Association of Unmet Social Needs With Missed In-Person Urology Clinic Appointments.

INTRODUCTION: Social determinants of health (SDH) are nonbiologic influencers of disease and health care disparities. This study focused on understanding the association between SDH and urology clinic "no-show" visits within a diverse urban population. METHODS: We retrospectively identified patients scheduled for urology clinic visits from October 2015 to June 2022 who completed a 10-question social needs screener. For each patient, demographic variables, and number of missed clinic appointments were abstracted. Multivariable logistic regression was performed to determine the association of unmet social needs and no-shows. RESULTS: Of 5761 unique patients seen in clinic, 5293 completed a social needs screener. Respondents were most commonly male (62.8%), Hispanic (50.3%), English-speaking (75.5%), and insured by Medicare (46.0%). Overall, 8.2%, 4.6%, and 6.1% reported 1, 2, and 3+ unmet social needs, respectively. Most patients (61.7%) had 0 no-shows; 38.3% had 1+ no-shows. Between the 0 and 1+ no-show groups, we found significant differences with respect to gender (P =.05), race/ethnicity (P = .002), preferred language (P = .006), insurance payer (P < .001), SDH status (P = .003), and total number of unmet social needs (P = .006). On multivariable analysis, patients concerned about housing quality (odds ratio [OR] = 1.50, P = .002), legal help (OR = 1.53, P = .009), and with 3+ unmet social needs (OR = 1.39, P = .006) were more likely to have 1+ no-shows. CONCLUSIONS: Unmet social needs were associated with increased no-show urology clinic visits. Routine social needs screening could identify at-risk patients who would benefit from services. This may be particularly pertinent for patients with urgent diagnoses or those requiring frequent office visits where missing appointments could impact morbidity and mortality.

Public health nurse perspectives on predicting nonattendance for cervical cancer screening through classification, ensemble, and deep learning models.

OBJECTIVES: Women's attendance to cervical cancer screening (CCS) is a major concern for healthcare providers in community. This study aims to use the various algorithms that can accurately predict the most barriers of women for nonattendance to CS. DESIGN: The real-time data were collected from women presented at OPD of primary health centers (PHCs). About 1046 women's data regarding attendance and nonattendance to CCS were included. In this study, we have used three models, classification, ensemble, and deep learning models, to compare the specific accuracy and AU-ROC for predicting non-attenders for CC. RESULTS: The current model employs 22 predictors, with soft voting in ensemble models showing slightly higher specificity (96%) and sensitivity (93%) than weighted averaging. Bagging excels with the highest accuracy (98.49%), specificity (97.3%), and ideal sensitivity (100%) with an AUC of 0.99. Classification models reveal Naive Bayes with higher specificity (97%) but lower sensitivity (91%) than Logistic Regression. Random Forest and Neural Network achieve the highest accuracy (98.49%), with an AUC of 0.98. In deep learning, LSTM has an accuracy of 95.68%, higher specificity (97.60%), and lower sensitivity (93.42%) compared to other models. MLP and NN showed the highest AUC values of 0.99. CONCLUSION: Employing ensemble and deep learning models proved most effective in predicting barriers to nonattendance in cervical screening.

Factors associated with the absence of postpartum consultations in a high-risk population.

Objective: To assess the rate of missed postpartum appointments at a referral center for high-risk pregnancy and compare puerperal women who did and did not attend these appointments to identify related factors. Methods: This was a retrospective cross-sectional study with all women scheduled for postpartum consultations at a high-risk obstetrics service in 2018. The variables selected to compare women were personal, obstetric, and perinatal. The variables of interest were obtained from the hospital's electronic medical records. Statistical analyses were performed using the Chi-square, Fisher's exact, or Mann-Whitney tests. For the variable of the interbirth interval, a receiver operating characteristic curve (ROC) was used to best discriminate whether or not patients attended the postpartum consultation. The significance level for the statistical tests was 5%. Results: A total of 1,629 women scheduled for postpartum consultations in 2018 were included. The rate of missing the postpartum consultation was 34.8%. A shorter interbirth interval (p = 0.039), previous use of psychoactive substances (p = 0.027), current or former smoking (p = 0.003), and multiparity (p < 0.001) were associated with non-attendance. Conclusion: This study showed a high rate of postpartum appointment non-attendance. This is particularly relevant because it was demonstrated in a high-risk obstetric service linked to clinical severity or social vulnerability cases. This highlights the need for new approaches to puerperal women before hospital discharge and new tools to increase adherence to postpartum consultations, especially for multiparous women.

Telehealth Visits have Lower Non-attendance than Traditional Orthopaedic Clinic Visits.

The use of telemedicine has rapidly expanded in the wake of the COVID pandemic, but its effect on patient attendance remains unknown for different clinicians. This study compared traditional in-clinic visits with telehealth visits by retrospectively reviewing all scheduled orthopaedic clinic visits. Results demonstrated lower rates of cancellations in patients scheduled for telehealth visits as compared to in-clinic visits, during the initial COVID pandemic. In general, physicians can expect a lower cancellation rate than non-physician practitioners.

Patient Factors Associated with Missed Otolaryngology Appointments at an Urban Safety-Net Hospital.

OBJECTIVE: To determine if patient factors related to ethnicity, socioeconomic status (SES), medical comorbidities, or appointment characteristics increase the risk of missing an initial adult otolaryngology appointment. METHODS: This study is a retrospective case control study at Boston Medical Center (BMC) in Boston, Massachusetts, that took place in 2019. Patient demographic and medical comorbidity data as well as appointment characteristic data were collected and compared between those that attended their initial otolaryngology appointment versus those who missed their initial appointment. Chi-square and ANOVA tests were used to calculate differences between attendance outcomes. Multivariate analysis was used to compare the odds of missing an appointment based on various patient- and appointment-related factors. RESULTS: Patients who were more likely to miss their appointments were more often female, of lower education, disabled, not employed, Black or Hispanic, and Spanish-speaking. Spring and Fall appointments were more likely to be missed. When a multivariate regression was conducted to control for social determinants of health (SDOH) such as race, insurance status, employment, and education status, the odds of females, Spanish-speaking, students, and disabled patients missing their appointment were no longer statistically significant. CONCLUSION: A majority of patients at BMC come from lower SES backgrounds and have multiple medical comorbidities. Those who reside closer to BMC, often areas of lower average income, had higher rates of missed appointments. Interventions such as decreasing lag time, providing handicap-accessible free transportation, and increasing accessibility of telemedicine for patients could help improve attendance rates at BMC. LEVEL OF EVIDENCE: IV Laryngoscope, 134:4003-4010, 2024.

The Impact of Telemedicine on Human Immunodeficiency Virus (HIV)-Related Clinical Outcomes During the COVID-19 Pandemic.

The coronavirus disease of 2019 (COVID-19) pandemic exacerbated barriers to care for people living with human immunodeficiency virus (HIV) (PLWH). The quick uptake of telemedicine in the outpatient setting provided promise for care continuity. In this study, we compared appointment and laboratory no-show rates in an urban outpatient HIV clinic during three time periods: (1) Pre-COVID-19: 9/15/2019-3/14/2020 (predominately in-person), (2) "Early" COVID-19: 3/15/2020-9/14/2020 (predominately telemedicine), and (3) "Later" COVID-19: 9/15/2020-3/14/2021 (mixed in-person/telemedicine). Multivariable logistic regression models evaluated the two study hypotheses: (i) equivalence of Period 2 with Period 1 and of Period 3 with Period 1 and (ii) improved outcomes with telemedicine over in-person visits. No-show rates were 1% in Period 1, 4% in Period 2, and 18% in Period 3. Compared to the pre-pandemic period, individuals had a higher rate of appointment no-shows during Period 2 [OR (90% CI): 7.67 (2.68, 21.93)] and 3 [OR (90% CI): 30.91 (12.83 to 75.06). During the total study period, those with telemedicine appointments were less likely to no-show than those with in-person appointments [OR (95% CI): 0.36 (0.16-0.80), p = 0.012]. There was no statistical difference between telemedicine and in-person appointments for laboratory completion rates. Our study failed to prove that no-show rates before and during the pandemic were similar; in fact, no-show rates were higher during both the early and later pandemic. Overall, telemedicine was associated with lower no-show rates compared to in-person appointments. In future pandemics, telemedicine may be a valuable component to maintain care in PLWH.

No-Show Rates in a Urogynecology Clinic.

IMPORTANCE: No-show appointments, or scheduled appointments that patients do not attend without giving notice of cancellation, are a prevalent problem in the outpatient setting. OBJECTIVE: The objective of this study was to compare the proportion of patients by decades of life who "no-show" to their urogynecology appointments. STUDY DESIGN: This retrospective cohort included women 20 years and older who did not show to their urogynecologic clinical encounters at an academic practice between January 1, 2022, and December 31, 2022. Demographics and visit history were recorded. The primary outcome was the proportion of patients by decade of age who were a "no-show" to their appointments. All decades were compared with women in their 70s, the decade with the most patients seen. Secondary outcomes included descriptive data of patients. Descriptive statistics and χ2 analyses were used. RESULTS: The cohort of 450 no-show encounters (composed of 391 patients), out of 6729 encounters, demonstrated an overall no-show rate of 6.7%. Baseline demographics of "no-show" patients were 67.3% White and 27.4% Black. The odds of women in their 20s-50s who no-show was 2-3 times higher than women in their 70s (P < 0.01). The highest no-show rates occurred in 20s (12.6%) and 40s (11.8%). Forty-six patients missed multiple appointments. The odds of a Black patient having multiple no-shows was 3.15 times higher than the odds of a White patient. CONCLUSIONS: No-show rates are low in this urogynecology practice. Younger women are more likely to no-show. This knowledge can facilitate potential double bookings necessary for urgent appointments and to maximize resource utilization.

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.

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.

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

IMPORTANCE: Missed appointments lead to decreased clinical productivity and poor health outcomes. OBJECTIVES: The objectives of this study were to describe sociodemographic and clinical characteristics of patients who miss urogynecology appointments and identify risk factors for missed appointments. STUDY DESIGN: We conducted an institutional review board-approved case-control study of women 18 years or older scheduled for a urogynecology appointment at 1 of 4 sites associated with an urban academic tertiary care center over 4 months. Patients were included in the missed appointment group if they canceled their appointments the same day or did not show up for them. For comparison, we included a control group consisting of patients immediately preceding or following the ones who missed their appointments with the same visit type. Logistic regression was used to identify risk factors for missed appointments. RESULTS: Four hundred twenty-six women were included: 213 in the missed appointment group and 213 in the control group. Women who missed appointments were younger (60 years [interquartile range (IQR), 47-72 years] vs 69 years [IQR, 59-78 years], P < 0.0001). More women in the missed appointment group were Hispanic (24.4% vs 13.1%) and non-Hispanic Black (7.5% vs 3.8%, P = 0.009), had Medicaid (17.4% vs 6.57%, P = 0.0006), missed previous appointments (24.9% vs 11.7% P = 0.0005), waited longer for appointments (39 days [IQR, 23.5-55.5 days] vs 30.5 days [IQR, 12.8-47.0 days], P = 0.002), and made appointments for urinary incontinence (44.1% vs 26.8%, P = 0.0002). On multivariate logistic regression, women with Medicaid had significantly higher odds of missing appointments (adjusted OR, 2.11 [1.04-4.48], P = 0.044). CONCLUSIONS: Women with Medicaid were more likely to miss urogynecology appointments. Further research is needed to address barriers this group faces when accessing care.

Effect of Standardized Reminder Calls on Trauma Patient No-Show Rate.

BACKGROUND: Most patients who sustain a traumatic injury require outpatient follow-up. A common barrier to outpatient postadmission care is patient failure to follow-up. One of the most significant factors resulting in failure to follow-up is age more than 35 years. Recent work has shown that follow-up telephone calls reduce readmission rates. Our aim was to decrease no-show appointments by 10% in 12 months. STUDY DESIGN: The electronic medical records at our level I and II trauma centers were queried for all outpatient appointments for trauma between July 1, 2020, and June 9, 2021, and whether the patient attended their follow-up appointment. Patients with visits scheduled after August 1, 2021, received 24- and 48-hour previsit reminder calls. Patients with visits scheduled between July 1, 2020, and August 1, 2021, did not receive previsit calls. Both groups were compared using multivariable direct logistic regression models. RESULTS: A total of 1,822 follow-up opportunities were included in the study. During the pre-implementation phase, there was a no-show rate of 30.9% (329 of 1,064 visits). Postintervention, a 12.2% reduction in overall no-show rate occurred. A statistically significant 11.2% decrease (p < 0.001) was seen in elderly patients. Multivariate analysis showed standardized calls resulted in significantly decreased odds of failing to keep an appointment (adjusted odds ratio = 0.610, p < 0.001). CONCLUSIONS: Reminder calls led to a 12.2% reduction in no-show rate and were an independent predictor of a patient's likelihood of attending their appointment. Other predictors of attendance included insurance status and abdominal injury.

The Psychological, Physiological, and Behavioral Responses of Patients to Magnetic Resonance Imaging (MRI): A Systematic Review and Meta-Analysis.

BACKGROUND: MRI is generally well-tolerated although it may induce physiological stress responses and anxiety in patients. PURPOSE: Investigate the psychological, physiological, and behavioral responses of patients to MRI, their evolution over time, and influencing factors. STUDY TYPE: Systematic review with meta-analysis. POPULATION: 181,371 adult patients from 44 studies undergoing clinical MRI. ASSESSMENT: Pubmed, PsycInfo, Web of Science, and Scopus were systematically searched according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Quality appraisal was conducted with the Joanna Briggs Institute critical appraisal tools. Meta-analysis was conducted via Meta-Essentials workbooks when five studies were available for an outcome. Psychological and behavioral outcomes could be analyzed. Psychological outcomes were anxiety (State-Trait-Anxiety Inventory, STAI-S; 37) and willingness to undergo MRI again. Behavioral outcomes included unexpected behaviors: No shows, sedation, failed scans, and motion artifacts. Year of publication, sex, age, and positioning were examined as moderators. STATISTICAL TESTS: Meta-analysis, Hedge's g. A P value <0.05 was considered to indicate statistical significance. RESULTS: Of 12,755 initial studies, 104 studies were included in methodological review and 44 (181,371 patients) in meta-analysis. Anxiety did not significantly reduce from pre- to post-MRI (Hedge's g = -0.20, P = 0.051). Pooled values of STAI-S (37) were 44.93 (pre-MRI) and 40.36 (post-MRI). Of all patients, 3.9% reported unwillingness to undergo MRI again. Pooled prevalence of unexpected patient behavior was 11.4%; rates for singular behaviors were: Failed scans, 2.1%; no-shows, 11.5%; sedation, 3.3%; motion artifacts, 12.2%. Year of publication was not a significant moderator (all P > 0.169); that is, the patients' response was not improved in recent vs. older studies. Meta-analysis of physiological responses was not feasible since preconditions were not met for any outcome. DATA CONCLUSION: Advancements of MRI technology alone may not be sufficient to eliminate anxiety in patients undergoing MRI and related unexpected behaviors. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 5.

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.

Factors Associated with No-Show Rates in a Pediatric Audiology Clinic.

OBJECTIVE: To evaluate factors associated with no-show rates in a pediatric audiology clinic. STUDY DESIGN: Retrospective review. SETTING: Tertiary referral center. PARTICIPANTS: All pediatric patients younger than 18 years whose parents/guardians scheduled an appointment at a tertiary Audiology Clinic between June 1, 2015, and July 1, 2017. MAIN OUTCOME MEASURES: Data included whether the patient came to their appointment, patient age, sex, race, insurance type, appointment type, location, season of appointment, and day of the week of the appointment. RESULTS: Of the 7,784 pediatric appointments scheduled with audiology, the overall no-show rate was 24.3% (n = 1893). Lower age was significantly associated with no-shows ( p = 0.0003). Black/African American children were more likely to no-show compared with White/Caucasians ( p = 0.0001). Compared with self-pay/military/other insurance, those with Medicaid were more likely to no-show ( p = 0.0001). The highest rate of no-shows occurred during summer (27%). On multivariate analysis, younger age, Black/African American race, and Medicaid insurance were associated with increased no-show rates. CONCLUSION: A variety of factors influence no-show rates in a pediatric audiology setting. No-shows can affect treatment quality and affect overall hearing outcomes. Further investigation is necessary to assess barriers to appointment adherence and to develop interventions to improve adherence and care.

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.

Intraday dynamic rescheduling under patient no-shows.

Patient no-shows are a major source of uncertainty for outpatient clinics. A common approach to hedge against the effect of no-shows is to overbook. The trade-off between patient's waiting costs and provider idling/overtime costs determines the optimal level of overbooking. Existing work on appointment scheduling assumes that appointment times cannot be updated once they have been assigned. However, advances in communication technology and the adoption of online (as opposed to in-person) appointments make it possible for appointments to be flexible. In this paper, we describe an intraday dynamic rescheduling model that adjusts upcoming appointments based on observed no-shows. We formulate the problem as a Markov Decision Process in order to compute the optimal pre-day schedule and the optimal policy to update the schedule for every scenario of no-shows. We also propose an alternative formulation based on the idea of 'atomic' actions that allows us to apply a shortest path algorithm to solve for the optimal policy more efficiently. Based on a numerical study using parameter estimates from existing literature, we find that intraday dynamic rescheduling can reduce expected cost by 15% compared to static scheduling.

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

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

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.

Estudo transversal sobre ausências de pacientes em consultas médicas agendadas em ambulatórios de hospital terciário / Cross-sectional Study On Patient Absences In Medical Consultations Scheduled In Outpatient Clinics Of Tertiary Hospital / Estudio Transversal Sobre Ausencias de Pacientes Em Citas Médicas Programadas Em Pacientes Ambulatorios de Hospitales Terciarios

Objetivo: Analisar variáveis relacionadas ao agendamento de consultas ambulatoriais que não se realizaram pela ausência dos pacientes. Métodos: Estudo transversal produzido em hospital público terciário de referência para o Sistema Único de Saúde. Foram sorteados aleatoriamente 493 pacientes, e a amostra final totalizou 317 pacientes que faltaram a consultas médicas. Variáveis investigadas: recebimento de mensagens via celular informando a data da consulta, intervalo entre a data do agendamento e da consulta e a realização de reagendamento. Os dados foram oriundos de relatórios informatizados de faltosos a consultas agendadas e entrevistas telefônicas. Análises estatísticas realizadas no programa SAS® for Windows, versão 9.3. Resultados: 49.8% dos pacientes informaram não terem recebido mensagem lembrando sobre a data da consulta. O tempo entre o agendamento e a data da consulta ficou entre 180 e 365 dias para 36,6% dos pacientes. Reagendaram a consulta 24,6% dos pacientes. Conclusão: É necessário ampliar procedimentos para prevenção de faltas às consultas, independentemente do tempo entre o agendamento e a consulta. (AU)

Objective: To analyze variables related to the scheduling of outpatient appointments that did not take place due to patient no-show. Methods: Cross-sectional study conducted in a tertiary public referral hospital providing services to the Brazilian Unified Health System. A total of 493 patients were randomly selected, and the final sample totaled 317 no-show patients. Variables investigated: receipt of messages via cell phones informing the appointment date, interval between the scheduling date and the appointment date, and rescheduling. Data were obtained from computerized reports regarding absenteeism from scheduled appointments and telephone interviews. Statistical analyses were performed using the SAS® for Windows software, version 9.3. Results: 49.8% of patients reported not having received a message reminding them of the appointment dates. The interval between scheduling and the appointment dates was from 180 to 365 days for 36.6% of patients. A total of 24.6% of patients rescheduled their appointments. Conclusion: It is necessary to expand procedures to prevent missed appointments regardless of the time between scheduling and the appointment. (AU)

Objetivo: Analizar variables relacionadas con la programación de consultas externas que no se realizaron por ausencia del paciente. Métodos: Estudio transversal realizado en un hospital público terciario de referencia del Sistema Único de Salud. Se seleccionó aleatoriamente a 493 pacientes y la muestra final fue de 317 pacientes que faltaron a las citas médicas. Variables investigadas: recepción de mensajes vía celular informando la fecha de la cita, intervalo entre la cita y la fecha de la cita y reprogramación. Los datos provienen de informes computarizados de ausencias n citas programadas y entrevistas telefónicas. Análisis estadísticos realizados en SAS® para Windows versión 9.3. Resultados: En el 49.8% de los pacientes informaron no haber recibido un mensaje recordando la fecha de la cita. Conclusión: Es necesario ampliar los procedimientos para evitar citas perdidas independientemente del tiempo entre citas y citas. (AU)

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.