.007) while more patients with private insurance and fewer patients with Medicaid completed their hybrid telemedicine visits (p = 0.026). No associations were found between hybrid telemedicine visit adherence and age, sex, race/ethnicity, marital status, or ADI. When the 145 patients who failed to complete their hybrid visits were compared to a time-matched cohort of patients who failed to complete their standard in-person visit, we found that patients who missed hybrid visits were similar to those who missed standard in-person visits except for patients insured by Medicare. These patients were more likely to miss a hybrid visit than a standard in-person visit (Odds Ratio 2.199, 95% confidence interval 1.136-4.259, p = 0.019). No associations were found between patient nonadherence with hybrid telemedicine versus with standard in-person visits based on age, sex, primary language, race/ethnicity, marital status, or ADI.

CONCLUSION

The hybrid telemedicine model was associated with insurance and language-based disparities. Patients with non-English primary language and Medicaid recipients were more likely to miss a hybrid visit than their counterparts. Our findings support developing deliberate interventions to ensure hybrid telemedicine care is delivered equitably to all patients.

Importance of primary care for underserved cancer patients with multiple chronic conditions

PURPOSE

To understand the impact of pre-existing conditions on healthcare utilization among under- and uninsured patients in the transition from cancer treatment to post-treatment survivorship.

METHODS

Using electronic health record data, we constructed a cohort of patients seen in an integrated county health system between 1/1/2010 and 12/31/2016. Six hundred thirty-one adult patients diagnosed with non-metastatic breast or colorectal cancer during this period (cases) were matched 1:1 on sex and Charlson comorbidity index to non-cancer patients who had at least two chronic conditions and with at least one visit to the health system during the study period (controls). Conditional fixed effects Poisson regression models compared number of primary care and emergency department (ED) visits and completed [vs. no show or missed] appointments between cancer and non-cancer patients.

RESULTS

Cancer patients had significantly lower number of visits compared with non-cancer patients (N = 46,965 vs. 85,038). Cancer patients were less likely to have primary care (IRR = 0.25; 95% CI: 0.24, 0.27) and ED visits (IRR = 0.57; 95% CI: 0.50, 0.64) but more likely to complete a scheduled appointment (AOR = 4.83; 95% CI: 4.32, 5.39) compared with non-cancer patients. Cancer patients seen in primary care at a higher rate were more likely to visit the ED (IRR = 2.06; 95% CI: 1.52, 2.80) than those seen in primary care at a lower rate.

CONCLUSION

Health systems need to find innovative, effective solutions to increase primary care utilization among cancer patients with chronic care conditions to ensure optimal management of both chronic conditions and cancer.

IMPLICATIONS FOR CANCER SURVIVORS

Maintaining regular connections with primary care providers during active cancer treatment should be promoted.

Implementation of substance use screening in rural federally-qualified health center clinics identified high rates of unhealthy alcohol and cannabis use among adult primary care patients

BACKGROUND

Screening for substance use in rural primary care clinics faces unique challenges due to limited resources, high patient volumes, and multiple demands on providers. To explore the potential for electronic health record (EHR)-integrated screening in this context, we conducted an implementation feasibility study with a rural federally-qualified health center (FQHC) in Maine. This was an ancillary study to a NIDA Clinical Trials Network study of screening in urban primary care clinics (CTN-0062).

METHODS

Researchers worked with stakeholders from three FQHC clinics to define and implement their optimal screening approach. Clinics used the Tobacco, Alcohol, Prescription Medication, and Other Substance (TAPS) Tool, completed on tablet computers in the waiting room, and results were immediately recorded in the EHR. Adult patients presenting for annual preventive care visits, but not those with other visit types, were eligible for screening. Data were analyzed for the first 12 months following implementation at each clinic to assess screening rates and prevalence of reported unhealthy substance use, and documentation of counseling using an EHR-integrated clinical decision support tool, for patients screening positive for moderate-high risk alcohol or drug use.

RESULTS

Screening was completed by 3749 patients, representing 93.4% of those with screening-eligible annual preventive care visits, and 18.5% of adult patients presenting for any type of primary care visit. Screening was self-administered in 92.9% of cases. The prevalence of moderate-high risk substance use detected on screening was 14.6% for tobacco, 30.4% for alcohol, 10.8% for cannabis, 0.3% for illicit drugs, and 0.6% for non-medical use of prescription drugs. Brief substance use counseling was documented for 17.4% of patients with any moderate-high risk alcohol or drug use.

CONCLUSIONS

Self-administered EHR-integrated screening was feasible to implement, and detected substantial alcohol, cannabis, and tobacco use in rural FQHC clinics. Counseling was documented for a minority of patients with moderate-high risk use, possibly indicating a need for better support of primary care providers in addressing substance use. There is potential to broaden the reach of screening by offering it at routine medical visits rather than restricting to annual preventive care visits, within these and other rural primary care clinics.

Development of an evidence-based model for predicting patient, provider, and appointment factors that influence no-shows in a rural healthcare system

BACKGROUND

No-show appointments pose a significant challenge for healthcare providers, particularly in rural areas. In this study, we developed an evidence-based predictive model for patient no-shows at the Marshfield Clinic Health System (MCHS) rural provider network in Wisconsin, with the aim of improving overbooking approaches in outpatient settings and reducing the negative impact of no-shows in our underserved rural patient populations.

METHODS

Retrospective data (2021) were obtained from the MCHS scheduling system, which included 1,260,083 total appointments from 263,464 patients, as well as their demographic, appointment, and insurance information. We used descriptive statistics to associate variables with show or no-show status, logistic regression, and random forests utilized, and eXtreme Gradient Boosting (XGBoost) was chosen to develop the final model, determine cut-offs, and evaluate performance. We also used the model to predict future no-shows for appointments from 2022 and onwards.

RESULTS

The no-show rate was 6.0% in both the train and test datasets. The train and test datasets both yielded 5.98. Appointments scheduled further in advance (> 60 days of lead time) had a higher (7.7%) no-show rate. Appointments for patients aged 21-30 had the highest no-show rate (11.8%), and those for patients over 60 years of age had the lowest (2.9%). The model predictions yielded an Area Under Curve (AUC) of 0.84 for the train set and 0.83 for the test set. With the cut-off set to 0.4, the sensitivity was 0.71 and the positive predictive value was 0.18. Model results were used to recommend 1 overbook for every 6 at-risk appointments per provider per day.

CONCLUSIONS

Our findings demonstrate the feasibility of developing a predictive model based on administrative data from a predominantly rural healthcare system. Our new model distinguished between show and no-show appointments with high performance, and 1 overbook was advised for every 6 at-risk appointments. This data-driven approach to mitigating the impact of no-shows increases treatment availability in rural areas by overbooking appointment slots on days with an elevated risk of no-shows.

Rural patient and provider perceptions of telehealth implemented during the COVID-19 pandemic

BACKGROUND

Understanding perceptions of telehealth  implementation from patients and providers can improve the utility and sustainability of these programs, particularly in under-resourced rural settings. The purpose of this study was to evaluate both patient and provider perceptions of telehealth visits in a large rural healthcare system during the COVID-19 pandemic. To promote sustainability of telehealth approaches, we also assessed whether the percentage of missed appointments differed between in-person and telehealth visits.

METHODS

Using anonymous surveys, we evaluated patient preferences and satisfaction with telehealth visits from November 2020 -March 2021 and assessed perceptions of telehealth efficiency and value among rural providers from September-October 2020. We examined whether telehealth perceptions differed according to patients' age, educational attainment, insurance status, and distance to clinical site and providers' age and length of time practicing medicine using ANOVA test. We also examined whether the percentage of missed appointments differed between in-person and telehealth visits at a family practice clinic within the rural healthcare system from April to September 2020 using a Chi-square test.

RESULTS

Over 73% of rural patients had favorable perceptions of telehealth visits, and satisfaction was generally higher among younger patients. Patients reported difficulty with scheduling follow-up appointments, lack of personal contact and technology challenges as common barriers. Over 80% of the 219 providers responding to the survey reported that telehealth added value to their practice, while 36.6% agreed that telehealth visits are more efficient than in-person visits. Perception of telehealth value and efficiency did not differ by provider age (p = 0.67 and p = 0.67, respectively) or time in practice (p = 0.53 and p = 0.44, respectively). Technology challenges for the patient (91.3%) and provider (45.1%) were commonly reported. The percentage of missed appointments was slightly higher for telehealth visits compared to in-person visits, but the difference was not statistically significant (8.7% vs. 8.0%; p = 0.39).

CONCLUSIONS

Telehealth perceptions were generally favorable among rural patients and providers, although satisfaction was lower among older patients and providers. Our findings suggest that telehealth approaches may add value and efficiency to rural clinical practice. However, technology issues for both patients and providers and gaps in care coordination need to be addressed to promote sustainability of telehealth approaches in rural practice.

Racial, Ethnic, and Socioeconomic Differences in Primary Care No-Show Risk with Telemedicine During the COVID-19 Pandemic

BACKGROUND

The coronavirus 2019 (COVID-19) pandemic resulted in rapid implementation of telemedicine. Little is known about the impact of telemedicine on both no-show rates and healthcare disparities on the general primary care population during the pandemic.

OBJECTIVE

To compare no-show rates between telemedicine and office visits in the primary care setting, while controlling for the burden of COVID-19 cases, with focus on underserved populations.

DESIGN

Retrospective cohort study.

SETTING

Multi-center urban network of primary care clinics between April 2021 and December 2021.

PARTICIPANTS

A total of 311,517 completed primary care physician visits across 164,647 patients.

MAIN MEASURES

The primary outcome was risk ratio of no-show incidences (i.e., no-show rates) between telemedicine and office visits across demographic sub-groups including age, ethnicity, race, and payor type.

RESULTS

Compared to in-office visits, the overall risk of no-showing favored telemedicine, adjusted risk ratio of 0.68 (95% CI 0.65 to 0.71), absolute risk reduction (ARR) 4.0%. This favorability was most profound in several cohorts with racial/ethnic and socioeconomic differences with risk ratios in Black/African American 0.47 (95% CI 0.41 to 0.53), ARR 9.0%; Hispanic/Latino 0.63 (95% CI 0.58 to 0.68), ARR 4.6%; Medicaid 0.58 (95% CI 0.54 to 0.62) ARR 7.3%; Self-Pay 0.64 (95% CI 0.58 to 0.70) ARR 11.3%.

LIMITATION

The analysis was limited to physician-only visits in a single setting and did not examine the reasons for visits.

CONCLUSION

As compared to office visits, patients using telemedicine have a lower risk of no-showing to primary care appointments. This is one step towards improved access to care.

Racial, Ethnic, and Socioeconomic Disparities Drive Appointment No-Show in Patients with Chronic Eye Disease

BACKGROUND

Visit no-shows (NS) reduce clinic efficiency and effective resource allocation. Inadequate follow-up among patients with chronic eye disease increases risk of disease progression. Our study identifies demographic, medical, and socioeconomic characteristics that increase odds of NS among patients with chronic eye conditions at high risk of vision-threatening complications.

METHODS

This is a retrospective case-control study of data abstracted over a 5-year period (January 2013-December 2018) in an urban academic ophthalmology practice. Follow-up appointments of patients ≥ 18 years of age with a diagnosis of glaucoma, diabetic retinopathy, or age-related macular degeneration were included. Age, sex, race, ethnicity, language preference, zip code, and relevant medical history were recorded. A multivariate mixed logistic regression model was utilized to determine any association between demographic factors and visit NS.

RESULTS

A total of 106,652 visits for 4,598 unique patients were included in this study. Of these, 13,240 (12.4%) visits were NS. Patient characteristics that increased the odds of NS included Hispanic ethnicity (p < 0.0001), Black race (p < 0.0001), and a history of mental illness (p < 0.0001). Socioeconomic factors that increased the odds of NS included median household income < $40,000 (p = 0.002), Medicare insurance (p < 0.0001), and Medicaid insurance (p < 0.0001).

CONCLUSIONS

Our results highlight the influence of ethnic, racial, medical, and socioeconomic characteristics on appointment NS among patients with chronic eye disease. Future interventions aimed at reducing appointment NS could channel resources to the at-risk populations identified in this analysis to improve access to care for those who need it most.

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.

Telehealth as a Tool to Improve Access and Reduce No-Show Rates in a Large Safety-Net Population in the USA

Low-income populations are at higher risk of missing appointments, resulting in fragmented care and worsening disparities. Compared to face-to-face encounters, telehealth visits are more convenient and could improve access for low-income populations. All outpatient encounters at the Parkland Health between March 2020 and June 2022 were included. No-show rates were compared across encounter types (face-to-face vs telehealth). Generalized estimating equations were used to evaluate the association of encounter type and no-show encounters, clustering by individual patient and adjusting for demographics, comorbidities, and social vulnerability. Interaction analyses were performed. There were 355,976 unique patients with 2,639,284 scheduled outpatient encounters included in this dataset. 59.9% of patients were of Hispanic ethnicity, while 27.0% were of Black race. In a fully adjusted model, telehealth visits were associated with a 29% reduction in odds of no-show (aOR 0.71, 95% CI: 0.70-0.72). Telehealth visits were associated with significantly greater reductions in probability of no-show among patients of Black race and among those who resided in the most socially vulnerable areas. Telehealth encounters were more effective in reducing no-shows in primary care and internal medicine subspecialties than surgical specialties or other non-surgical specialties. These data suggest that telehealth may serve as a tool to improve access to care in socially complex patient populations.

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.

Appointment attendance behaviors in multiple sclerosis: Understanding the factors that differ between no shows, short notice cancellations, and attended appointments

BACKGROUND

There has yet to be an examination of how appointment attendance behaviors in multiple sclerosis (MS) are related to scheduling metrics and certain demographic, clinical, and behavioral factors such as cognitive functioning and personality traits. This study aimed to examine the factors that differ between no shows (NS), short notice cancellations (SNC), and attended appointments.

METHODS

Participants (n = 110) were persons with MS who were enrolled in a larger cross-sectional study, during which they completed a battery of neuropsychological measures. Data about their appointments in three MS-related clinics the year prior to their study evaluation were extracted from the medical record. Bivariate analyses were done, with post-hoc tests conducted with Bonferroni corrections if there was an overall group difference.

RESULTS

A higher number of SNC were noted during the winter, with 22.4% being due to the weather. SNC were also more common on Thursdays, but less frequent during the early morning time slots (7am to 9am). In contrast, NS were associated with lower annual income, weaker healthcare provider relationships, lower self-efficacy, higher levels of neuroticism, depressive symptom severity, and health distress, and greater cognitive difficulties, particularly with prospective memory.

CONCLUSIONS

While SNC are related to clinic structure and situational factors like the weather, NS may be more influenced by behavioral issues, such as difficulty remembering an appointment and high levels of distress. These findings highlight potential targets for reducing the number of missed appointments in the clinic, providing opportunities for improved healthcare efficiency and most importantly health.

Non-adherence of surgical treatment in patients with non-melanoma skin cancer: a retrospective cohort pilot study

There is limited data on non-adherence for surgical treatment in non-melanoma skin cancer (NMSC) patients. The objective of this single-center, retrospective cohort study is to compare rates of non-adherence of surgical treatment options, determine factors associated with non-adherence, and identify barriers for non-adherence. All adult patients with NMSC (> 18 years) seen between 2015 and 2017 recommended surgical treatment (surgical excision and electrodessication and curettage (ED&C) or Mohs surgery) were eligible. Non-adherence was defined as not completing recommended treatment and reasons for non-adherence were collected. Out of 427 patients that met inclusion criteria, patients recommended surgical excision and ED&C had a lower non-adherence rate of 3.4% compared to those recommended Mohs (11.4%) (p = 0.006). Factors associated with non-adherence included self-pay patients (19.07% adherent vs. 43.24% non-adherent, p = 0.004). Multivariate logistic regression analysis confirmed that Mohs patients were more likely to be non-adherent (odds ratio (OR) = 3.839, 95% confidence interval (CI) (1.435-10.270), p = 0.007) compared to surgical excision and ED&C patients. Males were more likely to be non-adherent (OR = 2.474, 95% CI (1.105-5.542), p = 0.028) to females, and self-pay patients were more likely to be non-adherent than those with other payers (OR = 3.050, 95% CI (1.437-6.475), p = 0.004). Of the 37 patients who were non-adherent, the most common reasons were loss to follow-up (46%), social reasons (41%), medical reasons (38%), and financial reasons (22%). There was a significant difference in non-adherence rates between surgical treatments for NMSCs in our cohort. Our study suggests the need for future interventional studies that implement strategies and patient education to decrease non-adherence rates.

Primary Care Appointment Completion Rates and Telemedicine Utilization Among Black and Non-Black Patients from 2019 to 2020

Objective: To understand how differences in primary care appointment completion rates between Black and non-Black patients changed in 2020 within the context of the COVID-19 pandemic and when telemedicine utilization peaked. Materials and Methods: We conducted a retrospective cohort study using the electronic health record from January 1 to December 31, 2020, among all adults scheduled for a primary care appointment within a large academic medical center. We used mixed-effects logistic regression to estimate adjusted appointment completion rates for Black patients compared with those for non-Black patients in 2020 as compared with those in 2019 within four time periods: (1) prepandemic (January 1, 2020, to March 12, 2020), (2) shutdown (March 13, 2020, to June 3, 2020), (3) reopening (June 4, 2020, to September 30, 2020), and (4) second wave (October 1, 2020, to December 31, 2020). Results: Across 1,947,399 appointments, differences in appointment completion rates between Black and non-Black patients improved in all time periods: +1.4 percentage points prepandemic (95% confidence interval [CI]: +0.8 to +2.0), +11.7 percentage points during shutdown (95% CI: +11.0 to +12.3), +8.2 percentage points during reopening (95% CI: +7.8 to +8.7), and +7.1 percentage points during second wave (95% CI: +6.4 to +7.8) (all p-values <0.001). The types of conditions managed by primary care shifted during the shutdown period, but the remainder of 2020 mirrored those from 2019. Discussion: Racial differences in appointment completion rates narrowed significantly in 2020 even as the mix of disease conditions began to mirror patterns observed in 2019. Conclusions and Relevance: Telemedicine may be an important tool for improving access to primary care for Black patients. These findings should be key considerations as regulators and payors determine telemedicine's future.

Transition to telemedicine and its impact on missed appointments in community-based clinics

BACKGROUND AND OBJECTIVE

The Coronavirus Aid, Relief, and Economic Security Act led to the rapid implementation of telemedicine across health care office settings. Whether this transition to telemedicine has any impact on missed appointments is yet to be determined. This study examined the relationship between telemedicine usage and missed appointments during the COVID-19 pandemic.

METHOD

This retrospective study used appointment-level data from 55 Federally Qualified Health Centre clinics in Texas between March and November 2020. To account for the nested data structure of repeated appointments within each patient, a mixed-effects multivariable logistic regression model was used to examine associations between telemedicine use and missed appointments, adjusting for patient sociodemographic characteristics, geographic classification, past medical history, and clinic characteristics. The independent variable was having a telemedicine appointment, defined as an audiovisual consultation started and finalized via a telemedicine platform. The outcome of interest was having a missed appointment (yes/no) after a scheduled and confirmed medical appointment. Results from this initial model were stratified by appointment type (in-person vs. telemedicine).

RESULTS

The analytic sample included 278,171 appointments for 85,413 unique patients. The overall missed appointment rate was 18%, and 25% of all appointments were telemedicine appointments. Compared to in-person visits, telemedicine visits were less likely to result in a missed appointment (OR = 0.87, p < .001). Compared to Whites, Asians were less likely to have a missed appointment (OR = 0.82, p < .001) while African Americans, Hispanics, and American Indians were all significantly more likely to have missed appointments (OR = 1.61, p < .001; OR = 1.19, p = .01; OR = 1.22, p < .01, respectively). Those accessing mental health services (OR = 1.57 for in-person and 0.78 for telemedicine) and living in metropolitan areas (OR = 1.15 for in-person and 0.82 for telemedicine) were more likely to miss in-person appointments but less likely to miss telemedicine appointments. Patients with frequent medical visits or those living with chronic diseases were more likely to miss in-person appointments but less likely to miss telemedicine appointments.

CONCLUSIONS

Telemedicine is strongly associated with fewer missed appointments. Although our findings suggest a residual lag in minority populations, specific patient populations, including those with frequent prior visits or chronic conditions, those seeking mental health services, and those living in metropolitan areas were less likely to miss telemedicine appointments than in-person visits. These findings highlight how telemedicine can enable effective and accessible care by reducing missed healthcare appointments.KEY MESSAGESTelemedicine was associated with 13% lower odds of missed appointments.Patients with frequent medical visits or those living with chronic diseases were less likely to miss telemedicine appointments but more likely to miss in-person appointments.Patients seeking mental health services were less likely to miss telemedicine appointments but more likely to miss in-person appointments.Similarly, those living in metropolitan areas were less likely to miss telemedicine appointments but more likely to miss in-person appointments.

Outpatient clinic attendance and outcomes among patients hospitalized with diabetic foot ulcers

BACKGROUND

There are limited data on post-hospital discharge clinic attendance rates and outcomes among patients with diabetic foot ulcers (DFUs).

METHODS

Retrospective study of patients hospitalized with a DFU from 2016 to 2019 in a large public hospital. We measured rates and predictors of clinic attendance with providers involved with DFU care within 30 days of hospital discharge ("30-day post-discharge clinic attendance"). Log-binomial regression was used to estimate risk ratios (RR) and 95 % confidence intervals (CI).

RESULTS

Among 888 patients, 60.0 % were between 45 and 64 years old, 80.5 % were Black, and 24.1 % were uninsured. Overall, 478 (53.8 %) attended ≥1 30-day post-discharge clinic appointment. Initial hospital outcomes were associated with clinic attendance. For example, the RR of 30-day post-discharge clinic attendance was 1.39 (95%CI 1.19-1.61) among patients who underwent a major amputation compared to patients with DFUs without osteomyelitis and did not undergo an amputation during the initial hospitalization. Among 390 patients with known 12-month outcome, 71 (18.2 %) had a major amputation or died ≤12 months of hospital discharge.

CONCLUSION

We found a low post-discharge clinic attendance and high post-discharge amputation and death rates among patients hospitalized with DFUs. Interventions to increase access to outpatient DFU care are needed and could prevent amputations.

Telemedicine and Socioeconomics in Orthopaedic Trauma Patients: A Quasi-Experimental Study During the COVID-19 Pandemic

INTRODUCTION

Socioeconomic factors may introduce barriers to telemedicine care access. This study examines changes in clinic absenteeism for orthopaedic trauma patients after the introduction of a telemedicine postoperative follow-up option during the COVID-19 pandemic with attention to patient socioeconomic status (SES).

METHODS

Patients (n = 1,060) undergoing surgical treatment of pelvic and extremity trauma were retrospectively assigned to preintervention and postintervention cohorts using a quasi-experimental design. The intervention is the April 2020 introduction of a telemedicine follow-up option for postoperative trauma care. The primary outcome was the missed visit rate (MVR) for postoperative appointments. We used Poisson regression models to estimate the relative change in MVR adjusting for patient age and sex. SES-based subgroup analysis was based on the Area Deprivation Index (ADI) according to home address.

RESULTS

The pre-telemedicine group included 635 patients; the post-telemedicine group included 425 patients. The median MVR in the pre-telemedicine group was 28% (95% confidence interval [CI], 10% to 45%) and 24% (95% CI, 6% to 43%) in the post-telemedicine group. Low SES was associated with a 40% relative increase in MVR (95% CI, 17% to 67%, P < 0.001) compared with patients with high SES. Relative MVR changes between pre-telemedicine and post-telemedicine groups did not reach statistical significance in any socioeconomic strata (low ADI, -6%; 95% CI, -25% to 17%; P = 0.56; medium ADI, -18%; 95% CI, -35% to 2%; P = 0.07; high ADI, -12%; 95% CI, -28% to 7%; P = 0.20).

CONCLUSIONS

Low SES was associated with a higher MVR both before and after the introduction of a telemedicine option. However, no evidence in this cohort demonstrated a change in absenteeism based on SES after the introduction of the telemedicine option. Clinicians should be reassured that there is no evidence that telemedicine introduces additional socioeconomic bias in postoperative orthopaedic trauma care.

LEVEL OF EVIDENCE

III.

Risk Factors for Operating Room No-Show in an Academic Otolaryngology Practice

OBJECTIVES/HYPOTHESIS

A no-show to the operating room date negatively impacts a hospital and can lead to increased costs for an institution in terms of time, materials, and manpower. Our objectives are to identify the factors associated with operating room no-shows in order to increase clinical efficiency, reduce hospital costs, and increase patient access to care.

STUDY DESIGN

Single institution retrospective chart review.

METHODS

A retrospective review was performed of all surgeries within the Otolaryngology department performed at a single tertiary academic center between 2006 and 2019. Demographic and surgical data were collected from the charts. Descriptive, univariate, and multivariate statistics were performed on the data.

RESULTS

There were a total of 1,752 no-shows and 46,440 patients who did show with an overall no-show rate of 3.63%. A multivariate logistic regression analysis was performed to compare patients who did not show for surgery to those who did. Analysis found multiple risk factors for not showing to surgery that were statistically significant (P < .05) and included decreasing age, planned outpatient case, head and neck oncology subspecialty, increasing distance from the facility, higher number of clinic no-shows, and not having private insurance. African-American race was more likely to show for surgery as scheduled.

CONCLUSIONS

Numerous factors may play a role on whether or not a patient attends a scheduled surgical date. Some of these factors may be preventable or modifiable to mitigate increased hospital costs associated with no-show to surgery and increase access to care.

LEVEL OF EVIDENCE

3 Laryngoscope, 132:1738-1742, 2022.

Racial and ethnic disparities in pediatric magnetic resonance imaging missed care opportunities

BACKGROUND

Imaging missed care opportunities (MCOs), previously referred to as "no shows," impact timely patient diagnosis and treatment and can exacerbate health care disparities. Understanding factors associated with imaging MCOs could help advance pediatric health equity.

OBJECTIVE

To assess racial/ethnic differences in pediatric MR imaging MCOs and whether health system and socioeconomic factors, represented by a geography-based Social Vulnerability Index (SVI), influence racial/ethnic differences.

MATERIALS AND METHODS

We conducted a retrospective analysis of MR imaging MCOs in patients younger than 21 years at a pediatric academic medical center (2015-2019). MR imaging MCOs were defined as: scheduled but appointment not attended, canceled within 24 h, and canceled but not rescheduled. Mixed effects multivariable logistic regression assessed the association between MCOs and race/ethnicity and community-level social factors, represented by the SVI.

RESULTS

Of 68,809 scheduled MRIs, 6,159 (9.0%) were MCOs. A higher proportion of MCOs were among Black/African-American and Hispanic/Latino children. Multivariable analysis demonstrated increased odds of MCOs among Black/African-American (adjusted odds ratio [aOR] 1.9, 95% confidence interval [CI] 1.7-2.3) and Hispanic/Latino (aOR 1.5, 95% CI 1.3-1.7) children compared to White children. The addition of SVI >90th percentile to the adjusted model had no effect on adjusted OR for Black/African-American (aOR 1.9, 95% CI 1.7-2.2) or Hispanic/Latino (aOR 1.5, 95% CI 1.3-1.6) children. Living in a community with SVI >90th percentile was independently associated with MCOs.

CONCLUSION

Black/African-American and Hispanic/Latino children were almost twice as likely to experience MCOs, even when controlling for factors associated with MCOs. Independent of race/ethnicity, higher SVI was significantly associated with MCOs. Our study supports that pediatric health care providers must continue to identify systemic barriers to health care access for Black/African-American and Hispanic/Latino children and those from socially vulnerable areas.

Adolescent attendance at transcultural psychotherapy: a retrospective cohort study

Migrant adolescents in therapy for psychological problems are at risk of poor attendance or even of dropping out. Transcultural psychotherapy has been developed in France to take cultural diversity into account in psychological treatment and to deal with the specific difficulties encountered in the psychotherapeutic treatment of this population. This study aims to assess adolescents' attendance rates to this form of psychotherapy and to explore the association of these rates with demographic, cultural, and clinical variables. We conducted a retrospective clinical cohort study of 148 adolescents aged from 11 to 20 years treated between 2008 and 2018 at two transcultural psychotherapy centers in Paris. Statistical analyses tested demographic, cultural, and clinical hypotheses. The main result was the high attendance rate at transcultural psychotherapy sessions among adolescents (77.8%). Attendance rates were not associated with age, gender, family size, generation of migration, or cultural area of origin, but were significantly linked to support in therapy, specifically, the presence at the first transcultural psychotherapy session of the first-line therapist, an interpreter, or both. Transcultural psychotherapy appears to be an effective method for addressing the complex symptoms experienced by migrant adolescents. Better attendance at sessions is statistically significantly associated with factors favoring a therapeutic alliance, specifically, the presence of the first-line therapist or an interpreter in TPT sessions and the existence of support from a social worker. The holistic approach of transcultural psychotherapy to adolescent care may explain the high attendance rates observed.

Automated patient self-scheduling: case study

This case study assesses the uptake, user characteristics, and outcomes of automated self-scheduling in a community-based physician group affiliated with an academic health system. We analyzed 1 995 909 appointments booked between January 1, 2019, and June 30, 2021 at more than 30 practice sites. Over the study period, uptake of self-scheduling increased from 4% to 15% of kept appointments. Younger, commercially insured patients were more likely to be users. Missed appointments were lower and cancelations were higher for self-scheduled patients. An examination of characteristics, benefits, and usage of automated self-scheduling provides insight to those organizations contemplating the implementation or expansion of similar consumer-facing digital self-scheduling platforms.

The effect of a public transportation expansion on no-show appointments

OBJECTIVE

To test whether there were fewer missed medical appointments ("no-shows") for patients and clinics affected by a significant public transportation expansion.

STUDY SETTING

A new light rail line was opened in a major metropolitan area in June 2014. We obtained electronic health records data from an integrated health delivery system in the area with over three million appointments at 97 clinics between 2013 and 2016.

STUDY DESIGN

We used a difference-in-differences research design to compare whether no-show appointment rates differentially changed among patients and clinics located near versus far from the new light rail line after it opened. Models included fixed effects to account for underlying differences across clinics, patient zip codes, and time.

DATA EXTRACTION METHODS

We obtained data from an electronic health records system representing all appointments scheduled at 97 outpatient clinics in this system. We excluded same-day, urgent care, and canceled appointments.

PRINCIPAL FINDINGS

The probability of no-show visits differentially declined by 0.5 percentage points (95% confidence interval [CI]: -0.9 to -0.1), or 4.5% relative to baseline, for patients living near the new light rail compared to those living far from it, after the light rail opened. The effects were stronger among patients covered by Medicaid (-1.6 percentage points [95% CI: -2.4 to -0.8] or 9.5% relative to baseline).

CONCLUSIONS

Improvements to public transit may improve access to health care, especially for people with low incomes.

Impact of United States 2017 Immigration Policy changes on missed appointments at two Massachusetts Safety-Net Hospitals

INTRODUCTION

Studies have shown mixed findings regarding the impact of immigration policy changes on immigrants' utilization of primary care.

METHODS

We used a difference-in-differences analysis to compare changes in missed primary care appointments over time across two groups: patients who received care in Spanish, Portuguese, or Haitian Creole, and non-Hispanic, white patients who received care in English.

RESULTS

After adjustment for age, sex, race, insurance, hospital system, and presence of chronic conditions, immigration policy changes were associated with an absolute increase in the missed appointment prevalence of 0.74 percentage points (95% confidence interval: 0.34, 1.15) among Spanish, Portuguese and Haitian-Creole speakers. We estimated that missed appointments due to immigration policy changes resulted in lost revenue of over $185,000.

CONCLUSIONS

We conclude that immigration policy changes were associated with a significant increase in missed appointments among patients who receive medical care in languages other than English.

Income Disparities in Outcomes of Horizontal Strabismus Surgery in a Pediatric Population

PURPOSE

To examine postoperative outcomes in pediatric patients undergoing strabismus surgery to determine the potential impact of socioeconomic disparities on ophthalmic outcomes.

METHODS

This study included 284 children undergoing strabismus surgery at a tertiary institution with at least 11 months of follow-up and no prior strabismus surgery or other neurologic or ophthalmologic conditions. Demographics, insurance, operative parameters, and appointments scheduled/attended were collected via chart review. Ocular alignment was recorded preoperatively and postoperatively at 3, 12, and 24 months. Two-sided t tests and chi-squared analyses were used to compare demographic and operative parameters. Logistic regression was employed to determine predictive factors for ophthalmic outcomes.

RESULTS

There was no difference in failure rates between patients with Medicaid and patients with private insurance 24 months postoperatively (45.9% vs 50.5%, respectively, P = .46). Patients with Medicaid were more likely to not follow up postoperatively (28.2% vs 9.6%, respectively, P < .01), whereas patients with private insurance were more likely to complete more than three follow-up appointments in 24 months (21.5% vs 39.0%, respectively, P < .01). Postoperative attendance was linked to Medicaid status (P < .01) but not travel time, neighborhood income levels, or social deprivation index factors.

CONCLUSIONS

There was no difference in failure rates between patients with Medicaid and patients with private insurance. Medicaid status was significantly predictive of loss to follow-up. [J Pediatr Ophthalmol Strabismus. 2022;59(3):156-163.].

Machine learning approaches to predicting no-shows in pediatric medical appointment

Patients’ no-shows, scheduled but unattended medical appointments, have a direct negative impact on patients’ health, due to discontinuity of treatment and late presentation to care. They also lead to inefficient use of medical resources in hospitals and clinics. The ability to predict a likely no-show in advance could enable the design and implementation of interventions to reduce the risk of it happening, thus improving patients’ care and clinical resource allocation. In this study, we develop a new interpretable deep learning-based approach for predicting the risk of no-shows at the time when a medical appointment is first scheduled. The retrospective study was conducted in an academic pediatric teaching hospital with a 20% no-show rate. Our approach tackles several challenges in the design of a predictive model by (1) adopting a data imputation method for patients with missing information in their records (77% of the population), (2) exploiting local weather information to improve predictive accuracy, and (3) developing an interpretable approach that explains how a prediction is made for each individual patient. Our proposed neural network-based and logistic regression-based methods outperformed persistence baselines. In an unobserved set of patients, our method correctly identified 83% of no-shows at the time of scheduling and led to a false alert rate less than 17%. Our method is capable of producing meaningful predictions even when some information in a patient’s records is missing. We find that patients’ past no-show record is the strongest predictor. Finally, we discuss several potential interventions to reduce no-shows, such as scheduling appointments of high-risk patients at off-peak times, which can serve as starting point for further studies on no-show interventions.

Exploring Primary Care Non-Attendance: A Study of Low-Income Patients

INTRODUCTION

While evidence has been established on the impact of medical appointment non-attendance on the healthcare system and patient health, previous research has not focused on how poverty and rurality may influence patient experiences with non-attendance. This paper explores patient perceptions of non-attendance among those experiencing poverty in a rural U.S county to better inform providers to the context in which their patients make attendance-related decisions.

METHODS

Using a grounded theory approach, we conducted semi-structured interviews with 32 U.S. low-income adults in the rural Western U.S. who recurrently missed primary care appointments. We also used a questionnaire to assess individual characteristics related to health, resiliency, personal mastery, medical mistrust, life chaos, and adverse childhood experiences.

RESULTS

Participants identified 3 barriers to attending appointments: appointment disinterest, competing demands, and insufficient systems. Appointment disinterest stemmed from physical and mental health issues, misalignment between needs and treatment, and comfort with the provider. Competing demands included family responsibilities, employment, and relationships. Finally, participants reported that current scheduling and transportation systems were helpful but insufficient. To provide further context, participants also reported low overall health, moderate levels of medical mistrust, life chaos, and mastery, moderate to low resilience, and very a high number of adverse childhood experiences.

CONCLUSIONS

Results point to the need for modified structures that allow low-income patients more control over their personal health and highlight opportunities for clinics to address patients' lack of interest and fear in the medical encounter.

Ethical Redress of Racial Inequities in AI: Lessons from Decoupling Machine Learning from Optimization in Medical Appointment Scheduling

An Artificial Intelligence algorithm trained on data that reflect racial biases may yield racially biased outputs, even if the algorithm on its own is unbiased. For example, algorithms used to schedule medical appointments in the USA predict that Black patients are at a higher risk of no-show than non-Black patients, though technically accurate given existing data that prediction results in Black patients being overwhelmingly scheduled in appointment slots that cause longer wait times than non-Black patients. This perpetuates racial inequity, in this case lesser access to medical care. This gives rise to one type of Accuracy-Fairness trade-off: preserve the efficiency offered by using AI to schedule appointments or discard that efficiency in order to avoid perpetuating ethno-racial disparities. Similar trade-offs arise in a range of AI applications including others in medicine, as well as in education, judicial systems, and public security, among others. This article presents a framework for addressing such trade-offs where Machine Learning and Optimization components of the algorithm are decoupled. Applied to medical appointment scheduling, our framework articulates four approaches intervening in different ways on different components of the algorithm. Each yields specific results, in one case preserving accuracy comparable to the current state-of-the-art while eliminating the disparity.

Family Experience of Caregiver Burden and Health Care Usage in a Statewide Medical Home Program

OBJECTIVE

To evaluate family-reported caregiver experiences and health care utilization of patients enrolled in the Pennsylvania Medical Home Program (PA-MHP) statewide practice network and compare results to PA-MHP practices' Medical Home Index (MHI) scores. We hypothesized families enrolled in higher-scoring patient-and-family-centered medical homes (PCMH) on completed MHIs would report decreased caregiver burden and improved health care utilization.

METHODS

We analyzed surveys completed by families receiving care coordination services in PA-MHP's network and each practice's mean MHI score. A total of 3221 caregivers completed surveys evaluating hours spent coordinating care/week, missed school/workdays, sick visits, and emergency department (ED) visits. A total of 222 providers from 54 participating PA-MHP practices completed the nationally recognized MHI. Family/practice demographics were collected. We developed multivariate logistic regression models assessing independent associations among family survey outcomes and corresponding practices' MHI scores.

RESULTS

Families enrolled in high-scoring PCMHs had decreased odds of spending >1 h/wk coordinating care (odds ratio [OR] 0.82, adjusted OR [aOR]: 0.70, 95% confidence interval [CI] 0.55-0.90), missing workdays in the past 6 months (OR 0.82, aOR: 0.72, 95% CI 0.69-0.97), and ED visits in the past 12 months (OR 0.83, aOR: 0.81, 95% CI 0.65-0.99) in comparison to families enrolled in lower-scoring PCMHs. Families enrolled in higher-scoring PCMHs did not report fewer sick visits despite fewer ED visits, indicating more appropriate health care utilization. High-scoring PCMHs had lower percentages of publicly insured and low-income children.

CONCLUSIONS

Higher-scoring PCMHs are associated with decreased caregiver burden and improved health care utilization across diverse PA practices. Future studies should evaluate interventions uniformly improving PCMH quality and equity.

Conversion of No-Show Patients to Telehealth in a Primary Medicine Clinic

No-shows in primary care clinics prevent patients from receiving essential care and decrease clinic productivity. The COVID-19 pandemic forced physicians to adjust to telemedicine as a necessary method to provide care. In this study no-show patients were converted to telehealth visits thereby allowing physicians to care for their patients and maintain hospital revenue. The most common reasons for "no-shows" were found to be forgetting appointments and transportation issues.

Decreasing Missed Appointments at a Community Health Center: A Community Collaborative Project

Introduction: Missed appointments are a problem for health care systems, causing lost revenue and concern for poor health outcomes. This is particularly true at Community Health Centers (CHCs), where clients may already face substantial barriers to optimal care and outcomes. Identified solutions to this problem are limited, and often focus on reminder calls and messages to clients. Methods: This project utilized a unique academic/CHC collaboration to investigate and initiate solutions to their high missed appointment rates. Client phone calls to determine clinic specific needs, monthly team meetings to brainstorm and choose initiatives, engaging stake holders, and phased implementation were the tools used to address the high missed appointment rates within the limitations of the clinic resources available. Results: Within one quarter, missed appointment rates at the clinic dropped by 6%-17% for different appointment types. Conclusion: While the project was interrupted due to the pandemic, early outcomes were promising and the model may be helpful to other CHCs with similar concerns.

Overbooking for physical examination considering late cancellation and set-resource relationship

BACKGROUND

Late cancellations of physical examination has severe impact on the operations of a physical examination center since it is often too late to fill vacancy. A booking control policy that considers overbooking is then one natural solution. Unlike appointment scheduling problems for clinics and hospitals, in which treating a patient mostly requires only one type of resource, a physical examination set typically requires multiple types of resources. Traditional methods that do not consider set-resource relationship thus may be inapplicable.

METHODS

We formulate a stochastic mathematical programming model that maximizes the expected net reward, which is the examination revenue minus overage cost. A complete search algorithm and a greedy search algorithm are designed to search for optimal booking limits for all examination sets. To estimate the late cancellation probability for each individual consumer, we apply logistic regression to identify significant factors affecting the probability. After clustering is used to estimate individual probabilities, Monte Carlo simulation is conducted to generate probability distributions for the number of consumers without late cancellations. A discrete-event simulation is performance to evaluate the effectiveness of our proposed solution.

RESULTS

We collaborate with a leading physical examination center to collect real data to evaluate our proposed overbooking policies. We show that the proposed overbooking policy may significantly increase the expected net reward. Our simulation results also help us understand the impact of overbooking on the expected number of customers and expected overage. A sensitivity analysis is conducted to demonstrate that the benefit of overbooking is insensitive to the accuracy of cost estimation. A Pareto efficiency analysis gives practitioners suggestions regarding policy determination considering multiple performance indications.

CONCLUSIONS

Our proposed overbooking policies may greatly enhance the overall performance of a physical examination center.

Efficient Prediction of Missed Clinical Appointment Using Machine Learning

Public health and its related facilities are crucial for thriving cities and societies. The optimum utilization of health resources saves money and time, but above all, it saves precious lives. It has become even more evident in the present as the pandemic has overstretched the existing medical resources. Specific to patient appointment scheduling, the casual attitude of missing medical appointments (no-show-ups) may cause severe damage to a patient's health. In this paper, with the help of machine learning, we analyze six million plus patient appointment records to predict a patient's behaviors/characteristics by using ten different machine learning algorithms. For this purpose, we first extracted meaningful features from raw data using data cleaning. We applied Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic Sampling Method (Adasyn), and random undersampling (RUS) to balance our data. After balancing, we applied ten different machine learning algorithms, namely, random forest classifier, decision tree, logistic regression, XG Boost, gradient boosting, Adaboost Classifier, Naive Bayes, stochastic gradient descent, multilayer perceptron, and Support Vector Machine. We analyzed these results with the help of six different metrics, i.e., recall, accuracy, precision, F1-score, area under the curve, and mean square error. Our study has achieved 94% recall, 86% accuracy, 83% precision, 87% F1-score, 92% area under the curve, and 0.106 minimum mean square error. Effectiveness of presented data cleaning and feature selection is confirmed by better results in all training algorithms. Notably, recall is greater than 75%, accuracy is greater than 73%, F1-score is more significant than 75%, MSE is lesser than 0.26, and AUC is greater than 74%. The research shows that instead of individual features, combining different features helps make better predictions of a patient's appointment status.

Characterising the nationwide burden and predictors of unkept outpatient appointments in the National Health Service in England: A cohort study using a machine learning approach

BACKGROUND

Unkept outpatient hospital appointments cost the National Health Service £1 billion each year. Given the associated costs and morbidity of unkept appointments, this is an issue requiring urgent attention. We aimed to determine rates of unkept outpatient clinic appointments across hospital trusts in the England. In addition, we aimed to examine the predictors of unkept outpatient clinic appointments across specialties at Imperial College Healthcare NHS Trust (ICHT). Our final aim was to train machine learning models to determine the effectiveness of a potential intervention in reducing unkept appointments.

METHODS AND FINDINGS

UK Hospital Episode Statistics outpatient data from 2016 to 2018 were used for this study. Machine learning models were trained to determine predictors of unkept appointments and their relative importance. These models were gradient boosting machines. In 2017-2018 there were approximately 85 million outpatient appointments, with an unkept appointment rate of 5.7%. Within ICHT, there were almost 1 million appointments, with an unkept appointment rate of 11.2%. Hepatology had the highest rate of unkept appointments (17%), and medical oncology had the lowest (6%). The most important predictors of unkept appointments included the recency (25%) and frequency (13%) of previous unkept appointments and age at appointment (10%). A sensitivity of 0.287 was calculated overall for specialties with at least 10,000 appointments in 2016-2017 (after data cleaning). This suggests that 28.7% of patients who do miss their appointment would be successfully targeted if the top 10% least likely to attend received an intervention. As a result, an intervention targeting the top 10% of likely non-attenders, in the full population of patients, would be able to capture 28.7% of unkept appointments if successful. Study limitations include that some unkept appointments may have been missed from the analysis because recording of unkept appointments is not mandatory in England. Furthermore, results here are based on a single trust in England, hence may not be generalisable to other locations.

CONCLUSIONS

Unkept appointments remain an ongoing concern for healthcare systems internationally. Using machine learning, we can identify those most likely to miss their appointment and implement more targeted interventions to reduce unkept appointment rates.

Characteristics Associated With New Patient Appointment No-Shows at an Academic Ophthalmology Department in the United States

PURPOSE

This study aimed to identify patient and appointment characteristics associated with no-shows to new patient appointments at a US academic ophthalmology department.

DESIGN

Cross-sectional study.

METHODS

This was a study of all adult patients with new patient appointments scheduled with an attending ophthalmologist at Penn State Eye Center between January 1^st and December 31^st of 2019. A multiple logistic regression model was used to assess the association between characteristics and no-show status.

RESULTS

Of 4,628 patients, 759 (16.4%) were no-shows. From the multiple logistic regression model, characteristics associated with no-shows were age (Odds Ratio (OR) for 18-40 years vs. >60 years: 3.41, 95% Confidence Interval (CI) 2.57, 4.51, p <0.001 and OR for 41-60 years vs. >60 years: 2.14, 95% CI 1.67, 2.74, p<0.001), median household income (OR for <$35,667 vs. >$59,445: 1.59, 95% CI 1.08, 2.34, p<0.001), insurance (OR for None vs. Medicare: 6.92, 95% CI 4.41, 10.86, p<0.001 and OR for Medicaid vs. Medicare: 1.54, 95% CI 1.18, 2.01, p=0.002), race (OR for Black vs. White: 2.62, 95% CI 2.00, 3.43, p<0.001 and OR for Other vs. White: 2.02, 95% CI 1.58, 2.59, p<0.001), and commute distance (OR for 5-10 mi vs. ≤5 mi: 1.73, 95% CI 1.17, 2.55, p=0.006). Appointments with longer lead times and scheduled with glaucoma or retina specialists were also significantly associated with greater no-shows.

CONCLUSION

Certain patient and appointment characteristics were associated with no-show status. These findings may assist in the development of targeted interventions at the patient, practice, and health system levels to improve appointment attendance.

Factors Associated with Nonattendance to a Geriatric Clinic among Mexican Older Adults

INTRODUCTION

Outpatient appointment nonattendance (NA) represents a public health problem, increasing the risk of unfavorable health-related outcomes. Although NA is significant among older adults, little is known regarding its correlates. This study aimed to identify the correlates (including several domains from the geriatric assessment) of single and repeated NA episodes in a geriatric medicine outpatient clinic, in general and in the context of specific comorbidities.

METHODS

This is a cross-sectional study including data from 3,034 older adults aged ≥60 years with ≥1 scheduled appointments between January 1, 2016, and December 31, 2016. Appointment characteristics as well as sociodemographic, geographical, and environmental information were obtained. Univariate and multivariate multinomial regression analyses were carried out.

RESULTS

The mean age was 81.8 years (SD 7.19). Over a third (37.4%) of participants missed one scheduled appointment, and 14.4% missed ≥2. Participants with a history of stroke (OR 1.336, p = 0.041) and those with a greater number of scheduled appointments during the study time frame (OR 1.182, p < 0.001) were more likely to miss one appointment, while those with Parkinson's disease (OR 0.346, p < 0.001), other pulmonary diseases (OR 0.686, p = 0.008), and better functioning for activities of daily living (ADL) (OR 0.883, p < 0.001) were less likely to do so. High socioeconomic level (OR 2.235, p < 0.001), not having a partner (OR 1.410, p = 0.006), a history of fractures (OR 1.492, p = 0.031), and a greater number of scheduled appointments (OR 1.668, p < 0.001) increased the risk of repeated NA, while osteoarthritis (OR 0.599, p = 0.001) and hypertension (OR 0.680, p = 0.002) decreased it. In specific comorbidity populations (hypertension, type 2 diabetes mellitus, and cancer), better ADL functioning protected from a single NA, while better mobility functioning protected from repeated NA in older patients with hypertension and cancer.

DISCUSSION/CONCLUSION

Identifying geriatric factors linked to an increased probability of NA may allow one to anticipate its likelihood and lead to the design and implementation of preventive strategies and to an optimization of the use of available health resources. The impact of these factors on adherence to clinical visits requires further investigation.

Comparison Between Short Text Messages and Phone Calls to Reduce No-Show Rates in Outpatient Medical Appointments: A Randomized Trial

The objective of this study was to evaluate the impact of telephone calls and short text messages (SMS) on no-show rates regarding scheduled appointments with a general practitioner. In a prospective, intervention-controlled, and randomized study, we divided 306 patients into 3 groups: telephone call, SMS, and no intervention. We compared no-show rates, as well as variables that influenced it. The lowest percentage of no-show (9.5%) occurred in the telephone call group, while the SMS group presented at 21% and the no-intervention group at 22.8% (P = .025). Telephone calls proved to be a superior strategy to text messaging.

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.

Which patients miss appointments with general practice and the reasons why: a systematic review

Background

Missed GP appointments have considerable time and cost implications for healthcare services.

Aim

This systematic review aims to explore the rate of missed primary care appointments, what the reported reasons are for appointments being missed, and which patients are more likely to miss appointments.

Design and setting

This study reports the findings of a systematic review. The included studies report the rate or reasons of missed appointments in a primary care setting.

Method

Databases were searched using a pre-defined search strategy. Eligible studies were selected for inclusion based on detailed inclusion criteria through title, abstract, and full text screening. Quality was assessed on all included studies, and findings were synthesised to answer the research questions.

Results

A total of 26 studies met the inclusion criteria for the review. Of these, 19 reported a rate of missed appointments. The mean rate of missed appointments was 15.2%, with a median of 12.9%. Twelve studies reported a reason that appointments were missed, with work or family/childcare commitments, forgetting the appointment, and transportation difficulties most commonly reported. In all, 20 studies reported characteristics of people likely to miss appointments. Patients who were likely to miss appointments were those from minority ethnicity, low sociodemographic status, and younger patients (<21 years).

Conclusion

Findings from this review have potential implications for targeted interventions to address missed appointments in primary care. This is the first step for clinicians to be able to target interventions to reduce the rate of missed appointments.