The effectiveness of outpatient appointment reminder systems in reducing no-show rates

Predictors of Subspecialty Appointment Scheduling and Completion for Patients Referred From a Pediatric Primary Care Clinic

Failure to complete subspecialty referrals decreases access to subspecialty care and may endanger patient safety. We conducted a retrospective analysis of new patient referrals made to the 14 most common referral departments at Boston Children's Hospital from January 1 to December 31, 2017. The sample included 2031 patient referrals. The mean wait time between referral and appointment date was 39.6 days. In all, 87% of referrals were scheduled and 84% of scheduled appointments attended, thus 73% of the original referrals were completed. In multivariate analysis, younger age, medical complexity, being a non-English speaker, and referral to a surgical subspecialty were associated with a higher likelihood of referral completion. Black and Hispanic/Latino race/ethnicity, living in a Census tract with Social Vulnerability Index (SVI) ≥ 90th percentile, and longer wait times were associated with a lower likelihood of appointment attendance. Future interventions should consider both health care system factors such as appointment wait times and community-level barriers to referral completion.

Relationship between syncopal symptoms and head-up tilt test modes

OBJECTIVE

Head-up tilt test (HUTT) is an important tool in the diagnosis of pediatric vasovagal syncope. This research will explore the relationship between syncopal symptoms and HUTT modes in pediatric vasovagal syncope.

METHODS

A retrospective analysis was performed on the clinical data of 2513 children aged 3-18 years, who were diagnosed with vasovagal syncope, from Jan. 2001 to Dec. 2021 due to unexplained syncope or pre-syncope. The average age was 11.76 ± 2.83 years, including 1124 males and 1389 females. The patients were divided into the basic head-up tilt test (BHUT) group (596 patients) and the sublingual nitroglycerine head-up tilt test (SNHUT) group (1917 patients) according to the mode of positive HUTT at the time of confirmed pediatric vasovagal syncope.

RESULTS

(1) Baseline characteristics: Age, height, weight, heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), and composition ratio of syncope at baseline status were higher in the BHUT group than in the SNHUT group (all P < 0.05). (2) Univariate analysis: Age, height, weight, HR, SBP, DBP, and syncope were potential risk factors for BHUT positive (all P < 0.05). (3) Multivariate analysis: syncope was an independent risk factor for BHUT positive, with a probability increase of 121% compared to pre-syncope (P<0.001).

CONCLUSION

The probability of BHUT positivity was significantly higher than SNHUT in pediatric vasovagal syncope with previous syncopal episodes.

Predicting Kidney Transplant Evaluation Non-attendance

Non-attendance to kidney transplant evaluation (KTE) appointments is a barrier to optimal care for those with kidney failure. We examined the medical and socio-cultural factors that predict KTE non-attendance to identify opportunities for integrated medical teams to intervene. Patients scheduled for KTE between May, 2015 and June, 2018 completed an interview before their initial KTE appointment. The interview assessed various social determinants of health, including demographic (e.g., income), medical (e.g. co-morbidities), transplant knowledge, cultural (e.g., medical mistrust), and psychosocial (e.g., social support) factors. We used multiple logistic regression analysis to determine the strongest predictor of KTE non-attendance. Our sample (N = 1119) was 37% female, 76% non-Hispanic White, median age 59.4 years (IQR 49.2-67.5). Of note, 142 (13%) never attended an initial KTE clinic appointment. Being on dialysis predicted higher odds of KTE non-attendance (OR 1.76; p = .02; 64% of KTE attendees on dialysis vs. 77% of non-attendees on dialysis). Transplant and nephrology teams should consider working collaboratively with dialysis units to better coordinate care, (e.g., resources to attend appointment or outreach to emphasize the importance of transplant) adjusting the KTE referral and evaluation process to address access issues (e.g., using tele-health) and encouraging partnership with clinical psychologists to promote quality of life for those on dialysis.

Automatic 1-year follow-up appointment creation and reminders can improve long-term follow-up after carotid revascularization

BACKGROUND

Long-term follow-up (LTFU) following carotid revascularization is important for post-surgical care, stroke risk optimization and post-market surveillance of new technologies.

METHODS

We instituted a quality improvement project to improve LTFU rates for carotid revascularizations (primary outcome) by scheduling perioperative and one-year follow-up appointments at time of surgery discharge. A temporal trends analysis (Q1 2019 through Q1 2022), multivariable regression, and interrupted time series (ITS) were performed to compare pre-post intervention LTFU rates.

RESULTS

269 consecutive patients were included (151 pre-intervention, 118 post-intervention; mean 71 ​± ​12 years-old, 39% female, 77% White). The overall LTFU rate improved (64.9%-78.8%; P ​= ​0.013) after the intervention. After controlling for patient factors, procedures performed after the intervention were associated with increased odds of being seen for 1-year follow-up (OR: 2.2 95%CI: 1.2-4.0). Quarterly ITS analysis corroborated this relationship (P ​= ​0.01).

CONCLUSIONS

Time-of-surgery appointment creation and automated patient reminders can improve LTFU rates following carotid revascularizations.

Follow-up non-attendance after long-bone fractures in a low-resource setting: a prospective study of predictors and interventions to increase attendance rates

BACKGROUND

While the majority of traumatic injuries occur in low- and middle-income countries, the published literature comes chiefly from high-income countries due to poor follow-up. Clinical and radiographic post-surgical trauma follow-up is essential to high-quality research and objective monitoring for healing and/or complications. This study aimed to identify the predictors of follow-up non-attendance in a low-resource setting and investigate the extent to which interventional efforts based on mobile phone technology (MPT) and home visits improved the follow-up rates for fractures treated with SIGN nails.

METHODS

This was a prospective study of 594 patients with long-bone fractures. Socio-demographic (e.g. age, gender, marital status, education level, etc.) and clinical (e.g. fracture type, concomitant injuries, comorbidity, etc.) data were collected on each patient. Before discharge, the importance of follow-up was explained to patients and their relations. They were encouraged to attend even if they felt well. Their residential addresses and telephone numbers were validated and securely stored. Patients who missed their appointments were contacted by phone. Those who failed to honour 2 or 3 rescheduled appointments were visited in their home. The patients were divided into those who returned for the primarily scheduled follow-up without prompting (volition group) and those who did not come (non-attenders). Univariate analyses and binary logistic regression were conducted to determine the significant predictors of non-attendance.

RESULTS

The proportion of patients in the volition group reduced from 96.1% at 6 weeks to 53.0% at 12 weeks and 39.2% at 6 months. However, interventional efforts increased these values to 98.5%, 92.5%, and 72.4% respectively. Walking unaided before the primarily scheduled 12-week appointment was the major reason for not attending the follow-up. Education, occupation, post-operative length of hospital stay (PLOS) and infection were significantly associated with non-attendance but younger age, long distances from the hospital, being separated or divorced, difficulty paying the in-patient care bill, closed fracture, having no (or a non-limb) concomitant injury, achieving painless weight bearing ≤ 6 weeks post-operatively and needing no additional surgery were independent predictors of non-attendance.

CONCLUSIONS

Our study sheds light on the predictors of follow-up non-attendance and demonstrates how interventional efforts improved attendance rates in a low-resource setting. In addition, efforts that better the socio-economic status of people such as more-encompassing health insurance coverage and greater work flexibility can improve the follow-up attendance rates.

Leveraging machine learning to create user-friendly models to mitigate appointment failure at dental school clinics

PURPOSE/OBJECTIVES

This study had a twofold outcome. The first aim was to develop an efficient, machine learning (ML) model using data from a dental school clinic (DSC) electronic health record (EHR). This model identified patients with a high likelihood of failing an appointment and provided a user-friendly system with a rating score that would alert clinicians and administrators of patients at high risk of no-show appointments. The second aim was to identify key factors with ML modeling that contributed to patient no-show appointments.

METHODS

Using de-identified data from a DSC EHR, eight ML algorithms were evaluated: simple decision tree, bagging regressor classifier, random forest classifier, gradient boosted regression, AdaBoost regression, XGBoost regression, neural network, and logistic regression classifier. The performance of each model was assessed using a confusion matrix with different threshold level of probability; precision, recall and predicted accuracy on each threshold; receiver-operating characteristic curve (ROC) and area under curve (AUC); as well as F1 score.

RESULTS

The ML models agreed on the threshold of probability score at 0.20-0.25 with Bagging classifier as the model that performed best with a F1 score of 0.41 and AUC of 0.76. Results showed a strong correlation between appointment failure and appointment confirmation, patient's age, number of visits before the appointment, total number of prior failed appointments, appointment lead time, as well as the patient's total number of medical alerts.

CONCLUSIONS

Altogether, the implementation of this user-friendly ML model can improve DSC workflow, benefiting dental students learning outcomes and optimizing personalized patient care.

Cost-effectiveness analysis of phone reminders for outpatient magnetic resonance imaging (MRI) appointments in Singapore

INTRODUCTION

Due to long wait times, rising demand and limited resources for Magnetic Resonance Imaging (MRI) services, phone call reminders were implemented as an intervention to increase scanner utilisation and improve non-attendance at the radiology department in Changi General Hospital, Singapore.

AIM

This study aims to evaluate the impact of phone reminders on outpatient MRI non-attendance rate as well as the operational efficiency and savings of this intervention through cost-effectiveness analysis.

METHODS

MRI outpatient records from January to December 2020 (pre-intervention period) and January to December 2021 (post-intervention period) were retrospectively obtained from the hospital systems. Non-attendance rates, costs and savings following the intervention were compared.

RESULTS

Outpatient appointment non-attendance rates reduced from 12.85% to 8.93% after intervention. Following the phone reminders, 2,953 patients (21.69%) decided to cancel or reschedule their appointments. Based on the 91.07% attendance rate (100% - 8.93%), another 2689 slots were recovered from the cancellation of these appointments and were given to other patients. The reduction in non-attendance rates (3.92%) after the intervention translates to an increase in attendance of 533 patients while the net revenue generation with the phone reminder intervention was $387,179.

CONCLUSION

Cost analysis indicates that phone reminders provide an inexpensive, easily implemented and personalised method to help increase adherence and improve appointment attendance. Reminding patients by phone calls two day before their appointments also leads to better optimization of appointment slots from cancelations and re-scheduling that can be used to allocate these appointments to other patients.

Impact of Automated Reminder Calls in a Safety-Net Setting on Surgical Clinic No-Show Rates

In surgical clinics, missed appointments may lead to delayed diagnosis and postponed surgical intervention. Automated reminder calls (robocalls) have replaced live staff phone calls in many systems as a cost-saving measure. This study aims to evaluate whether robocalls reduced the outpatient appointment no-show rate for surgical patients in a county hospital. Demographic and clinic data from two surgical clinics at a safety net hospital were collected over two time periods: 3-months immediately before robocalls went live and 3-months immediately after robocalls went live. No-show rates were compared between time periods. Multivariate analysis confirmed that robocalls were independently associated with reduced no-show rates (OR: 1.32; 95% CI: 1.0-1.7; P = .032). In addition, new appointments were independently predictive of higher no-show rates (OR: 1.32; 95% CI: 1.0-1.7; P = .048). Robocalls appear to be an effective tool for improving appointment attendance overall. Furthermore, robocalls may free limited staff to perform higher value work in the healthcare system.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

OBJECTIVE

To evaluate factors associated with no-show rates in a pediatric audiology clinic.

STUDY DESIGN

Retrospective review.

SETTING

Tertiary referral center.

PARTICIPANTS

All pediatric patients younger than 18 years whose parents/guardians scheduled an appointment at a tertiary Audiology Clinic between June 1, 2015, and July 1, 2017.

MAIN OUTCOME MEASURES

Data included whether the patient came to their appointment, patient age, sex, race, insurance type, appointment type, location, season of appointment, and day of the week of the appointment.

RESULTS

Of the 7,784 pediatric appointments scheduled with audiology, the overall no-show rate was 24.3% (n = 1893). Lower age was significantly associated with no-shows ( p = 0.0003). Black/African American children were more likely to no-show compared with White/Caucasians ( p = 0.0001). Compared with self-pay/military/other insurance, those with Medicaid were more likely to no-show ( p = 0.0001). The highest rate of no-shows occurred during summer (27%). On multivariate analysis, younger age, Black/African American race, and Medicaid insurance were associated with increased no-show rates.

CONCLUSION

A variety of factors influence no-show rates in a pediatric audiology setting. No-shows can affect treatment quality and affect overall hearing outcomes. Further investigation is necessary to assess barriers to appointment adherence and to develop interventions to improve adherence and care.

Reducing Disparities in No Show Rates Using Predictive Model-Driven Live Appointment Reminders for At-Risk Patients: a Randomized Controlled Quality Improvement Initiative

BACKGROUND

Appointment no shows are prevalent in safety-net healthcare systems. The efficacy and equitability of using predictive algorithms to selectively add resource-intensive live telephone outreach to standard automated reminders in such a setting is not known.

OBJECTIVE

To determine if adding risk-driven telephone outreach to standard automated reminders can improve in-person primary care internal medicine clinic no show rates without worsening racial and ethnic show-rate disparities.

DESIGN

Randomized controlled quality improvement initiative.

PARTICIPANTS

Adult patients with an in-person appointment at a primary care internal medicine clinic in a safety-net healthcare system from 1/1/2022 to 8/24/2022.

INTERVENTIONS

A random forest model that leveraged electronic health record data to predict appointment no show risk was internally trained and validated to ensure fair performance. Schedulers leveraged the model to place reminder calls to patients in the augmented care arm who had a predicted no show rate of 15% or higher.

MAINE MEASURES

The primary outcome was no show rate stratified by race and ethnicity.

KEY RESULTS

There were 5840 appointments with a predicted no show rate of 15% or higher. A total of 2858 had been randomized to the augmented care group and 2982 randomized to standard care. The augmented care group had a significantly lower no show rate than the standard care group (33% vs 36%, p < 0.01). There was a significant reduction in no show rates for Black patients (36% vs 42% respectively, p < 0.001) not reflected in white, non-Hispanic patients.

CONCLUSIONS

In this randomized controlled quality improvement initiative, adding model-driven telephone outreach to standard automated reminders was associated with a significant reduction of in-person no show rates in a diverse primary care clinic. The initiative reduced no show disparities by predominantly improving access for Black patients.

Intraday dynamic rescheduling under patient no-shows

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

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

BACKGROUND

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

OBJECTIVE

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

DESIGN

Cluster randomized controlled pragmatic trial.

PATIENTS

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

INTERVENTIONS

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

MAIN MEASURES

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

STATISTICAL ANALYSIS

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

KEY RESULTS

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

CONCLUSIONS

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

TRIAL NUMBER

ClinicalTrials.gov, Trial number NCT03850431.

Pragmatic randomized trial of a pre-visit intervention to improve the quality of telemedicine visits for vulnerable patients living with HIV

INTRODUCTION

The COVID-19 pandemic has required a shift of many routine primary care visits to telemedicine, potentially widening disparities in care access among vulnerable populations. In a publicly-funded HIV clinic, we aimed to evaluate a pre-visit phone-based planning intervention to address anticipated barriers to telemedicine.

METHODS

We conducted a pragmatic randomized controlled trial of patients scheduled for a phone-based HIV primary care visit at the Ward 86 HIV clinic in San Francisco from 15 April to 15 May 2020. Once reached by phone, patients were randomized to either have a structured pre-visit planning intervention to address barriers to an upcoming telemedicine visit versus a standard reminder call. The primary outcome was telemedicine visit attendance.

RESULTS

Of 476 scheduled telemedicine visits, 280 patients were reached by a pre-visit call to offer enrollment. Patients were less likely to be reached if virally unsuppressed (odds ratio (OR) 0.11, 95% confidence intervals (CI) 0.03-0.48), CD4 < 200 (OR 0.24, 95% CI 0.07-0.85), or were homeless (OR 0.24, 95% CI 0.07-0.87). There was no difference between intervention and control in scheduled visit attendance (83% v. 78%, OR 1.38, 95% CI 0.67-2.81).

CONCLUSIONS

A structured phone-based planning call to address barriers to telemedicine in a public HIV clinic was less likely to reach patients with poorly-controlled HIV and patients experiencing homelessness, suggesting additional interventions may be needed in this population to ensure access to telemedicine-based care. Among patients reachable by phone, telemedicine visit attendance was high and not improved with a structured pre-visit intervention, suggesting that standard reminders may be adequate in this population.

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.

Evaluation of an Automated Reminder System for Reducing Missed MRI Appointments

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

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

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

Understanding barriers of receiving short message service appointment reminders across African regions: a systematic review

OBJECTIVE

Patients frequently miss their medical appointments. Therefore, short message service (SMS) has been used as a strategy for medical and healthcare service appointment reminders. This systematic review aimed to identify barriers to SMS appointment reminders across African regions.

METHODS

PubMed, Google Scholar, Semantic Scholar and Web of Science were used for searching, and hand searching was done. Original studies written in English, conducted in Africa, and published since 1 December 2018, were included. The standard quality assessment checklist was used for the quality appraisal of the included studies. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses flowchart diagram was used for study selection and screening, and any disagreements were resolved via discussions.

RESULTS

A total of 955 articles were searched, 521 studies were removed due to duplication and 105 studies were assessed for eligibility. Consequently, nine studies met the inclusion criteria. Five out of nine included studies were done by randomised control trials. The barriers that hampered patients, mothers and other parental figures of children when they were notified via SMS of medical and health services were identified. Among the 11 identified barriers, illiteracy, issues of confidentiality, familiarised text messages, inadequate information communication technology infrastructure, being a rural resident and loss of mobile phones occurred in at least two studies.

CONCLUSIONS

SMS is an effective and widely accepted appointment reminder tool. However, it is hampered by numerous barriers. Hence, we gathered summarised information about users' barriers to SMS-based appointment reminders. Therefore, stakeholders should address existing identified barriers for better Mhealth interventions.

PROSPERO REGISTRATION NUMBER

CRD42022296559.

Parkland Diabetes Clinic

Quality Improvement Success Stories are published by the American Diabetes Association in collaboration with the American College of Physicians and the National Diabetes Education Program. This series is intended to highlight best practices and strategies from programs and clinics that have successfully improved the quality of care for people with diabetes or related conditions. Each article in the series is reviewed and follows a standard format developed by the editors of Clinical Diabetes. The following article describes an initiative to reduce the no-show rate for appointments at the Parkland Diabetes Clinic in Dallas, TX.

Factors predicting first appointment attendance at a traumatic brain injury clinical neuropsychology outpatient clinic: a logistic regression analysis

BACKGROUND

The purpose of our study was to investigate factors which predicted first appointment attendance within a traumatic brain injury (TBI) neuropsychology outpatient department.

MATERIALS AND METHODS

A newly introduced telephone triaging system was implemented in a clinical neuropsychology service for individuals with a TBI. The effects of receiving a triage telephone call, amongst other variables, were analysed as predictors of attendance at the first face-to-face clinic appointment. The data from 161 individuals were analysed using routine patient information collected by the clinical neuropsychology service. Logistic regression analyses were performed to investigate predictors of first appointment clinic attendance.

RESULTS

Logistic regression analyses identified higher age, shorter waiting times, and answering the triage call as potential predictors of attendance, highlighting where the service might focus efforts to facilitate attendance.

CONCLUSIONS

Both patient and service factors were found to be significant predictors of patient attendance. Further service evaluation could explore patients' experiences of triage telephone calls, and investigate relationships between waiting times and neuropsychological outcomes.IMPLICATIONS FOR REHABILITATIONIdentifying predictors of appointment attendance can allow the service to focus on the needs of particular patient groups.Implementing a telephone triage initiative had positive effects, both on waiting times and efficient use of face-to-face clinic time.The analysis highlighted the need to think about better ways of reaching out to younger individuals and those who have waited longer to attend appointments, who are less likely to attend once invited.

Telemedicine Orthopedic Consultations Duration and Timing in Outpatient Clinical Practice During the COVID-19 Pandemic

Introduction: Orthopedic associations advocated telemedicine during the COVID-19 pandemic to prevent disease transmission without hindering providing services to orthopedic patients. The study aimed to evaluate outpatient orthopedic teleconsultations' timing, length, and organizational issues in the circumstances of the COVID-19 pandemic based on consecutive orthopedic teleconsultations during the period of the first lockdown. Methods: Orthopedic telemedical consultations (OTCs) were provided from March 23, 2020, to June 1, 2020, and analyzed retrospectively based on mobile smartphone billing and electronic health record. Teleconsultations were based on the legal regulations of telemedicine services in Poland. Results: One thousand seventy-one patients (514 women and 557 men) with a mean age of 41.7 were teleconsulted. The length of the OTC averagely lasted 13.36 min (standard deviation 8.63). Consulted patients suffered from orthopedic disorders 65.3%, musculoskeletal injuries 26.3%, and other diseases 8.4%. Most OTCs were delayed (74.22%) concerning the planned schedule, with a median delay time of 12 min. Only 7.3% of teleconsultations were held precisely on time. Conclusions: Televisit length may not be dependent on gender, older age, or more diagnoses. The services like e-prescriptions, e-Referrals, e-Orders for orthotics, and e-Sick-leaves influence OTC length. Any extension of the patient's OTC may create a "snowball effect" of further delay for each subsequent OTC. Orthopedic teleconsultation requires new understanding and skills by both the patient and specialist physicians. Future research directions should concern the practical aspects of orthopedic teleconsultations, like legal, organizational, and technological issues and their implementation.

SMS reminders to improve outpatient attendance for public dental services: A retrospective study

Patients who miss scheduled appointments reduce clinical productivity and delay access to care for other patients. Reminders have improved attendance for healthcare appointments previously, but it is not known if short message service (SMS) implementation reduces incidence of patients unable to attend (UTA) or who fail to attend (FTA) appointments in the public dental service. This paper studied the effectiveness of SMS reminders in increasing appointment attendance at outpatient public dental services in Queensland. Data were sourced from the adult service and the children and adolescent oral health service (CAOHS) at West Moreton Hospital and Health Service, a public dental service in Queensland. A total of 63,238 appointments pre-implementation of SMS reminders and 55,028 appointments post-implementation over a period of 2 years were analysed for rates of attendance, UTA and FTA. Characteristics of UTA and FTA appointments were analysed to identify factors that hindered improvement after implementation of reminders. For the CAOHS, the attendance rate decreased 4% (95% CI: 2%, 6%) following SMS implementation. The UTA rate also increased by 20% (95% CI: 15%, 25%). Following SMS implementation in the adult service, the attendance rate increased from 73.5 (95% CI: 72.6, 74.4) to 77.7 (95% CI: 76.6-78.8) per 100 appointments. The FTA rate post-implementation was 1.08 (95% CI: 1.00, 1.16) times that from pre-intervention, and the UTA rate decreased from 21.7 (95% CI: 21.2, 22.2) to 17.1 (95% CI: 16.6, 17.7) per 100 appointments. The SMS reminders had a mixed effect on the attendance, UTA and FTA rates for the CAOHS and adult services. Reminders reduced the rates of UTA for the CAOHS service and increased the rate of attendance for the adult service. There was an increase in the FTA rate for both services.

Factors associated with the absence of Brazilians in specialized dental centers

Aim

To identify the individual and contextual factors associated with the absence of Brazilians at a scheduled appointment in Dental Specialties Centers (DSC).

Methods

This cross-sectional design uses the National Program for Improving Access and Quality of Dental Specialties Centers database, 2018. The outcome was the users' lack of at least one of the scheduled appointments. Contextual and individual independent variables were used, considering Andersen's behavioural model. The analyses were performed with the R Core Team and SAS (Studio 3.8, Institute Inc, North Carolina, U.S, 2019) programs.

Results

Of the 10,391 patients interviewed, 27.7% missed at least one of the consultations. In the adjusted multivariate model, the interpretation based on the effect size and 95% CI showed that the behaviour individual predisposing factors such as age ≤ 42 years (OR = 1.10; 95%CI:1.01–1.21), individual need factors such as participation in the “Bolsa Família” program (OR = 1,14; 95%CI:1.02–1.27), not being covered by the Family Health Strategy (OR = 1.15; 95% CI:1.02–1.30), and users of periodontics services (OR = 1.22;95%CI:1.05–1.40) were associated with absences. The behavioural factor associated with the outcome was that the DSC facilities were not in good condition (OR = 1.18; 95%CI:1.03–1.34). DSC located in the capital (OR = 1.12; 95% CI: 0.92–1.48) were 12% more likely to have dental absences than those in the interior region.

Conclusion

There are individual and contextual barriers associated with patients not attending specialised public dental consultations. DSC should offer adequate hours to patients, especially young adults and vulnerable people.

Effects of a brief pre-admission telephone reminder on no-show and dropout rates in substance use disorder treatment: a quasi-experimental study

Background

Appointment no-show and early dropout from treatment represent major challenges in outpatient substance use disorder treatment, adversely affecting clinical outcomes and health care productivity. In this quasi-experimental study, we examined how a brief reminder intervention for new patients before their first appointment affected treatment participation and retention. No-shows (not attending any sessions) and dropouts (discontinuation of initiated treatment because of three consecutively missed appointments) were compared between a period with pre-admission telephone calls (intervention) and a period without such reminders (non-intervention).

Methods

Participants were all eligible patients (N = 262) admitted to a Norwegian specialist clinic for substance use disorder treatment. We used the Chi-square test for the no-show analysis. Of the eligible patients, 147 were included in a subsequent dropout analysis. We used the number of visits up to 10 appointments as a measure for time to event. Group differences were analyzed using a Kaplan–Meier plot and the log-rank test. To control for relevant sociodemographic variables, as well as substance use and mental distress severity, we used Cox regression.

Results

No-show rates did not differ between the two periods (12% for non-intervention vs. 14% for intervention; χ2 = 0.20, p = 0.653). Of those consenting to participate in the dropout analysis (n = 147), 28 (19%) discontinued treatment within the time frame of 10 appointments, with no differences between the two periods (log-rank test = 0.328, p = 0.567). Controlling for baseline characteristics did not alter this finding. In fact, of the registered covariates at baseline, only higher education level was associated with attrition, linked to a reduced risk for dropout (hazard ratio = 0.85, 95% CI = 0.74–0.98, p = 0.025).

Conclusion

These findings do not provide support for the systematic use of a brief pre-admission telephone reminder in the current treatment setting.

Trial registration

The study was retrospectively registered 13 Jan 2021 at ClinicalTrials.gov, NCT04707599.

Study of Impact of Telehealth Use on Clinic "No Show" Rates at an Academic Practice

OBJECTIVE

To examine the clinic no-show rate across different modalities of care delivery (Face to Face, Telephone visits and Audio-Video visits).

METHODS

Clinic no show data for adult patients was extracted from the electronic health records used by the psychiatry clinic for 10 months before pandemic and 10 months during pandemic. No show rate was analyzed by visits type (new vs return) and across different modalities (face-to-face vs Telephone vs Audio-Video) before and during COVID pandemic.

RESULTS

There were 13,916 scheduled visits during the 10-month period before the pandemic of which 2,522 were no show. There were 13,251 scheduled visits during the 10-month period during the COVID pandemic of which 2,029 were no show. The overall clinic no show rate decreased from pre pandemic to pandemic period (18.1% vs 15.3%) after transitioning to telehealth. Across different modalities during the pandemic, the no-show rate for Telephone visits was significantly lower than for face- to-face visits. No difference was identified for no-show rates between face-to-face visits and audio-video visits during the pandemic. The no-show rate for face-to-face visits before the pandemic compared to during the pandemic also showed no difference.

CONCLUSION

Using technology in health care delivery can decrease the clinic no show rate. Digital literacy for patients and providers is critical for successful utilization of telehealth.

Predicting Patient No-Shows in an Academic Pediatric Neurology Clinic

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

Reducing non-attendance in outpatient appointments: predictive model development, validation, and clinical assessment

Background

Non-attendance to scheduled hospital outpatient appointments may compromise healthcare resource planning, which ultimately reduces the quality of healthcare provision by delaying assessments and increasing waiting lists. We developed a model for predicting non-attendance and assessed the effectiveness of an intervention for reducing non-attendance based on the model.

Methods

The study was conducted in three stages: (1) model development, (2) prospective validation of the model with new data, and (3) a clinical assessment with a pilot study that included the model as a stratification tool to select the patients in the intervention. Candidate models were built using retrospective data from appointments scheduled between January 1, 2015, and November 30, 2018, in the dermatology and pneumology outpatient services of the Hospital Municipal de Badalona (Spain). The predictive capacity of the selected model was then validated prospectively with appointments scheduled between January 7 and February 8, 2019. The effectiveness of selective phone call reminders to patients at high risk of non-attendance according to the model was assessed on all consecutive patients with at least one appointment scheduled between February 25 and April 19, 2019. We finally conducted a pilot study in which all patients identified by the model as high risk of non-attendance were randomly assigned to either a control (no intervention) or intervention group, the last receiving phone call reminders one week before the appointment.

Results

Decision trees were selected for model development. Models were trained and selected using 33,329 appointments in the dermatology service and 21,050 in the pneumology service. Specificity, sensitivity, and accuracy for the prediction of non-attendance were 79.90%, 67.09%, and 73.49% for dermatology, and 71.38%, 57.84%, and 64.61% for pneumology outpatient services. The prospective validation showed a specificity of 78.34% (95%CI 71.07, 84.51) and balanced accuracy of 70.45% for dermatology; and 69.83% (95%CI 60.61, 78.00) for pneumology, respectively. The effectiveness of the intervention was assessed on 1,311 individuals identified as high risk of non-attendance according to the selected model. Overall, the intervention resulted in a significant reduction in the non-attendance rate to both the dermatology and pneumology services, with a decrease of 50.61% (p<0.001) and 39.33% (p=0.048), respectively.

Conclusions

The risk of non-attendance can be adequately estimated using patient information stored in medical records. The patient stratification according to the non-attendance risk allows prioritizing interventions, such as phone call reminders, to effectively reduce non-attendance rates.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-07865-y.

No-show after extracorporeal shock wave lithotripsy treatment in endourology clinic: Can we build a typical patient profile?

Objectives

Patients “no‐show” in outpatient clinics is a worldwide challenge. Healthcare providers and patients suffer from negative impacts that include increased expenditure, clinical management ineffectiveness, and decreased access to care. This study aims to evaluate no‐show rate among extracorporeal shock wave lithotripsy patients visiting endourology clinic and to identify the demographic and clinical predictors of no‐show.

Methods

A cross‐sectional and historical cohort study using electronic medical records. We included 790 patients aged >18 years old referred for endourology clinic following shock wave lithotripsy during 2010–2017 at Hadassah Medical Center in Israel. We predicted no‐show rate following shock wave lithotripsy by various patient characteristics by a multivariate logistic regression model.

Results

Overall, 291 (36.8%) patients did not arrive for postoperative clinic. Of these, 91 (11.52%) patients referred to Emergency Department. Patients who were younger in age (odds ratio 1.49, 95% confidence interval 1.08–2.04), patients who underwent hospitalization ≥3 days (odds ratio 1.63, 95% confidence interval 1.11–2.41) and patients who had undergone a stent‐free shock wave lithotripsy (odds ratio 5.71, 95% confidence interval 2.40–13.57) were significantly associated with higher no‐show rate. Larger stone size was associated with reduction in no‐show rate with every millimeter increase of stone diameter was associated with a reduction of 6.1% probability for no‐show (odds ratio 0.94, 95% confidence interval 0.89–0.99).

Conclusions

Predicting patients' characteristics and no‐show patterns is necessary to improve clinical management efficiency, access to care, and costs. We showed that patients who were younger, patients who underwent stent‐free shock wave lithotripsy, patients who had a smaller stone, and patients who underwent a longer hospitalization were more prone to miss their appointment. Paying attention to the characteristics of individual patients may assist in implementing intervening program of patient scheduling.

How scheduling systems with automated appointment reminders improve health clinic efficiency

Missed clinic appointments or no-shows burden health care systems through inefficient use of staff time and resources. Scheduling software with automatic appointment reminders shows promise to improve clinics' management through timely cancellations and re-scheduling, but at-scale evidence is missing. We study a nationwide text message appointment reminder program in Chile implemented at primary care clinics for patients with chronic disease. Using longitudinal clinic-level data, we find that the program did not change the number of visits by chronic patients eligible to receive the reminder but visits from other patients ineligible to receive reminders increased by 5.0% in the first year and 7.4% in the second. Clinics treating more chronic patients and those with a relatively younger patient population benefited more from the program. Scheduling systems with automatic appointment reminders were effective in increasing clinics' ability to care for more patients, likely due to timely cancellations and re-scheduling.

Association of Use of Electronic Appointment Reminders With Waiting Times in the Veterans Affairs Health System

IMPORTANCE

Electronic appointment reminder systems are increasingly used across health systems. However, their association with patients' waiting times for their appointments, a measure of timely access to care, has yet to be assessed.

OBJECTIVE

To assess the associations between the introduction of an electronic appointment reminder system and the number of days patients had to wait from appointment booking to appointment completion in patients in the Veterans Affairs Health System.

DESIGN, SETTING, AND PARTICIPANTS

Cohort study of patients who completed appointments from January 1, 2018, to October 13, 2018, inclusive in all 130 Veterans Affairs (VA) health centers in the US. The study population comprised a census of all patients who received care at any VA health center during the period of the study for outpatient, procedural, rehabilitation, or radiology services. Data were analyzed from May 15, 2021, to December 15, 2021.

EXPOSURES

Phased introduction of an electronic appointment reminder system (VEText) in 6 waves spread across the study period.

MAIN OUTCOMES AND MEASURES

The unit of observation in this study was a completed appointment made by any such patients. Observations were excluded if the appointment was booked before but completed after the exposure, or if data were duplicated, missing, or incomplete. For each completed appointment, the number of days between which the appointment was booked and when it was completed.

RESULTS

The number of observations after exclusion comprised 39.5 million completed appointments from 5.1 million patients (91.1% male) with a mean (SD) age of 62.57 (16.24) years. The adoption of VEText was associated with an estimated reduction in patient waiting time by a mean of 6.51 days (95% CI, 5.51-7.52 days). Adoption of VEText was also associated with an increase of 8.54 (95% CI, 7.65-9.44) days of additional waiting per incomplete booking.

CONCLUSIONS AND RELEVANCE

Results of this study suggest that appointment reminder systems may be associated with decreases in the mean number of days patients in the VA system have to wait for their appointments but can potentially lengthen waiting times for patients who miss their bookings. Further study is warranted to assess whether these findings may be generalizable to other populations.

Enhanced PAtient Clinical Streamlining (EPACS): Quality Initiative to Improve Healthcare for New Surgical Outpatient Visits

Purpose

For patients who select a specialty hospital for cancer treatment, the wait time until the initial consultation leaves patients anxious and delays treatment. To improve quality of care, we implemented an enhanced patient clinical streamlining (EPACS) process that establishes an early connection and coordinates care before the first surgical outpatient visit at our specialty cancer center.

Methods

During a pre-visit EPACS phone call to new patients, an advanced practice provider (APP) collected medical history and ordered work-up tests or consultations if feasible. First visit cancellation rate, number of patients who started treatment, time to start of treatment, and satisfaction by the care team and patient were compared between patients treated with versus without EPACS.

Results

Among 5062 consecutive new patients, 720 (14%) received an EPACS call and 4342 did not (86%); work-up was ordered pre-visit in 34% and 16%, respectively. Fewer EPACS patients cancelled the first visit (4.6% vs. 12%, p < 0.001), more started treatment (55% vs. 50%, p = 0.037), and their time to treatment was shorter, but not significantly (median 17 vs. 19 days, p = 0.086). Patient interaction was considered to be improved by EPACS by 17 of 17 APPs and 14 of 16 surgeons, and outpatient clinic efficiency by 14 of 17 APPs and 13 of 16 surgeons. EPACS reduced anxiety and increased preparedness for the first visit in 29 of 31 patients.

Conclusions

EPACS improved effectiveness, timeliness, and physician and patient satisfaction with health care at our cancer center.

Supplementary Information

The online version contains supplementary material available at 10.1245/s10434-021-11126-3.

Characterization of Social Risk Factors Among Newborns Seen at an Urban Pediatric Primary Care Predictive of Appointment Nonattendance During the First 6 Months of Life

Purpose: Appointment attendance is critical in monitoring health and well-being of children. Low income Medicaid-insured families with newborns often experience social risks that may affect attendance. This project sought to characterize social risk factors present at first newborn visits predictive of future appointment nonattendance.

Methods: Retrospective cohort study of minority and Medicaid-insured population at St. Christopher's Hospital for Children using a standardized social risk screener administered at first newborn visits as part of routine clinical care. In total, 720 survey responses between December 2016 and June 2017 were correlated with electronic health record-derived sociodemographic and appointment attendance data in the first 6 months of life. Nonattendance included missed and canceled appointments. Caregiver-reported social risk factors were included as covariates in linear regressions predicting proportion nonattendance outcomes.

Results: Newborn caregivers identified many social risk factors including mental health diagnoses (14%), lack of child care support (45%), and food insecurity (9%). Approximately 74% had nonattendance with 41% missing or canceling a quarter or more appointments. Number of siblings (p<0.01) and maternal age (p<0.01) were most predictive for nonattendance, respectively. Other social risks were not significant except for maternal mental health (p=0.01) among those identifying number of risk factors above cohort average (16%).

Conclusion: Screening of newborns at first medical visits can be used to characterize social risks. Most social risk factors at first visits were not strongly predictive of nonattendance, although our results suggested associations between non-attendance and maternal demographics, mental health and household makeup.

Using artificial intelligence to reduce queuing time and improve satisfaction in pediatric outpatient service: A randomized clinical trial

INTRODUCTION

Complicated outpatient procedures are associated with excessive paperwork and long waiting times. We aimed to shorten queuing times and improve visiting satisfaction.

METHODS

We developed an artificial intelligence (AI)-assisted program named Smart-doctor. A randomized controlled trial was conducted at Shanghai Children's Medical Center. Participants were randomly divided into an AI-assisted and conventional group. Smart-doctor was used as a medical assistant in the AI-assisted group. At the end of the visit, an e-medical satisfaction questionnaire was asked to be done. The primary outcome was the queuing time, while secondary outcomes included the consulting time, test time, total time, and satisfaction score. Wilcoxon rank sum test, multiple linear regression and ordinal regression were also used.

RESULTS

We enrolled 740 eligible patients (114 withdrew, response rate: 84.59%). The median queuing time was 8.78 (interquartile range [IQR] 3.97,33.88) minutes for the AI-assisted group versus 21.81 (IQR 6.66,73.10) minutes for the conventional group (p < 0.01), and the AI-assisted group had a shorter consulting time (0.35 [IQR 0.18, 0.99] vs. 2.68 [IQR 1.82, 3.80] minutes, p < 0.01), and total time (40.20 [IQR 26.40, 73.80] vs. 110.40 [IQR 68.40, 164.40] minutes, p < 0.01). The overall satisfaction score was increased by 17.53% (p < 0.01) in the AI-assisted group. In addition, multiple linear regression and ordinal regression showed that the queuing time and satisfaction were mainly affected by group (p < 0.01), and missing the turn (p < 0.01).

CONCLUSIONS

Using AI to simplify the outpatient service procedure can shorten the queuing time of patients and improve visit satisfaction.

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

INTRODUCTION

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Contextual and local determinants associated with the achievement of goals in the endodontics specialty in Brazilian dental speciality centres: A multilevel analysis

OBJECTIVES

To assess which factors were associated with the achievement of endodontic goals.

METHODS

Cross-sectional study using secondary data from the second cycle of the Program for the Improvement of Access and Quality in the dental speciality centres-in Portuguese PMAQ-CEO. The independent variables extracted from this database were related to dental speciality centres (CEO in Portuguese). In addition, variables referring to the CEO host city were incorporated into the model. The outcome variable was the number of endodontic goals achieved calculated from the production of the CEO available in the Ambulatory Health Information System in 2018. Descriptive analyses and multilevel Poisson regression were performed with the software SPSS 23.0 and STATA 14.0.

RESULTS

CEOs with more than 20% of patients' absenteeism were 26% less likely to reach the goals of the endodontics specialty; CEOs with availability of endodontists for more than 40 hours a week were two times more likely to reach the goals than those with less than 40 hours in endodontics specialty. CEOs with a waiting time for endodontic procedures greater than 45 days achieved a number of goals 31% lower than those with a waiting time up to 45 days. CEO type I and CEO type II showed 2.10 and 1.20 higher likelihood to reach the number of goals of the endodontics specialty than CEO type III. The number of endodontic instruments in sufficient number was positively associated with the achievement of goals. CEOs located in municipalities that reached more than 5% in the supervised brushing indicator had 2.26 greater likelihood to achieve the goals than those that did not reach this percentage.

CONCLUSION

Contextual and local determinants are associated with the achievement of goals in the endodontic specialty in the dental speciality centres in Brazil.

Measuring Telehealth Visit Length and Schedule Adherence Using Videoconferencing Data

Background: The ability to measure clinical visit length is critical for operational efficiency, patient experience, and accurate billing. Despite the unprecedented surge in telehealth use in 2020, studies on visit length and schedule adherence in the telehealth setting are nonexistent in the literature. This article aims to demonstrate the use of videoconferencing data to measure telehealth visit length and schedule adherence. Materials and Methods: We used data from telehealth video visits at four clinical specialties at Nationwide Children's Hospital, including behavioral health (BH), speech pathology (SP), physical therapy/occupational therapy (PT/OT), and primary care (PC). We combined videoconferencing timestamp data with visit scheduling data to calculate the total visit length, examination length, and patient wait times. We also assessed schedule adherence, including patient on-time performance, examination on-time performance, provider schedule deviations, and schedule length deviations. Results: The analyses included a total of 175,876 telehealth video visits. On average, children with BH appointments spent a total of 57.2 min for each visit, followed by PT/OT (50.8 min), SP (42.1 min), and PC (25.0 min). The average patient wait times were 4.1 min (BH), 2.7 min (PT/OT), 2.8 min (SP), and 3.1 min (PC). The average examination lengths were 48.8 min (BH), 44.5 min (PT/OT), 34.9 min (SP), and 16.6 min (PC). Regardless of clinical specialty, actual examination lengths of most visits were shorter than the scheduled lengths, except that appointments scheduled for 15 min tended to run overtime. Conclusions: Videoconferencing data provide a low-cost, accurate, and readily available resource for measuring telehealth visit length and schedule adherence.

Factors to Effective Telemedicine Visits During the COVID-19 Pandemic: Cohort Study

Background

With COVID-19 there was a rapid and abrupt rise in telemedicine implementation often without sufficient time for providers or patients to adapt. As telemedicine visits are likely to continue to play an important role in health care, it is crucial to strive for a better understanding of how to ensure completed telemedicine visits in our health system. Awareness of these barriers to effective telemedicine visits is necessary for a proactive approach to addressing issues.

Objective

The objective of this study was to identify variables that may affect telemedicine visit completion in order to determine actions that can be enacted across the entire health system to benefit all patients.

Methods

Data were collected from scheduled telemedicine visits (n=362,764) at the University of Miami Health System (UHealth) between March 1, 2020 and October 31, 2020. Descriptive statistics, mixed effects logistic regression, and random forest modeling were used to identify the most important patient-agnostic predictors of telemedicine completion.

Results

Using descriptive statistics, struggling telemedicine specialties, providers, and clinic locations were identified. Through mixed effects logistic regression (adjusting for clustering at the clinic site level), the most important predictors of completion included previsit phone call/SMS text message reminder status (confirmed vs not answered) (odds ratio [OR] 6.599, 95% CI 6.483-6.717), MyUHealthChart patient portal status (not activated vs activated) (OR 0.315, 95% CI 0.305-0.325), provider’s specialty (primary care vs medical specialty) (OR 1.514, 95% CI 1.472-1.558), new to the UHealth system (yes vs no) (OR 1.285, 95% CI 1.201-1.374), and new to provider (yes vs no) (OR 0.875, 95% CI 0.859-0.891). Random forest modeling results mirrored those from logistic regression.

Conclusions

The highest association with a completed telemedicine visit was the previsit appointment confirmation by the patient via phone call/SMS text message. An active patient portal account was the second strongest variable associated with completion, which underscored the importance of patients having set up their portal account before the telemedicine visit. Provider’s specialty was the third strongest patient-agnostic characteristic associated with telemedicine completion rate. Telemedicine will likely continue to have an integral role in health care, and these results should be used as an important guide to improvement efforts. As a first step toward increasing completion rates, health care systems should focus on improvement of patient portal usage and use of previsit reminders. Optimization and intervention are necessary for those that are struggling with implementing telemedicine. We advise setting up a standardized workflow for staff.

Using Behavioural Insights to Improve the Uptake of Services for Drug and Alcohol Misuse

In the U.K., 270,705 adults were in contact with drug and alcohol treatment services between April 2019 and March 2020. Within the same time period, 118,995 individuals exited the treatment system, and just over a third (36%) left treatment without completing it. The latter includes individuals declining further treatment and unsuccessful transfers between services. The aim of this study was to explore the factors that affect drug and alcohol treatment uptake within a drug and alcohol service in North East England. A mixed-methods approach was adopted. The exploration of factors affecting treatment uptake was captured through a behavioural insights survey and 1:1 in-depth qualitative interviews with service users within one council area within the North East of England. There were 53 survey participants, and a further 15 participants took part in qualitative interviews. We triangulated data sources to report consistencies and discrepancies in the data. Findings show that treatment services aiming to reduce missed appointments and increase retention rates need to implement several strategies. Consistently distributing appointment cards, using text message reminders, displaying a timetable presenting all treatment options, and displaying information in a format to ensure it is accessible to individuals with lower health literacy and reducing wait times for appointments will all improve appointment attendance.

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

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

Non-attendance at an out-patient otolaryngology and head and neck clinic in New Zealand: impact of coronavirus disease 2019, and demographic, clinical and environmental factors

BACKGROUND

Fear of contracting coronavirus disease 2019 may be the latest addition to the barriers to clinic attendance. This study aimed to examine the impact of coronavirus disease 2019 and other variables on non-attendance rate at an out-patient clinic.

METHODS

Clinic attendance at the Department of Otolaryngology and Head and Neck Surgery, Waikato Hospital, New Zealand, was assessed. For each appointment, the impact of coronavirus disease 2019 and other variables on non-attendance rate were analysed.

RESULTS

In total, 1963 appointments were scheduled, with 194 non-attendances (9.9 per cent). Patients who had their appointments confirmed beforehand were 10.0 times more likely to attend their appointment. Sex, socioeconomic status, ethnicity and age were found to impact non-attendance rate.

CONCLUSION

In New Zealand, coronavirus disease 2019 does not appear to be a barrier to out-patient clinic appointment attendance. The patient's age, sex, ethnicity, socioeconomic status and prior appointment confirmation were found to influence clinic attendance.

PEC e-SUS APS online appointment scheduling system: a tool to facilitate access to Primary Care in Brazil

Barriers faced by health services providing scheduled care result in high no-show rates. This article describes the main characteristics of an online appointment scheduling system incorporated into the citizens' electronic health record system (PEC e-SUS APS). Developed by the Bridge Laboratory, Federal University of Santa Catarina, which also developed the PEC e-SUS APS, the system allows patients to schedule appointments using the national patient communications hub, Conecte SUS Cidadão. The PEC e-SUS APS includes a professional's agenda module that allows patients to view available time slots and book and cancel appointments. Unfortunately, despite the benefits of online scheduling systems, their potential has been poorly exploited in Brazil. The main reasons for this include lack of information and training of health professionals on how to use the system and its potential benefits for Primary Health Care (PHC) services. Wider dissemination is needed to improve the adoption of the system and promote the routine use of this tool in health services in order to facilitate access to primary health care.

Impact of demographics and appointment characteristics on patient attendance in a university dental clinic

INTRODUCTION

Failed patient attendance in a university dental clinic is detrimental to the student learning experience, the university as a business, and to members of the public awaiting urgent dental treatment.

PURPOSE

This study aimed to identify the demographic, appointment characteristics, and time-related factors associated with patient attendance in a university dental clinic from 2015 to 2019.

METHODS

A 5-year retrospective analysis was conducted in 2020 on data extracted from the Griffith University Dental Clinic patient management system. Following data cleaning and categorization, the dataset was downloaded into SPSS for statistical analysis. Frequencies, odds ratio, and chi squared were used to determine the demographic and time-related factors of patients who had completed, cancelled, and failed to attend (FTA) appointments.

RESULTS

A total of 23.4% of appointments were cancelled, and 6.6% were FTA. Demographics associated with cancellations include females, adults aged 25 to 44, and private paying patients. FTA were higher in young adults aged 19 to 24, low to mid-range socioeconomic status (SES) and those eligible for publicly funded dental treatment. Mondays and Fridays experienced the greatest number of FTA and cancellations, respectively. Emergency appointments had the greatest attendance rates and endodontic procedures the lowest.

CONCLUSION

The loss of clinical teaching hours, resources, and revenue necessitates the implementation of targeted strategies to minimize cancellations and FTA based on demographic and appointment characteristics that may render individual as high risk for failed attendance.

Changing Patterns of Medical Visits and Factors Associated with No-show in Patients with Rheumatoid Arthritis during COVID-19 Pandemic

BACKGROUND

The main barrier to the effective rheumatoid arthritis (RA) therapy is poor adherence. Coronavirus disease 2019 (COVID-19) pandemic have led to a significant change in the pattern and the number of medical visits. We assessed changing patterns of medical visits and no-show, and identified factors associated with no-show in patients with RA during COVID-19 pandemic.

METHODS

RA patients treated with disease-modifying antirheumatic drugs at least 6 months who had been in remission or those with mild disease activity were observed for 6 months from February to July 2020. No-show was defined as a missed appointment that was not previously cancelled by the patient and several variables that might affect no-show were examined.

RESULTS

A total of 376 patients and 1,189 appointments were evaluated. Among 376 patients, 164 patients (43.6%) missed appointment more than one time and no-show rate was 17.2% during COVID-19 pandemic. During the observation, face-to-face visits gradually increased and no-show gradually decreased. The logistic regression analysis identified previous history of no-show (adjusted odds ratio [OR], 2.225; 95% confidence interval [CI], 1.422-3.479; P < 0.001) and fewer numbers of comorbidities (adjusted OR, 0.749; 95% CI, 0.584-0.961; P = 0.023) as the independent factors associated with no-show.

CONCLUSION

Monthly analysis showed that the no-show rate and the pattern of medical visits gradually changed in patients with RA during COVID-19 pandemic. Moreover, we found that previous history of no-show and fewer numbers of comorbidities as the independent factors associated with no-show.

Benefits and barriers to pediatric tele-urology during the COVID-19 pandemic

INTRODUCTION

Telemedicine video visits are an under-utilized form of delivering health care. However due to the COVID-19 pandemic, practices are rapidly adapting telemedicine for patient care. We describe our experience in rapidly introducing video visits in a tertiary academic pediatric urology practice, serving primarily rural patients during the COVID-19 pandemic.

OBJECTIVE

The primary aim of this study was to assess visit success rate and identify barriers to completing video visits. The secondary aim identified types of pathologies feasible for video visits and travel time saved. We hypothesize socioeconomic status is a predictor of a successful visit.

MATERIALS AND METHODS

Data was prospectively collected and analyzed on video visits focusing on visit success, defined by satisfactory completion of the visit as assessed by the provider. Other variables collected included duration, video platform and technical problems. Retrospective data was collected via chart review and analyzed including demographics, insurance, and distance to care. Socioeconomic status was estimated using the Distressed Communities Index generated for patient zip code.

RESULTS/DISCUSSION

Out of 116 attempted visits, 81% were successful. The top two reasons for failure were "no-show" (64%) and inability to connect (14%). Success versus failure of visit was similar for patient age (p = 0.23), sex (p = 0.42), type of visit (initial vs. established) (p = 0.51), and socioeconomic status (p = 0.39). After adjusting for race, socioeconomic status, and type of provider, having public insurance remained a significant predictor of failure (p = 0.017). Successful visits were conducted on multiple common pediatric urologic problems (excluding visits requiring palpation on exam), and video was sufficient for physical exams in most cases (Summary Table). A median of 2.25 h of travel time was saved.

CONCLUSIONS

While socioeconomic status, estimated using the Distressed Communities Index, did not predict success of video visits, patients with public insurance were more likely to have a failed video visit. There is compelling evidence that effective video visits for certain pathologies can be rapidly achieved in a pediatric urology practice with minimal preparation time.

Managing patient flows in radiation oncology during the COVID-19 pandemic : Reworking existing treatment designs to prevent infections at a German hot spot area University Hospital

PURPOSE

The described work aimed to avoid cancellations of indispensable treatments by implementing active patient flow management practices and optimizing infrastructure utilization in the radiation oncology department of a large university hospital and regional COVID-19 treatment center close to the first German SARS-CoV‑2 hotspot region Heinsberg in order to prevent nosocomial infections in patients and personnel during the pandemic.

PATIENTS AND METHODS

The study comprised year-to-date intervention analyses of in- and outpatient key procedures, machine occupancy, and no-show rates in calendar weeks 12 to 19 of 2019 and 2020 to evaluate effects of active patient flow management while monitoring nosocomial COVID-19 infections.

RESULTS

Active patient flow management helped to maintain first-visit appointment compliance above 85.5%. A slight appointment reduction of 10.3% daily (p = 0.004) could still significantly increase downstream planning CT scheduling (p = 0.00001) and performance (p = 0.0001), resulting in an absolute 20.1% (p = 0.009) increment of CT performance while avoiding overbooking practices. Daily treatment start was significantly increased by an absolute value of 18.5% (p = 0.026). Hypofractionation and acceleration were significantly increased (p = 0.0043). Integrating strict testing guidelines, a distancing regimen for staff and patients, hygiene regulations, and precise appointment scheduling, no SARS-CoV‑2 infection in 164 tested radiation oncology service inpatients was observed.

CONCLUSION

In times of reduced medical infrastructure capacities and resources, controlling infrastructural time per patient as well as optimizing facility utilization and personnel workload during treatment evaluation, planning, and irradiation can help to improve appointment compliance and quality management. Avoiding recurrent and preventable exposure to healthcare infrastructure has potential health benefits and might avert cross infections during the pandemic. Active patient flow management in high-risk COVID-19 regions can help Radiation Oncologists to continue and initiate treatments safely, instead of cancelling and deferring indicated therapies.

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

Purpose

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

Methods

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

Results

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

Conclusion

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

"If You Call Them, They Will Come": A Telephone Call Reminder to Decrease the No-Show Rate in an Outpatient Palliative Medicine Clinic

INTRODUCTION

A high outpatient clinic no-show rate affects clinical outcomes, increases healthcare costs, and reduces both access to care and provider productivity. In an effort to reduce the no-show rate at a busy palliative medicine outpatient clinic, a quality improvement project was launched consisting of a telephone call made by clinic staff prior to appointments. The study aimed to determine the effect of this intervention on the no-show rate, and assess the financial impact of a decreased no-show rate.

METHODS AND MATERIALS

The outpatient clinic no-show rate was measured from September 1 to December 31, 2015. Data from the first 8 months of the calendar year was removed since these could not be verified. Starting January 1, 2016, patients received a telephone call reminder 24 hours prior to their scheduled outpatient appointment for confirmation. No-show rate was again measured for the calendar year 2016. Opportunity costs were calculated for unfulfilled clinic visits.

RESULTS

Of the 1224 completed visits from September 1 to December 31, 2015, 271 were no-shows with an average rate of 11.8%. After the intervention, there were 4368 completed visits and 562 no-shows. The no-show rate for 2016 averaged 6.9% (p < 0.001), down 4.9% from the last 4 months of 2015. Estimated opportunity costs were about 396 no-show visits avoided, equivalent to an annual savings of about $79,200.

CONCLUSION

A telephone call reminder to patients 24 hours prior to their appointment decreased the no-show rate in an outpatient palliative medicine clinic. Avoiding unfulfilled visits resulted in substantial opportunity costs.