Evaluating the Impact of Patient No-Shows on Service Quality

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

BACKGROUND

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Clinic of Hope: bridging infectious disease and orthopaedic surgery in prosthetic joint infection management

Prosthetic joint infections (PJIs) following total joint arthroplasty are a significant and costly complication. To address fragmented care typically seen with separate management, we established a combined infectious disease and orthopaedic surgery clinic at Duke Health in July 2020. This clinic focuses on patients experiencing acute deterioration or multiple PJI episodes, often at the stage where amputation is the only option offered. From July 2021 to March 2024, the clinic completed 974 visits with 319 unique patients. The clinic maintained a low no-show rate of 5.0%. Treatment plans included procedures such as debridement, antibiotics and implant retention (38%), as well as implant explantation and one-stage exchange (32% each), with amputation required in only 4% of cases. The integrated clinic model facilitated real-time, multidisciplinary care, improving patient outcomes and operational efficiency. This approach offers a promising model for managing complex infections.

Implementation of a patient reminder system in Haiti in a socio-political crisis context: evaluation of outcomes

BACKGROUND

In Haiti, patient's adherence to treatment and compliance with medical appointments are very challenging due to different local factors. We aimed to assess the effectiveness of a reminder system implemented in health facilities in Haiti in a context of socio-political crisis.

METHODS

We used appointment data from patients aged 15 years and older between January 2021 and November 2023 from four healthcare centers in the Port-au-Prince metropolitan area. We performed descriptive analysis, crossing covariates with appointment attendance. We performed Pearson's Chi-squared test, and multivariate regression analysis using a mixed-effect logistic regression model in order to explore the association between sending reminders and appointment attendance, with and without adjustment for other patient-level covariates.

RESULTS

A total of 14 108 appointments were registered on the reminder systems, with 2 479 (17.6%) attendances. Among those to whom reminders were sent, the number of attendances was 167 (17.4%) for email recipients only, 199 (36.7%) for SMS recipients only, and 19 (42.2%) for both SMS and email recipients - versus 2 094 (16.7%) for non-reminders. After adjusting on all other covariates, we found that patients to whom a reminder was sent via email (aOR: 1.45; CI: 1.08, 1.94), SMS (aOR: 2.95; CI: 2.41, 3.60), and both SMS and email (aOR: 2.86, CI: 1.37, 5.96) were more likely to show up on their appointment day compared to those who did not receive any reminder. Other socio-demographic factors such as being 50 years and older (aOR: 1.31; CI: 1.10, 1.56) compared to under 30 years, living as couple (aOR: 1.23; CI: 1.10, 1.37), and not having children (aOR: 1.21; CI: 1.07, 1.37) were significantly associated with appointment attendance.

CONCLUSIONS

Our study suggests that patient reminder systems may be used to reduce non-attendance in Haiti, even in a context of socio-political crisis.

Sociodemographic Factors Associated with Outpatient Radiology No-shows Versus Cancellations

RATIONALE AND OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

Background

To assess the relationship between patients' demographic, health system-related, and geosocial characteristics and the risk of missed appointments among patients in family medicine practice.

Methods

The study was based on a retrospective cross-sectional design using electronic health records and neighborhood-level social determents of health metrics linked by geocoded patients' home address. The study population consisted of patients who had a primary care provider and at least one appointment at 14 family medicine clinics in rural and suburban areas in January-December 2022. Negative binomial regression was utilized to examine the impact of personal, health system, and geosocial effects on the risk of no-shows and same-day cancellations.

Results

A total of 258,614 appointments were made from 75,182 patients during the study period, including 7.8% no-show appointments from 20,256 patients. The analysis revealed that individuals in the ethnic minority groups were 1.24-1.65 times more likely to miss their appointments than their White counterpart. Females and English speakers had 14% lower risk for no-show. A significant increase (32%-64%) in the odds of no-shows was found among individuals on Medicaid and uninsured. Persons with prior history of no-shows or same day cancellations were 6%-27% more likely to miss their appointments. The no-show risk was also higher among people living in areas experiencing socioeconomic disadvantage.

Conclusion

The risk of missed appointments is affected by personal, health system, and geosocial contexts. Future efforts aiming to reduce no-shows could develop personalized interventions targeting the at-risk populations identified in the analysis.

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

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

Phase-Based Care in Community Mental Health: A Cost-Effective Innovation Using Algorithms, Rating Scales and Treatment Teams for Depression Management

This retrospective, observational report describes an innovative quality improvement process, Phase-based Care (PBC), that eliminated wait times and achieved positive clinical outcomes in a community mental health center's (CMHC) mood disorder clinic without adding staff. PBC accomplishes this by eliminating the ingrained cultural practice of routinely scheduling stable patients at rote intervals of 1-3 months, regardless of clinical need or medical necessity. Based on four organizational transformations and using mathematical algorithms developed for this process, PBC re-allocates therapy and medical resources away from routinely scheduled appointments and front-loads those resources to patients in an acute phase of illness. To maintain wellness for patients in recovery, lower frequency and intensity approaches are used. This report describes the development of the PBC methodology focusing on the Rapid Recovery Clinic (RRC) comprised of 182 patients with a primary diagnosis of a mood disorder, the largest of the 14 PBC clinics created. Over an 18-month period, wait times were reduced from several months to less than one week and recovery rates, meaning no longer in an acute phase, were 63% and 78% at weeks 6 and 12, respectively for patients who engaged in the program.

Going Beyond Waitlists in Mental Healthcare

Managing waitlists for outpatient mental health services particularly in community health settings is difficult to standardize, poses an administrative burden, and are barriers rather than gateways to access to care particularly for low-income communities. While telehealth has initially expanded access to mental healthcare at the onset of the COVID-19 pandemic, it has not resolved the challenges associated with the increasing demand for services and the shortage of available providers. This commentary explores the intricate interconnections between wait times, readiness for and appropriateness of therapy, and engagement in treatment. Drawing on insights from waiting line theory to question the attachment to and utility of waitlists for non-emergency, outpatient mental healthcare, this commentary questions the utility and efficacy of waitlists. Alternative solutions that capitalize on community resources and collaboration and harness patients' agency for change are discussed.

Sociodemographic factors and Child Opportunity Index disparities associated with missed care opportunities in pediatric patients with lymphoma and leukemia referred for FDG-PET/CT

BACKGROUND

Little data exists on the association of missed care opportunities (MCOs) in children referred for nuclear medicine/nuclear oncology imaging examinations and socioeconomic disparities.

OBJECTIVE

To determine the prevalence of MCOs in children with lymphoma/leukemia scheduled for fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) and the impact of sociodemographic factors and Child Opportunity Index (COI).

MATERIALS AND METHODS

Retrospective analysis of MCOs in children with lymphoma/leukemia scheduled for FDG-PET/CT (2012 to 2022) was performed. In univariate analysis, patient, neighborhood, and appointment data were assessed across MCOs and completed appointments. Logistic regression evaluated independent effects of patient-, neighborhood-, and appointment-level factors with MCOs. Two-sided P-value < .05 was considered statistically significant.

RESULTS

In 643 FDG-PET/CT appointments (n = 293 patients; median age 15 years (IQR 11.0-17.0 years); 37.9% female), there were 20 MCOs (3.1%) involving 16 patients. Only 8.2% appointments involved Black/African American non-Hispanic/Latino patients, yet they made up a quarter of total MCOs. Patients living in neighborhoods with very low or low COI experienced significantly higher MCOs versus zip codes with very high COI (6.9% vs. 0.8%; P = 0.02). Logistic regression revealed significantly increased likelihood of MCOs for patients aged 18 to 21 [odds ratio (OR) 4.50; 95% CI 1.53-13.27; P = 0.007], Black/African American non-Hispanic/Latino (OR 3.20; 95% CI 1.08-9.49; P = 0.04), zip codes with very low or low COI (OR 9.60; 95% CI 1.24-74.30; P = 0.03), and unknown insurance status.

CONCLUSION

Children with lymphoma/leukemia, living in zip codes with very low or low COI, and who identified as Black/African American non-Hispanic/Latino experienced more MCOs. Our study supports the need to address intersecting sociodemographic, neighborhood, and health system factors that will improve equitable access to necessary healthcare imaging for children.

Impact of Insurance Type on Access to Pediatric Surgical Care

Background

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

Methods

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

Results

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

Conclusions

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

Reducing No-Show Appointments in Preanesthesia Screening

PURPOSE

Preanesthesia screening is critical to evaluate the patient's medical and surgical history before any procedure or surgery to assess for risks and to optimize outcomes during the perioperative period. 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.

DESIGN

The 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 were collected to compare missed appointment rates from a 3-month period before the project implementation and a 3-month period afterward.

METHODS

Predata collection included the number of no-shows in the electronic health record system from the previous 3 months. Participants included all adult patients who are scheduled for a preanesthesia surgical screening appointment. Provider satisfaction was assessed using a 5-question survey, pre and postinnovation.

FINDINGS

Reminder systems had a statistically significant impact on reducing the number of no-shows in the preanesthesia and surgical screening clinic. No significant impact was shown in provider satisfaction.

CONCLUSIONS

Implementation 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

OBJECTIVE

Patients who miss screening mammogram appointments without notifying the health care system (no-show) risk care delays. We investigate sociodemographic characteristics of patients who experience screening mammogram no-shows at a community health center and whether and when the missed examinations are completed.

METHODS

We included patients with screening mammogram appointments at a community health center between January 1, 2021, and December 31, 2021. Language, race, ethnicity, insurance type, residential ZIP code tabulation area (ZCTA) poverty, appointment outcome (no-show, same-day cancelation, completed), and dates of completed screening mammograms after no-show appointments with ≥1-year follow-up were collected. Multivariable analyses were used to assess associations between patient characteristics and appointment outcomes.

RESULTS

Of 6,159 patients, 12.1% (743 of 6,159) experienced no-shows. The no-show group differed from the completed group by language, race and ethnicity, insurance type, and poverty level (all P < .05). Patients with no-shows more often had: primary language other than English (32.0% [238 of 743] versus 26.7% [1,265 of 4,741]), race and ethnicity other than White non-Hispanic (42.3% [314 of 743] versus 33.6% [1,595 of 4,742]), Medicaid or means-tested insurance (62.0% [461 of 743] versus 34.4% [1,629 of 4,742]), and residential ZCTAs with ≥20% poverty (19.5% [145 of 743] versus 14.1% [670 of 4,742]). Independent predictors of no-shows were Black non-Hispanic race and ethnicity (adjusted odds ratio [aOR], 1.52; 95% confidence interval [CI], 1.12-2.07; P = .007), Medicaid or other means-tested insurance (aOR, 2.75; 95% CI, 2.29-3.30; P < .001), and ZCTAs with ≥20% poverty (aOR, 1.76; 95% CI, 1.14-2.72; P = .011). At 1-year follow-up, 40.6% (302 of 743) of patients with no-shows had not completed screening mammogram.

DISCUSSION

Screening mammogram no-shows is a health equity issue in which socio-economically disadvantaged and racially and ethnically minoritized patients are more likely to experience missed appointments and continued delays in screening mammogram completion.

Pattern of Virtual Consultations in the Kingdom of Saudi Arabia: An Epidemiological Nationwide Study

BACKGROUND

In the Kingdom of Saudi Arabia (KSA), little is known about the adoption of virtual consultations (VCs), with most studies being survey-based leading to varying results. This study aims to utilise secondary collected data on the use of both kinds of VCs currently available, and to epidemiologically describe the adoption of these consultations.

METHODS

This retrospective study analysed data provided by the Ministry of Health between January 1st 2021 and June 30th 2022. For both the home-based and the hospital-based consultations, variables included the age and sex of patients, date of consultation, duration in minutes, closure status for the appointment and the governorate in which the patient is residing. A heat map was drawn to present patterns of utilisation across the country.

RESULTS

The total number of VCs for both types were 1,008,228. For both types, females were higher adopters (54.73%). Of the total number of consultations, 751,156 were hospital-based. Of these consultations, family medicine consultations were the most common (20.42%), followed by internal medicine. Maternity follow-up clinics were higher in home-based clinics. The proportion of patient no-shows was high overall (48.30%). Utilisation was high in urban governorates, and low in rural ones.

CONCLUSION

Findings have several implications on health policy. It provides further evidence of the importance of family medicine, where it was the most common speciality even in hospital-based settings. The high variability in the adoption of consultations across rural and urban areas as well as the extremely high number of patient-no-shows warrants further investigation.

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

IMPORTANCE

Missed appointments lead to decreased clinical productivity and poor health outcomes.

OBJECTIVES

The objectives of this study were to describe sociodemographic and clinical characteristics of patients who miss urogynecology appointments and identify risk factors for missed appointments.

STUDY DESIGN

We conducted an institutional review board-approved case-control study of women 18 years or older scheduled for a urogynecology appointment at 1 of 4 sites associated with an urban academic tertiary care center over 4 months. Patients were included in the missed appointment group if they canceled their appointments the same day or did not show up for them. For comparison, we included a control group consisting of patients immediately preceding or following the ones who missed their appointments with the same visit type. Logistic regression was used to identify risk factors for missed appointments.

RESULTS

Four hundred twenty-six women were included: 213 in the missed appointment group and 213 in the control group. Women who missed appointments were younger (60 years [interquartile range (IQR), 47-72 years] vs 69 years [IQR, 59-78 years], P < 0.0001). More women in the missed appointment group were Hispanic (24.4% vs 13.1%) and non-Hispanic Black (7.5% vs 3.8%, P = 0.009), had Medicaid (17.4% vs 6.57%, P = 0.0006), missed previous appointments (24.9% vs 11.7% P = 0.0005), waited longer for appointments (39 days [IQR, 23.5-55.5 days] vs 30.5 days [IQR, 12.8-47.0 days], P = 0.002), and made appointments for urinary incontinence (44.1% vs 26.8%, P = 0.0002). On multivariate logistic regression, women with Medicaid had significantly higher odds of missing appointments (adjusted OR, 2.11 [1.04-4.48], P = 0.044).

CONCLUSIONS

Women with Medicaid were more likely to miss urogynecology appointments. Further research is needed to address barriers this group faces when accessing care.

Improving access to melarsomine therapy: treating canine heartworm infection in a high-volume, outpatient community clinic setting

BACKGROUND

Models that provide high-quality veterinary care for more affordable prices are emerging, but not well documented outside of wellness and preventative care. Effective treatment guidelines for heartworm disease have been developed by the American Heartworm Society; however, not all owners are able to access treatment due to the high costs associated with sick and emergency care services.

METHODS

To increase access to high-quality adulticidal treatment of canine heartworm disease, we developed and implemented a technician-leveraged heartworm treatment protocol for high-volume, outpatient community clinic settings based on the American Heartworm Society guidelines. Modifications were few and included limited pre-treatment blood work, pre-injection sedation, post-injection pain medication, and a reduced exercise restriction period. We monitored retention rates for 556 dogs throughout treatment, evaluated treatment success (defined as no antigen detection 9 months post treatment) for patients that returned for post-treatment antigen testing, and reported on adverse reactions and therapy adherence throughout treatment.

RESULTS

Of the patients that began adulticide therapy, 539/556 (97%) successfully completed the three-injection series. No microfilariae were detected in 99% (428/433) of those who returned for post-injection microfilaria testing. Among those that returned for or reported the results of post-injection antigen testing, no antigen was detected for 99% (245/248) and no microfilariae were detected for 99.5% (200/201). During the course of treatment, 483/539 (90%) of patients experienced at least one adverse reaction, with the most frequently reported types being behavioral and injection site reactions. 25/539 (4.6%) of owners sought additional medical care for adverse reactions at some point during the treatment course. The overall mortality rate was 1.3% (7/556).

CONCLUSIONS

This study represents the first evaluation of a heartworm treatment protocol optimized for implementation in a high-volume, outpatient community clinic setting. Our findings align with those previously reported in private practice or tertiary referral centers, illustrating that through the inclusion of pre-treatment blood work, employing short-acting or reversible sedatives, ensuring proper analgesia, minimizing the use of ancillary diagnostics, reducing the duration of in-clinic monitoring while focusing on outpatient care, and maximizing technician involvement, we can deliver effective and safe melarsomine therapy at a more affordable cost to pet owners.

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

BACKGROUND

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Integration of e-Health Strategies for Post-COVID-19 Pandemic Pediatric Weight Management Programs

Background: The COVID-19 pandemic catalyzed a renewed urgency to address the obesity pandemic and accelerated the use of technology to treat pediatric obesity. Yet, there are significant incongruities between the existing literature on technology for obesity management and the current health care system that may lead to suboptimal outcomes and increased costs. This study reviewed the types of e-health strategies currently in use, highlighted inconsistencies and overlap in terminology, and identified future research directions in e-health for childhood obesity, including gaps in implementation science. Methods: This narrative literature review synthesized seminal articles from the literature, as well as recent articles, using PubMed and Google Scholar that focused on the use of technology in treating pediatric obesity. This inclusive strategy was intended to elucidate the heterogeneity in how different disciplines are using digital health terminology in pediatric obesity research. Results: Both the prevalence of e-health interventions and its associated terminology are increasing in the peer-reviewed literature, especially since the beginning of the COVID-19 pandemic. Yet, their definitions and usage are unstandardized, leading to a lack of cohesion in the research and between disciplines. There is a gap in implementation science outcomes, including reimbursement, that may significantly impact external validity and uptake. Conclusion: A more systematic and precise approach to researching e-health that can assess specific technologies and combinations of technologies, their short-term and long-term effect sizes, and feasibility can produce the necessary data that may lead to reimbursement policies and, ultimately, improved pediatric weight management outcomes.

Patient and Clinician Satisfaction in Teledermatology: Key Factors for Successful Implementation

Purpose of review

Teledermatology has emerged as a promising solution for remote dermatologic care, especially during COVID-19 pandemic. It improves access to care through information and communication technologies. This paper explores patient and clinician satisfaction in teledermatology.

Recent findings

Patient satisfaction encompasses various aspects, including future willingness, the quality of care, technical quality, and access to care. Clinician satisfaction is influenced by quality of care, implementation, technical aspects, clinician-patient rapport, and financial considerations. It is important to evaluate patient and clinician satisfaction in different teledermatology models, including store-and-forward, live interactive, and hybrid interactive approaches.

Summary

By evaluating satisfaction within different teledermatology models and addressing the factors that impact satisfaction, teledermatology can ensure high-quality care and promote positive outcomes. With proper implementation and ongoing evaluation, teledermatology has the potential to become a widely accepted and valuable component of dermatologic care, offering enhanced access to specialized services.

Effect of a same day appointment initiative on racial disparities in access for radiation oncology

PURPOSE

We present our single-institution experience with the development of a same day access scheduling initiative for an outpatient radiation oncology unit, focusing on its potential influence on ameliorating racial disparities.

METHODS AND MATERIALS

From March 2021 to August 2022, a pilot initiative was conducted such that all new patients referred to a tertiary care-based radiation oncology department were offered the ability to be seen as a same day consultation. The timespan of this analysis was categorized into 2 distinct successive periods over 36 months-a 18-month pre-initiative period (September 2019 to February 2021) and another subsequent one (March 2021 to August 2022). Descriptive statistics were used to study the impact of this initiative on access-related benchmarks.

RESULTS

A total of 2897 patients were referred. Among the 2107 patients scheduled, three hundred and sixteen (15 %) opted for same day appointments. Black, Latino, and Asian patients were significantly more likely to use the same day access initiative versus Caucasian patients (p = 0.01). The same day access initiative increased the proportion of patients seen within 5 days from referral from 8 % to 34 % for Blacks, 12-57 % for Latinos, and 18-67 % for Asians, compared to 39-55 % for Caucasians (p < 0.001). The no-show rate was reduced from 20 % to 7 % and 14-5 %, for Black and Latino patients, respectively (p < 0.001).

CONCLUSIONS

The implementation of a same day access initiative narrowed disparities with respect to access-related benchmarks.

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

BACKGROUND

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

OBJECTIVES

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

METHOD

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

RESULTS

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

CONCLUSION

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

Introduction

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

Methods

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

Results

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

Conclusion

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

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.

Racial disparities in telehealth use during the coronavirus disease 2019 pandemic

OBJECTIVE

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

DESIGN

Retrospective cohort.

SETTING

Fertility practice in an academic medical center.

PATIENTS

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

EXPOSURE

Coronavirus disease 2019 pandemic.

MAIN OUTCOME MEASURES

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

RESULTS

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

CONCLUSION

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

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

BACKGROUND

No-show appointments pose a significant challenge for healthcare providers, particularly in rural areas. In this study, we developed an evidence-based predictive model for patient no-shows at the Marshfield Clinic Health System (MCHS) rural provider network in Wisconsin, with the aim of improving overbooking approaches in outpatient settings and reducing the negative impact of no-shows in our underserved rural patient populations.

METHODS

Retrospective data (2021) were obtained from the MCHS scheduling system, which included 1,260,083 total appointments from 263,464 patients, as well as their demographic, appointment, and insurance information. We used descriptive statistics to associate variables with show or no-show status, logistic regression, and random forests utilized, and eXtreme Gradient Boosting (XGBoost) was chosen to develop the final model, determine cut-offs, and evaluate performance. We also used the model to predict future no-shows for appointments from 2022 and onwards.

RESULTS

The no-show rate was 6.0% in both the train and test datasets. The train and test datasets both yielded 5.98. Appointments scheduled further in advance (> 60 days of lead time) had a higher (7.7%) no-show rate. Appointments for patients aged 21-30 had the highest no-show rate (11.8%), and those for patients over 60 years of age had the lowest (2.9%). The model predictions yielded an Area Under Curve (AUC) of 0.84 for the train set and 0.83 for the test set. With the cut-off set to 0.4, the sensitivity was 0.71 and the positive predictive value was 0.18. Model results were used to recommend 1 overbook for every 6 at-risk appointments per provider per day.

CONCLUSIONS

Our findings demonstrate the feasibility of developing a predictive model based on administrative data from a predominantly rural healthcare system. Our new model distinguished between show and no-show appointments with high performance, and 1 overbook was advised for every 6 at-risk appointments. This data-driven approach to mitigating the impact of no-shows increases treatment availability in rural areas by overbooking appointment slots on days with an elevated risk of no-shows.

Rural patient and provider perceptions of telehealth implemented during the COVID-19 pandemic

BACKGROUND

Understanding perceptions of telehealth  implementation from patients and providers can improve the utility and sustainability of these programs, particularly in under-resourced rural settings. The purpose of this study was to evaluate both patient and provider perceptions of telehealth visits in a large rural healthcare system during the COVID-19 pandemic. To promote sustainability of telehealth approaches, we also assessed whether the percentage of missed appointments differed between in-person and telehealth visits.

METHODS

Using anonymous surveys, we evaluated patient preferences and satisfaction with telehealth visits from November 2020 -March 2021 and assessed perceptions of telehealth efficiency and value among rural providers from September-October 2020. We examined whether telehealth perceptions differed according to patients' age, educational attainment, insurance status, and distance to clinical site and providers' age and length of time practicing medicine using ANOVA test. We also examined whether the percentage of missed appointments differed between in-person and telehealth visits at a family practice clinic within the rural healthcare system from April to September 2020 using a Chi-square test.

RESULTS

Over 73% of rural patients had favorable perceptions of telehealth visits, and satisfaction was generally higher among younger patients. Patients reported difficulty with scheduling follow-up appointments, lack of personal contact and technology challenges as common barriers. Over 80% of the 219 providers responding to the survey reported that telehealth added value to their practice, while 36.6% agreed that telehealth visits are more efficient than in-person visits. Perception of telehealth value and efficiency did not differ by provider age (p = 0.67 and p = 0.67, respectively) or time in practice (p = 0.53 and p = 0.44, respectively). Technology challenges for the patient (91.3%) and provider (45.1%) were commonly reported. The percentage of missed appointments was slightly higher for telehealth visits compared to in-person visits, but the difference was not statistically significant (8.7% vs. 8.0%; p = 0.39).

CONCLUSIONS

Telehealth perceptions were generally favorable among rural patients and providers, although satisfaction was lower among older patients and providers. Our findings suggest that telehealth approaches may add value and efficiency to rural clinical practice. However, technology issues for both patients and providers and gaps in care coordination need to be addressed to promote sustainability of telehealth approaches in rural practice.

GPT Technology to Help Address Longstanding Barriers to Care in Free Medical Clinics

The implementation of technology in healthcare has revolutionized patient-centered decision making by providing contextualized information about a patient's healthcare journey, leading to increased efficiency (Keyworth et al. in BMC Med Inform Decis Mak 18:93, 2018, https://doi.org/10.1186/s12911-018-0661-3 ). Artificial intelligence has been integrated within Electronic Health Records (EHR) to prompt screenings or diagnostic tests based on a patient's holistic health profile. While larger hospitals have already widely adopted these technologies, free clinics hold lower utilization of these advanced capability EHRs. The patient population at a free clinic faces a multitude of factors that limits their access to comprehensive care, thus requiring necessary efforts and measures to close the gap in healthcare disparities. Emerging Artificial Intelligence (AI) technology, such as OpenAI's ChatGPT, GPT-4, and other large language models (LLMs) have remarkable potential to improve patient care outcomes, promote health equity, and enhance comprehensive and holistic care in resource-limited settings. This paper aims to identify areas in which integrating these LLM AI advancements into free clinics operations can optimize and streamline healthcare delivery to underserved patient populations. This paper also identifies areas of improvements in GPT that are necessary to deliver those services.

Feasibility of Postoperative Day One or Day Two Discharge After Robotic Cardiac Surgery

INTRODUCTION

The robotic platform reduces the invasiveness of cardiac surgical procedures, thus facilitating earlier discharge in select patients. We sought to evaluate the characteristics, perioperative management, and early outcomes of patients who underwent postoperative day 1 or 2 (POD1-2) discharge after robotic cardiac surgery at our institution.

METHODS

Retrospective review of 169 patients who underwent robotic cardiac surgery at our facility between 2019 and 2021 identified 57 patients discharged early on POD1 (n = 19) or POD2 (n = 38) and 112 patients who underwent standard discharge (POD3 or later). Relevant data were extracted and compared.

RESULTS

In the early discharge group, median patient age was 62 [IQR: 55, 66] (IQR = interquartile range) years, and 70.2% (40/57) were male. Median Society of Thoracic Surgeons predictive risk of mortality score was 0.36 [IQR: 0.25, 0.56] %. The most common procedures performed were mitral valve repair [66.6%, (38/57)], atrial mass resection [10.5% (6/57)], and coronary artery bypass grafting [10.5% (6/57)]. The only significant differences between the POD1 and POD2 groups were shorter operative time, higher rate of in-operating room extubation, and shorter ICU length of stay in the POD1 group. Lower in-hospital morbidity and comparable 30-day mortality and readmission rates were observed between the early and standard discharge groups.

CONCLUSIONS

POD1-2 discharge after various robotic cardiac operations afforded lower morbidity and similar 30-day readmission and mortality rates compared to discharge on POD3 or later. Our findings support the feasibility of POD1-2 discharge after robotic cardiac surgery for patients with low preoperative risk, an uncomplicated postoperative course, and appropriate postoperative management protocols.

A comparative analysis of outpatient nutrition clinic scheduling outcomes based on in-person and telehealth patient care delivery modalities☆

During the start of the global COVID-19 pandemic in March 2020, patient care modalities changed from in-person to telehealth to comply with physical distancing guidelines. Our study uniquely examines operations data from three distinct periods: before the transition to telehealth, early transition from in-person care to telehealth, and the eventual adoption of telehealth. We present a comparative analysis of outpatient nutrition clinic scheduling outcomes based on care delivery modality. We used descriptive statistics to report means and variance and frequencies. We used inferential statistics to make comparisons: categorical data were compared using chi- square analysis with post-hoc comparisons using a z-test with alpha at 0.05. Means of continuous variables were compared using ANOVA with Tukey HSD post-hoc analysis. We found patient demographics remained widely unchanged across the three distinct periods as the demand for telehealth visits increased, with a notable rise in return patient visits, signaling both adaptability across the patient population and acceptance of the telehealth modality. These analyses along with evidence from the included literature review point to many the benefits of telehealth, thus telehealth as a healthcare delivery modality is here to stay. Our work serves as a foundation for future studies in this field, provides information for decision-makers in telehealth-related strategic planning, and can be utilized in advocacy for the extension of telehealth coverage.

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.

Doctor for Every Citizen: Telehealth Visits at Dubai Health Authority during COVID-19 Pandemic in Dubai, United Arab Emirates

BACKGROUND

Digital health significantly affects healthcare delivery. Moreover, empirical studies on the utilization of telehealth in Dubai are limited. Accordingly, this study examines the utilization of telehealth services in Dubai Health Authority (DHA) facilities and the factors associated with telehealth appointment completion and turnaround time.

METHODS

This cross-sectional study examines patients who used telehealth services in DHA from 2020 through 2021 using 241,822 records. A binary logistic regression model was constructed to investigate the association between appointment turnaround time as a dependent variable and patient and visit characteristics as independent variables.

RESULTS

Of the total scheduled telehealth visits, more than three-quarter (78.55%) were completed. Older patients, non-Emiratis, patients who had their visits in 2020, patients who had video visits, and those who sought family medicine as a specialty had a shorter turnaround time to receive their appointment.

CONCLUSIONS

This study identifies several characteristics associated with the turnaround time. Moreover, technological improvements focusing on specialties that can readily be addressed through telehealth and further research in this domain will improve service provision and support building an evidence-base in the government sector of the emirate of Dubai.

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

BACKGROUND AND OBJECTIVE

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

METHOD

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

RESULTS

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

CONCLUSIONS

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

Predicting no-shows for dental appointments

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

Reducing missed psychotherapy appointments: An advanced practice nurse-initiated telephone orientation protocol

PURPOSE

Missed appointments pose significant challenges to the health care system as they contribute to the inefficient use of resources, increase costs, and result in poor outcomes. The aim of this project was to reduce the rate of missed adult psychotherapy appointments by implementing a Psychiatric-Mental Health Nurse Practitioner-initiated telephone orientation protocol (TOP).

DESIGN

Quasi-experimental.

METHODS

The TOP intervention combined an attendance policy review, psychoeducation, and motivational interviewing techniques. Missed appointment rates were compared before and after initiation of the TOP.

FINDINGS

The TOP significantly reduced the rates of missed appointments by 55% over 12 weeks.

PRACTICE IMPLICATIONS

The TOP can be implemented as standard care to increase client engagement, improve appointment attendance, and boost clinician productivity.

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

ABSTRACT

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

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

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

A critical review of implementing lean and simulation to improve resource utilization and patient experience in outpatient clinics

Purpose

The purpose of this paper is to explore and critically review the existing literature on applications of Lean Methodology (LM) and Discrete-Event Simulation (DES) to improve resource utilization and patient experience in outpatient clinics. In doing, it is aimed to identify how to implement LM in outpatient clinics and discuss the advantages of integrating both lean and simulation tools towards achieving the desired outpatient clinics outcomes.

Design/methodology/approach

A theoretical background of LM and DES to define a proper implementation approach is developed. The search strategy of available literature on LM and DES used to improve outpatient clinic operations is discussed. Bibliometric analysis to identify patterns in the literature including trends, associated frameworks, DES software used, and objective and solutions implemented are presented. Next, an analysis of the identified work offering critical insights to improve the implementation of LM and DES in outpatient clinics is presented.

Findings

Critical analysis of the literature on LM and DES reveals three main obstacles hindering the successful implementation of LM and DES. To address the obstacles, a framework that integrates DES with LM has been recommended and proposed. The paper provides an example of such a framework and identifies the role of LM and DES towards improving the performance of their implementation in outpatient clinics.

Originality/value

This study provides a critical review and analysis of the existing implementation of LM and DES. The current roadblocks hindering LM and DES from achieving their expected potential has been identified. In addition, this study demonstrates how LM with DES combined to achieve the desired outpatient clinic objectives.

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.

Sociodemographic and Appointment Factors Affecting Missed Opportunities to Provide Neonatal Ultrasound Imaging

PURPOSE

The aim of this study was to assess disparities in outpatient imaging missed care opportunities (IMCOs) for neonatal ultrasound by sociodemographic and appointment factors at a large urban pediatric hospital.

METHODS

A retrospective review was performed among patients aged 0 to 28 days receiving one or more outpatient appointments for head, hip, renal, or spine ultrasound at the main hospital or satellite sites from 2008 to 2018. An IMCO was defined as a missed ultrasound or cancellation <24 hours in advance. Population-average correlated logistic regression modeling estimated the odds of IMCOs for six sociodemographic (age, sex, race/ethnicity, language, insurance, and region of residence) and seven appointment (type of ultrasound, time, day, season, site, year, and distance to appointment) factors. The primary analysis included unknown values as a separate category, and the secondary analysis used multiple imputation to impute genuine categories from unknown variables.

RESULTS

The data set comprised 5,474 patients totaling 6,803 ultrasound appointments. IMCOs accounted for 4.4% of appointments. IMCOs were more likely for Black (odds ratio [OR], 3.31; P < .001) and other-race neonates (OR, 2.66; P < .001) and for patients with public insurance (OR, 1.78; P = .002). IMCOs were more likely for appointments at the main hospital compared with satellites (P < .001), during work hours (P = .021), and on weekends (P < .001). Statistical significance for primary and secondary analyses was quantitatively similar and qualitatively identical.

CONCLUSIONS

Marginalized racial groups and those with public insurance had a higher rate of IMCOs in neonatal ultrasound. This likely represents structural inequities faced by these communities, and more research is needed to identify interventions to address these inequities in care delivery for vulnerable neonatal populations.

Missed Appointments at a Child Development Centre and Barriers to Access Special Needs Services for Children in Klang Valley, Malaysia: A Mixed Methods Study

Attending appointments is vital for children with special needs, as such appointments involve long-term interdisciplinary care to ensure continuity of care and improve health and well-being. This study was performed to determine the prevalence of missed clinic appointments and identify the factors among those who have ever missed appointments and barriers of access to children's special needs services at the Child Development Centre (CDC) at the Universiti Kebangsaan Malaysia Medical Centre (UKMMC). Moreover, suggestions for improvement from the caregivers' perspectives were explored. This is an explanatory sequential mixed methods study among caregivers of children with developmental disabilities aged up to 17 years old. Of 197 caregivers, 62 (31.5%) had missed clinic appointments. Forgetfulness was the most frequently cited reason. The bi-variable analysis showed significant differences in missed appointment rates by gender of caregivers and duration of follow-up. The final logistic regression model demonstrated that, when combined with the effect of being a male caregiver as an independent variable, follow-up duration of more than 6 years increased 2.67 times the risk of missing an appointment. Caregivers' perceived barriers were transportation, caregiver, child and healthcare services factors. Policies and strategic plans should be focused on key findings from these factors to improve appointment adherence and accessibility to services for children with special needs.

Efficient Prediction of Missed Clinical Appointment Using Machine Learning

Public health and its related facilities are crucial for thriving cities and societies. The optimum utilization of health resources saves money and time, but above all, it saves precious lives. It has become even more evident in the present as the pandemic has overstretched the existing medical resources. Specific to patient appointment scheduling, the casual attitude of missing medical appointments (no-show-ups) may cause severe damage to a patient's health. In this paper, with the help of machine learning, we analyze six million plus patient appointment records to predict a patient's behaviors/characteristics by using ten different machine learning algorithms. For this purpose, we first extracted meaningful features from raw data using data cleaning. We applied Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic Sampling Method (Adasyn), and random undersampling (RUS) to balance our data. After balancing, we applied ten different machine learning algorithms, namely, random forest classifier, decision tree, logistic regression, XG Boost, gradient boosting, Adaboost Classifier, Naive Bayes, stochastic gradient descent, multilayer perceptron, and Support Vector Machine. We analyzed these results with the help of six different metrics, i.e., recall, accuracy, precision, F1-score, area under the curve, and mean square error. Our study has achieved 94% recall, 86% accuracy, 83% precision, 87% F1-score, 92% area under the curve, and 0.106 minimum mean square error. Effectiveness of presented data cleaning and feature selection is confirmed by better results in all training algorithms. Notably, recall is greater than 75%, accuracy is greater than 73%, F1-score is more significant than 75%, MSE is lesser than 0.26, and AUC is greater than 74%. The research shows that instead of individual features, combining different features helps make better predictions of a patient's appointment status.

Effect of disclosure of HIV status on patient representation and adherence to clinic visits in eastern Uganda: A propensity-score matched analysis

BACKGROUND

Disclosure of human immunodeficiency virus (HIV) status improves adherence to antiretroviral therapy (ART) and increases the chance of virological suppression and retention in care. However, information on the effect of disclosure of HIV status on adherence to clinic visits and patient representation is limited. We evaluated the effects of disclosure of HIV status on adherence to clinic visits and patient representation among people living with HIV in eastern Uganda.

METHODS

In this quasi-randomized study, we performed a propensity-score-matched analysis on observational data collected between October 2018 and September 2019 from a large ART clinic in eastern Uganda. We matched participants with disclosed HIV status to those with undisclosed HIV status based on similar propensity scores in a 1:1 ratio using the nearest neighbor caliper matching technique. The primary outcomes were patient representation (the tendency for patients to have other people pick-up their medications) and adherence to clinic visits. We fitted a logistic regression to estimate the effects of disclosure of HIV status, reported using the odds ratio (OR) and 95% confidence interval (CI).

RESULTS

Of 957 participants, 500 were matched. In propensity-score matched analysis, disclosure of HIV status significantly impacts adherence to clinic visits (OR = 1.63; 95% CI, 1.13-2.36) and reduced patient representation (OR = O.49; 95% CI, 0.32-0.76). Sensitivity analysis showed robustness to unmeasured confounders (Gamma value = 2.2, p = 0.04).

CONCLUSIONS

Disclosure of HIV status is associated with increased adherence to clinic visits and lower representation to collect medicines at the clinic. Disclosure of HIV status should be encouraged to enhance continuity of care among people living with HIV.

Reduce Patient Treatment wait time in a Proton Beam Facility - A Gatekeeper Approach

Patient wait time can negatively impact treatment quality in a proton therapy center, where multiple treatment rooms share one proton beam. Wait time increases patient discomfort that can lead to patient motion, dissatisfaction, and longer treatment delay. This study was to develop a patient call-back model that reduced patient wait while efficiently utilizing the proton beam. A "Gatekeeper" logic allowing therapists to adjust the time of a patient's call-back to the treatment room was developed. It uses a two-pronged approach to minimize overlap of long treatment and the possibility of excessive wait in the queue to receive the proton beam. The goal was to reduce the maximum wait time to less than eight minutes per field for a four-room facility. The effectiveness of this logic was evaluated through simulation, and five scenarios were compared. Four scenarios implementing various levels of gatekeeper logic were compared with the original scenario without the logic. The best performing model provided a reduction of the maximum field wait by 26% and met the predefined goal. Adjusting call-back extended the treatment day length by an average of 6 min and a maximum of 12 min in total. The use of this gatekeeper logic significantly reduces patient field wait with minimal impact on treatment day length for a four-room proton facility. A sample interface that adopts this logic for therapists to make informed decision on patient call-back time is demonstrated.

Digital Health: Unlocking Value in a Post-Pandemic World

The COVID-19 pandemic has forever changed health care, spurring a revolution in digital health technologies. Across the world, hundreds of thousands of health care systems are considering a central question: how do we connect with our patients? Digital health has been used as a stopgap in many cases to continue the essential functions of health systems. As the post-pandemic world and our "new normal" come into focus, further needs will have to be met with a digital patient interaction, with an eye toward value transformation. One barrier to fully leveraging digital tools is the lack of a framework for classifying the type of digital health care. This can limit our ability to design, deploy, evaluate, and communicate through digital means. This article presents 3 categories of digital health and their relationships to value metrics: (1) telehealth or direct care delivery, (2) digital access tools, and (3) digital monitoring. An evidence-based discussion reveals past successes, current promises, and future challenges in reducing defects in value through digital care. In the coming years, value transformation will become more crucial to the success of health care systems. By using the taxonomy in this article, health systems can better implement digital tools with a value-driven purpose. Defining the role of digital health in the post-pandemic world is needed to assist health systems and practices to build a bridge to value-based care.