No-show Appointments Research Articles

Evaluating the Impact of the COVID-19 Pandemic on Late-Canceled and No-Show Appointments at the Department of Neurological Surgery.

The coronavirus disease 2019 (COVID-19) pandemic resulted in unprecedented restrictions on the general public and disturbances to the routines of hospitals worldwide. These restrictions are now being relaxed as the number of vaccinated individuals increases and as the rates of incidence and prevalence decrease; however, they left a lasting impact on healthcare systems that is still being felt today. This retrospective study evaluated the total number of canceled or missed outpatient clinic appointments in a Neurological Surgery department before and after peak COVID-19 restrictions and attempted to assess the impact of these disruptions on neurosurgical clinical attendance. We also attempted to compare our data with the data from another surgical subspecialty department. We evaluated 32,558 scheduled appointments at the Loyola University Medical Center Department of Neurological Surgery, as well as 139,435 scheduled appointments with the Department of Otolaryngology. Appointments before April 2020 were defined as pre-COVID, while appointments during or after April 2020 were defined as post-COVID. Here, we compare no-show and non-attendance rates (no-shows plus late-canceled appointments) within the respective time range. Overall, we observed that before COVID-19 restrictions were put into place, there was an 8.9% no-show rate and a 17.4% non-attendance rate for the Department of Neurological Surgery. After COVID restrictions were implemented, these increased to 10.9% and 18.3%, respectively.Greater no-show and cancellation rates (9.8% in the post-COVID era vs 8.0% in the pre-COVID era) were associated with varying socioeconomic and racial demographics. African-American patients (2.56 times higher), new-visit patients (1.67 times higher), and those with Medicaid/Medicare insurance policies (1.48 times higher) were at the highest risk of no-show in the post-COVID era compared to the pre-COVID era.

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

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. 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. 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. 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.

Reducing No-Show Appointments in Preanesthesia Screening

PurposePreanesthesia screening is critical to evaluate the patient’s medical and surgical history prior to any procedure or surgery to assess for risks and to optimize outcomes during the perioperative period. Missed appointments impede the ability to identify comorbidities, perform cardiac testing, review laboratory results and current medications. No-shows also result in a waste of clinical resources and impacts the schedules of Advance Practice Providers (APPs). The purpose of this quality improvement project was to decrease the number of missed appointments in the outpatient preanesthesia and surgical screening clinic and the impact on provider satisfaction. DesignThe design of this quality improvement project was pre and post design. Automated and live phone calls reminders were provided for patients scheduled in the outpatient preanesthesia. Data was collected to compare missed appointment rates from a 3-month period prior to the project implementation and a 3-month period afterwards. MethodsPre-data collection included the number of no-shows in the EHR system from the previous 3 months. Participants included all adult patients who are scheduled for a pre-anesthesia surgical screening appointment. Provider satisfaction was assessed using a 5-question survey, pre and post innovation. FindingsReminder systems had a statistically significant impact on reducing the number of no-shows in the preanesthesia and surgical screening clinic (PASS). No significant impact was shown in provider satisfaction. ConclusionImplementation of a reminder system can help to reduce no show rates in clinics. Patient no-shows overload the health system by reducing the productivity of providers and waste resources including use of clinic staff, longer wait times for other patients, and the timing providers put into chart preparation.

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

ObjectivePatients who miss screening mammogram appointments without notifying the health care system (no-show) risk care delays. We investigate sociodemographic characteristics of patients who experience screening mammogram no-shows at a community health center and whether and when the missed examinations are completed. MethodsWe included patients with screening mammogram appointments at a community health center between January 1, 2021, and December 31, 2021. Language, race, ethnicity, insurance type, residential ZIP code tabulation area (ZCTA) poverty, appointment outcome (no-show, same-day cancelation, completed), and dates of completed screening mammograms after no-show appointments with ≥1-year follow-up were collected. Multivariable analyses were used to assess associations between patient characteristics and appointment outcomes. ResultsOf 6,159 patients, 12.1% (743 of 6,159) experienced no-shows. The no-show group differed from the completed group by language, race and ethnicity, insurance type, and poverty level (all P < .05). Patients with no-shows more often had: primary language other than English (32.0% [238 of 743] versus 26.7% [1,265 of 4,741]), race and ethnicity other than White non-Hispanic (42.3% [314 of 743] versus 33.6% [1,595 of 4,742]), Medicaid or means-tested insurance (62.0% [461 of 743] versus 34.4% [1,629 of 4,742]), and residential ZCTAs with ≥20% poverty (19.5% [145 of 743] versus 14.1% [670 of 4,742]). Independent predictors of no-shows were Black non-Hispanic race and ethnicity (adjusted odds ratio [aOR], 1.52; 95% confidence interval [CI], 1.12-2.07; P = .007), Medicaid or other means-tested insurance (aOR, 2.75; 95% CI, 2.29-3.30; P < .001), and ZCTAs with ≥20% poverty (aOR, 1.76; 95% CI, 1.14-2.72; P = .011). At 1-year follow-up, 40.6% (302 of 743) of patients with no-shows had not completed screening mammogram. DiscussionScreening mammogram no-shows is a health equity issue in which socio-economically disadvantaged and racially and ethnically minoritized patients are more likely to experience missed appointments and continued delays in screening mammogram completion.

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

BackgroundHealthcare 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.MethodsMailed 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).ResultsResponse 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).ConclusionsPractices 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.

No-Show Rates in a Urogynecology Clinic.

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

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

PurposeTo assess the efficacy of electronic health record (EHR) messaging for re-engaging patients with ophthalmology care after a missed appointment. DesignProspective, randomized clinical trial. MethodsThe study setting was an academic ophthalmology department. The patient population comprised return patients age 18 years or older with an appointment “no show,” or missed appointment. Over two phases of recruitment, 362 patients with an active patient portal in the EHR were selected consecutively each business day. Patients were randomized using a web-based tool to receive a reminder to reschedule via a standard mailed letter only (control) or the mailed letter plus an electronic message through the electronic record within one business day of the missed appointment (intervention). Reengagement with eye care was defined as attendance of a rescheduled appointment within 30 days of the no-show visit. Patient charts were reviewed for demographic and clinical data. ResultsThe average age of recruited patients was 59.9 years, just under half of the sample was male (42.5%, 154/362), and most patients were White (56.9%, 206/362) or Black (36.2%, 131/362). Patients were most commonly recruited from the retina service (39.2%, 142/362) followed by the glaucoma service (29.3%, 106/362). Many patients in this study had previous no-show appointments, with an average no-show rate of 18.8% out of all scheduled visits across our health system. In total, 22.2% (42/189) of patients in the intervention group attended a follow-up appointment within 30 days of their no-show visit compared to 11.6% (20/173) of the control group (OR, 2.186; 95% CI, 1.225-3.898; p=0.008). When including only the 74 patients in the intervention group who read the intervention message in the patient portal, 28.4% (21/74) attended a follow-up compared to 11.6% (20/173) of the control group (p=0.001). ConclusionsEHR-based reminder messages sent within a business day of a missed appointment may promote re-engagement in ophthalmology care after appointment no-show.

Psychosocial Risk Exposure Limits Routine Pediatric Oral Health Care

ObjectivesTo identify social, psychological, and contextual factors that influenced attendance at routine oral health visits in a cohort of 189 preschool children who were followed over a 2-year period. MethodsGeneralized estimating equation was used to examine the association between clinic attendance and the predictors. Odds ratios and 95% confidence intervals were reported in the multiple logistic regression models. The study was conducted in Rochester, NY between February 2016-February 2021. ResultsPrior to the COVID-19 pandemic declaration, the rate of cancelled and no-show appointments was greater for routine clinic visits (20% and 24%, respectively) than for research visits (14% and 9% respectively) for the same participants; these rates increased during the pandemic. After adjusting for socio-demographic factors, the likelihood of a cancelled or no-show appointment was associated with parental depression (OR 1.06 CI 1.03-1.09), regardless of the type or occurrence of the visit. ConclusionsFindings from this study demonstrate that attendance to oral health care in young children is reliably reduced with parental depression and that this may provide one mechanism for early emerging health inequalities of oral health.

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

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

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

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

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

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

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

BackgroundNo-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.MethodsRetrospective 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.ResultsThe 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.ConclusionsOur 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.

Digital dashboards for oral anticoagulation management: a literature scoping review

This scoping review summarizes the extent and characteristics of the published literature describing digital population management dashboards implemented to improve the quality of anticoagulant management. A standardized search protocol was executed to identify relevant manuscripts published between January 1, 2015 and May 31, 2022. The resulting records were systematically evaluated by multiple blinded reviewers and the findings from selected papers were evaluated and summarized. Twelve manuscripts were identified, originating from 5 organizations within the US and 2 from other countries. The majority (75%) described implementation in the outpatient setting. The identified papers described a variety of positive results of dashboard use, including a 24.5% reduction of questionable direct oral anticoagulant dosing in one organization, a 33.3% relative improvement in no-show appointments in an ambulatory care clinic, and a 75% improvement in intervention efficiency. One medical center achieved a 98.4% risk-appropriate venous thromboembolism risk prophylaxis prescribing rate and 40.6% reduction in anticoagulation-related adverse event rates. The manuscripts primarily described retrospective findings from single-center dashboard implementation experiences. Digital dashboards have been successfully implemented to support the anticoagulation of acute and ambulatory patients and available manuscripts suggest a positive impact on care-related processes and relevant patient outcomes. Prospective studies are needed to better characterize the implementation and impact of dashboards for anticoagulation management. Published reports suggest that digital dashboards may improve the quality, safety, and efficiency of anticoagulation management. Additional research is needed to validate these findings and to understand how best to implement these tools.

Visit Data and Telehealth in a Clinic for Trafficked Persons: Virtual Care and Human Trafficking During the COVID-19 Pandemic

Abstract: Background. Human trafficking involves coerced labor or sex. THRIVE, a multidisciplinary University of Miami clinic for trafficked persons, transitioned to a hybrid telehealth model during the COVID-19 pandemic. Th is paper presents appointment data across THRIVE clinic services during this transition. Methods. A review of appointments for THRIVE patients (n=156) was conducted to compare pre-pandemic (February 2019 to February 2020) and pandemic (March 2020 to December 2021) appointment trends. Results. There were 15% more scheduled (n=51.1 versus n=44) and 8% more completed (n=30.2 versus n=27.9) appointments per month early in the pandemic period with telehealth use compared to the pre-pandemic period. Telehealth was most used within psychiatry. Rescheduled and no-show appointments per month significantly increased during the pandemic period (p=0.010 in pandemic period 1 and and p=0.028 in pandemic period 2). There were few significant differences in appointment trends according to demographic variables. Conclusion. Telehealth succeeded in connecting THRIVE patients during the pandemic, highlighting its potential for long-term use amongst trafficked persons.

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

BackgroundThe 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.ObjectiveExamine the relationship between unmet social needs and care access.DesignCross-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.SubjectsA national stratified random sample of VA-enrolled Veterans with and at risk for CVD who responded to the survey.Main MeasuresNo-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 ResultsGreater 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.ConclusionsFindings 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.

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

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. 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. 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]). 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.

A novel pathway for insurance-based breast reductions: A method for identifying appropriate surgical candidates

Breast reductions are one of the most common plastic surgery procedures performed. The purpose of this study was to streamline the evaluation of patients requiring breast reduction by using a nurse practitioner (NP)-led class to funnel appropriate surgical candidates through the preoperative process. We performed a retrospective review of patients interested in breast reduction who enrolled in this class from March 2015 to August 2021. Of the 1310 unique patients enrolled in the initial class, 386 patients passed the initial screening and were scheduled with the NP, whereas 924 were screened out at this stage either for being an inadequate surgical candidate or not showing up for clinical visits (36.7%). An additional 185 were screened out after the consultation with NP for reasons such as lack of insurance coverage and no-show visits (20.2%). MD visits had a no-show rate of 7.08%. The decrease in no-show rates between the class-NP visit and the NP-MD visit were both significant (p < 0.001). There was no significant difference in gram estimates between providers and pathology (p = 0.5). In total, 171 patients underwent breast reduction (13.05% of the patients screened). The average time from class to surgery was 278.15 days, NP consultation to surgery was 171.48 days, and MD consultation to surgery was 59.51 days. Using a screening pathway allows for early identification of inadequate surgical candidates and therefore streamlines the screening process for optimized breast reduction candidates. Appropriate use of NP visits functions to streamline the funnel, thus decreasing the number of visits and no-show appointments for a surgeon.

Visual Cue to Reduce False No-Show Registration in Riyadh Primary Healthcare: A Randomized Controlled Trial

Introduction: Preliminary field visits to the Saudi Ministry of Health’s primary healthcare centers (PHCs) confirmed a proportion of prior reported no-show appointments to be false due to staff registering patient arrivals inappropriately. We sought to investigate whether visual cue reminders would prime the staff to register patients properly. Methods: A randomized controlled trial was conducted in 2019 involving 35 PHCs in Riyadh, Saudi Arabia. Visual posters were installed in the intervention PHCs wherever patient arrivals were registered. The primary outcome was patient no-show appointments. Staff who registered appointment arrivals were observed and interviewed. Multilevel logistic regression analyzed the change in no-shows across an intervention group and a control group. Results: The intervention group had lower odds ratio (OR) of no-shows than the control group, although not significantly (OR 0.81, 95% confidence interval [CI] = 0.50–1.31). The period during Ramadan saw fewer no-shows than pre-Ramadan (OR 1.60, 95% CI = 1.55–1.66). Compared to family medicine appointments, no-shows were higher for the smoking cessation clinic (OR 3.95, 95% CI = 3.43–4.54), dental appointments (OR 2.14, 95% CI = 1.97–2.32), and vaccine appointments (OR 1.31, 95% CI = 1.27–1.35). Qualitative analysis shows that PHCs’ appointment processing was arbitrary without a unified structure. Conclusion: The reduction in no-show rates using visual cues was not significant. However, certain confounders, if not addressed prior to intervention implementation, can lead to cognitive overload and burnout, inviting unwanted behavior. Further implications are discussed.

Predicting no-show appointments in a pediatric hospital in Chile using machine learning

The Chilean public health system serves 74% of the country’s population, and 19% of medical appointments are missed on average because of no-shows. The national goal is 15%, which coincides with the average no-show rate reported in the private healthcare system. Our case study, Doctor Luis Calvo Mackenna Hospital, is a public high-complexity pediatric hospital and teaching center in Santiago, Chile. Historically, it has had high no-show rates, up to 29% in certain medical specialties. Using machine learning algorithms to predict no-shows of pediatric patients in terms of demographic, social, and historical variables. To propose and evaluate metrics to assess these models, accounting for the cost-effective impact of possible intervention strategies to reduce no-shows. We analyze the relationship between a no-show and demographic, social, and historical variables, between 2015 and 2018, through the following traditional machine learning algorithms: Random Forest, Logistic Regression, Support Vector Machines, AdaBoost and algorithms to alleviate the problem of class imbalance, such as RUS Boost, Balanced Random Forest, Balanced Bagging and Easy Ensemble. These class imbalances arise from the relatively low number of no-shows to the total number of appointments. Instead of the default thresholds used by each method, we computed alternative ones via the minimization of a weighted average of type I and II errors based on cost-effectiveness criteria. 20.4% of the 395,963 appointments considered presented no-shows, with ophthalmology showing the highest rate among specialties at 29.1%. Patients in the most deprived socioeconomic group according to their insurance type and commune of residence and those in their second infancy had the highest no-show rate. The history of non-attendance is strongly related to future no-shows. An 8-week experimental design measured a decrease in no-shows of 10.3 percentage points when using our reminder strategy compared to a control group. Among the variables analyzed, those related to patients’ historical behavior, the reservation delay from the creation of the appointment, and variables that can be associated with the most disadvantaged socioeconomic group, are the most relevant to predict a no-show. Moreover, the introduction of new cost-effective metrics significantly impacts the validity of our prediction models. Using a prototype to call patients with the highest risk of no-shows resulted in a noticeable decrease in the overall no-show rate.

Virtual care and COVID-19: A survey study of adoption, satisfaction and continuing education preferences of healthcare providers in Newfoundland and Labrador, Canada.

Virtual care has expanded during COVID-19 and enabled continued access to healthcare services. For many healthcare providers, the adoption of virtual care has been a new experience in the provision of healthcare services. The purpose of this survey study was to explore healthcare providers' experiences with virtual care during COVID-19. A web-based survey-questionnaire was developed by applying Rogers' theory of diffusion of innovation and distributed to healthcare providers (physicians, nurses and allied health professionals) in Newfoundland and Labrador, Canada to explore virtual care experiences, satisfaction and continuing professional development (CPD) needs. Analyses included descriptive statistics and thematic analysis of survey responses. Fifty-one percent of respondents (n = 432) indicated they were currently offering virtual care and a majority (68.9%) reported it has improved their work experience. Telephone appointments were preferred over videoconferencing by respondents, with key challenges including the inability to conduct a physical exam, patients' cell phone services being unreliable and patients knowing how to use videoconferencing. Majority of respondents (57.5%) reported quality of care by telephone was lower than in-person, whereas quality of care by videoconferencing was equivalent to in-person. Main benefits of virtual care included increased patient access, ability to work from home, and reduction in no-show appointments. Key supports for adopting virtual care included in-house organizational supports (e.g., technical support staff), local colleague support, and technology training. Important topics for virtual care CPD included complying with regulatory standards/rules, understanding privacy or ethical boundaries, and developing competency and digital professionalism while engaging in virtual care. Beyond the COVID-19 pandemic, virtual care will have a continuing role in enhancing continuity of care through access that is more convenient. Survey findings reveal a number of opportunities for supporting healthcare providers in use of virtual care, including CPD, guidelines and resources to support adaptation to virtual care provision (e.g., virtual examinations/assessments), as well as patient educational support.