Impact of Delayed Time to Advanced Imaging on Missed Appointments Across Different Demographic and Socioeconomic Factors

Reducing Delays in MRIs Under Sedation and General Anesthesia Using Quality Improvement Tools

OBJECTIVE

Develop structured, quality improvement interventions to achieve a 15%-point reduction in MRIs performed under sedation or general anesthesia (GA) delayed more than 15 min within a 6-month period.

METHODS

A prospective audit of MRIs under sedation or GA from January 2022 to June 2023 was conducted. A multidisciplinary team performed process mapping and root cause analysis for delays. Interventions were developed and implemented over four Plan, Do, Study, Act (PDSA) cycles, targeting workflow standardization, preadmission patient counseling, reinforcing adherence to scheduled scan times and written consent respectively. Delay times (compared with Kruskal-Wallis and Dunn's tests), delays more than 15 min and delays of 60 min or more at baseline and after each PDSA cycle were recorded.

RESULTS

In all, 627 MRIs under sedation or GA were analyzed, comprising 443 at baseline and 184 postimplementation. Of the 627, 556 (88.7%) scans were performed under sedation, 22 (3.5%) under monitored anesthesia care, and 49 (7.8%) under GA. At baseline, 71.6% (317 of 443) scans were delayed over 15 min and 28.2% (125 of 443) scans by 60 min or more, with a median delay of 30 min. Postimplementation, there was a 34.7%-point reduction in scans delayed more than 15 min, a 17.5%-point reduction in scans delayed by 60 min or more, and a reduction in median delay time by 15 min (P < .001).

DISCUSSION

Structured interventions significantly reduced delays in MRIs under sedation and GA, potentially improving outcomes for both patients and providers. Key factors included a diversity of perspectives in the study team, continued stakeholder engagement and structured quality improvement tools including PDSA cycles.

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.

Prevalence of financial hardship and health-related social needs among patients with missed radiology appointments

PURPOSE

We aimed to evaluate the prevalence of financial hardship and Health-Related Social Needs (HRSN) among patients who missed their radiology appointment.

METHODS

English-speaking adult patients, with a missed outpatient imaging appointment at any of a tertiary care imaging centers between 11/2022 and 05/2023 were eligible. We measured self-reported general financial worry using Comprehensive Score for Financial Toxicity (COST), imaging hardship (worry that the current imaging is a financial hardship to patient and their family), material hardship (e.g., medical debt), cost-related care nonadherence, and HRSNs including housing instability, food insecurity, transportation problems, and utility help needs.

RESULTS

282 patients were included (mean age 54.7 ± 15.0 years; 70.7 % female). Majority were non-Hispanic White (52.4 %), followed by Asian (23.0 %) and Hispanic (16.0 %) racial/ethnic background. Most missed appointments were patient-initiated (74.8 %); 13.5 % due to cost or insurance coverage and 6.4 % due to transportation and parking. Mean COST score was 26.8 with 44.4 % and 28.8 % reporting their illness and imaging as a source of financial hardship. 18.3 % and 35.2 % endorsed cost-related care nonadherence and material hardship. 32.7 % had at least one HRSNs with food insecurity the most common (25.4 %). Only 12.5 % were previously screened for financial hardship or HRSNs. Having comorbidity and living in more disadvantaged neighborhoods was associated with higher report of financial hardship and HRSNs.

CONCLUSION

Financial hardship and HRSNs are common among those who miss radiology appointments. There needs to be more rigorous screening for financial hardship and HRSNs at every health encounter and interventions should be implemented to address these.

Factors Associated With Timeliness and Equity of Access to Outpatient MRI Examinations

OBJECTIVE

The role of MRI in guiding patients' diagnosis and treatment is increasing. Therefore, timely MRI performance prevents delays that can impact patient care. We assessed the timeliness of performing outpatient MRIs using the socio-ecological model approach and evaluated multilevel factors associated with delays.

METHODS

This institutional review board-approved study included outpatient MRI examinations ordered between October 1, 2021, and December 31, 2022, for performance at a large quaternary care health system. Mean order-to-performed (OtoP) interval (in days) and prolonged OtoP interval (defined as >10 days) for MRI orders with an expected date of 1 day to examination performance were measured. Logistic regression was used to assess patient-level (demographic and social determinants of health), radiology practice-level, and community-level factors associated with prolonged OtoP interval.

RESULTS

There were 126,079 MRI examination orders with expected performance within 1 day placed during the study period (56% of all MRI orders placed). After excluding duplicates, there were 97,160 orders for unique patients. Of the MRI orders, 48% had a prolonged OtoP interval, and mean OtoP interval was 18.5 days. Factors significantly associated with delay in MRI performance included public insurance (odds ratio [OR] = 1.11, P < .001), female gender (OR = 1.11, P < .001), radiology subspecialty (ie, cardiac, OR = 1.71, P < .001), and patients from areas that are most deprived (ie, highest Area Deprivation Index quintile, OR = 1.70, P < .001).

DISCUSSION

Nearly half of outpatient MRI orders were delayed, performed >10 days from the expected date selected by the ordering provider. Addressing multilevel factors associated with such delays may help enhance timeliness and equity of access to MRI examinations, potentially reducing diagnostic errors and treatment delays.

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.

Social Determinants of Health Framework to Identify and Reduce Barriers to Imaging in Marginalized Communities

Social determinants of health (SDOH) are conditions influencing individuals' health based on their environment of birth, living, working, and aging. Addressing SDOH is crucial for promoting health equity and reducing health outcome disparities. For conditions such as stroke and cancer screening where imaging is central to diagnosis and management, access to high-quality medical imaging is necessary. This article applies a previously described structural framework characterizing the impact of SDOH on patients who require imaging for their clinical indications. SDOH factors can be broadly categorized into five sectors: economic stability, education access and quality, neighborhood and built environment, social and community context, and health care access and quality. As patients navigate the health care system, they experience barriers at each step, which are significantly influenced by SDOH factors. Marginalized communities are prone to disparities due to the inability to complete the required diagnostic or screening imaging work-up. This article highlights SDOH that disproportionately affect marginalized communities, using stroke and cancer as examples of disease processes where imaging is needed for care. Potential strategies to mitigate these disparities include dedicating resources for clinical care coordinators, transportation, language assistance, and financial hardship subsidies. Last, various national and international health initiatives are tackling SDOH and fostering health equity.

Decision analysis framework for predicting no-shows to appointments using machine learning algorithms

BACKGROUND

No-show to medical appointments has significant adverse effects on healthcare systems and their clients. Using machine learning to predict no-shows allows managers to implement strategies such as overbooking and reminders targeting patients most likely to miss appointments, optimizing the use of resources.

METHODS

In this study, we proposed a detailed analytical framework for predicting no-shows while addressing imbalanced datasets. The framework includes a novel use of z-fold cross-validation performed twice during the modeling process to improve model robustness and generalization. We also introduce Symbolic Regression (SR) as a classification algorithm and Instance Hardness Threshold (IHT) as a resampling technique and compared their performance with that of other classification algorithms, such as K-Nearest Neighbors (KNN) and Support Vector Machine (SVM), and resampling techniques, such as Random under Sampling (RUS), Synthetic Minority Oversampling Technique (SMOTE) and NearMiss-1. We validated the framework using two attendance datasets from Brazilian hospitals with no-show rates of 6.65% and 19.03%.

RESULTS

From the academic perspective, our study is the first to propose using SR and IHT to predict the no-show of patients. Our findings indicate that SR and IHT presented superior performances compared to other techniques, particularly IHT, which excelled when combined with all classification algorithms and led to low variability in performance metrics results. Our results also outperformed sensitivity outcomes reported in the literature, with values above 0.94 for both datasets.

CONCLUSION

This is the first study to use SR and IHT methods to predict patient no-shows and the first to propose performing z-fold cross-validation twice. Our study highlights the importance of avoiding relying on few validation runs for imbalanced datasets as it may lead to biased results and inadequate analysis of the generalization and stability of the models obtained during the training stage.

Evaluation of a STAT MRI Protocol for Emergent Ophthalmology Patients

BACKGROUND

Evaluating patients with potentially sight-threatening conditions frequently involves urgent neuroimaging, and some providers recommend expediting emergency department (ED) evaluation. However, several factors may limit the practicality of ED evaluation. This pilot study assessed the feasibility and safety of a STAT magnetic resonance imaging (MRI) protocol, designed to facilitate outpatient MRI within 48 hours of referral, compared with ED evaluation for patients with optic disc edema.

METHODS

A retrospective chart review was performed. Demographics, clinical data, and baseline ophthalmic measures were compared between patients in STAT and ED groups using the t test or Fisher exact test. Multivariate analyses compared changes in visual acuity (VA), visual field mean deviation (VF MD), retinal nerve fiber layer thickness, and edema grade between presentation and follow-up using a mixed-effects model adjusting for age, sex, and baseline measures.

RESULTS

A total of 70 patients met the study criteria-24 (34.3%) in the STAT MRI cohort and 46 (65.7%) in the ED cohort. Demographic variables were similar between groups. Patients referred to the ED had worse VA ( P < 0.001), larger VF MD ( P < 0.001), and higher edema grade ( P = 0.002) at presentation. Four patients in the ED group and none in the STAT group were found to have space-occupying lesions. Multivariate analyses showed that follow-up measures were significantly associated with their baseline values (all P < 0.001) but not with referral protocol (all P > 0.099). The STAT MRI protocol was associated with lower average patient charges and hospital costs.

CONCLUSIONS

The STAT MRI protocol did not result in inferior visual outcomes or delay in life-threatening diagnoses. Urgent outpatient evaluation, rather than ED referral, seems safe for some patients with optic disc edema. These findings support continued utilization of the protocol and ongoing improvement efforts.

Lost in Translation: Challenges in the Diagnosis and Treatment of Early-Onset Schizophrenia

Early-onset schizophrenia (EOS) is a heterogeneous condition that has a serious, insidious clinical course and poor long-term mental health outcomes. The clinical presentations are highly complex due to the overlapping symptomatology with other illnesses, which contributes to a delay in the diagnosis. The objective of the review is to study if an earlier age of onset (AAO) of EOS has poor clinical outcomes, the diagnostic challenges of EOS, and effective treatment strategies. The review provides a comprehensive literature search of 5966 articles and summarizes 126 selected for empirical evidence to methodically consider challenges in diagnosing and treating EOS for practicing clinicians. The risk factors of EOS are unique but have been shared with many other neuropsychiatric illnesses. Most of the risk factors, including genetics and obstetric complications, are nonmodifiable. The role of early diagnosis in reducing the duration of untreated psychosis (DUP) remains critical to reducing overall morbidity. Many specific issues contribute to the risk and clinical outcomes. Therefore, issues around diagnostic ambiguity, treatment resistance, nonadherence, and rehospitalizations further extend the DUP. There is hesitancy to initiate clozapine early, even though the empirical evidence strongly supports its use. There is a growing body of research that suggests the use of long-acting injectables to address nonadherence, and these measures are largely underutilized in acute settings. The clinical presentations of EOS are complex. In addition to the presence of specific risk factors, patients with an early onset of illness are also at a higher risk for treatment resistance. While there is a need to develop tools for early diagnosis, established evidence-based measures to address nonadherence, psychoeducation, and resistance must be incorporated into the treatment planning.

Social determinants of health and the prediction of missed breast imaging appointments

BACKGROUND

Predictive models utilizing social determinants of health (SDH), demographic data, and local weather data were trained to predict missed imaging appointments (MIA) among breast imaging patients at the Boston Medical Center (BMC). Patients were characterized by many different variables, including social needs, demographics, imaging utilization, appointment features, and weather conditions on the date of the appointment.

METHODS

This HIPAA compliant retrospective cohort study was IRB approved. Informed consent was waived. After data preprocessing steps, the dataset contained 9,970 patients and 36,606 appointments from 1/1/2015 to 12/31/2019. We identified 57 potentially impactful variables used in the initial prediction model and assessed each patient for MIA. We then developed a parsimonious model via recursive feature elimination, which identified the 25 most predictive variables. We utilized linear and non-linear models including support vector machines (SVM), logistic regression (LR), and random forest (RF) to predict MIA and compared their performance.

RESULTS

The highest-performing full model is the nonlinear RF, achieving the highest Area Under the ROC Curve (AUC) of 76% and average F1 score of 85%. Models limited to the most predictive variables were able to attain AUC and F1 scores comparable to models with all variables included. The variables most predictive of missed appointments included timing, prior appointment history, referral department of origin, and socioeconomic factors such as household income and access to caregiving services.

CONCLUSIONS

Prediction of MIA with the data available is inherently limited by the complex, multifactorial nature of MIA. However, the algorithms presented achieved acceptable performance and demonstrated that socioeconomic factors were useful predictors of MIA. In contrast with non-modifiable demographic factors, we can address SDH to decrease the incidence of MIA.

Racial and ethnic disparities in pediatric magnetic resonance imaging missed care opportunities

BACKGROUND

Imaging missed care opportunities (MCOs), previously referred to as "no shows," impact timely patient diagnosis and treatment and can exacerbate health care disparities. Understanding factors associated with imaging MCOs could help advance pediatric health equity.

OBJECTIVE

To assess racial/ethnic differences in pediatric MR imaging MCOs and whether health system and socioeconomic factors, represented by a geography-based Social Vulnerability Index (SVI), influence racial/ethnic differences.

MATERIALS AND METHODS

We conducted a retrospective analysis of MR imaging MCOs in patients younger than 21 years at a pediatric academic medical center (2015-2019). MR imaging MCOs were defined as: scheduled but appointment not attended, canceled within 24 h, and canceled but not rescheduled. Mixed effects multivariable logistic regression assessed the association between MCOs and race/ethnicity and community-level social factors, represented by the SVI.

RESULTS

Of 68,809 scheduled MRIs, 6,159 (9.0%) were MCOs. A higher proportion of MCOs were among Black/African-American and Hispanic/Latino children. Multivariable analysis demonstrated increased odds of MCOs among Black/African-American (adjusted odds ratio [aOR] 1.9, 95% confidence interval [CI] 1.7-2.3) and Hispanic/Latino (aOR 1.5, 95% CI 1.3-1.7) children compared to White children. The addition of SVI >90th percentile to the adjusted model had no effect on adjusted OR for Black/African-American (aOR 1.9, 95% CI 1.7-2.2) or Hispanic/Latino (aOR 1.5, 95% CI 1.3-1.6) children. Living in a community with SVI >90th percentile was independently associated with MCOs.

CONCLUSION

Black/African-American and Hispanic/Latino children were almost twice as likely to experience MCOs, even when controlling for factors associated with MCOs. Independent of race/ethnicity, higher SVI was significantly associated with MCOs. Our study supports that pediatric health care providers must continue to identify systemic barriers to health care access for Black/African-American and Hispanic/Latino children and those from socially vulnerable areas.

Improving intervention design to promote cervical cancer screening among hard-to-reach women: assessing beliefs and predicting individual attendance probabilities in Bogotá, Colombia

Background

Despite being a preventable disease, cervical cancer continues to be a public health concern, affecting mainly lower and middle-income countries. Therefore, in Bogotá a home-visit based program was instituted to increase screening uptake. However, around 40% of the visited women fail to attend their Pap smear test appointments. Using this program as a case study, this paper presents a methodology that combines machine learning methods, using routinely collected administrative data, with Champion’s Health Belief Model to assess women’s beliefs about cervical cancer screening. The aim is to improve the cost-effectiveness of behavioural interventions aiming to increase attendance for screening. The results presented here relate specifically to the case study, but the methodology is generic and can be applied in all low-income settings.

Methods

This is a cross-sectional study using two different datasets from the same population and a sequential modelling approach. To assess beliefs, we used a 37-item questionnaire to measure the constructs of the CHBM towards cervical cancer screening. Data were collected through a face-to-face survey (N = 1699). We examined instrument reliability using Cronbach’s coefficient and performed a principal component analysis to assess construct validity. Then, Kruskal–Wallis and Dunn tests were conducted to analyse differences on the HBM scores, among patients with different poverty levels. Next, we used data retrieved from administrative health records (N = 23,370) to fit a LASSO regression model to predict individual no-show probabilities. Finally, we used the results of the CHBM in the LASSO model to improve its accuracy.

Results

Nine components were identified accounting for 57.7% of the variability of our data. Lower income patients were found to have a lower Health motivation score (p-value < 0.001), a higher Severity score (p-value < 0.001) and a higher Barriers score (p-value < 0.001). Additionally, patients between 25 and 30 years old and with higher poverty levels are less likely to attend their appointments (O.R 0.93 (CI: 0.83–0.98) and 0.74 (CI: 0.66–0.85), respectively). We also found a relationship between the CHBM scores and the patient attendance probability. Average AUROC score for our prediction model is 0.9.

Conclusion

In the case of Bogotá, our results highlight the need to develop education campaigns to address misconceptions about the disease mortality and treatment (aiming at decreasing perceived severity), particularly among younger patients living in extreme poverty. Additionally, it is important to conduct an economic evaluation of screening options to strengthen the cervical cancer screening program (to reduce perceived barriers). More widely, our prediction approach has the potential to improve the cost-effectiveness of behavioural interventions to increase attendance for screening in developing countries where funding is limited.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12905-022-01800-3.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Targeting Missed Care Opportunities Using Modern Communication Methods: A Quality Improvement Initiative to Improve Access to CT and MRI Appointments

PURPOSE

To evaluate the impact of automated text and phone call reminder systems on CT (computed tomography) and MRI (magnetic resonance imaging) missed care opportunities.

METHODS

This was an IRB (institutional review board) exempt prospective interventional quality improvement study. The proportion of missed care opportunities (appointment made, no imaging performed) related to scheduled CT and MRI examinations were evaluated over 2 months (Month 1: reminder phone calls by staff 48-96 hours prior and mailed letter 1-2 weeks prior; Month 2: no manual call or letter, automated text message 24 hours prior, automated phone call 72 hours prior, automated patient portal message 7 days prior). The proportion of missed care opportunities was calculated in aggregate and by modality. Process control p-charts were generated. An a priori power analysis was performed. Chi-squared tests were performed. p-value < 0.017 was considered significant after Bonferroni correction.

RESULTS

Missed care opportunities occurred for 2.82% (292/10348; 95% CI: 2.51-3.16) of all CT and MRI appointments using traditional communication and 2.44% (262/10719; 95% CI: 2.16-2.75) using automated communication (p = 0.09). Automated messaging did not significantly change the proportion of missed care opportunities for CT (traditional: 2.62% [95% CI: 2.23-3.06] vs. automated: 2.06% [95% CI: 1.70-2.48], p = 0.05) or MRI (traditional: 3.1% [95% CI: 2.60-3.66] vs. automated: 2.83% [95% CI: 2.40-3.30], p = 0.43). Process control p-charts showed dominance of common cause variation.

CONCLUSION

Automated messaging did not meaningfully change the overall proportion of missed care opportunities compared to traditional human-initiated phone calls. Automated communications may reduce cost and improve efficiency without adversely affecting access to care.

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.

Shorter outpatient wait-times for buprenorphine are associated with linkage to care post-hospital discharge

BACKGROUND

Inpatient addiction consult services (ACS) lower barriers to accessing medications for opioid use disorder (MOUD), however not every patient recommended for MOUD links to outpatient care. We hypothesized that fewer days between discharge date and outpatient appointment date was associated with improved linkage to buprenorphine treatment among patients evaluated by an ACS.

METHODS

We extracted appointment and demographic data from electronic medical records and conducted retrospective chart review of adults diagnosed with opioid use disorder (OUD) evaluated by an ACS in Boston, MA between July 2015 and August 2017. These patients were initiated on or recommended buprenorphine treatment on discharge and provided follow-up appointment at our hospital post-discharge. Multivariable logistic regression assessed whether arrival to the appointment post-discharge was associated with shorter wait-times (0-1 vs. 2+ days).

RESULTS

In total, 142 patients were included. Among patients who had wait-times of 0-1 day, 63 % arrived to their appointment compared to wait-times of 2 or more days (42 %). There were no significant differences between groups based on age, gender, distance of residence from the hospital, insurance status, co-occurring alcohol use disorder diagnosis, or discharge with buprenorphine prescription. After adjusting for covariates, patients with 0-1 day of wait-time had 2.6 times the odds of arriving to their appointment [95 % CI 1.3-5.5] compared to patients who had 2+ days of wait-time.

CONCLUSION

For hospitalized patients with OUD evaluated for initiating MOUD, same- and next-day appointments are associated with increased odds of linkage to outpatient MOUD care post-discharge compared to waiting two or more days.

Workers' Societal Costs After Knee and Shoulder Injuries and Diagnosis with In-Office Arthroscopy or Delayed MRI: A Cost-Minimization Analysis

The goal of this study was to evaluate the societal costs of using in-office diagnostic arthroscopy (IDA) compared with magnetic resonance imaging (MRI) for the diagnosis of intra-articular knee and shoulder pathology in employed patients receiving Workers' Compensation or disability coverage. The prevalence is estimated at 260,000 total cases per year.

METHODS

A cost-minimization analysis of IDA compared with MRI was conducted. Direct costs (in 2018 U.S. dollars) were calculated from private reimbursement amounts and Medicare. Indirect costs were estimated from a societal perspective including effects of delayed surgical procedures on the ability to work, lost income, Workers' Compensation or disability coverage, and absenteeism. Four regions were selected: Boston, Massachusetts; Detroit, Michigan; Denver, Colorado; and San Bernadino, California. Sensitivity analyses were performed using TreeAge Pro 2019 software. The base assumption was that it would take approximately 4 weeks for a diagnosis with MRI and 0 weeks for a diagnosis with IDA.

RESULTS

Direct costs to determine a knee diagnosis with IDA were $556 less expensive (California) to $470 more expensive (Massachusetts) than MRI. Assuming a 4-week wait, societal costs (indirect and direct) for knee diagnosis were anywhere from $7,852 (Denver) to $11,227 (Boston) less using IDA. Direct costs were similar for shoulder pathology. In order for MRI to be the less costly option, the MRI and the follow-up visit to the physician would need to occur directly after consultation. Under Medicare, direct costs were similar for both the knee and shoulder when comparing IDA and MRI. Including indirect costs resulted in IDA being the less costly option.

CONCLUSIONS

The use of IDA instead of MRI for the diagnosis of knee and shoulder pathology reduced costs. The potential savings to society were approximately $7,852 to $11,227 per operative patient and were dependent on scheduling and follow-up using MRI and on Workers' Compensation.

LEVEL OF EVIDENCE

Economic and Decision Analysis Level IV. See Instructions for Authors for a complete description of levels of evidence.

The impact of common variables on non-attendance at a radiology centre in Singapore

INTRODUCTION

With the increasing demand for medical imaging, non-attendance inhibits private and public radiology practices in Singapore from providing timely care and achieving maximal efficiency. Missed radiological appointments adversely affect clinical and economic outcomes and strain the finite healthcare resources. We examined the prevalence and predictors of patient non-attendance for radiological services at a regional public hospital in Singapore and compared them against other medical imaging centres globally.

METHODS

Outpatient records of patients who were scheduled for specialised medical imaging obtained from Radiological Information System (RIS) were retrospectively reviewed. Analysed variables include patient demographics, radiology modalities, visit statuses and appointment lead times where Pearson's chi-square test and Fisher's exact test were used for categorical variables, and independent sample t-test was used for continuous variables. The association between each patient characteristic and non-attendance status was assessed using Binary Logistics Regression. Variables that showed statistical significance in univariate analysis were included in the multivariate logistic regression model to identify the independent risk factors associated with non-attendance.

RESULTS

Among the 59,748 outpatient appointments with medical imaging requests, 15.5% did not turn up for their appointments. Logistic regression indicated that patient's age, ethnicity, subsidy status, house ownership, living vicinity to regional hospital cluster, appointment wait times, appointment hours and appointment months were significant factors associated with the failure to attend scheduled radiological examinations.

CONCLUSION

Even though predictors of non-attendance remained consistent across medical imaging centres worldwide, Singapore reported a higher prevalence of missed appointments calling for future exploratory studies to understand the population's health-seeking behaviours and ordering patterns of clinicians.

IMPLICATIONS FOR PRACTICE

Comparison and identification of these predictors will assist in the design of targeted interventions that may improve patient's adherence and utilisation of imaging services.

Analysis of socioeconomic and demographic factors and imaging exam characteristics associated with missed appointments in pediatric radiology

BACKGROUND

Missed appointments can have an adverse impact on health outcomes by delaying appropriate imaging, which can be critical in influencing treatment decisions.

OBJECTIVE

To assess for socioeconomic and imaging exam factors associated with missed appointments among children scheduled for diagnostic imaging.

MATERIALS AND METHODS

We retrospectively analyzed children (<18 years) scheduled for outpatient diagnostic imaging during a 12-month period. In doing so, we obtained socioeconomic and radiology exam characteristics (modality, intravenous contrast administration, radiation and use of sedation) data from the electronic medical record. We employed multivariate logistic regression to assess the association of socioeconomic, demographic and imaging exam characteristics with imaging missed appointments.

RESULTS

In total, 7,275 children met inclusion criteria. The mean age was 8.8 years (standard deviation [SD] = 6.2 years) and the study population consisted of 52% female gender, 69% White race, 38% adolescent age group and 32% with a median household income by ZIP-code category of <$50,000. Logistic regression showed increased likelihood of missed appointments among children of Black/African-American race (odds ratio [OR] = 1.9; 95% confidence interval [CI] = 1.4-2.5); with insurance categories including Medicaid (OR=2.0; 95% CI=1.6-2.4), self-pay (OR=2.1; 95% CI=1.3-3.6) and other (OR=2.7; 95% CI=1.3-5.4); with <$50,000 median household income by ZIP-code category (OR=1.7; 95% CI=1.4-2.0); and with examination wait time of 7-21 days (OR=2.7; 95% CI=2.1-3.5) and >21 days (OR=3.7; 95% CI=2.9-4.8). The use of radiation, intravenous contrast agent or sedation was not associated with increased likelihood of missed appointments.

CONCLUSION

Expanding our knowledge of how different socioeconomic and imaging-related factors influence missed appointments among children can serve as a foundational step to better understand existing and emerging disparities and inform strategies to advance health equity efforts in radiology.

Disparities in PET Imaging for Prostate Cancer at a Tertiary Academic Medical Center

The purpose of this study was to evaluate differences between patients receiving ¹⁸F-fluciclovine and ⁶⁸Ga-prostate-specific membrane antigen (⁶⁸Ga-PSMA-11) for biochemically recurrent prostate cancer at a tertiary medical center. Methods: All ¹⁸F-fluciclovine and ⁶⁸Ga-PSMA-11 PET studies performed at the University of California San Francisco from October 2015 to January 2020 were reviewed. Age, race/ethnicity, primary language, body mass index, insurance type, and home address were obtained through the electronic medical record. A logistic regression model was used to evaluate the predictor variables. Results: In total, 1,502 patients received ⁶⁸Ga-PSMA-11 and 254 patients received ¹⁸F-fluciclovine. Black patients had increased odds of receiving imaging with ¹⁸F-fluciclovine versus ⁶⁸Ga-PSMA-11 compared with non-Hispanic White patients (odds ratio, 3.88; 95% CI, 1.90-7.91). There were no other statistically significant differences. Conclusion: In patients receiving molecular imaging for prostate cancer at a single U.S. tertiary medical center, access to ⁶⁸Ga-PSMA-11 for Black patients was limited, compared with non-Hispanic White patients, by a factor of nearly 4.

Factors affecting patient adherence to lung cancer screening: A multisite analysis

OBJECTIVE

To identify factors associated with delayed adherence to follow-up in lung cancer screening.

METHODS

Utilizing a data warehouse and lung cancer screening registry, variables were collected from a referred sample of 3110 unique participants with follow-up CT during the study period (1 January 2016 to 17 October 2018). Adherence was defined as undergoing chest CT within 90 days and 30 days of the recommended time for follow-up and was determined using proportions and multiple variable logistic regression models across the American College of Radiology Lung Imaging Reporting and Data System (Lung-RADS®) categories.

RESULTS

Of 1954 lung cancer screening participants (51.9% (1014/1954) males, 48.1% (940/1954) female; mean age 65.7 (range 45-87), smoking history median 40 pack-years, 60.2% and 44.5% did not follow-up within 30 and 90 days, respectively. Participants receiving Lung-RADS® category 1 or 2 presented later than those with Lung-RADS® category 3 at 90 days (coefficient -27.24, 95% CI -51.31, -3.16, p = 0.027). Participants with Lung-RADS® category 1 presented later than those with Lung-RADS® category 2 at both 90- and 30-days past due (OR 0.76 95% CI [0.59-0.97], p = 0.029 and OR 0.63 95% CI [0.48-0.83], p = 0.001, respectively).

CONCLUSIONS

Adherence to follow-up was higher among participants receiving more suspicious Lung-RADS® results at index screening CT and among those who had undergone more non-lung cancer screening imaging examinations prior to index lung cancer screening CT. These observations may inform strategies aimed at prospectively identifying participants at risk for delayed or nonadherence to prevent potential morbidity and mortality from incident lung cancers.

Analysis of the Effects of a Patient-Centered Rideshare Program on Missed Appointments and Timeliness for MRI Appointments at an Academic Medical Center

PURPOSE

The aim of this study was to assess the differences in timeliness to MRI appointments and missed MRI appointment rates before and after the implementation of a rideshare program.

METHODS

Retrospective analysis of a rideshare program was performed 9 months after implementation to compare the effects before and after implementation. Variables obtained included demographics, MRI appointment variables, and data related to rideshare use. Descriptive statistics and linear and logistic regression analyses were used to compare demographic characteristics among patients using the rideshare program with (1) those who did not use the rideshare program after implementation and (2) patients before rideshare implementation. Rates of missed appointments derived from patient-related, same-day appointment cancellations were analyzed using logistic regression analyses. Timeliness was analyzed using linear regression analyses. All analyses were adjusted for potential confounders.

RESULTS

Of 7,707 patients scheduled for MRI appointments during the postintervention period, 151 patients used the rideshare service (1.95%). There were no statistically significant differences in missed appointment rates after rideshare implementation (adjusted odds ratio, 1.09; 95% confidence interval, 0.93-1.27; P = .275). Patients using the rideshare service were more likely to be on time (adjusted coefficient = 13.0; 95% confidence interval, 5.4-20.5; P = .001). Older patients (P = .001), unemployed patients (P < .001), and patients without commercial insurance (P < .001) were more likely to use the rideshare service.

CONCLUSIONS

Implementation of a rideshare program did not significantly decrease missed appointment rates, but it significantly improved timeliness to MRI appointments while assisting at-risk patient populations reporting transportation barriers.

Appointment wait-times and arrival for patients at a low-barrier access addiction clinic

INTRODUCTION

Same-day or next-day access to outpatient medication for addiction treatment (MAT) for both alcohol and opioid use disorders may facilitate sustained treatment with evidence-based therapies for substance use disorders (SUD). This study evaluates the association between appointment wait-times and odds of arrival to appointment for patients seeking outpatient MAT.

METHODS

The study sample consisted of patients who scheduled an appointment with a low-barrier access addiction clinic between August 1, 2016, and July 31, 2017. The outcome of interest was the status of the appointment as a dichotomous variable: arrive or no-show/cancel. The primary independent variable (wait-time) was the number of overnights between the date a patient scheduled a clinic appointment and the date of service, categorized as 0 days, 1 day, and 2+ days. We conducted bivariable and multivariable logistic regressions to calculate unadjusted and adjusted odds ratios for arrival. Multivariable analyses were adjusted for gender, age, distance of residence from the clinic, and insurance type.

RESULTS

Our analysis included 657 patients, of whom 410 (62%) arrived to their first appointment. Among the 657 patients, 47% (308) were scheduled the same day (0 days) and 82% (252) of them were seen, 23% (151) waited 1 day (next-day) and 53% (80) of them were seen, and 30% (198) waited 2+ days and 39% (78) of them were seen. Patients were more likely to be seen when they had a same-day (OR 6.9 [95% CI 4.6-10.4]; AOR 7.5 [4.9-11.4]) or next-day (OR 1.7 [1.1-2.7]; AOR 1.7 [1.1-2.6]) appointment compared to waiting 2+ days.

CONCLUSION

Patients seeking MAT through a clinic that schedules same-day and next-day appointments for treatment are more likely to attend addiction appointments compared to patients who wait longer. Clinics should strive to reduce wait-times for patients seeking MAT.

Location, Location, Location: The Association Between Imaging Setting and Follow-Up of Findings of Indeterminate Malignant Potential

PURPOSE

To evaluate the relationship between patient location at time of imaging and completion of relevant imaging follow-up for findings with indeterminate malignant potential.

METHODS

We used a mandatory hospital-wide standardized assessment categorization system to analyze all ultrasound, CT, and MRI examinations performed over a 7-month period. Multivariate logistic regression, adjusted for imaging modality, characteristics of patients, ordering clinicians, and interpreting radiologists, was used to evaluate the relationship between patient location (outpatient, inpatient, or emergency department) at the time of index examination and completion of relevant outpatient imaging follow-up.

RESULTS

Relevant follow-up occurred in 49% of index examinations, with a greater percentage among those performed in the outpatient setting compared with those performed in the inpatient or emergency department settings (62% versus 18% versus 17%, respectively). Compared with examinations obtained in the outpatient setting, examinations performed in the emergency department (adjusted odds ratio [aOR] 0.07; 95% confidence interval [CI], 0.03-0.19) and inpatient (aOR 0.14; 95% CI, 0.09-0.23) settings were less likely to be followed up. Black patients and those residing in lower-income neighborhoods were also less likely to receive relevant follow-up. Few lesions progressed to more suspicious lesions (4.6%).

CONCLUSIONS

Patient location at time of imaging is associated with the likelihood of completing relevant follow-up imaging for lesions with indeterminate malignant potential. Future work should evaluate health system-level care processes related to care setting, as well as their effects on appropriate follow-up imaging. Doing so would support efforts to improve appropriate follow-up imaging and reduce health care disparities.