No-shows in appointment scheduling - a systematic literature review

Impact of Patient Factors on Attendance at Remote Telehealth Swallow Therapy

In-person swallow therapy is a primary and effective treatment for dysphagia. However, remote telehealth is now a widely utilized component of healthcare delivery for therapeutic interventions. This study evaluates potential factors influencing attendance at telehealth swallow therapy. Retrospective review of 308 patients referred for telehealth swallow therapy from April 2020-November 2021 included patient referral diagnosis, diagnostic swallowing evaluations, and sociodemographic information including age, race, health insurance, interpreter use, and socioeconomic status. Univariable and multivariable analyses compared patient and appointment factors for those who attended telehealth swallow therapy with those who did not attend. Overall, 71.8% of patients attended at least one telehealth swallow therapy appointment while 28.2% did not attend any. The most common referral diagnoses were "Cancer" (19.2%) and "Dysphagia Unspecified" (19.2%). Patients diagnosed with "Cancer" and "Muscle Tension" were significantly less likely to attend telehealth swallow therapy compared to those with "Dysphagia Unspecified," "Globus," and "Gastroesophageal Reflux Disease/Laryngopharyngeal Reflux" after adjusting for covariates. Lower socioeconomic status (p = 0.023), no interpreter use (p < 0.001), and more diagnostic evaluations (p = 0.001) correlated with higher telehealth swallow therapy attendance. Race and sex did not correlate with attendance. Most patients referred to telehealth swallow therapy attended at least one appointment. Patients with dysphagia associated with cancer and muscle tension, those with higher socioeconomic status, interpreter use, and fewer diagnostic swallowing evaluations were less likely to attend telehealth swallow therapy. Future research should investigate and compare attendance and efficacy of telehealth swallow therapy with in-person therapy.

Impact of Race in Missed Appointments in Pediatric Neurology Resident Clinic at a Large Tertiary Medical Center

Missed medical appointments are a common problem across specialties. The discontinuity of care leads to unplanned health care utilization, increased costs, and poor health outcomes. Previous studies evaluating pediatric epilepsy have shown significant socioeconomic barriers to care. In several specialties, resident clinic no-show rates are higher than faculty clinics because of socioeconomic barriers. We sought to understand the relationship between race, socioeconomic factors, and missed appointments in a pediatric neurology resident clinic at a large tertiary care hospital. Resident clinic encounters for 1 year were extracted and analyzed for missed appointments, socioeconomic factors, and health care utilization. We found that missed appointments occur for 1 in 5 patients and correlate with socioeconomic factors (eg, income and insurance) and race. Race was a more significant factor than socioeconomic factors for missed appointments. These results provide areas to target and track interventions to improve health outcomes in children in pediatric neurology clinics.

Telemedicine Reduces Missed Appointments but Disparities Persist

INTRODUCTION

Missed appointments also known as no-shows adversely affect clinical outcomes, clinic efficiency, and quality of care and have been attributed to barriers such as work schedule conflicts and lack of transportation. The widespread telemedicine implementation and adoption that has occurred as a consequence of the COVID-19 pandemic has the potential to address these barriers and improve missed appointment rates. This study aims to analyze the relationship between telemedicine and missed appointments.

METHODS

This retrospective cohort study used electronic health records data from a safety-net academic health center with federally qualified clinics (March 2020-December 2022). Bivariate and multivariable generalized estimating equations were used to analyze the relationship between no-show and appointment type (in-person versus telemedicine appointment). Stratified adjusted regression analyses were used to calculate the average change in the marginal effect of telemedicine appointments on no-shows across covariates. The data were analyzed from September 2023 to October 2023.

RESULTS

Hispanic patients accounted for 60% of the 474,212 appointments, followed by non-Hispanic White (22.5%), non-Hispanic Black (13.3%), Asian (2.7%), Native American (1%), and other race/ethnicity patients (0.6%). The no-show rate for telemedicine appointments was 12% compared with 25% for in-person appointments. Multivariable analysis showed that telemedicine appointment was associated with a decreased likelihood of no-show compared with in-person appointments (OR=0.40, 95% CI=0.40, 0.41). The average change in the marginal effect of telemedicine appointments on the reduction of no-shows across race/ethnicity was greatest for Native American and non-Hispanic Black patients.

CONCLUSIONS

Telemedicine appointments were associated with a decreased likelihood of no-shows, and the protective effect of telemedicine appointments on missed appointments was greatest for underserved groups. Strategies to increase telemedicine uptake, especially for underserved groups, are critical.

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

Background

Patients' missed appointments can cause interference in the functions of the clinics and the visit of other patients. One of the most effective strategies to solve the problem of no-show rate is the use of an open access scheduling system (OA). This systematic review was conducted with the aim of investigating the impact of OA on the rate of no-show of patients in outpatient clinics.

Methods

Relevant articles in English were investigated based on the keywords in title and abstract using PubMed, Scopus, and Web of Science databases and Google Scholar search engine (July 23, 2023). The articles using OA and reporting the no-show rate were included. Exclusion criteria were as follows: (1) review articles, opinion, and letters, (2) inpatient scheduling system articles, and (3) modeling or simulating OA articles. Data were extracted from the selected articles about such issues as study design, outcome measures, interventions, results, and quality score.

Findings

From a total of 23,403 studies, 16 articles were selected. The specialized fields included family medicine (62.5%, 10), pediatrics (25%, four), ophthalmology, podiatric, geriatrics, internal medicine, and primary care (6.25%, one). Of 16 articles, 10 papers (62.5%) showed a significant decrease in the no-show rate. In four articles (25%), the no-show rate was not significantly reduced. In two papers (12.5%), there were no significant changes.

Conclusions

According to this study results, it seems that in most outpatient clinics, the use of OA by considering some conditions such as conducting needs assessment and system design based on the patients' and providers' actual needs, and cooperating of all system stakeholders through consistent training caused a significant decrease in the no-show rate.

Early Access to First-Seizure Clinics, Subsequent Outcomes, and Factors Associated With Attendance

Importance

First-seizure clinics (FSCs) aim to deliver prompt specialist care to patients with new-onset undifferentiated seizure events.

Objective

To determine whether FSC attendance and time to FSC are associated with subsequent health care utilization and mortality and to investigate factors associated with FSC nonattendance.

Design, Setting, and Participants

This was a record-linkage, retrospective, cohort study of patients who booked appointments at 2 FSCs between 2007 and 2018. Patients' records were linked to state-wide administrative databases between 2000 and 2021. The setting comprised the FSCs of 2 major metropolitan public hospitals in Melbourne, Australia, providing national inpatient and outpatient adult epilepsy services. Of patients who booked appointments at the FSCs, those who were successfully linked for analysis were included in the study. Patients who recorded only canceled appointments were excluded from analysis of outcomes. Study data were analyzed from January 2000 to December 2021.

Exposure

FSC attendance.

Main Outcomes and Measures

Subsequent all-cause and seizure-related emergency department (ED) presentations and hospital admissions.

Results

Of 10 162 patients with appointments at FSCs, 9392 were linked for analysis, with mean (SD) follow-up time 6.9 (2.8) years after FSC referral. A total of 703 patients were excluded. Among 9392 linked patients, 5398 were male (57.5%; mean [SD] age, 59.7 [11.2] years). FSC attendance was associated with reduced subsequent all-cause emergency presentations (adjusted incidence rate ratio [aIRR], 0.72; 95% CI, 0.66-0.79) and all-cause hospitalization (aIRR, 0.81; 95% CI, 0.75-0.88). Those who attended at the first-scheduled appointment, compared with those who attended only a rescheduled, delayed appointment, had reduced subsequent all-cause emergency presentations (aIRR, 0.83; 95% CI, 0.76-0.91), all-cause hospitalization (aIRR, 0.71; 95% CI, 0.65-0.79), seizure-related presentations (aIRR, 0.40; 95% CI, 0.33-0.49), and mortality (hazard ratio, 0.82; 95% CI, 0.69-0.98). Male sex was associated with nonattendance (adjusted relative risk [aRR], 1.12; 95% CI, 1.03-1.22), as were injury at emergency presentation (aRR, 1.12; 95% CI, 1.01-1.24), psychiatric comorbidity (aRR, 1.68; 95% CI, 1.55-1.81), previous seizure-related presentations (aRR, 1.35; 95% CI, 1.22-1.49), and delays (>14 days) between FSC referral and appointment (aRR, 1.35; 95% CI, 1.18-1.54). Hospitalization at referral was associated with reduced nonattendance (aRR, 0.80; 95% CI, 0.72-0.90), as were non-English language preference (aRR, 0.81; 95% CI, 0.69-0.94), distance greater than 6 mi from home to clinic (aRR, 0.85; 95% CI, 0.76-0.95), and physical comorbidity (aRR, 0.80; 95% CI, 0.72-0.89).

Conclusions and Relevance

Results of this cohort study suggest that FSC attendance, particularly early attendance, was associated with reduced rates of subsequent hospital utilization. This knowledge may support adequately resourcing FSCs to improve equitable, timely access. Future study directions include assessing interventions that may support FSC attendance for at-risk groups.

Effect of a Patient Portal Reminder Message After No-Show on Appointment Reattendance in Ophthalmology: A Randomized Clinical Trial

PURPOSE

To assess the efficacy of electronic health record (EHR) messaging for re-engaging patients with ophthalmology care after a missed appointment.

DESIGN

Prospective, randomized clinical trial.

METHODS

The study setting was an academic ophthalmology department. The patient population comprised of return patients age 18 years or older with an appointment "no show," or missed appointment. Over 2 phases of recruitment, 362 patients with an active patient portal in the EHR were selected consecutively each business day. Patients were randomized using a web-based tool to receive a reminder to reschedule via a standard mailed letter only (control) or the mailed letter plus an electronic message through the EHR within 1 business day of the missed appointment (intervention). Reengagement with eye care was defined as attendance of a rescheduled appointment within 30 days of the no-show visit. Patient charts were reviewed for demographic and clinical data.

RESULTS

The average age of recruited patients was 59.9 years, just under half of the sample was male (42.5%, 154/362), and most patients were White (56.9%, 206/362) or Black (36.2%, 131/362). Patients were most commonly recruited from the retina service (39.2%, 142/362) followed by the glaucoma service (29.3%, 106/362). Many patients in this study had previous no-show appointments, with an average no-show rate of 18.8% out of all scheduled visits across our health system. In total, 22.2% (42/189) of patients in the intervention group attended a follow-up appointment within 30 days of their no-show visit compared to 11.6% (20/173) of the control group (OR, 2.186; 95% CI, 1.225-3.898; P = .008). When including only the 74 patients in the intervention group who read the intervention message in the patient portal, 28.4% (21/74) attended a follow-up compared to 11.6% (20/173) of the control group (P = .001).

CONCLUSIONS

EHR-based reminder messages sent within a business day of a missed appointment may promote re-engagement in ophthalmology care after appointment no-show.

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.

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

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

Time Series Analysis: Associations Between Temperature and Primary Care Utilization in Philadelphia, Pennsylvania

INTRODUCTION

Earth's temperature has risen by an average of 0.11°F per decade since 1850 and experts predict continued global warming. Studies have shown that exposure to extreme temperatures is associated with adverse health outcomes. Missed primary care visits can lead to incomplete preventive health screenings and unmanaged chronic diseases. This study examines the associations between extreme temperature conditions and primary care utilization among adult Philadelphians.

METHODS

A total of 1,048,575 appointments from 91,580 patients age ≥ 18 years enrolled in the study at thirteen university-based outpatient clinics in Philadelphia from January 1, 2009 to December 31, 2019. Statistical analysis was performed from June to December 2023. Data on attended and missed appointments was linked with measures of daily maximum temperature and precipitation, stratified by warm and cold seasons. Sociodemographic variables and associations with chronic disease status were explored.

RESULTS

Rates of missed appointments increased by 0.72% for every 1°F decrease in daily maximum temperatures below 39°F and increased by 0.64% for every 1°F increase above 89°F. Individuals ≥ 65 years and those with chronic conditions had stronger associations with an increased rate of missed appointments.

CONCLUSIONS

Temperature extremes were associated with higher rates of missed primary care appointments. Individuals with chronic diseases were more likely to have missed appointments associated with extreme temperatures. The findings suggest the need for primary care physicians to explore different modes of care delivery to support vulnerable populations, such as making telemedicine during extreme weather events a viable and affordable option.

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.

Impact of Insurance Type on Access to Pediatric Surgical Care

Background

This study aimed to measure the impact of insurance type on access to pediatric surgical care, clinical and surgical scheduling decisions, provider-driven cancelations, and missed care opportunities (MCOs). We hypothesize that patients with public health insurance experience longer scheduling delays and more frequently canceled surgical appointments compared with patients with private health insurance.

Methods

This retrospective study reviewed the demographics and clinical characteristics of patients who underwent a surgical procedure within the plastic and oral surgery department at our institution in 2019. Propensity score matching and linear regressions were used to estimate the effect of insurance type on hospital scheduling and patient access outcomes while controlling for procedure type and sex.

Results

A total of 457 patients were included in the demographic and clinical characteristics analyses; 354 were included in propensity score matching analyses. No significant differences in the number of days between scheduling and occurrence of initial consultation or number of clinic cancelations were observed between insurance groups (P > 0.05). However, patients with public insurance had a 7.4 times higher hospital MCO rate (95% CI [5.2-9.7]; P < 0.001) and 4.7 times the number of clinic MCOs (P = 0.007).

Conclusions

No significant differences were found between insurance groups in timely access to surgical treatment or cancelations. Patients with public insurance had more MCOs than patients with private insurance. Future research should investigate how to remove barriers that impact access to care for marginalized patients.

Healthy rural hearts: The feasibility of a telehealth nutrition randomised controlled trial for rural people at risk of cardiovascular disease

INTRODUCTION

Improving dietary patterns using medical nutrition therapy delivered via telehealth could make an effective contribution to reducing cardiovascular disease burden in rural Australia. However, it is important that medical nutrition therapy programmes are developed in collaboration with rural stakeholders, to increase feasibility for the rural context and the likelihood of successful implementation. The aim of this study was to evaluate the preliminary feasibility outcomes of integration (implementation), practicality, acceptability, demand, and preliminary effectiveness at the 3-month timepoint of the Healthy Rural Hearts randomised control trial.

METHODS

Feasibility measures were collected from participants in the Healthy Rural Hearts medical nutrition therapy trial. Study participants were patients from eligible primary care practices who had been assessed by their general practitioner as being at moderate to high risk of developing cardiovascular disease in the next five years. The sample in this analysis includes those who had completed the first 3-months of the study. Feasibility outcomes were measured over the first 3-months of the trial intervention. A process evaluation survey was used to collect measures relating to intervention implementation, practicality, acceptability, and demand. Completion rates of the Australian Eating Survey Heart version, Personalised Nutrition Questionnaire, pathology tests and telehealth medical nutrition therapy consultations delivered by Accredited Practising Dietitians were also used to measure intervention practicality. Preliminary effectiveness was evaluated by comparing the intervention group's dietary change, measured using Australian Eating Survey Heart with data from the control group.

RESULTS

A total of 105 participants (75 intervention, 30 control participants) were eligible for inclusion in analysis. Attendance rates at the first 3-months of dietitian consultations ranged from 94.7% to 89.3% between the first and 3-month consultations, and most participants were able to complete the Australian Eating Survey Heart and Personalised Nutrition Questionnaire prior to their initial consultation [Australian Eating Survey Heart (n = 57, 76%) and Personalised Nutrition Questionnaire (n = 61, 81.3%)] and the Australian Eating Survey Heart prior to their 3-month consultation (n = 52, 69.3%). Of the participants who completed a pathology test at the 3-month time-point (n = 54, 72%), less than half were able to do so prior to their dietitian consultation (n = 35, 46.7%). Of the 75 intervention participants, 28 (37.3%) completed the process evaluation survey. Intervention participants ranked acceptability of the Healthy Rural Hearts intervention highly (mean rank out of 10 = 9.5, SD 1.9), but provided mixed responses on whether they would access the intervention outside of the study (mean rank out of 10 = 6.0, SD 3.5). There were statistically significant increases in percentage total energy intake derived from nutrient-dense core foods compared to the control group (p ≤ 0.05).

DISCUSSION

The positive findings related to acceptability and implementation outcomes suggest that the Healthy Rural Hearts intervention was acceptable, practical, and able to be implemented within this population living in rural NSW. This, combined with the small to medium effect size in the proportion of total energy derived from nutrient-dense core foods compared to the control group indicates that long-term intervention effectiveness on other cardiovascular disease outcomes is important to evaluate in the future.

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.

Post-discharge Care Practices, Challenges, and Outcomes in Newborn Infants of Mothers With SARS-CoV-2 Infection: Insights From Public Hospitals

Background The data regarding the care at home and outcomes in infants of mothers infected with SARS-CoV-2 continue to evolve. There is a paucity of studies beyond the immediate newborn period. Our research aims to improve the understanding in these areas by studying the newborn population discharged from public hospitals in several boroughs of New York City (NYC) through the first year of the COVID-19 pandemic. Objective The objective of this study is to assess parental perspective and describe post-discharge care practices, patterns of healthcare utilization, challenges in obtaining care, and outcomes in infants between six and 12 months of age born to mothers infected with SARS-CoV-2 at the time of delivery. Methods We conducted an institutional review board (IRB)-approved multi-center retrospective cohort study of infants born to SARS-CoV-2-positive mothers at five NYC public hospitals between March and December of 2020. Clinical and demographic data were collected from electronic medical records. A phone interview of the caregivers using a standard questionnaire was conducted to collect data about care at home, healthcare utilization patterns, and challenges with access to healthcare. Results Our study cohort included 216 infants born to SARS-CoV-2-positive mothers with 16 (7.4%) mothers being symptomatic at discharge. Ten infants tested positive, and two showed symptoms before discharge. Two hundred seven (95.8%) infants were discharged home to their parents, and eight (3.7%) were transferred to other facilities. One hundred thirty-eight (66%) infants had at least one visit to the emergency room (ER) for various complaints where two were found to have COVID-19 with one needing hospitalization. One hundred seventy-two (79.6%) families responded to the phone interview. Most mothers (78%) cohabitated with their infants at home, and 70.3% elected to breastfeed. However, only 56.3% of mothers reported using all the recommended infection prevention practices at home. More than half (57%) of the families reported financial hardship related to the pandemic. Although 46.2% of patients missed their in-person health maintenance visits, telemedicine was highly utilized for follow-up with most being phone visits (70.3%). The majority of the infants (95.5%) remained up-to-date with their routine immunizations. Conclusions Our results suggest that infants born to SARS-CoV-2-infected mothers showed increased utilization of medical care and telemedicine between six and 12 months of age. Mothers reported low adherence to infection prevention practices at home; however, infants rarely showed clinically significant SARS-CoV-2 infection while maintaining high breastfeeding rates after discharge.

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

BACKGROUND

Healthcare accessibility and utilization are important social determinants of health. Lack of access to healthcare, including missed or no-show appointments, can have negative health effects and be costly to patients and providers. Various office-based approaches and community partnerships can address patient access barriers.

OBJECTIVES

(1) To understand provider perceptions of patient barriers; (2) to describe the policies and practices used to address late or missed appointments, and (3) to evaluate access to patient support services, both in-clinic and with community partners.

METHODS

Mailed cross-sectional survey with online response option, sent to all Nebraska primary care clinics (n = 577) conducted April 2020 and January through April 2021. Chi-square tests compared rural-urban differences; logistic regression of clinical factors associated with policies and support services computed odds ratios (OR) and 95% confidence intervals (CI).

RESULTS

Response rate was 20.3% (n = 117), with 49 returns in 2020. Perceived patient barriers included finances, higher among rural versus urban clinics (81.6% vs. 56.1%, p =.009), and time (overall 52.3%). Welcoming environment (95.5%), telephone appointment reminders (74.8%) and streamlined admissions (69.4%) were the top three clinic practices to reduce missed appointments. Telehealth was the most commonly available patient support service in rural (79.6%) and urban (81.8%, p =.90) clinics. Number of providers was positively associated with having a patient navigator/care coordinator (OR = 1.20, CI = 1.02-1.40). For each percent increase in the number of privately insured patients, the odds of providing legal aid decreased by 4% (OR = 0.96, CI = 0.92-1.00). Urban clinics were less likely than rural clinics to provide social work services (OR = 0.16, CI = 0.04-0.67) or assist with applications for government aid (OR = 0.22, CI = 0.06-0.90).

CONCLUSIONS

Practices to reduce missed appointments included a variety of reminders. Although finances and inability to take time off work were the most frequently reported perceived barriers for patients' access to timely healthcare, most clinics did not directly address them. Rural clinics appeared to have more community partnerships to address underlying social determinants of health, such as transportation and assistance applying for government aid. Taking such a wholistic partnership approach is an area for future study to improve patient access.

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.

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

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

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

BACKGROUND

The phenomenon of patients missing booked appointments without canceling them-known as Did Not Show (DNS), Did Not Attend (DNA), or Failed To Attend (FTA)-has a detrimental effect on patients' health and results in massive health care resource wastage.

OBJECTIVE

Our objective was to develop machine learning (ML) models and evaluate their performance in predicting the likelihood of DNS for hospital outpatient appointments at the MidCentral District Health Board (MDHB) in New Zealand.

METHODS

We sourced 5 years of MDHB outpatient records (a total of 1,080,566 outpatient visits) to build the ML prediction models. We developed 3 ML models using logistic regression, random forest, and Extreme Gradient Boosting (XGBoost). Subsequently, 10-fold cross-validation and hyperparameter tuning were deployed to minimize model bias and boost the algorithms' prediction strength. All models were evaluated against accuracy, sensitivity, specificity, and area under the receiver operating characteristic (AUROC) curve metrics.

RESULTS

Based on 5 years of MDHB data, the best prediction classifier was XGBoost, with an area under the curve (AUC) of 0.92, sensitivity of 0.83, and specificity of 0.85. The patients' DNS history, age, ethnicity, and appointment lead time significantly contributed to DNS prediction. An ML system trained on a large data set can produce useful levels of DNS prediction.

CONCLUSIONS

This research is one of the very first published studies that use ML technologies to assist with DNS management in New Zealand. It is a proof of concept and could be used to benchmark DNS predictions for the MDHB and other district health boards. We encourage conducting additional qualitative research to investigate the root cause of DNS issues and potential solutions. Addressing DNS using better strategies potentially can result in better utilization of health care resources and improve health equity.

A Novel Nephropsychology Clinic: Partnering With Patients in the Era of Value-Based Care in Nephrology

CKD and end-stage kidney disease are highly prevalent and complex chronic conditions with a high disease burden that corresponds to a high cost of care. Mental health conditions have a high prevalence in this population and add to the burden of disease, increase the cost of care, and are co-related with worse clinical outcomes. Despite these clear co-relations, mental health disorders remain underdiagnosed and undertreated in this population, secondary to multiple reasons, including patient-specific factors as well as systematic issues, including difficulty in accessing mental health experts. Here we describe a novel collaborative care model for patients with advanced CKD within the nephrology clinic space, in the form of a nephropsychology clinic. We present the details of our clinic, our preliminary findings, and propose that an integrated behavioral health model offers convenience for the patient and improves workflow for the physician, allowing a pathway to timely mental health interventions.

Social Determinants of Health and Associations With Outcomes in Pediatric Patients With Brain Tumors

BACKGROUND AND OBJECTIVES

Social determinants of health (SDOH) are nonmedical factors that affect health outcomes. Limited investigation has been completed on the potential association of these factors to adverse outcomes in pediatric populations. In this study, the authors aimed to analyze the effects of SDOH disparities and their relationship with outcomes after brain tumor resection or biopsy in children.

METHODS

The authors retrospectively reviewed the records of their center's pediatric patients with brain tumor. Black race, public insurance, median household income, and distance to hospital were the investigated SDOH factors. Univariate analysis was completed between number of SDOH factors and patient demographics. Multivariate linear regression models were created to identify coassociated determinants and outcomes.

RESULTS

A total of 272 patients were identified and included in the final analysis. Among these patients, 81 (29.8%) had no SDOH disparities, 103 (37.9%) had 1, 71 (26.1%) had 2, and 17 (6.2%) had 3. An increased number of SDOH disparities was associated with increased percentage of missed appointments ( P = .002) and emergency room visits ( P = .004). Univariate analysis demonstrated increased missed appointments ( P = .01), number of postoperative imaging ( P = .005), and number of emergency room visits ( P = .003). In multivariate analysis, decreased median household income was independently associated with increased length of hospital stay ( P = .02).

CONCLUSION

The SDOH disparities are prevalent and impactful in this vulnerable population. This study demonstrates the need for a shift in research focus toward identifying the full extent of the impact of these factors on postoperative outcomes in pediatric patients with brain tumor.

Telemedicine Improves Access to Care for Spine Patients With Low Socioeconomic Status

STUDY DESIGN

Retrospective cohort study.

OBJECTIVES

The objective of this study is to compare the likelihood of missing a scheduled telemedicine and in-person appointments for spine patients. The secondary objective is to assess the impact of socioeconomic status on missed telemedicine and in-person appointments.

METHODS

Patients with scheduled outpatient appointments with orthopedic spine faculty between 2019 and 2021 were divided by appointment type: telemedicine (N = 4,387) and in-person (N = 3810). Socioeconomic status was assessed using Area Deprivation Index (ADI) stratified based on percentile: low (<25), medium (25-75), and high (>75) levels of socioeconomic disadvantage. The primary outcome measure was missed clinic appointments, which was defined as having at least one appointment that was cancelled or labeled "no show."

RESULTS

Patients with in-person appointments missed appointments more often than patients with telemedicine visits (51.3% vs 24.7%, P < .001). Patients with high ADI missed their in-person appointments more often than patients with medium and low ADI (59.5% vs 52.2% and 47.5%, P < .001). There was no difference in missed telemedicine visits between patients with high, medium, and low ADI (27.6% vs 24.8% vs 23.8%, P = .294). Patients that missed an appointment were 41.9% more likely to be high ADI (OR 1.42, 95% CI 1.20-1.68, P < .001) and 13.4% more likely to be medium ADI (OR 1.13, 95% CI 1.03-1.26, P = .015) compared with low ADI patients.

CONCLUSIONS

Telemedicine may serve a role in reducing disparity in appointment attendance. While further studies are needed to validate these findings, spine surgeons should consider offering telemedicine as an option to patients.

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.

Rapid methods for identifying barriers and solutions to improve access to community health services: a scoping review

BACKGROUND

The advancement of universal health coverage (UHC) is largely based on identifying and addressing barriers to accessing community health services. Traditional qualitative research approaches provide excellent insights but have unfeasibly high resource requirements for most care providers.

AIM

To identify, categorise, and evaluate methods that have been used to identify barriers to and/or solutions for improving access to community-based health services, grounded in engagement with affected communities, excluding approaches that take >14 days.

DESIGN & SETTING

This was a scoping review.

METHOD

Following Joanna Briggs Institute (JBI) guidelines, a search was undertaken using the Cochrane Library, Ovid MEDLINE, Ovid Embase, Ovid Global Health, and Google Scholar. An information specialist designed the search, and dual independent review and data charting were used.

RESULTS

In total, 44 studies were included from 30 countries, reporting on 18 different clinical services. Thirty studies used self-described 'rapid' approaches; however, the majority of these did not justify what they meant by this term. Nearly half of the studies used mixed- or multi-methods and triangulation to verify early findings. All of the qualitative studies used interviews and/or focus groups, which were often supplemented with observations, document review, and mapping activities. The use of in situ snowball and convenience sampling; community members as data collectors and cultural guides; collaborative summarisation (review of findings with community members and end-users); and deductive framework analysis expedited the research processes. There were no data on costs.

CONCLUSION

There are a wide range of methods that can be used to deliver timely information about barriers to access. The methods employed in the articles reviewed tended to use traditional data collection approaches in innovative ways.

Policy-makers' conception of patient non-attendance fees in specialist healthcare: a qualitative document analysis

OBJECTIVES

Patients missing their scheduled appointments in specialist healthcare without giving notice can undermine efficient care delivery. To reduce patient non-attendance and possibly compensate healthcare providers, policy-makers have noted the viability of implementing patient non-attendance fees. However, these fees may be controversial and generate public resistance. Identifying the concepts attributed to non-attendance fees is important to better understand the controversies surrounding the introduction and use of these fees. Patient non-attendance fees in specialist healthcare have been extensively debated in Norway and Denmark, two countries that are fairly similar regarding political culture, population size and healthcare system. However, although Norway has implemented a patient non-attendance fee scheme, Denmark has not. This study aimed to identify and compare how policy-makers in Norway and Denmark have conceptualised patient non-attendance fees over three decades.

DESIGN

A qualitative document study with a multiple-case design.

METHODS

A theory-driven qualitative analysis of policy documents (n=55) was performed.

RESULTS

Although patient non-attendance fees were seen as a measure to reduce non-attendance rates in both countries, the specific conceptualisation of the fees differed. The fees were understood as a monetary disincentive in Norwegian policy documents. In the Danish documents, the fees were framed as an educative measure to foster a sense of social responsibility, as well as serving as a monetary disincentive. The data suggest, however, a recent change in the Danish debate emphasising fees as a disincentive. In both countries, fees were partly justified as a means of compensating providers for the loss of income.

CONCLUSIONS

The results demonstrate how, as a regulative policy tool, patient non-attendance fees have been conceptualised and framed differently, even in apparently similar contexts. This suggests that a more nuanced and complex understanding of why such fees are debated is needed.

Mapping out the arguments for and against patient non-attendance fees in healthcare: an analysis of public consultation documents

BACKGROUND

Patients not attending their appointments without giving notice burden healthcare services. To reduce non-attendance rates, patient non-attendance fees have been introduced in various settings. Although some argue in narrow economic terms that behavioural change as a result of financial incentives is a voluntary transaction, charging patients for non-attendance remains controversial. This paper aims to investigate the controversies of implementing patient non-attendance fees.

OBJECTIVE

The aim was to map out the arguments in the Norwegian public debate concerning the introduction and use of patient non-attendance fees at public outpatient clinics.

METHODS

Public consultation documents (2009-2021) were thematically analysed (n=84). We used a preconceived conceptual framework based on the works of Grant to guide the analysis.

RESULTS

A broad range of arguments for and against patient non-attendance fees were identified, here referring to the acceptability of the fees' purpose, the voluntariness of the responses, the effects on the individual character and institutional norms and the perceived fairness and comparative effectiveness of patient non-attendance fees. Whereas the aim of motivating patients to keep their appointments to avoid poor utilisation of resources and increased waiting times was widely supported, principled and practical arguments against patient non-attendance fees were raised.

CONCLUSION

A narrow economic understanding of incentives cannot capture the breadth of arguments for and against patient non-attendance fees. Policy makers may draw on this insight when implementing similar incentive schemes. The study may also contribute to the general debate on ethics and incentives.

Predicting ophthalmic clinic non-attendance using machine learning: Development and validation of models using nationwide data

BACKGROUND

Ophthalmic clinic non-attendance in New Zealand is associated with poorer health outcomes, marked inequities and costs NZD$30 million per annum. Initiatives to improve attendance typically involve expensive and ineffective brute-force strategies. The aim was to develop machine learning models to accurately predict ophthalmic clinic non-attendance.

METHODS

This multicentre, retrospective observational study developed and validated predictive models of clinic non-attendance. Attendance data for 3.1 million appointments from all New Zealand government-funded ophthalmology clinics from 2009 to 2018 were aggregated for analysis. Repeated ten-fold cross validation was used to train and optimise XGBoost and logistic regression models on several demographic and clinic-related variables. Models developed using the entire training set were compared with those restricted to regional subsets of the data.

RESULTS

In the testing data set from 2019, there were 407 574 appointments (median [range] age, 66 [0-105] years; 210 365 [51.6%] female) with a non-attendance rate of 5.7% (n = 23 309 missed appointments), XGBoost models trained on each region's data achieved the highest mean AUROC of 0.764 (SD 0.058) and mean AUPRC of 0.157 (SD 0.072). XGBoost performed better than logistic regression (mean AUROC = 0.756, p = 0.002). Training individual XGBoost models for each region led to better performance than training a single model on the complete nationwide dataset (mean AUROC = 0.754, p = 0.04).

CONCLUSION

Machine learning algorithms can predict ophthalmic clinic non-attendance with relatively basic demographic and clinic data. These findings suggest further research examining implementation of such algorithms in scheduling systems or public health interventions may be useful.

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

BACKGROUND

Appointment non-attendance - often referred to as "missed appointments", "patient no-show", or "did not attend (DNA)" - causes volatility in health systems around the world. Of the different approaches that can be adopted to reduce patient non-attendance, behavioural economics-oriented mechanisms (i.e., psychological, cognitive, emotional, and social factors that may impact individual decisions) are reasoned to be better suited in such contexts - where the need is to persuade, nudge, and/ or incentivize patients to honour their scheduled appointment. The aim of this systematic literature review is to identify and summarize the published evidence on the use and effectiveness of behavioural economic interventions to reduce no-shows for health care appointments.

METHODS

We systematically searched four databases (PubMed/Medline, Embase, Scopus, and Web of Science) for published and grey literature on behavioural economic strategies to reduce no-shows for health care appointments. Eligible studies met four criteria for inclusion; they were (1) available in English, Spanish, or French, (2) assessed behavioural economics interventions, (3) objectively measured a behavioural outcome (as opposed to attitudes or preferences), and (4) used a randomized and controlled or quasi-experimental study design.

RESULTS

Our initial search of the five databases identified 1,225 articles. After screening studies for inclusion criteria and assessing risk of bias, 61 studies were included in our final analysis. Data was extracted using a predefined 19-item extraction matrix. All studies assessed ambulatory or outpatient care services, although a variety of hospital departments or appointment types. The most common behaviour change intervention assessed was the use of reminders (n = 56). Results were mixed regarding the most effective methods of delivering reminders. There is significant evidence supporting the effectiveness of reminders (either by SMS, telephone, or mail) across various settings. However, there is a lack of evidence regarding alternative interventions and efforts to address other heuristics, leaving a majority of behavioural economic approaches unused and unassessed.

CONCLUSION

The studies in our review reflect a lack of diversity in intervention approaches but point to the effectiveness of reminder systems in reducing no-show rates across a variety of medical departments. We recommend future studies to test alternative behavioural economic interventions that have not been used, tested, and/or published before.

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

Introduction

A no-show appointment occurs when a patient does not attend a previously booked appointment. This situation can cause other problems, such as discontinuity of patient treatments as well as a waste of both human and financial resources. One of the latest approaches to address this issue is predicting no-shows using machine learning techniques. This study aims to propose a predictive analytical approach for developing a patient no-show appointment model in Hospital Kuala Lumpur (HKL) using machine learning algorithms.

Methods

This study uses outpatient data from the HKL's Patient Management System (SPP) throughout 2019. The final data set has 246,943 appointment records with 13 attributes used for both descriptive and predictive analyses. The predictive analysis was carried out using seven machine learning algorithms, namely, logistic regression (LR), decision tree (DT), k-near neighbours (k-NN), Naïve Bayes (NB), random forest (RF), gradient boosting (GB) and multilayer perceptron (MLP).

Results

The descriptive analysis showed that the no-show rate was 28%, and attributes such as the month of the appointment and the gender of the patient seem to influence the possibility of a patient not showing up. Evaluation of the predictive model found that the GB model had the highest accuracy of 78%, F1 score of 0.76 and area under the curve (AUC) value of 0.65.

Conclusion

The predictive model could be used to formulate intervention steps to reduce no-shows, improving patient care quality.

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.

To charge or not to charge: reducing patient no-show

BACKGROUND

In order to reduce patient no-show, the Israeli government is promoting legislation that will allow Health Plans to require a co-payment from patients when reserving an appointment. It is hoped that this will create an incentive for patients to cancel in advance rather than simply not show up. The goal of this policy is to improve patient access to medical care and ensure that healthcare resources are utilized effectively. We explore this phenomenon to support evidence-based decision making on this issue, and to determine whether the proposed legislation is aligned with the findings of previous studies.

MAIN BODY

No-show rates vary across countries and healthcare services, with several strategies in place to mitigate the phenomenon. There are three key stakeholders involved: (1) patients, (2) medical staff, and (3) insurers/managed care organizations, each of which is affected differently by no-shows and faces a different set of incentives. The decision whether to impose financial penalties for no-shows should take a number of considerations into account, such as the fine amount, service type, the establishment of an effective fine collection system, the patient's socioeconomic status, and the potential for exacerbating disparities in healthcare access. The limited research on the impact of fines on no-show rates has produced mixed results. Further investigation is necessary to understand the influence of fine amounts on no-show rates across various healthcare services. Additionally, it is important to evaluate the implications of this proposed legislation on patient behavior, access to healthcare, and potential disparities in access.

CONCLUSION

It is anticipated that the proposed legislation will have minimal impact on attendance rates. To achieve meaningful change, efforts should focus on enhancing medical service availability and improving the ease with which appointments can be cancelled or alternatively substantial fines should be imposed. Further research is imperative for determining the most effective way to address the issue of patient no-show and to enhance healthcare system efficiency.

Children with uncontrolled asthma from economically disadvantaged neighborhoods: Needs assessment and the development of a school-based telehealth and electronic inhaler monitoring system

BACKGROUND

Children from economically disadvantaged communities often encounter healthcare access barriers, increasing risk for poorly controlled asthma and subsequent healthcare utilization. This highlights the need to identify novel intervention strategies for these families.

OBJECTIVE

To better understand the needs and treatment preferences for asthma management in children from economically disadvantaged communities and to develop a novel asthma management intervention based on an initial needs assessment and stakeholder feedback.

METHODS

Semistructured interviews and focus groups were conducted with 19 children (10-17 years old) with uncontrolled asthma and their caregivers, 14 school nurses, 8 primary care physicians, and three school resource coordinators from economically disadvantaged communities. Interviews and focus groups were audio-taped and transcribed verbatim and then analyzed thematically to inform intervention development. Using stakeholder input, an intervention was developed for children with uncontrolled asthma and presented to participants for feedback to fully develop a novel intervention.

RESULTS

The needs assessment resulted in five themes: (1) barriers to quality asthma care, (2) poor communication across care providers, (3) problems identifying and managing symptoms and triggers among families, (4) difficulties with adherence, and (5) stigma. A proposed video-based telehealth intervention was proposed to stakeholders who provided favorable and informative feedback for the final development of the intervention for children with uncontrolled asthma.

CONCLUSIONS

Stakeholder input and feedback provided information critical to the development of a multicomponent (medical and behavioral) intervention in a school setting that uses technology to facilitate care, collaboration, and communication among key stakeholders to improve asthma management for children from economically disadvantaged neighborhoods.

High-risk antenatal women's perceptions of dietitian appointments and information

Problem

The dietitian service at a metropolitan health service in Queensland, Australia has a non-engagement rate for high-risk antenatal women of 50%.

Aim

Determine which attributes are related to non-attendance at dietitian appointments, and women's perceptions and attitudes towards dietitian appointments during pregnancy.

Methods

An explanatory mixed-methods design was utilised, with first phase including 103 antenatal women referred to a dietitian in 2021 and compared the attributes of those who attended with those who did not engage. Queensland Health electronic databases were used to collect attribute data, which were then analysed with Jamovi (version 1.6) for descriptive, correlational, multivariate analyses of variance MANOVA. Second phase included seven semi-structured interviews with women attending a dietitian appointment, and subsequently analysed through thematic analysis.

Results

Distance from clinic was not related to clinic attendance, and women reported they would attend regardless of distance or work status. Non-attendance was related to higher gravidity, parity, and if referred for obesity, but not previous gastric sleeve or underweight referral. Six themes were identified from the interview data: "Women want to be treated like an individual," "It's all about expectations," "Midwives hold the key," "Preferences in receiving dietary information," "Weight has been a long-term problem and is a sensitive topic," and "Barriers to attendance."

Conclusion

Antenatal services can adjust service delivery to improve engagement in weight management services during pregnancy. Telehealth appointments may reduce non-engagement due to distance from clinic. Demystifying the dietitian appointment, ensuring non-judgemental referral processes and collaboration between midwives and dietitians will ensure that women value the service.

The Positive Effect of an Online Appointment Portal on a Breast Cancer Screening Program

BACKGROUND

The adoption of web-based appointment methods by health care systems is increasing.

OBJECTIVES

This study primarily aimed to evaluate in the setting of an organized breast cancer screening program the actual usage of an online appointment portal by the target population, i.e., how the online tool was used (type and timing of the actions performed) and by whom (users' characteristics); the effect of coronavirus disease 2019 (COVID-19) on its usage was also investigated. The effect of adopting this tool on the attendance to breast cancer screening was contextually investigated.

METHODS

Electronic data records of 75,903 women (45-74 years old, residing in the territory of Bologna Local Health Authority) were retrospectively reviewed.

RESULTS

In total, 12.4% of women logged into the online portal at least once. Most of them (79.9%) rescheduled, 15.7% viewed, and 4.4% cancelled their own appointment. In addition, 40.6% of all rescheduling actions were performed by the online portal; the remaining was performed by the toll-free number/dedicated email address. The highest peak (13.8%) of web accesses was registered at 10 a.m. Monday to Friday, when the toll-free number service is available. Percentages of portal usage were higher: (1) among the younger women, of Italian nationality, and for the first time invited to mammographic screening (p < 0.0001), and (2) in the pandemic period versus the prepandemic period (12.5 vs. 8.6%, respectively; p < 0.001). Finally, when compared to not using, the online portal usage led to an overall reduction in the no-show rate of almost 20% (p < 0.0001).

CONCLUSION

The action mainly performed by using the online appointment portal was the appointment rescheduling. The usage of this tool had a positive effect on the no-show rate and it can be speculated that has led to a reduction of the request load to be handled by the center's screening staff. Finally, this study confirmed that the COVID-19 pandemic boosted the use of digital technologies.

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.

Experiences and Perceptions of Telehealth Visits in Diabetes Care During and After the COVID-19 Pandemic Among Adults With Type 2 Diabetes and Their Providers: Qualitative Study

BACKGROUND

Since the COVID-19 pandemic, telehealth has been widely adopted in outpatient settings in the United States. Although telehealth visits are publicly accepted in different settings, little is known about the situation after the wide adoption of telehealth from the perspectives of adults with type 2 diabetes mellitus (T2D) and their providers.

OBJECTIVE

This study aims to identify barriers and facilitators of maintaining continuity of care using telehealth for patients with T2D in a diabetes specialty clinic.

METHODS

As the second phase of a multimethod study to understand missed appointments among adults with T2D, we conducted semistructured, individual, in-depth phone or Zoom interviews with 23 adults with T2D (14/23, 61% women; mean age 55.1, SD 14.4, range 35-77 years) and 10 providers from diabetes clinics in a tertiary academic medical center in Maryland. Interviews were audio-recorded, transcribed, and analyzed using thematic content analysis by the research team.

RESULTS

Adults with T2D and their providers generally reported positive experiences with telehealth visits for diabetes care with some technical challenges resulting in the need for in-person visits. We identified the following 3 themes: (1) "perceived benefits of telehealth visits," such as convenience, time and financial efficiencies, and independence from caregivers, benefits shared by both patients and providers; (2) "perceived technological challenges of telehealth visits," such as disparities in digital health literacy, frustration caused by unstable internet connection, and difficulty sharing glucose data, challenges shared by both patients and providers; and (3) "impact of telehealth visits on the quality of diabetes care," including lack of diabetes quality measures and needs and preferences for in-person visits, shared mainly from providers' perspectives with some patient input.

CONCLUSIONS

Telehealth is generally received positively in diabetes care with some persistent challenges that might compromise the quality of diabetes care. Telehealth technology and glucose data platforms must incorporate user experience and user-centered design to optimize telehealth use in diabetes care. Clinical practices need to consider new workflows for telehealth visits to facilitate easier follow-up scheduling and lab completion. Future research to investigate the ideal balance between in-person and telehealth visits in diabetes care is warranted to enhance the quality of diabetes care and to optimize diabetes outcomes. Policy flexibilities should also be considered to broaden access to diabetes care for all patients with T2D.

Outpatient visit behavior in patients with epilepsy: Generalized Epilepsy is more frequently non-attendance than Focal Epilepsy

BACKGROUND

Patients with epilepsy (PWE), especially those with Idiopathic Epilepsy (GE), are at a high risk of disadvantage caused by non-adherence. It has been suggested that medical visit behavior may be a surrogate indicator of medication adherence. We hypothesized that patients with IGE would adhere poorly to visits.

METHODS

This was a retrospective study of PWE who visited the Department of Psychiatry and Neurology at Hokkaido University Hospital between January 2017 and December 2019. Demographic and clinical information on PWE were extracted from medical records and visit data from the medical information system. Non-attendance of outpatient appointments was defined as "not showing up for the day of an appointment without prior notice." Mixed-effects logistic regression analysis was conducted with non-attendance as the objective variable.

RESULTS

Of the 9151 total appointments, 413 were non-attendances, with an overall non-attendance rate of 4.5%. IGE was a more frequent non-attendance than Focal Epilepsy (FE) (odds ratio (OR) 1.94; 95% confidence interval (CI) 1.17-3.21; p = 0.010). History of public assistance receipt was associated with higher non-attendance (OR 2.04; 95% CI 1.22-3.43; p = 0.007), while higher education (OR 0.64; 95% CI 0.43-0.93; p = 0.021) and farther distance to a hospital (OR 0.33; 95% CI 0.13-0.88; p = 0.022), and higher frequency of visits (OR 0.18; 95% CI 0.04-0.86; p = 0.031) were associated with fewer non-attendances. In a subgroup analysis of patients with GE, women were associated with fewer non-attendance (OR 0.31; 95% CI 0.14-0.72; p = 0.006).

CONCLUSIONS

GE was more frequent in the non-attendance group than in the FE group. Among patients with GE, females were found to have non-attendance less frequently; however, there was no clear difference in the odds of non-attendance between Juvenile Myoclonic Epilepsy (JME) and IGE other than JME.

Using Nudges to Reduce Missed Appointments in Primary Care and Mental Health: a Pragmatic Trial

BACKGROUND

Missed appointments ("no-shows") are a persistent and costly problem in healthcare. Appointment reminders are widely used but usually do not include messages specifically designed to nudge patients to attend appointments.

OBJECTIVE

To determine the effect of incorporating nudges into appointment reminder letters on measures of appointment attendance.

DESIGN

Cluster randomized controlled pragmatic trial.

PATIENTS

There were 27,540 patients with 49,598 primary care appointments, and 9420 patients with 38,945 mental health appointments, between October 15, 2020, and October 14, 2021, at one VA medical center and its satellite clinics that were eligible for analysis.

INTERVENTIONS

Primary care (n = 231) and mental health (n = 215) providers were randomized to one of five study arms (four nudge arms and usual care as a control) using equal allocation. The nudge arms included varying combinations of brief messages developed with veteran input and based on concepts in behavioral science, including social norms, specific behavioral instructions, and consequences of missing appointments.

MAIN MEASURES

Primary and secondary outcomes were missed appointments and canceled appointments, respectively.

STATISTICAL ANALYSIS

Results are based on logistic regression models adjusting for demographic and clinical characteristics, and clustering for clinics and patients.

KEY RESULTS

Missed appointment rates in study arms ranged from 10.5 to 12.1% in primary care clinics and 18.0 to 21.9% in mental health clinics. There was no effect of nudges on missed appointment rate in primary care (OR = 1.14, 95%CI = 0.96-1.36, p = 0.15) or mental health (OR = 1.20, 95%CI = 0.90-1.60, p = 0.21) clinics, when comparing the nudge arms to the control arm. When comparing individual nudge arms, no differences in missed appointment rates nor cancellation rates were observed.

CONCLUSIONS

Appointment reminder letters incorporating brief behavioral nudges were ineffective in improving appointment attendance in VA primary care or mental health clinics. More complex or intensive interventions may be necessary to significantly reduce missed appointments below their current rates.

TRIAL NUMBER

ClinicalTrials.gov, Trial number NCT03850431.

Improvement studies for equitable and evidence-based innovation: an overview of the 'IM-SEEN' model

BACKGROUND

Health inequalities are ubiquitous, and as countries seek to expand service coverage, they are at risk of exacerbating existing inequalities unless they adopt equity-focused approaches to service delivery.

MAIN TEXT

Our team has developed an equity-focused continuous improvement model that reconciles prioritisation of disadvantaged groups with the expansion of service coverage. Our new approach is based on the foundations of routinely collecting sociodemographic data; identifying left-behind groups; engaging with these service users to elicit barriers and potential solutions; and then rigorously testing these solutions with pragmatic, embedded trials. This paper presents the rationale for the model, a holistic overview of how the different elements fit together, and potential applications. Future work will present findings as the model is operationalised in eye-health programmes in Botswana, India, Kenya, and Nepal.

CONCLUSION

There is a real paucity of approaches for operationalising equity. By bringing a series of steps together that force programme managers to focus on groups that are being left behind, we present a model that can be used in any service delivery setting to build equity into routine practice.

Sociodemographic and Clinical Factors Associated with Nonattendance at the Hepatology Clinic

BACKGROUND

Absenteeism from clinic appointments reduces efficiency, wastes resources, and contributes to longer wait times. There are limited data regarding factors associated with nonattendance in hepatology clinics. Identifying factors related to appointment nonattendance may help in the design of interventions for reducing absenteeism.

METHODS

We aim to identify sociodemographic, clinical, and appointment-related factors associated with absenteeism following referral to a liver clinic in a tertiary academic center located in the US Midwest. We designed a case-control study using data from electronic medical records of patients scheduled for appointments between January 2016 and December 2021. Cases were defined as patients who canceled appointments on the same day or resulting in no-shows, and controls were those who completed the referral visit. Information about patients' sociodemographic characteristics, appointment details, and etiology of liver disease were recorded. Hierarchical logistic regression was used to analyze factors related to nonattendance.

RESULTS

Of 3404 scheduled appointments, 460 (13.5%) missed visits were recorded. In the multivariable logistic regression models, hepatitis C and alcohol-associated liver disease were associated with greater odds of nonattendance [odds ratio (OR) 4.0 (95% CI 3.2-4.9), OR 2.7 (1.7-4.2), respectively] compared to those with other liver disease. Sociodemographic characteristics associated with risk of nonattendance included being Black [OR 2.6, (1.8-3.7)], Medicaid insurance or no insurance [OR 2.3 (1.7-2.9), OR 2.5 (1.6-3.7), respectively], non-English speaking [OR 1.8 (1.1-3.1)], being unmarried [OR 1.8 (1.4-2.2)], and longer wait time (> 30 days) until appointments [OR 1.8 (1.5-2.2)].

CONCLUSION

Several sociodemographic and administrative characteristics, as well as hepatitis C and alcohol-associated liver disease were associated with appointment nonattendance. Targeted future interventions may help to decrease nonattendance.

Rapid methods for identifying barriers and solutions to improve access to community health services: a scoping review protocol

OBJECTIVES

Low attendance rates for community health services reflect important barriers that prevent people from receiving the care they need. Services and health systems that seek to advance Universal Health Coverage need to understand and act on these factors. Formal qualitative research is the best way to elicit barriers and identify potential solutions, however traditional approaches take months to complete and can be very expensive. We aim to map the methods that have been used to rapidly elicit barriers to accessing community health services and identify potential solutions.

METHODS AND ANALYSIS

We will search MEDLINE, Embase, the Cochrane Library and Global Health for empirical studies that use rapid methods (<14 days) to elicit barriers and potential solutions from intended service beneficiaries. We will exclude hospital-based and 100% remotely delivered services. We will include studies conducted in any country from 1978 to present. We will not limit by language. Two reviewers will independently perform screening and data extraction, with disagreements resolved by a third reviewer. We will tabulate the different approaches used and present data on time, skills and financial requirements for each approach, as well as the governance framework and any strengths and weaknesses presented by the study authors. We will follow Joanna Briggs Institute (JBI) scoping review guidance and report the review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews.

ETHICS AND DISSEMINATION

Ethical approval is not required. We will share our findings in the peer-reviewed literature, at conferences, and with WHO policymakers working in this space.

REGISTRATION

Open Science Framework (https://osf.io/a6r2m).

Co-designing a shared book reading environment at a community hub

PURPOSE

Community hubs often provide support to families in areas of high vulnerability and can provide unique opportunities for delivering early literacy programs. This study used a co-design process to engage families, staff, and community partners within a community hub to design an environment that supported shared book reading.

METHOD

Co-design was enacted in four phases: 1) interviews to understand user experiences relating to shared book reading; 2) focus groups to refine ideas into actions to support shared book reading and prioritise these actions; 3) implementation of changes; and 4) understanding of participants' experiences of involvement.

RESULT

Participant identified changes were implemented within four categories: 1) changing how books are organised, 2) showing families how to share books, 3) giving families information about how books can be borrowed, and 4) running more activities about books. Participants indicated they enjoyed being a part of a co-design process to affect change at the community hub.

CONCLUSION

Co-design enabled the development of collaborative changes to support book reading that were valued and owned by families, staff, and community partners. Community hubs can provide unique opportunities to engage with families in areas of vulnerability to support the development of early language and literacy skills.

Clinic-Based Financial Coaching and Missed Pediatric Preventive Care: A Randomized Trial

OBJECTIVES

Poverty is a common root cause of poor health and disrupts medical care. Clinically embedded antipoverty programs that address financial stressors may prevent missed visits and improve show rates. This pilot study evaluated the impact of clinic-based financial coaching on adherence to recommended preventive care pediatric visits and vaccinations in the first 6 months of life.

METHODS

In this community-partnered randomized controlled trial comparing clinic-based financial coaching to usual care among low-income parent-infant dyads attending pediatric preventive care visits, we examined the impact of the longitudinal financial intervention delivered by trained coaches addressing parent-identified, strengths-based financial goals (employment, savings, public benefits enrollment, etc.). We also examined social needs screening and resource referral on rates of missed preventive care pediatric visits and vaccinations through the 6-month well-child visit.

RESULTS

Eighty-one parent-infant dyads were randomized (35 intervention, 46 control); nearly all parents were mothers and more than one-half were Latina. The rate of missed visits among those randomized to clinic-based financial coaching was half that of controls (0.46 vs 1.07 missed of 4 recommended visits; mean difference, 0.61 visits missed; P = .01). Intervention participants were more likely to have up-to-date immunizations each visit (relative risk, 1.26; P = .01) with fewer missed vaccinations by the end of the 6-month preventive care visit period (2.52 vs 3.8 missed vaccinations; P = .002).

CONCLUSIONS

In this pilot randomized trial, a medical-financial partnership embedding financial coaching within pediatric primary care improved low-income families' adherence to recommended visits and vaccinations. Clinic-based financial coaching may improve care continuity and quality in the medical home.

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.

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.

Evaluation of an Automated Reminder System for Reducing Missed MRI Appointments

Background: The high frequency of missed appointments continues to be a burden on healthcare providers, leading to decreased productivity, quality of service, and quality of outcome. The purpose of this study is to evaluate the effectiveness of Televox's automated appointment reminder service in reducing the missed appointment rate for MRI (magnetic resonance imaging). The appointment reminders were sent 72 h in advance. The total and no-show numbers were tallied to calculate missed appointment rates. Comparison of the missed appointment rate with and without Televox implementation and different payment types was performed. Temporal comparisons were also made across the corresponding time periods in order to control for seasonal fluctuations. Results: An insignificant decline in missed appointment rates was found in locations implementing Televox (P = .495) overall, although a significant decrease in missed appointments was found among Medicaid patients (P = .0381). Conclusion: Implementation of Televox appointment reminder systems did not significantly affect appointment attendance overall, but could be more useful specifically for encouraging Medicaid patients to attend MRI appointments.

Exploring potential barriers in equitable access to pediatric diagnostic imaging using machine learning

In this work, we examine magnetic resonance imaging (MRI) and ultrasound (US) appointments at the Diagnostic Imaging (DI) department of a pediatric hospital to discover possible relationships between selected patient features and no-show or long waiting room time endpoints. The chosen features include age, sex, income, distance from the hospital, percentage of non-English speakers in a postal code, percentage of single caregivers in a postal code, appointment time slot (morning, afternoon, evening), and day of the week (Monday to Sunday). We trained univariate Logistic Regression (LR) models using the training sets and identified predictive (significant) features that remained significant in the test sets. We also implemented multivariate Random Forest (RF) models to predict the endpoints. We achieved Area Under the Receiver Operating Characteristic Curve (AUC) of 0.82 and 0.73 for predicting no-show and long waiting room time endpoints, respectively. The univariate LR analysis on DI appointments uncovered the effect of the time of appointment during the day/week, and patients' demographics such as income and the number of caregivers on the no-shows and long waiting room time endpoints. For predicting no-show, we found age, time slot, and percentage of single caregiver to be the most critical contributors. Age, distance, and percentage of non-English speakers were the most important features for our long waiting room time prediction models. We found no sex discrimination among the scheduled pediatric DI appointments. Nonetheless, inequities based on patient features such as low income and language barrier did exist.

The impact of loneliness on compliance with COVID-19 prevention guidelines

Many individuals have been reluctant to follow the COVID-19 prevention guidelines (e.g., wearing a mask, physical distancing, and vigilant handwashing) set forth by the U.S. Center for Disease Control to reduce the spread of COVID-19. In this research, we use reciprocal altruism theory to investigate the role of loneliness and its impact on compliance with these guidelines. Our findings indicate that lonely individuals are less willing to comply with COVID-19 prevention guidelines than non-lonely individuals. Process evidence suggests that this occurs as loneliness can inhibit an individual's sense of obligation to reciprocate to others. However, we demonstrate that framing information about COVID-19 through agentic (vs. communal) advertising messaging strategies can offset the negative impact of loneliness on compliance with COVID-19 prevention guidelines. Thus, marketers and policymakers may want to consider the important role of loneliness when tailoring messaging appeals that encourage compliance with COVID-19 prevention guidelines.

Improving Specialty Care Access via Telemedicine

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