Utilization Metrics Research Articles

Systematic Review of Generative Modelling Tools and Utility Metrics for Fully Synthetic Tabular Data

Systematic Review of Generative Modelling Tools and Utility Metrics for Fully Synthetic Tabular Data

Clinical Tools to Assess Functional Capacity During Risk Assessment Before Elective Noncardiac Surgery : A Scoping Review.

Functional capacity is critical to preoperative risk assessment, yet guidance on its measurement in clinical practice remains lacking. To identify functional capacity assessment tools studied before surgery and characterize the extent of evidence regarding performance, including in populations where assessment is confounded by noncardiopulmonary reasons. MEDLINE, EMBASE, and EBM Reviews (until July 2024). Studies evaluating performance of functional capacity assessment tools administered before elective noncardiac surgery to stratify risk for postoperative outcomes. Study details, measurement properties, pragmatic qualities, and/or clinical utility metrics. 6 categories of performance-based tests and 5 approaches using patient-reported exercise tolerance were identified. Cardiopulmonary exercise testing (CPET) was the most studied tool (132 studies, 32 662 patients) followed by field walking tests (58 studies, 9393 patients) among performance-based tests. Among patient-reported assessments, the Duke Activity Status Index (14 studies, 3303 patients) and unstructured assessments (19 studies, 28 520 patients) were most researched. Most evidence focused on predictive validity (92% of studies), specifically accuracy in predicting cardiorespiratory complications. Several tools lacked evidence on reliability (test consistency across similar measurements), pragmatic qualities (feasibility of implementation), or concurrent criterion validity (correlation to gold standard). Only CPET had evidence on clinical utility (whether administration improved postoperative outcomes). Older adults (≥65 years) were well represented across studies, whereas there were minimal data in patients with obesity, lower-limb arthritis, and disability. Synthesis focused on reported data without requesting missing information. Though several tools for preoperative functional capacity assessment have been studied, research has overwhelmingly focused on CPET and only 1 aspect of validity (predictive validity). Important evidence gaps remain among vulnerable populations with obesity, arthritis, and physical disability. None. (Open Science Framework: https://osf.io/ah7u5).

Automated Virtual Machine Migration with Load Shifting Technique Avoiding Latency in Cloud Environment

Efficient management of Virtual Machines (VMs) in cloud computing is one key aspect to achieve the best utilization of resources and performance. The study is about improving Automated Virtual Machine Migration with a Load Shifting Technique in order to reduce latency through the cloud domain. This methodology dynamically distribute the load across multiple VMs by migrating their workloads to other physical hosts determined according certain resource utilization metrics available in real-time. The dynamic and unpredictable nature of cloud workloads requires a solution that is more responsive in the way it manages VMs. Live migration and pre-copy migration offer suboptimal performance in terms of time to migrate, downtime due to the prolonged copying period as heaps swell/collapse during live migrations. Utilizing sophisticated algorithms and real-time monitoring to avoid these pitfalls, our methodology allows us for proper load balancing with minimal latency. The primary elements of the proposed framework are a detailed monitoring module, an intelligent load shifting algorithm, high-performance migration method and feedback loop for continuous optimization. The monitoring module is responsible for collecting on-the-fly information about CPU, memory, network traffic and disk I/O such that can be exploited by the load shifting algorithm. The algorithm processes this data to decide which VMs should be migrated with the purpose of ensuring load balancing and decreasing latency achieved using these migrations. In order to test the proposed approach, comprehensive simulations and comparatives analyses utilizing other models were performed. This results in better latency reduction, increased resource utilization and improved overall system performance. In fact, the proposed method can handle not only current challenges in cloud computing such as load balancing and latency issued but also a scalable and effective approach for general cloud infrastructure usage. Finally, the Automated Virtual Machine Migration with Load Shifting Technique proves to be a reliable and successful solution for assisting in managing dynamic cloud workloads. Through real-time monitoring, advanced algorithms and continuous optimization this method has now been able to improve cloud services performance and reliability thereby allowing it to better serve service level agreements as well meet up with user satisfaction. We hope to adapt the algorithm and generalize it for even more advanced predictive models in future iterations, as well as evaluation of practical implementation on cloud-based deployment environments.

Enhancing sound source localization and music teaching through integrated computational resource allocation

In contemporary educational and computational settings, the incorporation of cutting-edge technologies like sound source localization and personalized music teaching helps in offering an effective resource allocation strategies. Previous systems for sound localization and music teaching frequently lacked real-time flexibility and effective resource use, reducing their efficiency in dynamic learning settings and tasks involving computation. To overcome these shortcomings, the SoundLocMusicTeachRA (SLMTRA) algorithm is presented, a single, integrated platform made to maximize sound localization accuracy, improve music teaching efficiency, and enhance computational resource oversight. However, the existing study did not highlight the importance of computation resource allocation but this proposed algorithm will address it. SLMTRA uses a new Bagging ensemble approach incorporating Random Forest (RF), Decision Trees (DT), Naive Bayes (NB), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), with hyperparameter tuning to enhance the effectiveness of the approach. These classifiers are trained utilizing sound localization datasets from recordings made with microphones, music teaching feedback datasets from data on student performance, and resource allocation datasets from metrics for computer utilization. Experimental findings indicate SLMTRA’s high accuracy in sound source localization, improved music teaching feedback capacities, as well as effective resource allocation tactics, guaranteeing the best performance of the system. The implementation of SLMTRA represents a noteworthy development in combining sound localization, music teaching, and resource allocation within a unified computational framework, offering a more flexible and effective system compared to previous methodologies.

Resource use efficiency: Developing a generic framework for protein production systems and its application on dairy and fish

Resource use efficiency has become increasingly important as the global demand for natural resources continues to grow, necessitating innovative and sustainable utilization strategies. Despite the availability of tools to quantify resource use and environmental impacts, an integrated assessment of total resource use efficiency remains underdeveloped. Addressing this gap, particularly within the context of the protein transition from animal-based to plant-based sources, this work introduces a novel resource efficiency framework. This framework advances current environmental assessments by enabling the precise calculation of efficiency in protein production processes and process chains based on comprehensive resource utilization metrics. It uniquely incorporates the nature of process inputs, including their renewability and circularity, and supports both foreground and life cycle perspectives at the process and process chain levels. To demonstrate its applicability, two case studies—fish and dairy production systems—are evaluated using exergy and mass analyses.

Optimizing last-mile delivery of essential medicines in Nigeria: Insights from Niger State’s health-care supply chain

Last-mile delivery of health commodities is a critical component of efficient supply chains, and third-party logistics (3PL) providers are pivotal in ensuring cost-effective and reliable product distribution. This study explores the implementation of last-mile delivery outsourcing by government-operated warehouses and drug management agencies (DMAs). This research focuses on the relationship between 3PL utilization and key performance metrics, such as cost reduction, timely delivery, and end-user satisfaction. This study combines a literature review, surveys, and interviews conducted with employees of the DMAs in Minna, Niger State, to establish significant correlations. The findings show that using 3PLs significantly reduced operational costs, improved the frequency of timely deliveries, and enhanced end-user satisfaction. These findings underscore the need for continued engagement with 3PL providers to optimize supply chain performance and improve service delivery outcomes. Managers are recommended to maintain and expand such collaborations to sustain these benefits.

Anesthetic Techniques for Type-1 (Medialization) Thyroplasty: A Scoping Review

ObjectiveTo explore different anesthesia techniques for medialization thyroplasty and determine how these anesthesia techniques may influence patient safety, patient experience, and surgical outcomes during medialization thyroplasty in adult patients. Data SourcesA comprehensive librarian-designed strategy was used to search EMBASE, MEDLINE, and Web of Science for English language studies from database inception to July 2023. The study was registered on Open Science Framework (10.17605/OSF.IO/R3BV2). Review MethodsStudy selection was independently performed by two investigators for all English language studies of adult patients investigating anesthetic techniques for medialization thyroplasty with a minimum of 5 patients. Surgical outcomes (voice, perioperative complications, swallowing), healthcare resource utilization metrics (operating time, length of stay), and patient-reported outcomes measures (PROMs) were analyzed. Study quality was assessed with the Oxford Levels of Evidence tool. ResultsFrom 354 articles, 28 studies were included. The most common anesthetic techniques were combined procedural sedation and local anesthesia (13/28[46%]), local anesthesia alone (8/28 [29%]), and general anesthesia (GA) (7/28 [25%]). Six (6) studies (21%) reported intraoperative complications (e.g., desaturation), and 8 (29%) studies reported postoperative complications (e.g., airway obstruction). Voice outcomes were assessed in 14 (50%) studies. PROMs, including Voice Handicap Index (3/28 [11%]) were less commonly assessed. Intraoperative fiberoptic visualization was utilized in 8 (29%) studies. Only one study assessed swallowing. Only two studies compared outcomes between anesthetic techniques. The median Oxford Level of Evidence was 4. ConclusionMedialization thyroplasty is performed under local anesthetic alone, with combined procedural sedation, and local anesthetic or with GA, with diverse approaches to airway management and minimal perioperative complications. Future research using standardised outcome measures is warranted due to the current paucity in the literature.

Association of inpatient and outpatient pediatric palliative care with healthcare utilization and end-of-life outcomes in pediatric oncology.

Pediatric palliative care (PPC) is associated with improved end-of-life (EOL) outcomes. Inpatient and outpatient PPC have unique roles during the disease course. Yet, it is unknown whether the location of PPC receipt (inpatient vs. outpatient) is associated with healthcare utilization and EOL outcomes for pediatric and adolescent and young adult oncology patients. A retrospective single-institution chart review of pediatric patients (age 0-28) with cancer who died between January 2015 and December 2022 was performed to compare EOL outcomes and healthcare utilization metrics among inpatient PPC, any outpatient PPC, and non-PPC recipients. Demographics and clinical factors were analyzed by PPC receipt location. Among 450 patients, 292 (64.9%) received PPC (inpatient only 35%, any outpatient 65%). Patients who died without receiving PPC dropped from 69% to 22% following development of an outpatient PPC clinic (p<.001). In the last 6months, 1month, and last week of life, inpatient PPC recipients spent more days admitted to the hospital and intensive care unit (all p<.001), and had more intensive medical interventions performed (p<.01). Outpatient PPC recipients were less likely to receive intravenous (IV) chemotherapy (p<.01) or intubation (p=.05), and more likely to receive hospice, die at home, and have an outpatient do-not-resuscitate order (all p<.001). PPC receipt substantially increased after the creation of an outpatient PPC clinic, suggesting that outpatient PPC is critical in the provision of PPC to children with cancer. Outpatient PPC was associated with fewer hospital days, IV chemotherapy, and intubation at EOL, while increasing hospice enrollment and home death.

A cache-aware congestion control mechanism using deep reinforcement learning for wireless sensor networks

In Wireless Sensor Networks (WSN) communication protocols, rule-based approaches have been traditionally used for managing caching and congestion control. These approaches rely on explicitly defined, unchanging models. Recently, a trend has been toward incorporating adaptive methods that leverage machine learning (ML), including its subset deep learning (DL), during network congestion conditions. However, an adaptive cache-aware congestion control mechanism using Deep Reinforcement Learning (DRL) in WSN has not yet been explored. Therefore, this study developed a DRL-based adaptive cache-aware congestion control mechanism called DRL-CaCC to alleviate WSN during congestion scenarios. The DRL-CaCC uses intermediate caching parameters as its state space and adaptively moves the congestion window as its action space through the Rapid Start and DRL algorithms. The mechanism aims to find the optimal congestion window movement to avoid further network congestion while ensuring maximum cache utilization. Results show that DRL-CaCC achieved an average improvement gain between 20% and 40% compared to its baseline protocol, RT-CaCC. Finally, DRL-CaCC outperformed other caching-based and DRL-based congestion control protocols in terms of cache utilization, throughput, end-to-end delay, and packet loss metrics, with improvement gains between 10% and 30% in various congestion scenarios in WSN.

Unlocking success: Full year quality and utilization metrics of payer-provider OMH partnership.

41 Background: For more than a decade, CMS and commercial payers have sought to engage provider organizations in value-based cancer care payment models (VBPMs). Success of VBPMs requires payer/provider alignment on quality/utilization measures indicative of high-value care and the efficiency of data exchange between providers and payers. The following describes several key metrics from the first full year 2022 of the Tennessee Oncology (TO) and BlueCross BlueShield of Tennessee (BCBST) Oncology Medical Home (OMH) partnership. Methods: TO and BCBST implemented reporting methodologies for a combination of practice-reported quality and utilization measures and payer claims-based measures: Patient experience, Pathway adherence, Distress screening / interventions, Admissions/1000 patients, ED visits/1000 patients, Imaging utilization, and Observation stays. Bi-weekly meetings between practice and payer were necessary to review quality/utilization measure reporting, coordinate overall program administration, and monitor member attribution for care management and performance-based payments. Results: TO/BCBST co-developed a VBPM that included bi-directional reporting of practice-reported quality and utilization measures and payer claims-based measures. Technical challenges in data sharing and reporting occurred frequently and required regular troubleshooting. Questions regarding trends in observed data arose frequently for both TO and BCBST and required regular discussions between clinical/operational leaders of payer and practice (Table). Conclusions: These data suggest that payers and community oncology practices can co-develop value-based payment models that yield transparency in quality and utilization data reporting, and observed differences in performance and cost-efficiency among different practices in a market. Payers can gain increased transparency in quality and cost-efficiency of cancer care for their members. Practices can leverage care management payments and performance-based payments to support care delivery and quality/utilization data reporting improvements. Provider and payer capabilities to execute transparent QM/UM reporting is a major challenge in implementing OMH programs. 2022 OMH results. Measure % higher or lower than benchmarks Admissions /k -7.0 ED visit /k -8.4 Imaging utilization + 9.4 Observation stays -10.0 Patient experience NA Pathway adherence NA Distress screening / interventions NA Hospice reporting NA NA = not applicable, no benchmark data available/measured; /k = per 1000 patients; negative = lower than benchmarks; positive = higher than benchmarks.

A Benchmark Proposal for Non‐Generative Fair Adversarial Learning Strategies Using a Fairness‐Utility Trade‐off Metric

ABSTRACTAI systems for decision‐making have become increasingly popular in several areas. However, it is possible to identify biased decisions in many applications, which have become a concern for the computer science, artificial intelligence, and law communities. Therefore, researchers are proposing solutions to mitigate bias and discrimination among decision‐makers. Some explored strategies are based on GANs to generate fair data. Others are based on adversarial learning to achieve fairness by encoding fairness constraints through an adversarial model. Moreover, it is usual for each proposal to assess its model with a specific metric, making comparing current approaches a complex task. Therefore, this work proposes a systematical benchmark procedure to assess the fair machine learning models. The proposed procedure comprises a fairness‐utility trade‐off metric (), the utility and fairness metrics to compose this assessment, the used datasets and preparation, and the statistical test. A previous work presents some of these definitions. The present work enriches the procedure by increasing the applied datasets and statistical guarantees when comparing the models' results. We performed this benchmark evaluation for the non‐generative adversarial models, analyzing the literature models from the same metric perspective. This assessment could not indicate a single model which better performs for all datasets. However, we built an understanding of how each model performs on each dataset with statistical confidence.

Perioperative outcomes using computer navigation in primary total shoulder arthroplasty

IntroductionThe utilization of computer navigation to improve glenoid component placement in primary total shoulder arthroplasty (TSA) is becoming increasingly prevalent. However, there is a paucity of literature examining the perioperative outcomes and cost of this technology compared to standard instrumentation. The purpose of this study was to compare the perioperative complications and healthcare utilization between computer navigation and standard instrumentation in primary TSA. MethodsThe Nationwide Readmissions Database was queried from 2016 to 2020 to identify patients who underwent elective primary TSA. Patients who underwent TSA with computer navigation (n = 752) were propensity score matched in a 1:3 proportion to patients who underwent TSA with standard instrumentation (n = 2256) for age, sex, smoking status, alcohol abuse, hypertension, diabetes mellitus, congestive heart failure, obesity, peripheral vascular disease, chronic kidney disease, chronic liver disease, and chronic lung disease. Bivariate statistical analyses were performed to compare preoperative demographic and comorbidity data, postoperative complications, and hospital utilization metrics between the 2 groups. Chi-square and student t-tests identified differences in categorical and continuous variables, respectively. ResultsPatients undergoing TSA with computer navigation exhibited lower rates of revision within 180 days (P < .001) and lower rates of certain postoperative medical and surgical complications, including acute renal failure (P = .006), urinary tract infection (P = .015), acute respiratory distress syndrome (P = .045), surgical site infection (P = .022), dislocation (P = .012), and prosthetic loosening (P = .032). Computer navigation patients had a higher cost of initial admission (P < .001) but were less likely to have extended hospital stays (P = .047), less likely to be discharged with home healthcare (P < .001), and more likely to be discharged routinely (P < .001). ConclusionTSA with computer navigation was associated with lower revision rates and reduced postoperative complications. Although greater initial costs were observed, decreased extended hospital stays and more routine discharges show promise for long-term cost efficiency. These findings underscore the advantages of computer navigation in TSA and should be considered by orthopedic surgeons when evaluating choice of instrumentation.

Assigning credit where it is due: an information content score to capture the clinical value of multiplexed assays of variant effect

BackgroundA variant can be pathogenic or benign with relation to a human disease. Current classification categories from benign to pathogenic reflect a probabilistic summary of the current understanding. A primary metric of clinical utility for multiplexed assays of variant effect (MAVE) is the number of variants that can be reclassified from uncertain significance (VUS). However, a gap in this measure of utility is that it underrepresents the information gained from MAVEs. The aim of this study was to develop an improved quantification metric for MAVE utility. We propose adopting an information content approach that includes data that does not reclassify variants will better reflect true information gain. We adopted an information content approach to evaluate the information gain, in bits, for MAVEs of BRCA1, PTEN, and TP53. Here, one bit represents the amount of information required to completely classify a single variant starting from no information.ResultsBRCA1 MAVEs produced a total of 831.2 bits of information, 6.58% of the total missense information in BRCA1 and a 22-fold increase over the information that only contributed to VUS reclassification. PTEN MAVEs produced 2059.6 bits of information which represents 32.8% of the total missense information in PTEN and an 85-fold increase over the information that contributed to VUS reclassification. TP53 MAVEs produced 277.8 bits of information which represents 6.22% of the total missense information in TP53 and a 3.5-fold increase over the information that contributed to VUS reclassification.ConclusionsAn information content approach will more accurately portray information gained through MAVE mapping efforts than by counting the number of variants reclassified. This information content approach may also help define the impact of guideline changes that modify the information definitions used to classify groups of variants.

Cross-sectional analysis of healthcare worker mental health and utilisation of a digital mental health platform from 2020 to 2023

BackgroundHealthcare worker (HCW) anxiety and depression worsened during the pandemic, prompting the expansion of digital mental health platforms as potential solutions offering online assessments, access to resources and counselling. The...

Characteristics of Admissions from the Pediatric Emergency Department Observation Unit.

Emergency department observation units (EDOUs) are used to manage common pediatric illnesses and reduce the admission rate to the hospital. Most of these patients require a short duration of observation before a determination can be made whether they need to be admitted to the hospital or safely discharged home. The purpose of this study was to determine the characteristics of admissions from a pediatric EDOU for the top 10 diagnoses admitted to the unit. This will help standardize the disposition of such types of patients from the ED, hence improving the efficiency of the unit. We did a retrospective surveillance study of admitted patients from 0 to 18 years of age from the EDOU for the top 10 diagnoses. Descriptive data were reported using percentages and medians with interquartile ranges. Pearson χ2 tests were used to determine significant differences (P < 0.05) between the reason for admission and medical history. In total, 520 patients were admitted from the EDOU during the study period. The median patient age was 3.39 years, with most being Hispanic and female. The top three primary diagnoses of all admitted patients were cellulitis and abscess, gastroenteritis, and bronchiolitis. Sixty-three percent of all admitted patients had secondary diagnoses. Most of these patients were admitted to the inpatient unit due to progression of the primary condition. The characteristics of admissions from the EDOU may help us to understand historical experience regarding diagnoses, timing, and indications of deterioration, resource utilization, and other metrics that resulted in transfers of EDOU patients to the intensive care unit/operating room/inpatient units.

Radiofrequency catheter ablation of persistent atrial fibrillation by pulmonary vein isolation with or without left atrial posterior wall isolation: long-term outcomes of the CAPLA trial.

Posterior wall isolation (PWI) is commonly incorporated into catheter ablation (CA) strategies for persistent atrial fibrillation (AF) in an attempt to improve outcomes. In the CAPLA randomized study, adjunctive PWI did not improve freedom from atrial arrhythmia at 12 months compared with pulmonary vein isolation (PVI) alone. Whether additional PWI reduces arrhythmia recurrence over the longer term remains unknown. In this multicenter, international, randomized study patients with persistent AF undergoing index CA using radiofrequency (RF) were randomized to PVI+PWI versus PVI alone. Patients underwent regular follow-up including rhythm monitoring for a minimum of 3 years post CA. AF burden at 3 years post-ablation was evaluated with either 28-day continuous ambulatory ECG monitoring, twice daily single-lead ECG or from cardiac implanted device. Evaluated endpoints included freedom from any documented atrial arrhythmia recurrence after a single procedure, AF burden, need for redo catheter ablation, rhythm at last clinical follow-up, healthcare utilisation metrics and AF-related quality of life. 333 of 338 (98.5%) patients (mean age 64.3±9.4 years, 23% female) completed 3-year follow-up, with 169 patients randomized to PVI+PWI and 164 patients to PVI alone. At a median of 3.62 years post-index ablation, freedom from recurrent atrial arrhythmia occurred in 59 patients (35.5%) randomized to PVI+PWI vs 68 patients (42.1%) randomized to PVI alone (HR 1.15, 95% CI 0.88-1.51, p=0.55). Median time to recurrent atrial arrhythmia was 0.53 years (IQR 0.34-1.01 years). Redo ablation was performed in 54 patients (32.0%) in the PVI+PWI group vs 49 patients (29.9%, p=0.68) in the PVI alone group. Pulmonary vein reconnection was present in 54.5% (mean number of reconnected PVs 2.2±0.9) and posterior wall reconnection in 75%. Median AF burden at 3 years was 0% in both groups (IQR 0-0.85% PVI+PWI vs 0-1.43% PVI alone, p=0.49). Sinus rhythm at final clinical follow-up was present in 85.1% with PVI+PWI vs 87.1% with PVI alone (p=0.60). Mean AF Effect On Quality-Of-Life (AFEQT) score at 3 years post-ablation was 88.0±14.8 with PVI+PWI vs 88.9±15.4 with PVI alone (p=0.63). In patients with persistent AF, the addition of PWI to PVI alone at index RF catheter ablation did not significantly improve freedom from atrial arrhythmia recurrence at long-term follow-up. Median AF burden remains low and AF quality of life high at 3 years with either ablation strategy.

Towards Regional Population Health Management: A Prospective Analysis Using the Adjusted Clinical Groups Classification.

This research seeks to assess the potential of regionally integrated health management for specific sub-populations, including the incorporation of self-management initiatives. It will achieve this by conducting a thorough stratification analysis of hospital data, utilizing the Adjusted Clinical Groups (ACG) classification system. The approach involves a retrospective review of healthcare data spanning five years, which includes patient demographics, health outcomes, and healthcare utilization metrics. We intend to use the ACG method to classify the patient population into pertinent groups that mirror their health requirements and resource use. The insights obtained from this analysis will be used to create a localized adaptation of the Kaiser Permanente Pyramid Model of Care. This adaptation aims to identify the distribution of costs among patients treated in the Rivierenland Hospital. We anticipate that stratifying data with the ACG method will identify distinct multimorbid subgroups. These subgroups will have unique healthcare requirements. Early interventions and customized health management strategies, based on these insights, could enhance health outcomes and resource efficiency for high-risk patients. This analysis will serve as a foundation for constructive discussions with hospital management and clinical staff, fostering a deeper comprehension of the patients' burden of disease. It might also foster multidisciplinary collaboration opportunities between medical specialties as with regional healthcare partners such as general practitioners (GPs), mental health and other long-term care organizations. Moreover, we anticipate that self-care initiatives, supported by customized health information, will encourage increased patient engagement and strategies for enhancing lifestyle improvements. This strategy is expected to enable the personalization of advanced care planning based on individual needs profiles, thereby improving the management of complex and chronic conditions, and encouraging self-care practices. Our anticipated findings highlight the potential benefits of a data-informed approach to advancing healthcare outcomes and present opportunities for future investigations to refine and implement such integrated care models across the region.

Investigating the Visual Utility of Differentially Private Scatterplots.

Increasingly, visualization practitioners are working with, using, and studying private and sensitive data. There can be many stakeholders interested in the resulting analyses-but widespread sharing of the data can cause harm to individuals, companies, and organizations. Practitioners are increasingly turning to differential privacy to enable public data sharing with a guaranteed amount of privacy. Differential privacy algorithms do this by aggregating data statistics with noise, and this now-private data can be released visually with differentially private scatterplots. While the private visual output is affected by the algorithm choice, privacy level, bin number, data distribution, and user task, there is little guidance on how to choose and balance the effect of these parameters. To address this gap, we had experts examine 1,200 differentially private scatterplots created with a variety of parameter choices and tested their ability to see aggregate patterns in the private output (i.e. the visual utility of the chart). We synthesized these results to provide easy-to-use guidance for visualization practitioners releasing private data through scatterplots. Our findings also provide a ground truth for visual utility, which we use to benchmark automated utility metrics from various fields. We demonstrate how multi-scale structural similarity (MS-SSIM), the metric most strongly correlated with our study's utility results, can be used to optimize parameter selection.

Polypharmacy in spinal cord injury: Matched cohort analysis comparing drug classes, medical complications, and healthcare utilization metrics with 24-month follow-up

Objective: Polypharmacy in spinal cord injury (SCI) is common and predisposes patients to increased risk of adverse events. Evaluation of long-term health consequences and economic burden of polypharmacy in patients with SCI is explored. Design: Retrospective cohort. Methods: The IBM Marketscan Research Databases claims-based dataset was queried to search for adult patients with SCI with a 2-year follow-up. Participants: Two matched cohorts were analyzed: those with and without polypharmacy, analyzing index hospitalization, readmissions, payments, and health outcomes. Results: A total of 11 569 individuals with SCI were included, of which 7235 (63%) were in the polypharmacy group who took a median of 11 separate drugs over two years. Opioid analgesics were the most common medication, present in 57% of patients with SCI meeting the criteria of polypharmacy, followed by antidepressant medications (46%) and muscle relaxants (40%). Risk of pneumonia was increased for the polypharmacy group (58%) compared to the non-polypharmacy group (45%), as were urinary tract infection (79% versus 63%), wound infection (30% versus 21%), depression (76% versus 57%), and adverse drug events (24% versus 15%) at 2 years. Combined median healthcare payments were higher in polypharmacy at 2 years ($44 333 vs. $10 937, P < .0001). Conclusion: Majority of individuals with SCI met the criteria for polypharmacy with nearly 60% of those prescribed opioids and taking drugs from high-risk side effect profiles. Polypharmacy in SCI was associated with a greater risk of pneumonia, depression, urinary tract infections, adverse drug events, and emergency room visits over two years with four times higher overall healthcare payments at 1-year post-injury.

Racial Disparities in the Utilization of Lateral Ankle Ligament Reconstruction for Chronic Ankle Instability in the United States

Background: Previous studies have reported on the impact of race and ethnicity on access to orthopaedic procedures in the United States. However, there are few studies evaluating racial disparities in elective foot and ankle surgeries. Recognizing and addressing disparities in the access of health resources are steps to improve care rationing. Our study specifically investigated potential racial disparities with utilization and perioperative metrics of lateral ankle ligament reconstruction. Methods: A retrospective study was performed using the National Surgical Quality Improvement Program (NSQIP) database. Current Procedural Terminology ( CPT) code 27698 was used to identify patients who underwent LALR from 2011 to 2020. Sixty-one percent of these patients had available race/ethnicity data and were included in the study. Designations of race and ethnicity were standardized as White non-Hispanic, Black non-Hispanic, Hispanic, Asian American and Pacific Islander, and Other. Race/ethnicity stratifications of NSQIP patients were compared to 2010 and 2020 census data from the United States (US) Census Bureau. Thirty-day postoperative complications and total length of hospital stay, for all complications, were also compared between White patients and all other patients combined. Independent t tests, χ2, and Fisher exact tests were performed to compare differences including age, gender, and postoperative complications. Results: Overall, 1295 patients were included in the study. White non-Hispanic patients underwent 3.3 times more LALR than all other patients during the study period. This finding shows a higher prevalence of surgery on White non-Hispanic patients than all others reported in the US Census Bureau data for 2010 and 2020, where the ratio of White non-Hispanic vs all others were 1.9 and 1.5-fold, respectively. White patients were marginally older with a mean age of 38 years, compared with 36 years for Black patients ( P &lt; .05). There were no differences in 30-day complication rates or total length of hospital stay based on race and ethnicity. Conclusion: Our study found that there was increased utilization of LALR for White non-Hispanic patients compared with all other racial/ethnic groups. These numbers do not mimic the current population trends based on the national census data. Despite this discrepancy, no differences in postoperative complications and length of stay were found based on race and ethnicity. These results suggest that there may be barriers for patients who are not White non-Hispanic to undergo care for LALR. Level of Evidence: Level III, retrospective cohort study.