Evaluation of claims data from a commercial value-based insurance product shows pediatric imaging is not a major driver of overall or pediatric healthcare expenditures.

BACKGROUND: Initiatives to reduce healthcare expenditures often focus on imaging, suggesting that imaging is a major driver of cost. OBJECTIVE: To evaluate medical expenditures and determine if imaging was a major driver in pediatric as compared to adult populations. METHODS: We reviewed all claims data for members in a value-based contract between a commercial insurer and a healthcare system for calendar years 2021 and 2022. For both pediatric (<18 years of age) and adult populations, we analyzed average per member per year (PMPY) medical expenditures related to imaging as well as other categories of large medical expenses. Average PMPY expenditures were compared between adult and pediatric patients. RESULTS: Children made up approximately 20% of members and 21% of member months but only 8-9% of expenditures. Imaging expenditures in pediatric members were 0.2% of the total healthcare spend and 2.9% of total pediatric expenditures. Imaging expenditures per member were seven times greater in adults than children. The rank order of imaging expenditures and imaging modalities was also different in pediatric as compared to adult members. CONCLUSION: Evaluation of claims data from a commercial value-based insurance product shows that pediatric imaging is not a major driver of overall, nor pediatric only, healthcare expenditures.

The Association Between Central Line-Associated Bloodstream Infection and Central Line Access.

OBJECTIVES: Identifying modifiable risk factors associated with central line-associated bloodstream infections (CLABSIs) may lead to modifications to central line (CL) management. We hypothesize that the number of CL accesses per day is associated with an increased risk for CLABSI and that a significant fraction of CL access may be substituted with non-CL routes. DESIGN: We conducted a retrospective cohort study of patients with at least one CL device day from January 1, 2015, to December 31, 2019. A multivariate mixed-effects logistic regression model was used to estimate the association between the number of CL accesses in a given CL device day and prevalence of CLABSI within the following 3 days. SETTING: A 395-bed pediatric academic medical center. PATIENTS: Patients with at least one CL device day from January 1, 2015, to December 31, 2019. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: There were 138,411 eligible CL device days across 6,543 patients, with 639 device days within 3 days of a CLABSI (a total of 217 CLABSIs). The number of per-day CL accesses was independently associated with risk of CLABSI in the next 3 days (adjusted odds ratio, 1.007; 95% CI, 1.003-1.012; p = 0.002). Of medications administered through CLs, 88% were candidates for delivery through a peripheral line. On average, these accesses contributed a 6.3% increase in daily risk for CLABSI. CONCLUSIONS: The number of daily CL accesses is independently associated with risk of CLABSI in the next 3 days. In the pediatric population examined, most medications delivered through CLs could be safely administered peripherally. Efforts to reduce CL access may be an important strategy to include in contemporary CLABSI-prevention bundles.

Evaluating the Accuracy of 2020 Census Block-Level Estimates in California.

In this study, we provide an assessment of data accuracy from the 2020 Census. We compare block-level population totals from a sample of 173 census blocks in California across three sources: (1) the 2020 Census, which has been infused with error to protect respondent confidentiality; (2) the California Neighborhoods Count, the first independent enumeration survey of census blocks; and (3) projections based on the 2010 Census and subsequent American Community Surveys. We find that, on average, total population counts provided by the U.S. Census Bureau at the block level for the 2020 Census are not biased in any consistent direction. However, subpopulation totals defined by age, race, and ethnicity are highly variable. Additionally, we find that inconsistencies across the three sources are amplified in large blocks defined in terms of land area or by total housing units, blocks in suburban areas, and blocks that lack broadband access.

Higher Moments for Optimal Balance Weighting in Causal Estimation.

We expand upon a simulation study that compared three promising methods for estimating weights for assessing the average treatment effect on the treated for binary treatments: generalized boosted models, covariate-balancing propensity scores, and entropy balance. The original study showed that generalized boosted models can outperform covariate-balancing propensity scores, and entropy balance when there are likely to be nonlinear associations in both the treatment assignment and outcome models and when the other two models are fine-tuned to obtain balance only on first-order moments. We explore the potential benefit of using higher-order moments in the balancing conditions for covariate-balancing propensity scores and entropy balance. Our findings showcase that these two models should, by default, include higher-order moments and focusing only on first moments can result in substantial bias in estimated treatment effect estimates from both models that could be avoided using higher moments.

Patient and family experience with telemedicine and in-person pediatric and obstetric ambulatory encounters throughout 2020, during the COVID-19 epidemic: the distance effect.

BACKGROUND: Telemedicine has grown significantly in recent years, mainly during the COVID-19 pandemic, and there has been a growing body of literature on the subject. Another topic that merits increased attention is differences in patient and family experience between telehealth and in-person visits. To our team's knowledge, this is the first study evaluating pediatric and obstetrics outpatients experience with telemedicine and in-person visit types in an academic maternal and children's hospital, and its correlation with geographic distance from the medical center throughout 2020, during the COVID-19 crisis. METHODS: We aim to evaluate and compare patients' telemedicine and in-person experience for ambulatory encounters based on survey data throughout 2020, during the COVID-19 pandemic, with particular focus on the influence of distance of the patient's home address from the medical facility. A total of 9,322 patient experience surveys from ambulatory encounters (6,362 in-person and 2,960 telemedicine), in a maternal and children's hospital during 2020 were included in this study. The percentage of patients who scored the question "Likelihood to recommend practice" with a maximum 5/5 (top box) score was used to evaluate patient experience. The k-means model was used to create distance clusters, and statistical t-tests were conducted to compare mean distances and Top Box values between telemedicine and in-person models. Logistic regression analysis was used to evaluate the correlation between Top Box scores and patients' distance to the hospital. RESULTS: Top Box likelihood to recommend percentages for in-person and telemedicine were comparable (in-person = 81.21%, telemedicine = 81.70%, p-value = 0.5624). Mean distance from the hospital was greater for telemedicine compared to in-person patients (in-person = 48.89 miles, telemedicine = 61.23 miles, p-value < 0.01). Patients who live farther displayed higher satisfaction scores regardless of the visit type (p-value < 0.01). CONCLUSIONS: There is a direct relationship between the family experience and the distance from the considered medical center, during year 2020, i.e., patients who live farther from the hospital record higher Top Box proportion for "Likelihood to Recommend" than patients who live closer to the medical center, regardless of the approach, in-person or telemedicine.

Propensity score matching for multilevel spatial data: accounting for geographic confounding in health disparity studies.

BACKGROUND: Diabetes is a public health burden that disproportionately affects military veterans and racial minorities. Studies of racial disparities are inherently observational, and thus may require the use of methods such as Propensity Score Analysis (PSA). While traditional PSA accounts for patient-level factors, this may not be sufficient when patients are clustered at the geographic level and thus important confounders, whether observed or unobserved, vary by geographic location. METHODS: We employ a spatial propensity score matching method to account for "geographic confounding", which occurs when the confounding factors, whether observed or unobserved, vary by geographic region. We augment the propensity score and outcome models with spatial random effects, which are assigned scaled Besag-York-Mollié priors to address spatial clustering and improve inferences by borrowing information across neighboring geographic regions. We apply this approach to a study exploring racial disparities in diabetes specialty care between non-Hispanic black and non-Hispanic white veterans. We construct multiple global estimates of the risk difference in diabetes care: a crude unadjusted estimate, an estimate based solely on patient-level matching, and an estimate that incorporates both patient and spatial information. RESULTS: In simulation we show that in the presence of an unmeasured geographic confounder, ignoring spatial heterogeneity results in increased relative bias and mean squared error, whereas incorporating spatial random effects improves inferences. In our study of racial disparities in diabetes specialty care, the crude unadjusted estimate suggests that specialty care is more prevalent among non-Hispanic blacks, while patient-level matching indicates that it is less prevalent. Hierarchical spatial matching supports the latter conclusion, with a further increase in the magnitude of the disparity. CONCLUSIONS: These results highlight the importance of accounting for spatial heterogeneity in propensity score analysis, and suggest the need for clinical care and management strategies that are culturally sensitive and racially inclusive.

Appropriate analyses of bimodal substance use frequency outcomes: a mixture model approach.

Background: In addiction research, outcome measures are often characterized by bimodal distributions. One mode can be for individuals with low substance use and the other mode for individuals with high substance use. Applying standard statistical procedures to bimodal data may result in invalid inference. Mixture models are appropriate for bimodal data because they assume that the sampled population is composed of several underlying subpopulations.Objectives: To introduce a novel mixture modeling approach to analyze bimodal substance use frequency data.Methods: We reviewed existing models used to analyze substance use frequency outcomes and developed multiple alternative variants of a finite mixture model. We applied all methods to data from a randomized controlled study in which 30-day alcohol abstinence was the primary outcome. Study data included 73 individuals (38 men and 35 women). Models were implemented in the software packages SAS, Stata, and Stan.Results: Shortcomings of existing approaches include: 1) inability to model outcomes with multiple modes, 2) invalid statistical inferences, including anti-conservative p-values, 3) sensitivity of results to the arbitrary choice to model days of substance use versus days of substance abstention, and 4) generation of predictions outside the range of common substance use frequency outcomes. Our mixture model variants avoided all of these shortcomings.Conclusions: Standard models of substance use frequency outcomes can be problematic, sometimes overstating treatment effectiveness. The mixture models developed improve the analysis of bimodal substance use frequency.

The approach to improving patient experience at children's hospitals: a primer for pediatric radiologists.

Increasing attention is being given to improving patient experience in health care. Most children's hospitals have a patient experience office or team that champions and measures patient experience and partners with operations to optimize performance in this area. We outline the activities that our patient experience team undertakes at our pediatric health system to advocate for, measure and improve the experience of our patients and families. The framework we propose for such activities includes those that are proactive in improving patient experience as well as those that are reactive to when patients and families have had a poor experience. Those reactive practices are often centered on the management of patient complaints and grievances and early intervention into patient complaints so that they do not become grievances.

The American Board of Radiology B. Leonard Holman Research Pathway to Initial Certification: Opportunities Lost for Diagnostic Radiology.

OBJECTIVE: In 1998, the American Board of Radiology introduced the B. Leonard Holman Research Pathway (HRP) to initial certification for trainees in diagnostic radiology (DR) and radiation oncology (RO) motivated to pursue research-oriented careers in academic DR and RO. CONCLUSION: The HRP Committee anticipated that there would be a relatively even distribution between DR and RO participants, but with 18 years of experience that has not been the case. This article focuses on the HRP and DR.

Review of learning opportunity rates: correlation with radiologist assignment, patient type and exam priority.

BACKGROUND: Common cause analysis of learning opportunities identified in a peer collaborative improvement process can gauge the potential risk to patients and opportunities to improve. OBJECTIVE: To study rates of learning opportunities based on radiologist assignment, patient type and exam priority at an academic children's hospital with 24/7 in-house attending coverage. MATERIALS AND METHODS: Actively submitted peer collaborative improvement learning opportunities from July 2, 2016, to July 31, 2018, were identified. Learning opportunity rates (number of learning opportunities divided by number of exams in each category) were calculated based on the following variables: radiologist assignment at the time of dictation (daytime weekday, daytime weekend and holiday, evening, and night) patient type (inpatient, outpatient or emergency center) and exam priority (stat, urgent or routine). A statistical analysis of rate differences was performed using a chi-square test. Pairwise comparisons were made with Bonferroni method adjusted P-values. RESULTS: There were 1,370 learning opportunities submitted on 559,584 studies (overall rate: 0.25%). The differences in rates by assignment were statistically significant (P<0.0001), with the highest rates on exams dictated in the evenings (0.31%) and lowest on those on nights (0.19%). Weekend and holiday daytime (0.26%) and weekday daytime (0.24%) rates fell in between. There were significantly higher rates on inpatients (0.33%) than on outpatients (0.22%, P<0.0001) or emergency center patients (0.16%, P<0.0001). There were no significant differences based on exam priority (stat 0.24%, urgent 0.26% and routine 0.24%, P=0.55). CONCLUSION: In this study, the highest learning opportunity rates occurred on the evening rotation and inpatient studies, which could indicate an increased risk for patient harm and potential opportunities for improvement.

Practical Suggestions on How to Move From Peer Review to Peer Learning.

OBJECTIVE: The purpose of this article is to outline practical steps that a department can take to transition to a peer learning model. CONCLUSION: The 2015 Institute of Medicine report on improving diagnosis emphasized that organizations and industries that embrace error as an opportunity to learn tend to outperform those that do not. To meet this charge, radiology must transition from a peer review to a peer learning approach.

Optimizing Performance by Preventing Disruptive Behavior in Radiology.

Disruptive behaviors impede delivery of high-value health care by negatively impacting patient outcomes and increasing costs. Health care is brimming with potential triggers of disruptive behavior. Given omnipresent environmental and cultural factors such as constrained resources, stressful environments, commercialization, fatigue, unrealistic expectation of perfectionism, and burdensome documentation, a burnout epidemic is raging, and medical providers are understandably at tremendous risk to succumb and manifest these unprofessional behaviors. Each medical specialty has its own unique challenges. Radiology is not exempt; these issues do not respect specialty or professional boundaries. Unfortunately, preventive measures are too frequently overlooked, provider support programs rarely exist, and often organizations either tolerate or ineffectively manage the downstream disruptive behaviors. This review summarizes the background, key definitions, contributing factors, impact, prevention, and management of disruptive behavior. Every member of the health care team can gain from an improved understanding and awareness of the contributing factors and preventive measures. Application of these principles can foster a just culture of understanding, trust, support, respect, and teamwork balanced with accountability. The authors discuss these general topics along with specific issues for radiologists in the current medical environment. Patients, providers, health care organizations, and society all stand to benefit from better prevention of these behaviors. There is a strong moral, ethical, and business case to address this issue head-on. ©RSNA, 2018.

Costs of Quality and Safety in Radiology.

With the movement toward at-risk population health management-related payment models, a core factor for the success and survival of health care organizations has become understanding and decreasing costs. In medical specialties such as radiology, understanding models for procedure-based costing will become increasingly important. Using bottom-up models for procedure-based costing, such as time-driven activity-based costing, is more advantageous than using the inaccurate ratio of costs to charges approach; however, these approaches are more resource intensive when compared to top-down approaches. Understanding the costs of quality is also important for creating an accounting and budgeting process that reflects the total cost of quality. The costs of quality are divided into two main categories: the cost of control (also referred to as the costs of conformance) and the costs of failure of control (also referred to as the costs of nonconformance). The costs of control are the expenditures that occur to ensure quality. The costs of noncontrol are the expenses that arise from the lack of quality and safety. The cost of control has two subcategories: prevention costs and appraisal costs. The cost of noncontrol also has two subcategories: internal failure costs and external failure costs. Adopting a mind-set that takes into account the costs of control, or the costs to ensure high-quality care, and the costs of noncontrol, or the hidden costs of poor-quality care, will be essential for successful health care organizations in the future. ©RSNA, 2018.

Radiographic appearance and clinical significance of fidget spinner ingestions.

BACKGROUND: According to anecdotal press reports, there have been medically significant ingestions of fidget spinner toys, including ingestions that required endoscopic intervention. Fidget spinners have been marketed to improve attention and have been suggested as a therapeutic alternative to medications in children with attention deficit hyperactivity disorder (ADHD). OBJECTIVE: To describe the radiographic appearance and features of ingested fidget spinner components. To evaluate clinical significance via rates of endoscopic intervention, incidence in patients on ADHD medications, and mean age compared to other accidental foreign body ingestions. MATERIALS AND METHODS: A nested retrospective case control study analyzed pediatric accidental foreign body ingestions identified via electronic medical record search between March 1, 2017, and Feb. 28, 2018. Radiographic identifiability, component type and maximum diameter of ingested fidget spinner components were described. A nested cohort of non-fidget spinner ingestions between May 1 and Aug. 31, 2017, was compared with the fidget spinner ingestions for rates of endoscopic intervention (a), concomitant use of ADHD medication (b) and mean age (c) using the Fisher exact test (a and b) and independent samples t-test (c). RESULTS: There were 1,095 unintentional foreign body ingestions. Ten were ingested fidget spinner component ingestions. Eight of the 10 ingested components were radiographically identifiable. Compared with the nested cohort of non-fidget spinner ingestions, fidget spinner ingestions were more likely to undergo endoscopic intervention (P=0.009, 5/10 fidget spinner ingestions vs. 54/383 other ingestions). Fidget spinner patients were more likely to be on ADHD medication (P=0.011, 2/10 fidget spinners vs. 5/383 other). Fidget spinner mean patient age was significantly older than other ingestions (P=0.015, mean: 7.1 years fidget spinner ingestions vs. 4.0 years for other ingestions). CONCLUSION: Compared with other foreign body ingestions, patients who ingested fidget spinner components were more likely to undergo endoscopic intervention, had a higher rate of ADHD medication use and were older. Familiarity with the radiographic appearance of ingested fidget spinner components is important for patient management.

The Daily Operational Brief: Fostering Daily Readiness, Care Coordination, and Problem-Solving Accountability in a Large Pediatric Health Care System.

At a pediatric health system, the Daily Operational Brief (DOB) was updated in 2015 after three years of operation. Quality and safety metrics, the patient volume and staffing assessment, and the readiness assessment are all presented. In addition, in the problem-solving accountability system, problematic issues are categorized as Quick Hits or Complex Issues. Walk-the-Wall, a biweekly meeting attended by hospital senior administrative leadership and quality and safety leaders, is conducted to chart current progress on Complex Issues. The DOB provides a daily standardized approach to evaluate readiness to provide care to current patients and improvement in the care to be provided for future patients.

Gauging potential risk for patients in pediatric radiology by review of over 2,000 incident reports.

BACKGROUND: Incident reporting can be used to inform imaging departments about adverse events and near misses. OBJECTIVE: To study incident reports submitted during a 5-year period at a large pediatric imaging system to evaluate which imaging modalities and other factors were associated with a greater rate of filed incident reports. MATERIALS AND METHODS: All incident reports filed between 2013 and 2017 were reviewed and categorized by modality, patient type (inpatient, outpatient or emergency center) and use of sedation/anesthesia. The number of incident reports was compared to the number of imaging studies performed during that time period to calculate an incident report rate for each factor. Statistical analysis of whether there were differences in these rates between factors was performed. RESULTS: During the study period, there were 2,009 incident reports filed and 1,071,809 imaging studies performed for an incident report rate of 0.19%. The differences in rates by modality were statistically significant (P=0.0001). There was a greater rate of incident reports in interventional radiology (1.54%) (P=0.0001) and in magnetic resonance imaging (MRI) (0.62%) (P=0.001) as compared to other imaging modalities. There was a higher incident report rate for inpatients (0.34%) as compared to outpatient (0.1%) or emergency center (0.14%) (P=0.0001). There was a higher rate of incident reports for patients under sedation (1.27%) as compared to non-sedated (0.12%) (P=0.0001). CONCLUSION: Using incident report rates as a proxy for potential patient harm, the areas of our pediatric radiology service that are associated with the greatest potential for issues are interventional radiology, sedated patients, and inpatients. The areas associated with the least risk are ultrasound (US) and radiography. Safety improvement efforts should be focused on the high-risk areas.

Magnetic resonance imaging of obstructive sleep apnea in children.

Sleep-disordered breathing has a spectrum of severity that spans from snoring and partial airway collapse with increased upper airway resistance, to complete upper airway obstruction with obstructive sleep apnea during sleeping. While snoring occurs in up to 20% of children, obstructive sleep apnea affects approximately 1-5% of children. The obstruction that occurs in obstructive sleep apnea is the result of the airway collapsing during sleep, which causes arousal and impairs restful sleep. Adenotonsillectomy is the first-line treatment of obstructive sleep apnea and is usually effective in otherwise healthy nonsyndromic children. However, there are subgroups in which this surgery is less effective. These subgroups include children with obesity, severe obstructive sleep apnea preoperatively, Down syndrome, craniofacial anomalies and polycystic ovarian disease. Continuous positive airway pressure (CPAP) is the first-line therapy for persistent obstructive sleep apnea despite previous adenotonsillectomy, but it is often poorly tolerated by children. When CPAP is not tolerated or preferred by the family, surgical options beyond adenotonsillectomy are discussed with the parent and child. Dynamic MRI of the airway provides a means to identify and localize the site or sites of obstruction for these children. In this review the authors address clinical indications for imaging, ideal team members to involve in an effective multidisciplinary program, basic anesthesia requirements, MRI protocol techniques and interpretation of the findings on MRI that help guide surgery.

Peer Feedback, Learning, and Improvement: Answering the Call of the Institute of Medicine Report on Diagnostic Error.

In September 2015, the Institute of Medicine (IOM) published a report titled "Improving Diagnosis in Health Care," in which it was recommended that "health care organizations should adopt policies and practices that promote a nonpunitive culture that values open discussion and feedback on diagnostic performance." It may seem counterintuitive that a report addressing a highly technical skill such as medical diagnosis would be focused on organizational culture. The wisdom becomes clearer, however, when examined in the light of recent advances in the understanding of human error and individual and organizational performance. The current dominant model for radiologist performance improvement is scoring-based peer review, which reflects a traditional quality assurance approach, derived from manufacturing in the mid-1900s. Far from achieving the goals of the IOM, which are celebrating success, recognizing mistakes as an opportunity to learn, and fostering openness and trust, we have found that scoring-based peer review tends to drive radiologists inward, against each other, and against practice leaders. Modern approaches to quality improvement focus on using and enhancing interpersonal professional relationships to achieve and maintain high levels of individual and organizational performance. In this article, the authors review the recommendations set forth by the recent IOM report, discuss the science and theory that underlie several of those recommendations, and assess how well they fit with the current dominant approach to radiology peer review. The authors also offer an alternative approach to peer review: peer feedback, learning, and improvement (or more succinctly, "peer learning"), which they believe is better aligned with the principles promoted by the IOM. © RSNA, 2016.

The Right Tool for the Job: Choosing Between Covariate-balancing and Generalized Boosted Model Propensity Scores.

Estimating the causal effect of an exposure (vs. some control) on an outcome using observational data often requires addressing the fact that exposed and control groups differ on pre-exposure characteristics that may be related to the outcome (confounders). Propensity score methods have long been used as a tool for adjusting for observed confounders in order to produce more valid causal effect estimates under the strong ignorability assumption. In this article, we compare two promising propensity score estimation methods (for time-invariant binary exposures) when assessing the average treatment effect on the treated: the generalized boosted models and covariate-balancing propensity scores, with the main objective to provide analysts with some rules-of-thumb when choosing between these two methods. We compare the methods across different dimensions including the presence of extraneous variables, the complexity of the relationship between exposure or outcome and covariates, and the residual variance in outcome and exposure. We found that when noncomplex relationships exist between outcome or exposure and covariates, the covariate-balancing method outperformed the boosted method, but under complex relationships, the boosted method performed better. We lay out criteria for when one method should be expected to outperform the other with no blanket statement on whether one method is always better than the other.