Prevalence, predictors and economic consequences of no-shows

Telemedicine appointments are more likely to be completed than in-person healthcare appointments: a retrospective cohort study

Objectives

Missed appointments can lead to treatment delays and adverse outcomes. Telemedicine may improve appointment completion because it addresses barriers to in-person visits, such as childcare and transportation. This study compared appointment completion for appointments using telemedicine versus in-person care in a large cohort of patients at an urban academic health sciences center.

Materials and Methods

We conducted a retrospective cohort study of electronic health record data to determine whether telemedicine appointments have higher odds of completion compared to in-person care appointments, January 1, 2021, and April 30, 2023. The data were obtained from the University of South Florida (USF), a large academic health sciences center serving Tampa, FL, and surrounding communities. We implemented 1:1 propensity score matching based on age, gender, race, visit type, and Charlson Comorbidity Index (CCI).

Results

The matched cohort included 87 376 appointments, with diverse patient demographics. The percentage of completed telemedicine appointments exceeded that of completed in-person care appointments by 9.2 points (73.4% vs 64.2%, P < .001). The adjusted odds ratio for telemedicine versus in-person care in relation to appointment completion was 1.64 (95% CI, 1.59-1.69, P < .001), indicating that telemedicine appointments are associated with 64% higher odds of completion than in-person care appointments when controlling for other factors.

Discussion

This cohort study indicated that telemedicine appointments are more likely to be completed than in-person care appointments, regardless of demographics, comorbidity, payment type, or distance.

Conclusion

Telemedicine appointments are more likely to be completed than in-person healthcare appointments.

Predictors of No-Shows and Cancellations in an Outpatient Neuropsychology Clinic in a Large Healthcare System

BACKGROUND

The purpose of this study was to evaluate potential predictors of no-shows and late cancellations in an outpatient clinic within a large healthcare system serving vulnerable communities.

METHODS

Demographic data and appointment status were recorded for 537 consecutive patients scheduled for neuropsychological evaluation in an outpatient psychiatry clinic. Patients include 220 males and 317 females with an average formal education of 11.01 years (SD = 3.87) and age of 55.64 years (SD = 16.20).

RESULTS

The overall rate of no-shows or late cancellations was 20%. Of the 106 patients who no-showed/late cancelled, 41% rescheduled, and of those, 23% missed or late cancelled their second appointment. No-shows and late cancellations were associated with historical/prior no-show rate, while race/ethnicity and activation of MyChart had slight impacts.

CONCLUSIONS

These data suggest that prior no-show rates and MyChart access may be targets for interventions to improve show rates. This is important for the patients' gaining access to care as well as minimizing financial strains for the system and increasing wait times/delays to care for other patients.

Consumer Preferences for a Healthcare Appointment Reminder in Australia: A Discrete Choice Experiment

BACKGROUND

It is essential to consider the evidence of consumer preferences and their specific needs when determining which strategies to use to improve patient attendance at scheduled healthcare appointments.

OBJECTIVES

This study aimed to identify key attributes and elicit healthcare consumer preferences for a healthcare appointment reminder system.

METHODS

A discrete choice experiment was conducted in a general Australian population sample. The respondents were asked to choose between three options: their preferred reminder (A or B) or a 'neither' option. Attributes were developed through a literature review and an expert panel discussion. Reminder options were defined by four attributes: modality, timing, content and interactivity. Multinomial logit and mixed multinomial logit models were estimated to approximate individual preferences for these attributes. A scenario analysis was performed to estimate the likelihood of choosing different reminder systems.

RESULTS

Respondents (n = 361) indicated a significant preference for an appointment reminder to be delivered via a text message (β = 2.42, p < 0.001) less than 3 days before the appointment (β = 0.99, p < 0.001), with basic details including the appointment cost (β = 0.13, p < 0.10), and where there is the ability to cancel or modify the appointment (β = 1.36, p < 0.001). A scenario analysis showed that the likelihood of choosing an appointment reminder system with these characteristics would be 97%.

CONCLUSIONS

Our findings provide evidence on how healthcare consumers trade-off between different characteristics of reminder systems, which may be valuable to inform current or future systems. Future studies may focus on exploring the effectiveness of using patient-preferred reminders alongside other mitigation strategies used by providers.

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.

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.

Risk Factors for Missed Follow-up Appointments among Facial Trauma Patients

A retrospective case-control study was performed to characterize the rate of missed follow-up appointments after facial trauma and identify associated risk factors.Follow-up appointments for facial trauma over a 3-month period at a single, safety net hospital were analyzed. Appointment-specific, sociodemographic, trauma, and management data were compared between cases (missed appointments) and controls (attended appointments). Univariate testing and multivariable logistic regression were employed.A total of 116 cases and 259 controls were identified, yielding a missed appointment rate of 30.9% (116/375). Missed appointments were significantly associated with initial clinic appointments compared to return visits (odds ratio [OR] 2.21 [1.38-3.54]), afternoon visits compared to morning (OR 3.14 [1.94-5.07]), lack of private health insurance (OR 2.91 [1.68-5.18]), and presence of midface fractures (OR 2.04 [1.28-3.27]). Missed appointments were negatively associated with mandible fractures (OR 0.56 [0.35-0.89]), surgical management (OR 0.48 [0.30-0.77]), and the presence of nonremovable hardware (OR 0.39 [0.23-0.64]). Upon multivariable logistic regression, missed appointments remained independently associated with afternoon visits (adjusted OR [aOR] 1.95 [1.12-3.4]), lack of private health insurance (aOR 2.73 [1.55-4.8]), and midface fractures (aOR 2.09 [1.21-3.59]).Nearly one-third of facial trauma patients missed follow-up appointments, with the greatest risk among those with afternoon appointments, lacking private health insurance, and with midface fractures.

Incidence, characteristics, and risk factors of new liver disorders 3.5 years post COVID-19 pandemic in the Montefiore Health System in Bronx

PURPOSE

To determine the incidence of newly diagnosed liver disorders (LD) up to 3.5-year post-acute COVID-19, and risk factors associated with new LD.

METHODS

We analyzed 54,699 COVID-19 patients and 1,409,547 non-COVID-19 controls from March-11-2020 to Jan-03-2023. New liver disorders included abnormal liver function tests, advanced liver failure, alcohol and non-alcohol related liver disorders, and cirrhosis. Comparisons were made with ambulatory non-COVID-19 patients and patients hospitalized for other lower respiratory tract infections (LRTI). Demographics, comorbidities, laboratory data, incomes, insurance status, and unmet social needs were tabulated. The primary outcome was new LD at least two weeks following COVID-19 positive test.

RESULTS

Incidence of new LD was not significantly different between COVID-19 and non-COVID-19 cohorts (incidence:1.99% vs 1.90% p>0.05, OR = 1.04[95%CI: 0.92,1.17], p = 0.53). COVID-19 patients with new LD were older, more likely to be Hispanic and had higher prevalence of diabetes, hypertension, chronic kidney disease, and obesity compared to patients without new LD. Hospitalized COVID-19 patients had no elevated risk of LD compared to hospitalized LRTI patients (2.90% vs 2.07%, p>0.05, OR = 1.29[0.98,1.69], p = 0.06). Among COVID-19 patients, those who developed LD had fewer patients with higher incomes (14.18% vs 18.35%, p<0.05) and more with lower incomes (21.72% vs 17.23%, p<0.01), more Medicare and less Medicaid insurance, and more patients with >3 unmet social needs (6.49% vs 2.98%, p<0.001) and fewer with no unmet social needs (76.19% vs 80.42%, p<0.001).

CONCLUSIONS

Older age, Hispanic ethnicity, and obesity, but not COVID-19 status, posed increased risk for developing new LD. Lower socioeconomic status was associated with higher incidence of new LD.

Implementing an osteoarthritis management program to deliver guideline-driven care for knee and hip osteoarthritis in a U.S. academic health system

Objective

Assess implementation feasibility and outcomes for an Osteoarthritis Management Program (OAMP) at an academic center.

Design

This open study assessed an OAMP designed to deliver care in 1-5 individual or group visits across ≤12 months. Eligibility included adults with knee or hip osteoarthritis with ≥1 visit from 7/1/2017-1/15/2021. A multidisciplinary care team provided: education on osteoarthritis, self-management, exercise, weight loss; pharmacologic management; assessments of mood, sleep, quality of life, and diet. Clinic utilization and growth are reported through 2022. Patient outcomes of body mass index (BMI), pain, and function were analyzed using multivariable general linear models. OAMP outcomes were feasibility and sustainability.

Results

Most patients were locally referred by primary care. 953 patients attended 2531 visits (average visits 2.16, treatment duration 187.9 days). Most were female (72.6%), older (62.1), white (91.1%), and had medical insurance (95.4%). Obesity was prevalent (84.7% BMI ≥30, average BMI 40.9), mean Charlson Comorbidity Index was 1.89, and functional testing was below average. Longitudinal modeling revealed statistically but not clinically significant pain reduction (4.4-3.9 on 0-10 scale, p ​= ​0.002). BMI did not significantly change (p ​= ​0.87). Higher baseline pain and BMI correlated with greater reductions in each posttreatment. Uninsured patients had shorter treatment duration. Increasing clinic hours (4-24 ​h weekly) and serving 953 patients over four years demonstrated OAMP sustainability.

Conclusions

OAMP implementation was feasible and sustainable. Patients with high baseline pain and BMI were more likely to improve. Noninsurance was a barrier. These results contribute to understanding OAMP outcomes in U.S. healthcare.

Sociodemographic Factors Associated with Outpatient Radiology No-shows Versus Cancellations

RATIONALE AND OBJECTIVES

To assess prevalence of missed outpatient radiology appointments and sociodemographic factors associated with no-shows vs. cancellations.

METHODS

Adults with outpatient radiology appointments in 2022 and January 2023 at a single tertiary academic health center were included. Generalized estimating equation regression was used to evaluate sociodemographic factors associated with missed vs. completed appointments, no-shows vs. cancellations and time interval between cancellations and appointments.

RESULTS

19,262 (24.3%) examinations were either a cancellation (22.3%) or no-show (2.0%) among 9713 patients (mean age 60.8 ± 15.5; 67.1% female, 63.9% White, 20.0% Asian, 22.0% Hispanics). Among cancellations, 70.19% were patient-initiated. Age ≥ 65 significantly decreased the probability of missed appointments by 5.4% point (pp) (95% CI: 3.7-7.2) or no-shows (4.2 pp; 95% CI, 1.4-6.9), while being single increased probability of missed appointments (2.2 pp; 95% CI, 1.2-3.1) or no-shows (2.6 pp; 95% CI, 1.2-4.1). Those uninsured or with public insurance were 1.3-4.9 pp more likely to miss appointments than commercial insurance, and 2.2-7.6 pp more likely to have no-shows than cancellations. Living in disadvantaged neighborhoods 4.9 pp (95% CI, 3.9-6.0) increased likelihood of missing appointment and was associated with shorter time interval between cancellation and appointment. English speakers were 2.2 pp (95% CI, 1.1-3.3) more likely to miss their exam, while 2.7 pp (95% CI, 1.1-0.4.3) less likely to be a no-show than cancellation.

CONCLUSION

Cancellations represented a significant portion of missed appointments. Specific sociodemographic subgroups exhibited higher tendencies for having missed appointments and no-shows.

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.

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.

Estimating the cost of visiting hospital outpatient

Objectives

This study aims to investigate the cost incurred by people travelling to the neurology outpatient clinic of a large metropolitan hospital. As outpatients are a substantial portion of a hospital's demographic, we aimed to understand the patient experience of various commuters.

Methods

We conducted an observational study collecting demographic details and travel information for how people attended the neurology clinic of Monash Medical Centre. Statistical analysis was performed using R. 165 participants were randomly selected and interviewed in-person. Data were collected via an anonymous questionnaire. The study was approved by the Monash Health Human Ethics Research Committee.

Results

155 responses were included in the analysis. Patients paid an average of $A16.64 to travel to Monash Medical Centre. Drivers paid on average $A16.70 and those taking public transport paid on average $A9.64, with the maximum cost overall being $A120.00. For patients driving to hospital, parking accounted for 60% of their travel costs. The average to Monash Medical centre was 20.82 km with the maximum being 190.88 km. Distance from hospital was correlated with a higher cost of travel (p<0.001, Spearman's rank correlation coefficient=0.48). There was also an inverse association between distance from hospital and socioeconomic status (p<0.001, Spearman's rank correlation coefficient=-0.26).

Conclusion

Travelling to hospital can be a costly endeavour. Driving is the most popular form of transport, but a large portion of the cost involved is hospital parking. Further research should be conducted at other tertiary centres with larger samples.

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.

Economic Evaluation of a Nonmedical Financial Assistance Program on Missed Treatment Appointments Among Adults With Cancer

PURPOSE

We retrospectively evaluated the clinical and economic impact of a program providing nonmedical financial assistance on missed treatment appointments among patients receiving cancer treatment at a large, Southeastern public hospital system.

MATERIALS AND METHODS

We used patient electronic health records, program records, and cancer registry data to examine the impact of the program on rates of missed (or no-show) radiation therapy and infusion chemotherapy/immunotherapy appointments in the 180 days after treatment initiation. We used propensity weighting to estimate the effect of the program, stratified by treatment appointment type (radiation therapy, infusion chemotherapy/immunotherapy). We developed a decision tree-based economic model to conduct a cost-consequence analysis from the health system perspective in a hypothetical cohort over a 6-month time horizon.

RESULTS

Of 1,347 patients receiving radiation therapy between 2015 and 2019, 53% (n = 715) had ≥1 no-shows and 28% (n = 378) received program assistance. Receipt of any assistance was associated with a 2.1 percentage point (95% CI, 0.6 to 3.5) decrease in the proportion of no-shows, corresponding to a 51% decrease in the overall mean no-show proportion. Under the current funding model, the program is estimated to save the health system $153 in US dollars per missed appointment averted, relative to not providing nonmedical financial assistance. Of the 1,641 patients receiving infusion chemotherapy/immunotherapy, 33% (n = 541) received program assistance, and only 14% (n = 223) had ≥1 no-shows. The financial assistance program did not have a significant effect on no-show proportions among infusion visits.

CONCLUSION

This study used a novel approach to retrospectively evaluate a nonmedical financial assistance program for patients undergoing active cancer treatment. Findings support investment in programs that address patients' nonmedical financial needs, particularly for those undergoing intensive radiation therapy.

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

INTRODUCTION

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

METHODS

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

RESULTS

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

CONCLUSION

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

Patient Opportunities to Self-Schedule in a Large Multisite, Multispecialty Medical Practice: Program Description and Uptake of 7 Unique Processes for Patients to Successfully Self-Schedule (and Reschedule) Their Medical Appointments

Introduction

Patient self-scheduling of medical appointments is becoming more common in many medical institutions. However, the complexity of scheduling multiple specialties, following scheduling guidelines, and managing appointment access requires a variety of processes for a diverse inventory of self-schedulable appointment types.

Methods

From 7 unique patient self-scheduling methods, we captured counts of successfully self-scheduled and completed appointments. A process map was created to show the paths of 5 different primary self-scheduling processes (new appointment self-scheduling) and 2 secondary self-scheduling processes (existing appointment self-rescheduling).

Results

There were 7 unique processes that led to 733,651 successfully self-scheduled completed visits from January 1 to December 31, 2023 at a multisite, multispecialty clinic. The self-scheduling processes consisted of the following: (1) Ticket offer (appointment "ticket" offers for specific visits generated by a provider order or system rules), the software "ticket" sent to the patient permits "admission" to self-schedule calendar templates (341,591 uses, 46.6%); (2) direct self-scheduled visit for prequalified visit types (203,593 uses, 27.6%); (3) self-reschedule option (patient option to reschedule existing appointment, 79,706 uses, 10.9%); (4) new patient self-scheduled visit via clinic website (does not require portal access, 54,367 uses, 7.4%). (5) automated waitlist self-rescheduled visit (38,649 uses, 5.3%); (6) automated waitlist self-scheduled visit of previously unscheduled visit (10,939 uses, 1.5%); and (7) self-triage self-scheduled visit (4806 uses, 0.7%).

Conclusion

The processes for self-scheduling are expanding. Our multispecialty clinic has implemented 7 different processes to help patients successfully self-schedule medical appointments. Some of the processes occur before initial scheduling (such as self-triage), and some are implemented after successful scheduling has already occurred (self-rescheduling option and self-rescheduling aided by an automated waitlist). Continued research is needed to look for measures of success beyond the ability to complete a self-scheduled visit, including the accuracy of the booking (right provider, location, and length of visit).

Patient "No-Show" Increases the Risk of 90-Day Complications Following Primary Total Knee Arthroplasty: A Retrospective Cohort Study of 6,776 Patients

BACKGROUND

Patients who "no-show" (NS) clinical appointments are at a high risk of adverse health outcomes. The objective of this study was to evaluate and characterize the relationship between NS visits prior to primary total knee arthroplasty (TKA) and 90-day complications after TKA.

METHODS

We retrospectively reviewed 6,776 consecutive patients undergoing primary TKA. Study groups were separated based on whether patients who NS versus always attended their appointment. A NS was defined as an intended appointment that was not canceled or rescheduled ≤2 hours before the appointment in which the patient did not show. Data collected included total number of follow-up appointments prior to surgery, patient demographics, comorbidities, and 90-day postoperative complications.

RESULTS

Patients who have ≥3 NS appointments had 1.5 times increased odds of a surgical site infection (odds ratio (OR) 1.54, P = .002) compared to always attended patients. Patients who were ≤65 years old (OR: 1.41, P < .001), smokers (OR: 2.01, P < .001), and had a Charlson comorbidity index ≥3 (OR: 4.48, P < .001) were more likely to miss clinical appointments.

CONCLUSION

Patients who have ≥3 NS appointments prior to TKA had an increased risk for surgical site infection. Sociodemographic factors were associated with higher odds of missing a scheduled clinical appointment. These data suggest that orthopaedic surgeons should consider NS data as an important clinical decision-making tool to assess risk for postoperative complications to minimize complications following TKA.

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.

Original Research Clinical attendance rate at a tertiary adult audiological service in South Africa

BACKGROUND

 Clinical non-attendance to audiological appointments may negatively affect early diagnosis and intervention as well as treatment outcomes for adults with hearing impairments.

OBJECTIVES

 This study aimed to explore the attendance rate and factors influencing attendance and non-attendance at an adult audiology diagnostic clinic at a tertiary hospital in Gauteng, South Africa.

METHOD

 A mixed-methods research design, utilising structured questionnaires and a retrospective record review was adopted. A total of 31 adult patients at a diagnostic audiology clinic were interviewed.

RESULTS

 Findings revealed an attendance rate of 47.62%, with 52.38% rate failure to return for follow-up appointments. Key reasons for attendance included understanding the need for appointments (57%), staff attitudes (42%) and appointment reminders (17%), and those for non-attendance included multiple appointments (33%), work commitments (28%), transport (8%) and forgetting about the appointment (8%). Six reasons for non-attendance were prominent in the current study: having multiple appointments (33%), work commitments (28%), forgetting the appointment (8%), transport difficulties (8%), attitudes and/or perceptions of the healthcare system (4%) and sequelae of hearing impairment (8%).

CONCLUSION

 This study reinforces previous research findings while highlighting that health literacy and Batho Pele (people first) ethos by staff positively influence attendance.Contribution: Current findings contribute towards contextually relevant evidence on the attendance rate in this sector for ear and hearing care, as well as additional insights into factors influencing this within the South African context. This information is crucial for clinical services provision planning as well as for policy formulation around resource allocation in the public healthcare sector.

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.

Racial disparities in telehealth use during the coronavirus disease 2019 pandemic

OBJECTIVE

To investigate the impact of coronavirus disease 2019 on initial infertility consultations.

DESIGN

Retrospective cohort.

SETTING

Fertility practice in an academic medical center.

PATIENTS

Patients presenting for initial infertility consultation between January 2019 and June 2021 were randomly selected for prepandemic (n = 500) and pandemic (n = 500) cohorts.

EXPOSURE

Coronavirus disease 2019 pandemic.

MAIN OUTCOME MEASURES

The primary outcome was a change in the proportion of African American patients using telehealth after pandemic onset compared with all other patients. Secondary outcomes included presentation to an appointment vs. no-show or cancellation. Exploratory outcomes included appointment length and in vitro fertilization initiation.

RESULTS

The prepandemic cohort vs. the pandemic cohort had fewer patients with commercial insurance (64.4% vs. 72.80%) and more African American patients (33.0% vs. 27.0%), although the racial makeup did not differ significantly between the two cohorts. Rates of missed appointments did not differ between the cohorts, but the prepandemic cohort vs. the pandemic cohort was more likely to no-show (49.4% vs. 27.8%) and less likely to cancel (50.6% vs. 72.2%). African American patients, compared with all other patients, during the pandemic were less likely to use telehealth (57.0% vs. 66.8%). African American patients, compared with all other patients, were less likely to have commercial insurance (prepandemic: 41.2% vs. 75.8%; pandemic: 57.0% vs. 78.6%), present to their scheduled appointment (prepandemic: 52.7% vs. 73.7%; pandemic: 48.1% vs. 74.8%), and cancel vs. no-show (prepandemic: 30.8% vs. 68.2%, pandemic: 64.3% vs. 78.3%). On multivariable analysis, African American patients were less likely (odds ratio 0.37, 95% confidence interval 0.28-0.50) and telehealth users were more likely (odds ratio 1.54, 95% confidence interval 1.04-2.27) to present to their appointments vs. no-show or cancel when controlling for insurance type and timing relative to the onset of the pandemic.

CONCLUSION

Telehealth implementation during the coronavirus disease 2019 pandemic decreased the overall no-show rate, but this shift did not apply to African American patients. This analysis highlights disparities in insurance coverage, telehealth utilization, and presentation for an initial consultation in the African American population during the pandemic.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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.

Association between no-show rates and interpreter use in a pediatric otolaryngology clinic

OBJECTIVES

To understand how primary language and interpreter use affect no-show rates in pediatric otolaryngology.

METHODS

This is a retrospective cohort study using medical records of new patients in a pediatric otolaryngology clinic from 2014 to 2019. Data was collected on patient demographics including age, primary language, insurance type, maternal education level, maternal primary language, interpreter use at the first visit, total number of appointments scheduled, number of missed appointments, and number of completed appointments. Inferential statistics using parametric (ANOVA) and non-parametric (Mann-Whitney U tests, Kruskal-Wallis tests, and Spearman correlation coefficient) methods were used.

RESULTS

Primary language was associated with significant differences in no-show rates (p = 0.0474), with Spanish and English speakers having the lowest no-show rate (33%). Overall, interpreter use at the first visit was not significantly associated with subsequent appointment attendance (p = 0.3674). Patients with a documented Spanish interpreter at the first visit had the lowest average no-show rate (31% ± 19%) compared to Haitian Creole (42% ± 18%) and all other languages (32% ± 19%) (p = 0.0265). Hispanic ethnicity, maternal education level, and maternal primary language were not associated with attendance.

CONCLUSION

Interpreter use at the first visit was not significantly correlated with no-show rates, but among patients that did require an interpreter at the first visit, those receiving services in Spanish had the best clinic attendance.

Dupuytren's Contracture Recurrence and Treatment Following Collagenase Clostridium Histolyticum Injection: A Longitudinal Assessment in a Veteran Population

INTRODUCTION

Dupuytren's contracture is a connective tissue disease characterized by an abnormal proliferation of collagen in the palm and fingers, which leads to a decline in hand function because of progressive joint flexion. In addition to surgical and percutaneous interventions, collagenase clostridium histolyticum (CCH, trade name Xiaflex) is an intralesional enzymatic treatment for adults with palpable cords. The objectives of this study are to evaluate factors predictive of recurrence following treatment with CCH and to review the outcomes of repeat treatments with CCH for recurrent contracture.

MATERIALS AND METHODS

An institutional review board-approved retrospective chart review was conducted for patients between 2010 and 2017 who received CCH injections for Dupuytren's contracture at a Veterans Affairs hospital. Demographics, comorbidities, affected finger and joint, pre/posttreatment contracture, time to recurrence, and treatment of recurrence were recorded. Successful treatment was defined as contracture ≤5° following CCH, and improvement was defined as ≥20° reduction from baseline contracture. Study cohorts were followed after their secondary treatment, and time to recurrence was recorded and plotted using a Kaplan-Meier curve. A Cox proportional hazards model was used to compare treatment group risk factors for recurrence with a P-value less than .05 defined as statistical significance.

RESULTS

Of 174 injections performed for the correction of flexion deformities in 109 patients, 70% (121) were successfully treated with CCH, and an additional 20% (35) had improvement. There was a recurrence of contractures in 43 joints (25%). Of these, 16 contractures were treated with repeat CCH, whereas another 16 underwent limited fasciectomy. In total, 75% (12 of 16) of the repeat CCH group and 75% of the fasciectomy group were successfully treated. Pre-injection contracture of ≥25° was found to be predictive of recurrence (P < .05).

CONCLUSIONS

Initial treatment of contracture with CCH had a 70% success rate with 25% recurrence during the study period. Compared with limited fasciectomy, CCH had decreased efficacy. Based on the findings of this study, we believe that the treatment of primary and/or recurrent Dupuytren's contracture with CCH is a safe and less invasive alternative to fasciectomy in the era of telemedicine. CCH treatment requires no suture removal, which allows the ability to assess motion virtually, and the potential consequences of CCH treatment such as skin tears can be assessed and managed conservatively. In the veteran and active duty population, CCH can facilitate faster recovery and return to service. Strengths of this study include a large series of veteran populations with longitudinal follow-up to determine treatment efficacy for primary Dupuytren's contracture and recurrence. Limitations include a smaller sample size compared to previous trials, a lack of standardized follow-up, and the retrospective nature of our study that prohibits randomization to compare outcomes between CCH treatment and fasciectomy efficacy over time. Directions for future research include stratification of patients by joint and specific digit involvement as well as comparison with percutaneous needle fasciotomy, another minimally invasive technique that could benefit the veteran population at increased risk for developing Dupuytren's disease.

A Nurse-Led Care Delivery App and Telehealth System for Patients Requiring Wound Care: Mixed Methods Implementation and Evaluation Study

BACKGROUND

Innovative solutions to nursing care are needed to address nurse, health system, patient, and caregiver concerns related to nursing wellness, work flexibility and control, workforce retention and pipeline, and access to patient care. One innovative approach includes a novel health care delivery model enabling nurse-led, off-hours wound care (PocketRN) to triage emergent concerns and provide additional patient health education via telehealth.

OBJECTIVE

This pilot study aimed to evaluate the implementation of PocketRN from the perspective of nurses and patients.

METHODS

Patients and part-time or per-diem, wound care-certified and generalist nurses were recruited through the Stanford Medicine Advanced Wound Care Center in 2021 and 2022. Qualitative data included semistructured interviews with nurses and patients and clinical documentation review. Quantitative data included app use and brief end-of-interaction in-app satisfaction surveys.

RESULTS

This pilot study suggests that an app-based nursing care delivery model is acceptable, clinically appropriate, and feasible. Low technology literacy had a modest effect on initial patient adoption; this barrier was addressed with built-in outreach and by simplifying the patient experience (eg, via phone instead of video calls). This approach was acceptable for users, despite total patient enrollment and use numbers being lower than anticipated (N=49; 17/49, 35% of patients used the app at least once beyond the orientation call). We interviewed 10 patients: 7 who had used the app were satisfied with it and reported that real-time advice after hours reduced anxiety, and 3 who had not used the app after enrollment reported having other resources for health care advice and noted their perception that this tool was meant for urgent issues, which did not occur for them. Interviewed nurses (n=10) appreciated working from home, and they reported comfort with the scope of practice and added quality of care facilitated by video capabilities; there was interest in additional wound care-specific training for nonspecialized nurses. Nurses were able to provide direct patient care over the web, including the few participating nurses who were unable to perform in-person care (n=2).

CONCLUSIONS

This evaluation provides insights into the integration of technology into standard health care services, such as in-clinic wound care. Using in-system nurses with access to electronic medical records and specialized knowledge facilitated app integration and continuity of care. This care delivery model satisfied nurse desires for flexible and remote work and reduced patient anxiety, potentially reducing postoperative wound care complications. Feasibility was negatively impacted by patients' technology literacy and few language options; additional patient training, education, and language support are needed to support equitable access. Adoption was impacted by a lack of perceived need for additional care; lower-touch or higher-acuity settings with a longer wait between visits could be a better fit for this type of nurse-led 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.

Relationship Between Unmet Social Needs and Care Access in a Veteran Cohort

BACKGROUND

The association between unmet social needs (e.g., food insecurity) and adverse health outcomes is well-established, especially for patients with and at risk for cardiovascular disease (CVD). This has motivated healthcare systems to focus on unmet social needs. Yet, little is known about the mechanisms by which unmet social needs impact health, which limits healthcare-based intervention design and evaluation. One conceptual framework posits that unmet social needs may impact health by limiting care access, but this remains understudied.

OBJECTIVE

Examine the relationship between unmet social needs and care access.

DESIGN

Cross-sectional study design using survey data on unmet needs merged with administrative data from the Veterans Health Administration (VA) Corporate Data Warehouse (September 2019-March 2021) and multivariable models to predict care access outcomes. Pooled and separate rural and urban logistic regression models were utilized with adjustments from sociodemographics, region, and comorbidity.

SUBJECTS

A national stratified random sample of VA-enrolled Veterans with and at risk for CVD who responded to the survey.

MAIN MEASURES

No-show appointments were defined dichotomously as patients with one or more missed outpatient visits. Medication non-adherence was measured as proportion of days covered and defined dichotomously as adherence less than 80%.

KEY RESULTS

Greater burden of unmet social needs was associated with significantly higher odds of no-show appointments (OR = 3.27, 95% CI = 2.43, 4.39) and medication non-adherence (OR = 1.59, 95% CI = 1.19, 2.13), with similar associations observed for rural and urban Veterans. Social disconnection and legal needs were especially strong predictors of care access measures.

CONCLUSIONS

Findings suggest that unmet social needs may adversely impact care access. Findings also point to specific unmet social needs that may be especially impactful and thus might be prioritized for interventions, in particular social disconnection and legal needs.

Multidisciplinary coordination of care for children with esophageal atresia and tracheoesophageal fistula

Esophageal Atresia/Tracheoesophageal Atresia (EA/TEF) is a multisystem congenital anomaly. Historically, children with EA/TEF lack coordinated care. A multidisciplinary clinic was established in 2005 to provide coordinated care and improve access to outpatient care. This single-center retrospective cohort study was conducted to describe our cohort of patients with EA/TEF born between March 2005 and March 2011, assess coordination of care, and to compare outcomes of children in the multidisciplinary clinic to the previous cohort without a multi-disciplinary clinic. A chart review identified demographics, hospitalizations, emergency visits, clinic visits, and coordination of outpatient care. Twenty-seven patients were included; 75.9% had a C-type EA/TEF. Clinics provided multidisciplinary care and compliance with the visit schedule was high with a median of 100% (IQR 50). Compared to the earlier cohort, the new cohort (N = 27) had fewer hospital admissions and LOS was reduced significantly in the first 2 years of life. Multidisciplinary care clinics for medically complex children can improve coordination of visits with multiple health care providers and may contribute to reduced use of acute care services.

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.

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.

A Predictive Decision Analytics Approach for Primary Care Operations Management: A Case Study of Double-Booking Strategy Design and Evaluation

Primary care plays a vital role for individuals and families in accessing care, keeping well, and improving quality of life. However, the complexities and uncertainties in the primary care delivery system (e.g., patient no-shows/walk-ins, staffing shortage, COVID-19 pandemic) have brought significant challenges in its operations management, which can potentially lead to poor patient outcomes and negative primary care operations (e.g., loss of productivity, inefficiency). This paper presents a decision analytics approach developed based on predictive analytics and hybrid simulation to better facilitate management of the underlying complexities and uncertainties in primary care operations. A case study was conducted in a local family medicine clinic to demonstrate the use of this approach for patient no-show management. In this case study, a patient no-show prediction model was used in conjunction with an integrated agent-based and discrete-event simulation model to design and evaluate double-booking strategies. Using the predicted patient no-show information, a prediction-based double-booking strategy was created and compared against two other strategies, namely random and designated time. Scenario-based experiments were then conducted to examine the impacts of different double-booking strategies on clinic's operational outcomes, focusing on the trade-offs between the clinic productivity (measured by daily patient throughput) and efficiency (measured by visit cycle and patient wait time for doctor). The results showed that the best productivity-efficiency balance was derived under the prediction-based double-booking strategy. The proposed hybrid decision analytics approach has the potential to better support decision-making in primary care operations management and improve the system's performance. Further, it can be generalized in the context of various healthcare settings for broader applications.

Missed appointments in a tertiary academic pediatric ophthalmology and adult strabismus service: cross-sectional study and literature review

PURPOSE

To investigate the rate of missed appointments in a Canadian academic hospital-based pediatric ophthalmology and adult strabismus practice and the demographic and clinical factors associated with missed appointments.

METHODS

This cross-sectional study included all consecutive patients seen from June 1, 2018, to May 31, 2019. Multivariable logistic regression model assessed associations between clinical and demographic variables with no-show status. A literature review on evidence-based interventions to reduce no-show appointments in ophthalmology was performed.

RESULTS

Of 3,922 visits, 718 (18.3%) were no-shows. Characteristics associated with no-shows included new patient (OR = 1.4; 95% CI, 1.1-1.7 [P = 0.001]), age 4-12 years (OR = 1.6; 95% CI, 1.1-2.3 [P = 0.011]) or age 13-18 years (OR = 1.8; 95% CI, 1.2-2.7 [P = 0.007]) compared with age 19+ years, history of previous no-shows (OR = 2.2; 95% CI, 1.8-2.7 [P = 0.001]), referrals from nurse practitioners (OR = 1.8; 95% CI, 1.0-3.2 [P = 0.037]), nonsurgical diagnoses such as retinopathy of prematurity (OR = 3.2; 95% CI, 1.8-5.6 [P < 0.001]), and winter season (OR = 1.4; 95% CI, 1.2-1.7 [P < 0.001]).

CONCLUSIONS

Missed appointments in our pediatric ophthalmology and strabismus academic center are more likely new patient referrals, prior no-shows, referrals from nurse practitioners, and nonsurgical diagnoses. These findings may facilitate targeted strategies to help improve utilization of healthcare resources.

The GROWBABY Research Network: A Framework for Advancing Health Equity Through Community Engaged Practice-Based Research

PURPOSE

Preventive health care, delivered through well child care visits, serves as a universal and primary entry point for promoting child wellbeing, yet children with lower socioeconomic status and children of color receive less consistent and lower quality preventive health care. Currently, limited research exists comparing models for delivering preventive care to children and their impact on longstanding racial/ethnic and socioeconomic inequities.

DESCRIPTION

Practice-based research networks can help to advance health equity by more rapidly studying and scaling innovative, local models of care to reduce racial/ethnic and socioeconomic inequities in primary care and preventive care utilization. This paper outlines a framework of community engagement that can be utilized by practice-based research networks to advance health equity and details the application of the framework using the GROWBABY Research Network (GROup Wellness Visits for BABies and FamilY Research Network).

ASSESSMENT

The GROWBABY Research Network launched in 2020, engaged clinical practices utilizing this unique model of group well childcare - CenteringParenting® - with the following goals: to promote collaboration among researchers, clinicians, patients, and community members; facilitate practice-based research; and increase the use of shared assessment measures and protocols. As a research collaborative, the GROWBABY Research Network connects clinical partners facing similar challenges and creates opportunities to draw upon the assets and strengths of the collective to identify solutions to the barriers to research participation.

CONCLUSION

Primary care, practice-based research networks like the GROWBABY Research Network that intentionally integrate community engagement principles and community-based participatory research methods can advance equitable health care systems and improve child wellbeing.

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.

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

BACKGROUND AND OBJECTIVE

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

METHOD

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

RESULTS

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

CONCLUSIONS

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

The Health Belief Model Modifying Factors Associated with Missed Clinic Appointments among Individuals with Sickle Cell Disease in the Jazan Province, Saudi Arabia

In treating chronic illnesses, such as sickle cell disease (SCD), outpatient care is essential; poor adherence in attending clinic appointments can lead to serious outcomes. SCD is highly prevalent in Saudi Arabia, and patients with SCD are advised to follow up with their treating physician in order to control this disease manifestation and to better forecast its complications. Studies evaluating missed appointments among patients with SCD are rare. Therefore, the current study aimed to use the health belief model's modifying factors in order to evaluate the variables associated with poor adherence in attending appointments. A total of 381 participants with SCD from various regions in the Jazan Province, southwestern Saudi Arabia, were included. The survey instrument included socioeconomic determinants, factors associated with poor adherence in attending outpatient appointments, and solutions under the conceptual framework of the health belief model. A descriptive analysis was conducted and the factors that impacted adherence in attending the appointments were evaluated. In the current sample, respondents with SCD from 21 to 30 years represented 41%, which was followed by participants who were 11 to 20 years at 21.5%. In addition, about 60% of the participants were women. Further, approximately 62% of the patients admitted were missing one or more outpatient appointments in the previous year, which was significantly related to various factors, such as socioeconomic characteristics and patient residence. Forgetting the appointment was the main reason for skipping outpatient appointments for patients with SCD; as such, reminders appear to be a good solution for most participants. Our findings indicated that modifying components of the health belief model, including age, level of education, income, patients' residence, and lacking cues to action (such as reminders) are important in explaining the reason for poor adherence in attending appointments. Thus, efforts are needed to address these factors and to ensure that SCD patients uphold their appointments. Future studies should examine the clinical, psychological, and epidemiological aspects that are linked with missed consultations.

Predicting no-shows for dental appointments

Patient no-shows is a significant problem in healthcare, reaching up to 80% of booked appointments and costing billions of dollars. Predicting no-shows for individual patients empowers clinics to implement better mitigation strategies. Patients' no-show behavior varies across health clinics and the types of appointments, calling for fine-grained studies to uncover these variations in no-show patterns. This article focuses on dental appointments because they are notably longer than regular medical appointments due to the complexity of dental procedures. We leverage machine learning techniques to develop predictive models for dental no-shows, with the best model achieving an Area Under the Curve (AUC) of 0.718 and an F1 score of 66.5%. Additionally, we propose and evaluate a novel method to represent no-show history as a binary sequence of events, enabling the predictive models to learn the associated future no-show behavior with these patterns. We discuss the utility of no-show predictions to improve the scheduling of dental appointments, such as reallocating appointments and reducing their duration.

Effect of COVID-19 pandemic on missed medical appointment among adults with chronic disease conditions in Northwest Ethiopia

BACKGROUND

COVID-19 had affected the health-care-seeking behavior of people with chronic medical conditions. The impact is even worse in resource-limited settings like Ethiopia. Therefore, this study was aimed to assess the extent and correlates of missed appointments among adults with chronic disease conditions before and during the COVID-19 pandemic in the Northwest Ethiopia.

METHODS

A retrospective chart review and cross-sectional survey were conducted from December 2020 to February 2021. A total of 1833 patients with common chronic disease were included by using a stratified systematic random sampling technique. Web-based data collection was done using Kobo collect. The data were explored using descriptive statistical techniques, the rate of missed appointments s before and during the COVID-19 pandemic was determined. A negative binomial regression model was fitted to identify the factors of missed appointment. An incidence rate ratio with its 95% confidence interval (CI) and p-value of the final model were reported.

RESULTS

The rate of missed appointments was 12.5% (95% CI: 11.13%, 14.20%) before the pandemic, increased to 26.8% (95% CI: 24.73%, 28.82%) during the pandemic (p-value < 0.001). Fear of COVID-19 infection and lack of transport was the most common reasons for missing appointments. Older patients (Adjusted Incidence Rate Ratio (AIRR) = 1.01, 95% CI: 1.001; 1.015), having treatment follow up more than 5 years (AIRR = 1.36, 95%CI: 1.103; 1.69), shorter frequency of follow-up (AIRR = 2.22, 95% CI: 1.63; 2.49), covering expense out of pocket (AIRR = 2.26, 95%CI: 1.41; 2.95), having a sedentary lifestyle (AIRR = 1.36, 95%CI: 1.12; 1.71), and history of missed appointments before COVID-19 pandemic (AIRR = 4.27, 95%CI: 3.35; 5.43) were positively associated with the incidence of missed appointments.

CONCLUSION

The rate of missed appointment increased significantly during the COVID-19 pandemic. Older age, longer duration of follow up, more frequent follow-up, out-of-pocket expenditure for health service, history of poor follow-up, and sedentary lifestyle had positive relationship with missed appointments during the pandemic. Therefore, it is important to give special emphasis to individuals with these risk factors while designing and implementing policies and strategies for peoples with chronic diseases to ensure the continuity of care and to avoid the long-term impact on their health.

The Role of Social Determinants of Health in the Use of Telemedicine for Asthma in Children

Asthma is the most common chronic health condition among children in the United States. The adverse impacts of social determinants of health often manifest in unmet health-related social needs, potentially contributing to worse asthma outcomes. With the onset and rapid spread of coronavirus disease 2019 (COVID-19) and the identification of asthma as a potential risk factor for more severe disease, our asthma program quickly pivoted to a remote-access telemedicine asthma population management platform to best meet the needs of our most at-risk patients. Our practice provides care to a large proportion of Black and Latino/a/e children in urban areas insured by the State Medicaid Program and impacted by unmet social needs. As we pivoted to telemedicine, we consistently reached a greater number of patients and families than prepandemic and observed decreased emergency department visits and hospitalizations. About 1 in 5 families received resource touch points spanning categories of transportation, food and supplies, clothing, utilities, and rent. Overall, families reported positive experiences with telemedicine, including the ability to connect remotely with our social work and resource teams. Telemedicine may be an effective strategy for addressing both the medical and the social needs of children with asthma at risk for worse outcomes.

Transportation barriers to care among frequent health care users during the COVID pandemic

Background

Transportation problems are known barriers to health care and can result in late arrivals and delayed or missed care. Groups already prone to greater social and economic disadvantage, including low-income individuals and people with chronic conditions, encounter more transportation barriers and experience greater negative health care consequences. Addressing transportation barriers is important not only for mitigating adverse health care outcomes among patients, but also for avoiding additional costs to the health care system. In this study, we investigate transportation barriers to accessing health care services during the COVID-19 pandemic among high-frequency health care users.

Methods

A web-based survey was administered to North Carolina residents aged 18 and older in the UNC Health system who were enrolled in Medicaid or Medicare and had at least six outpatient medical appointments in the past year. 323 complete responses were analyzed to investigate the prevalence of reporting transportation barriers that resulted in having arrived late to, delayed, or missed care, as well as relationships between demographic and other independent variables and transportation barriers. Qualitative analyses were performed on text response data to explain transportation barriers.

Results

Approximately 1 in 3 respondents experienced transportation barriers to health care between June 2020 and June 2021. Multivariate logistic regressions indicate individuals aged 18–64, people with disabilities, and people without a household vehicle were significantly more likely to encounter transportation barriers. Costs of traveling for medical appointments and a lack of driver or car availability emerged as major transportation barriers; however, respondents explained that barriers were often complex, involving circumstantial problems related to one’s ability to access and pay for transportation as well as to personal health.

Conclusions

To address transportation barriers, we recommend more coordination between transportation and health professionals and the implementation of programs that expand access to and improve patient awareness of health care mobility services. We also recommend transportation and health entities direct resources to address transportation barriers equitably, as barriers disproportionately burden younger adults under age 65 enrolled in public insurance programs.

A pilot study: the impact of clinic-provided transportation on missed clinic visits and system costs among teenage mother-child dyads

Transportation insecurity has profound impacts on the health and wellbeing of teenage parents and their children, who are at particularly high risk for missed clinic visits. In other settings, clinic-offered rideshare interventions have reduced the rates of missed visits. We conducted a one-arm pre-post time series analysis of missed visits before and after a pilot study rideshare intervention within a clinic specializing in the care of teenage parents and their children. We compared the number of missed visits during the study with the number during the preceding year (July 2019-March 2020), as well as the cost difference of missed visits, adjusting for inflation and clinic census. Of 153 rides scheduled, 106 (69.3%) were completed. Twenty-nine (29.9%) of 97 clinic visits were missed during the study period, compared to 145 (32.7%) of 443 comparison period visits (p-value = 0.59). The estimated cost difference of missed visits including intervention costs was a net savings of $90,830.32. However, the standardized cost difference was a net excess of $6.90 per clinic visit. We found no difference in rates of missed visits or costs, though likely impacted by the low census during the SARS-CoV-2 pandemic. Given the potential to improve health disparities exacerbated by the pandemic, further research is warranted into the impact and utility of clinic-offered rideshare interventions.

Predictors of Patient Return to a Tertiary Neurofibromatosis Subspecialty Clinic

OBJECTIVE

To evaluate sociodemographic and medical predictors of patient return to a neurofibromatosis subspecialty clinic.

STUDY DESIGN

Data were collected from the Washington University Neurofibromatosis Clinical Program electronic medical records. A total of 713 subjects with initial visits to the Washington University Neurofibromatosis Clinical Program between July 1, 2005 and December 18, 2020 were included. Variables collected included sex, race, ethnicity, age, date of first visit, place of residence, diagnosis, insurance payer, physician recommendation for return, and subject return. Return rates for each demographic group were calculated. Bivariate analyses were performed to inform variable inclusion in the model, and a binary logistic regression model was calculated to predict subject return.

RESULTS

The overall return rate was 76%. The binary logistic regression model was statistically significant (χ²₉ = 131.094; P < .001) and showed that subjects who self-identified as Black and/or African American, presented with or received a diagnosis of café-au-lait macules at their initial visit, were from a rural area, were older, or who lived farther from the Washington University Neurofibromatosis Clinical Program were less likely to return to clinic.

CONCLUSIONS

These findings support the implementation of tailored communication and monitoring interventions to improve the care for children with neurofibromatosis type 1.

Interprofessional Collaborative Practice Improves Access to Care and Healthcare Quality to Advance Health Equity

ABSTRACT

Healthcare disparites exist in cardiovascular care, including heart failure. Care that is not equitable can lead to higher incidence of heart failure, increased readmissions, and poorer outcomes. The Heart Failure Transitional Care Services for Adults Clinic is an interprofessional collaborative practice that provides guideline-directed medical therapy and education to underserved patients with heart failure. Little is known regarding healthcare equity and quality metrics in relation to interprofessional teams. Thus, the purpose of this study was to examine if an interprofessional collaborative practice care delivery model can affect access to care and healthcare quality outcomes in underserved patients with heart failure. As evidenced by control charts over a two and a half year period, the Heart Failure Transitional Care Services for Adults Clinic was able to show improvements in access to care and quality metrics results without variation. An interprofessional collaborative practice can be an effective delivery model to address health equity and quality of care outcomes.

Predicting Patient Wait Times by Using Highly Deidentified Data in Mental Health Care: Enhanced Machine Learning Approach

BACKGROUND

Wait times impact patient satisfaction, treatment effectiveness, and the efficiency of care that the patients receive. Wait time prediction in mental health is a complex task and is affected by the difficulty in predicting the required number of treatment sessions for outpatients, high no-show rates, and the possibility of using group treatment sessions. The task of wait time analysis becomes even more challenging if the input data has low utility, which happens when the data is highly deidentified by removing both direct and quasi identifiers.

OBJECTIVE

The first aim of this study was to develop machine learning models to predict the wait time from referral to the first appointment for psychiatric outpatients by using real-time data. The second aim was to enhance the performance of these predictive models by utilizing the system's knowledge while the input data were highly deidentified. The third aim was to identify the factors that drove long wait times, and the fourth aim was to build these models such that they were practical and easy-to-implement (and therefore, attractive to care providers).

METHODS

We analyzed retrospective highly deidentified administrative data from 8 outpatient clinics at Ontario Shores Centre for Mental Health Sciences in Canada by using 6 machine learning methods to predict the first appointment wait time for new outpatients. We used the system's knowledge to mitigate the low utility of our data. The data included 4187 patients who received care through 30,342 appointments.

RESULTS

The average wait time varied widely between different types of mental health clinics. For more than half of the clinics, the average wait time was longer than 3 months. The number of scheduled appointments and the rate of no-shows varied widely among clinics. Despite these variations, the random forest method provided the minimum root mean square error values for 4 of the 8 clinics, and the second minimum root mean square error for the other 4 clinics. Utilizing the system's knowledge increased the utility of our highly deidentified data and improved the predictive power of the models.

CONCLUSIONS

The random forest method, enhanced with the system's knowledge, provided reliable wait time predictions for new outpatients, regardless of low utility of the highly deidentified input data and the high variation in wait times across different clinics and patient types. The priority system was identified as a factor that contributed to long wait times, and a fast-track system was suggested as a potential solution.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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