Request Rate Research Articles

SECMACE+: Upscaling Pseudonymous Authentication for Large Mobile Systems

The central building block of secure and privacy-preserving Vehicular Communication (VC) systems is a Vehicular Public Key Infrastructure (VPKI), which provides vehicles with multiple anonymized credentials, termed pseudonyms. These pseudonyms are used to ensure VC message authenticity and integrity while preserving vehicle (thus passenger) privacy. In the light of emerging large-scale multi-domain VC environments, the efficiency of the VPKI and, more broadly, its scalability are paramount. By the same token, preventing misuse of the credentials, in particular, Sybil-based misbehavior, and managing “honest-but-curious” VPKI entities are other facets of a challenging problem. In this paper, we leverage the state-of-the-art VPKI system and enhance its functionality towards a highly-available, dynamically-scalable, and resilient design; this ensures that the system remains operational in the presence of benign failures or resource depletion attacks, and that it dynamically scales out, or possibly scales in, according to request arrival rates. Our full-blown implementation on the Google Cloud Platform shows that deploying large-scale and efficient VPKI can be cost-effective: the processing latency to issue 100 pseudonyms is approximately 56 ms. More so, our experiments show that our VPKI system dynamically scales out or scales in according to the rate of pseudonyms requests. We formally assess the achieved security and privacy properties for the credential acquisition process. Overall, our scheme is a comprehensive solution that complements standards and can catalyze the deployment of secure and privacy-protecting VC systems.

Systematically Identifying Communication Deficits in Youth Under Psychiatric Care Through a Self-Report Scale

Background: Communication disorders are associated with psychopathology and social distress in adolescents, yet moderate or less severe deficits often go undiagnosed. Data from a quality improvement initiative was analyzed from the Communication Perception Estimate Scale (CoPES), a self-report scale for receptive-expressive and pragmatic language deficits in youth receiving mental health services. Objective: To identify the frequency of communication deficits (CDs) among youth in psychiatric care and identify patterns of self-reported symptoms. The scale may be used as a clinical screening tool for Language disorder and Social (Pragmatic) communication disorder. Methods: CoPES is a 31-item scale administered to 318 adolescents 11-18 years old, asking questions about receptive-expressive and pragmatic CDs. In the first 12 items, subjects identified the frequency of communication disorder symptoms using a forced-choice scale; in the following items, frustration with CDs, yes or no for earlier intervention, and current interest in getting help for CDs. Respondents are from five representative clinical services. Results: Patients were divided into self-identified groups: 14.5% significant Receptive-Expressive deficits only (R-ED); 6.6% significant Pragmatic deficits only (PD); 29.9% Both significant Receptive-Expressive and Pragmatic deficits (BCD); and 49.1% with limited or no deficits (NCD). The R-ED group expressed high rates of frustration and requests for help with Receptive-Expressive deficits. The PD group reported elevated rates of frustration in both areas. The BCD group indicated significantly more frustration and requests for help with both language areas compared to the NCD group. Conclusion: This data supports the need for greater awareness and consideration of communication disorders in youth presenting for psychiatric care.

Multiple Precaching Vehicle Selection Scheme Based on Set Ranking in Intermittently Connected Vehicular Networks

In vehicular networks, vehicles download vehicular information for various applications, including safety, convenience, entertainment, and social interaction, from the corresponding content servers via stationary roadside units. Since sufficient RSUs might be difficult to deploy due to rough geographical conditions or high deployment costs, vehicular networks can feature uncovered outage zones between two neighboring RSUs. In these outage zones, vehicles cannot download content, and thus the vehicle networks are defined as intermittently connected vehicular networks. In intermittently connected vehicular networks, the download delay and traffic overhead on the backhaul links are increased due to the large size of the content requested by vehicle users and the long distances between RSUs. Using the mobility information of vehicles, several schemes have been proposed to solve this issue by precaching and relaying content via multiple relaying vehicles in the outage zone. However, because they involved the individual ranking of vehicles for precaching and allocated all of the available precaching amounts to the top-ranking vehicles, they decreased the success rate of content requests and the fairness of vehicle precaching. To overcome the problem of these previous schemes, this paper proposes a multiple precaching vehicle selection (MPVS) scheme that efficiently selects a content-precaching vehicle group with multiple precaching vehicles to precache relayed content in outage zones. To achieve this, we first designed numerical models to decide the necessity and the amount of precaching and to calculate the available precaching amounts of vehicles. Next, MPVS calculates all available vehicle sets and ranks each set based on the available precaching amount. Then, the content-precaching vehicle group is identified from the sets by considering both set rankings and vehicle communication overheads. MPVS also provides a content downloading process through the content-precaching vehicle group in the outage zone. Simulation results conducted in various environments with a content request model and a highway mobility model verified that MPVS was superior to a representative previous scheme.

Intent Negotiation Framework for Intent-Driven Service Management

To automate network operations and deployment of compute services, intent-driven service management (IDSM) is essential. It enables network users to express their service requirements in a declarative manner as intents. To fulfill the intents, closed control-loop operations carry out required configurations and deployments without human intervention. Despite the fact that intents are fulfilled automatically, conflicts may arise between user's and service provider's intents due to limited resources availability. This triggers IDSM system to initialize an intent negotiation process among conflicting actors. Intent negotiation involves generating one or more alternate intents based on the current state of the underlying physical/virtual resources, which are then presented to the intent creator for acceptance or rejection. In this way, the quality of services (QoS) can be improved significantly by maximizing the acceptance rate of service requests in the scenario of limited resources. However, intent negotiation systems are still in their infancy. The available solutions are platform dependent which poses various challenges in their adoption to diverse platforms. The main focus of this work is to draft and evaluate a comprehensive and generic intent negotiation framework which can be used to develop intent negotiation solutions for diverse IDSM platforms. In this work, we have identified and defined various processes that are necessary for intent negotiation. Furthermore, a generic intent negotiation framework is presented representing interactions among the identified processes, while conflicting actors engage in the intent negotiation. The results demonstrated that the proposed intent negotiation framework increases the intent acceptance rate by up to 38 percent with processing overheads less than 10 percent.

Successful deployment of an Artificial Intelligence solution for primary diagnosis of prostate biopsies in clinical practice

In order to evaluate the feasibility of artificial intelligence within the setting of prostate histopathology, 3975 slides from 860 patients were digitally scanned and supplied to the IBEX-Artificial Intelligence (AI) system before evaluation by histopathology consultants with recommendations from the AI. Data comparing reporting with and without AI assistance were analysed along with accuracy of diagnosis for the AI. Request rates for additional immunohistochemistry from consultants in cases of diagnostic uncertainty dropped from 8.7 % to 4.5 %. Qualitative reporting confidence increased with AI assistance and valued the highlighting of the most suspicious areas within a biopsy. Positive and negative predictive values for the AI were 0.994 and 0.995 when using the consultants’ diagnosis as the true value. AI shows significant potential as an assistant for histopathologists in the field of cancer diagnosis.

Social media and volunteer rescue requests prediction with random forest and algorithm bias detection: a case of Hurricane Harvey

AI fairness is tasked with evaluating and mitigating bias in algorithms that may discriminate towards protected groups. This paper examines if bias exists in AI algorithms used in disaster management and in what manner. We consider the 2017 Hurricane Harvey when flood victims in Houston resorted to social media to request for rescue. We evaluate a Random Forest regression model trained to predict Twitter rescue request rates from social-environmental data using three fairness criteria (independence, separation, and sufficiency). The Social Vulnerability Index (SVI), its four sub-indices, and four variables representing digital divide were considered sensitive attributes. The Random Forest regression model extracted seven significant predictors of rescue request rates, and from high to low importance they were percent of renter occupied housing units, percent of roads in flood zone, percent of flood zone area, percent of wetland cover, percent of herbaceous, forested and shrub cover, mean elevation, and percent of households with no computer or device. Partial Dependence plots of rescue request rates against each of the seven predictors show the non-linear nature of their relationships. Results of the fairness evaluation of the Random Forest model using the three criteria show no obvious biases for the nine sensitive attributes, except that a minor imperfect sufficiency was found with the SVI Housing and Transportation sub-index. Future AI modeling in disaster research could apply the same methodology used in this paper to evaluate fairness and help reduce unfair resource allocation and other social and geographical disparities.

HIV genotyping prevalence analysis in population of Ponta Grossa

The Human Immunodeficiency Virus (HIV) has a high mutagenic degree and a high ability to integrate into the host genome, being able to create mechanisms of resistance to Antiretroviral Therapy drugs. The genotyping test is used to detect these mutations, allowing a reorganization of the therapeutic scheme and improvement of the patient's condition. The present study aimed, through the analysis of 691 medical records, to identify the prevalence of the genotyping test in the city of Ponta Grossa - Paraná, as well as the reasons why it was performed. Epidemiological findings indicated a higher prevalence of HIV in heterosexual individuals (59.04%) and men (58.61%). The prevalence of genotyping was, approximately, 10%. Of all the requests, the highest rate occurred in females, constituting 16.82% of the female sample, while in males the rate of requests was 7.44%. Regarding post-genotyping viral load, it was performed by only 5.4% of the patients (39 of the 69 who underwent the genotyping test), which reinforced the hypothesis of poor adherence to treatment as the main reason for requesting this test.

An Exploration of Journals Requested by Health Sciences Libraries Through DOCLINE Interlibrary Loan During the Early COVID-19 Pandemic

COVID-19 challenged information exchange globally, including interlibrary loan (ILL). This project explored DOCLINE ILL borrowing data from 15 academic, hospital, and association health sciences libraries before and during the pandemic to understand gaps in ILL coverage. We reviewed aggregate filled and unfilled borrowing data from March to August in 2019 and 2020. We compared these time periods to each other and to system-wide fill rates. We normalized journal titles, added journal price and language, calculated descriptive statistics and odds ratios, and conducted 2-proportion z-tests of differences. In our sample of 14,891 requests, the odds of requests being unfilled were 2.7 times higher in 2020 than in 2019. While the proportion of non-English language content requested did not change, a significantly higher proportion went unfilled in 2020. The rate of unfilled requests for older items also rose significantly between 2019 and 2020. Our findings support the conclusion that the COVID-19 pandemic significantly influenced ILL article request fulfillment in health sciences libraries. Libraries should consider collection development strategies to increase the accessibility of articles held only in print, and those with specialized print collections may want to prioritize digitization of older materials. Future research on the availability, utility, and expense of the materials more likely to remain unfilled should inform publisher backfile prioritization as well as consortial and individual library collection development practices.

Evaluating an audit and feedback intervention for reducing overuse of pathology test requesting by Australian general practitioners: protocol for a factorial cluster randomised controlled trial

IntroductionConsistent evidence shows pathology services are overused worldwide and that about one-third of testing is unnecessary. Audit and feedback (AF) is effective for improving care but few trials evaluating AF...

Dynamic truck–drone routing problem for scheduled deliveries and on-demand pickups with time-related constraints

The on-demand logistics services have risen continuously with the expansion of e-commerce. Logistics companies face challenges to meet customers’ expectations with high efficiency and reliability at a low cost. Hence, this paper investigates the dynamic truck–drone routing problem with scheduled deliveries and on-demand pickups (D-TDRP-SDOP) for an on-demand logistics system. Trucks and drones are deployed to serve a batch of deterministic deliveries and an uncertain set of pickup requests with deadlines subject to maximum working hour constraints. The drones can serve multiple requests per trip subject to load constraints and endurance capacity restrictions. The service provider aims to maximize the total profits by dynamic reassignment and recourse of the vehicles. We formulate the D-TDRP-SDOP problem as a Markov decision process (MDP) and propose a heuristic solution approach framework, consisting of an offline enhanced construction algorithm (OECA) and a segment-based heuristic, to solve the MDP. The comprehensive numerical experiments demonstrate the effectiveness of the proposed solution approach and the benefits of the model. Our model improves the total profits by 15% by considering on-demand requests, and the drone operations contribute to a 50% improvement in the acceptance rate of dynamic customer requests. Improved drone technology, such as a higher drone speed and a higher battery capacity, can enable the system to serve more on-demand requests and increase the final profits. However, the benefit diminishes when the drone capability reaches a certain threshold.

Joint inventory and transshipment decisions with consumer behavioral heterogeneity

Transshipment is an effective method for reducing mismatches between supply and demand among retailers. Consumers attempting to purchase out-of-stock items may wait for transshipment, purchase at another store, or choose not to buy. In this paper, the consumer behavioral heterogeneity is characterized using the transshipment request rate and consumer switching rate. It affects the replenishment and transshipment decisions, as well as the system profits. The inventory replenishment and transshipment decisions are studied in both centralized and decentralized two-location inventory systems with consumer behavioral heterogeneity. We characterize optimal replenishment decisions in a centralized system, prove the existence of a unique Nash equilibrium in a decentralized system with a specific demand distribution, and determine the coordinating transshipment price for some decentralized systems. In numerical studies, the performance of various systems is analyzed for consumers with identical (symmetric) or differing (asymmetric) behavior between retailers. For scenarios with symmetric consumers, a higher transshipment request rate and consumer switching rate resulted in increased total profit in all systems. For scenarios with asymmetric consumers, the retailer with the higher consumer switching rate should reduce ordering in a centralized system but increase ordering in a decentralized system. Moreover, the retailer with the higher transshipment request rate reduces ordering to increase profit, whereas the other retailer increases order quantity yet earns less profit.

Dynamic collaborative task offloading for delay minimization in the heterogeneous fog computing systems

Fog computing systems have been widely integrated in IoT-based applications to improve quality of services (QoS) such as low response service delays. This improvement is enabled by task offloading schemes, which perform task computation near the task generation sources (i.e., IoT devices) on behalf of remote cloud servers. However, reducing delay remains challenging for offloading strategies owing to the resource limitations of fog devices. In addition, a high rate of task requests combined with heavy tasks (i.e., large task size) may cause a high imbalance of the workload distribution among the heterogeneous fog devices, which severely impacts the offloading performance in terms of delay. To address these issues, this paper proposes a dynamic collaborative task offloading (DCTO) approach, which is based on the resource states of fog devices, to dynamically derive the task offloading policy. Accordingly, a task can be executed by either a single fog or multiple fog devices through the parallel computation of subtasks to reduce the task execution delay. Through extensive simulation analysis, the proposed offloading solution showed potential advantages in reducing the average delay significantly in systems with a high rate of service requests and heterogeneous fog environment compared with the existing solutions. In addition, the proposed scheme can be implemented online owing to its low computational complexity compared with the algorithms proposed in related works.

Dynamic Ridesharing With Minimal Regret: Towards an Enhanced Engagement Among Three Stakeholders

In dynamic ridesharing, the platform serves as the mediator by tailoring the assignment result between workers and riders with a focus on a certain objective. Existing studies generally focus on either one or two stakeholders when modelling the problem while the wellbeing of the other parties may be ignored or even undermined. For example, purely maximizing the total revenue of the ridesharing platform may cause the loss of riders and in turn lead to a low served rate, because those expensive orders will be processed in priority. In this paper, we for the first time study how to incorporate the willingness of all stakeholders (i.e., the platform, workers and riders). Given a set of workers and a set of rider requests, we aim to return the matchable worker-rider pairs in order to minimize the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">regret</i> . Specifically, two types of regret are defined: (i) the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">served rate regret</i> , which refers to the rate of unserved requests, catering for the reputation and profit of the platform and workers; (ii) the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">revenue regret</i> , which considers the portion of revenue loss from unserved riders, catering for the focus of workers and riders in the trip schedule. We prove the NP-hardness of this problem. To tackle this problem, we first propose a dynamic programming insertion algorithm to improve the efficiency of inserting a rider request into a trip schedule of a worker. Furthermore, two kinds of heuristic algorithms are devised to match rider requests with workers effectively. Comprehensive experiments on two real-world datasets verify the effectiveness, efficiency and scalability of our solutions in dealing with different supply-demand relationships in practice.

147 A Digital Approach to Syphilis Infection Prevention

OBJECTIVES/GOALS: The goal of this study is to explore the effectiveness of a digital health education curriculum aimed at increasing syphilis beliefs and screening behavior among young African American females residing in Jefferson County, AL. METHODS/STUDY POPULATION: Study Population: African American, cis-gender women, aged 18-29, who reside in Jefferson County, AL Sample Size: N = 50 Measurements: 1) Champion Health Belief Model scale adapted for measuring syphilis perceptions and syphilis screening behavior Analysis Plan: IBM SPSS Statistics 29 will be used to analyze intervention data. Pre-and post-intervention data will be analyzed to calculate a screening request rate for each period. We will use Cohen’s D effect sizes to measure screening requests rate differences post intervention completion. The small sample size and exploratory nature of this project make the descriptive statistics valuable. RESULTS/ANTICIPATED RESULTS: We anticipate that the digital syphilis prevention intervention will positively influence participants’ beliefs regarding syphilis and their syphilis screening behaviors. DISCUSSION/SIGNIFICANCE: Innovative preventive approaches to reducing syphilis disease spread is a national priority as rates have risen annually throughout the last decade. African American women experience the greatest disease burdens associated with syphilis. This study leverages tailored strategies to effectively address this ailment in the target population.

The pre-analytical process management status and influencing factors of laboratory test before prescribing antimicrobial in developing country

IntroductionThe results of laboratory testing are crucial basis for clinicians to prescribe antimicrobial. Laboratory testing is a highly complex process, and increasing evidence suggests that errors and obstacles in the pre-analytical process (PP) will affect reasonable antimicrobial use. However, PP was an easily neglected link in hospital infection management and the current situation of it and the influencing factors of management are not clear.MethodsA cross-sectional survey was conducted in the department of clinical, specimen collection, transportation, and inspection in 109 secondary and tertiary hospitals in Central China. The rate of antimicrobial susceptibility test request (AST) and related indexes of above departments were calculated to describe the situation. Management characteristics (frequency of training etc.) were described as proportions and fractional probit regression analysis was used to determine the influencing factors.ResultsThe average rate of non restricted-use antimicrobial was 63%, the restricted-use was 86%, the special-use was 95%. The zero obstacle rate of specimen collection was 27.3%, of specimen transportation was 19.4% and of inspection feedback was 61.7%. There was a difference between the secondary and tertiary hospitals on non restricted-use (X2 = 22.968, P < 0.001); restricted-use (X2 = 29.466, P < 0.001); special-use (X2 = 27.317, P < 0.001). Taking non restricted-use as an example, training (OR = 0.312, 95%CI: 0.148,0.429), low-frequency appraisal (OR = 0.153, 95%CI: 0.082,0.224), guidance (OR = 0.32, 95%CI: 0.237,0.403) and information technology (OR = 0.104, 95%CI: 0.009,0.199) were positive factors.ConclusionsThere were substantial differences in the rate of AST request in clinical department between secondary and tertiary hospitals. The zero obstacle rate in collection, transportation and inspection department were still low. In most departments, training and performance appraisal were positive factors, guidance and information technology were positive supporting factors.

An evaluation of relational and NoSQL distributed databases on a low-power cluster.

The constant growth of social media, unconventional web technologies, mobile applications, and Internet of Things (IoT) devices create challenges for cloud data systems in order to support huge datasets and very high request rates. NoSQL databases, such as Cassandra and HBase, and relational SQL databases with replication, such as Citus/PostgreSQL, have been used to increase horizontal scalability and high availability of data store systems. In this paper, we evaluated three distributed databases on a low-power low-cost cluster of commodity Single-Board Computers (SBC): relational Citus/PostgreSQL and NoSQL databases Cassandra and HBase. The cluster has 15 Raspberry Pi 3 nodes with Docker Swarm orchestration tool for service deployment and ingress load balancing over SBCs. We believe that a low-cost SBC cluster can support cloud serving goals such as scale-out, elasticity, and high availability. Experimental results clearly demonstrated that there is a trade-off between performance and replication, which provides availability and partition tolerance. Besides, both properties are essential in the context of distributed systems with low-power boards. Cassandra attained better results with its consistency levels specified by the client. Both Citus and HBase enable consistency but it penalizes performance as the number of replicas increases.

Implementation of an opioid reduction toolkit in pancreatectomy patients significantly increases patient awareness of safe practice and decreases amount prescribed and consumed

BackgroundPostoperative opioid abuse following surgery is a major concern. This study sought to create an opioid reduction toolkit to reduce the number of narcotics prescribed and consumed while increasing awareness of safe disposal in pancreatectomy patients. MethodsPrescription, consumption, and refill request data for postoperative opioids were collected from patients receiving an open pancreatectomy before and after the implementation of an opioid reduction toolkit. Outcomes included safe disposal practice awareness for unused medication. Results159 patients were included in the study: 24 in the pre-intervention and 135 in the post-intervention group. No significant demographic or clinical differences existed between groups. Median morphine milliequivalents (MMEs) prescribed were significantly reduced from 225 (225–310) to 75 (75–113) in the post-intervention group (p < 0.0001). Median MMEs consumed were significantly reduced from 109 (111–207) to 15 (0–75), p < 0.0001), as well. Refill request rates remained equivalent during the study (Pre: 17% v Post: 13%, p = 0.9) while patient awareness of safe disposal increased (Pre: 25% v Post: 62%, p < 0.0001). DiscussionAn opioid reduction toolkit significantly reduced the number of postoperative opioids prescribed and consumed after open pancreatectomy, while refill request rates remained the same and patients’ awareness of safe disposal increased.

An Improved Adaptive Service Function Chain Mapping Method Based on Deep Reinforcement Learning

With the vigorous development of the network functions virtualization (NFV), service function chain (SFC) resource management, which aims to provide users with diversified customized services of network functions, has gradually become a research hotspot. Usually, the network service desired by the user is randomness and timeliness, and the formed service function chain request (SFCR) is dynamic and real-time, which requires that the SFC mapping can be adaptive to satisfy dynamically changing user requests. In this regard, this paper proposes an improved adaptive SFC mapping method based on deep reinforcement learning (ISM-DRL). Firstly, an improved SFC request mapping model is proposed to abstract the SFC mapping process and decompose the SFC mapping problem into the SFCR mapping problem and the VNF reorchestration problem. Secondly, we use the deep deterministic policy gradient (DDPG), which is a deep learning framework, to jointly optimize the effective service cost rate and mapping rate to approximate the optimal mapping strategy for the current network. Then, we design four VNF orchestration strategies based on the VNF request rate and mapping rate, etc., to enhance the matching degree of the ISM-DRL method for different networks. Finally, the results show that the method proposed in this paper can realize SFC mapping processing under dynamic request. Under different experimental conditions, the ISM-DRL method performs better than the DDDPG and DQN methods in terms of average effective cost utilisation and average mapping rate.

Rhinoplasty Technique for Improving Nasal Base Aesthetics: Lateral Columellar Grafting.

One of the most important factors in nasal base aesthetics is symmetry. In the age of social media, the expectations of rhinoplasty patients and the rate of requests for a more symmetrical nose have increased. In this article, a lateral columellar grafting technique that can be applied to augment the more defective side of the columella and obtain a more symmetrical nasal base is presented. A total of 86 patients (women, 79; men, 7) were included in this study. The surfaces of the right and left lateral margins of the columella were evaluated from the basal view at the final stage of surgery, and a lateral columellar graft was placed on the more defective side. All patients who were included in the study were assessed with the Rhinoplasty Outcome Evaluation questionnaire before and 1 year after surgery. The median age of the patients was 28.3 years (range, 18-56 years). Eighty-two patients were primary rhinoplasty patients, and 4 patients were secondary rhinoplasty patients. The median Rhinoplasty Outcome Evaluation score was 68.3 points before surgery and 92.3 points 1 year after surgery, showing a significant increase (P = 0.003). Patient satisfaction was found to be excellent in 93% of the included patients. Using the lateral columellar grafting technique, greater symmetry of the columella and nostrils can be obtained by augmenting the more defective side of the lateral columellar surface.

Applying Implementation Science Principles to Systematize High-Quality Care for Potentially Significant Imaging Findings

ObjectiveUse principles of implementation science to improve the diagnosis and management of potentially significant imaging findings. MethodsMultidisciplinary stakeholders codified the diagnosis and management of potentially significant imaging findings in eight organs and created a finding tracking management system that was embedded in radiologist workflows and IT systems. Radiologists were trained to use this system. An automated finding tracking management system was created to support consistent high-quality care through care pathway visualizations, increased awareness of specific findings in the electronic medical record, templated notifications, and creation of an electronic safety net. Primary outcome was the rate of quality reviews related to eight targeted imaging findings. Secondary outcome was radiologist use of the finding tracking management tool. ResultsIn the 4 years after implementation, the tool was used to track findings in 7,843 patients who received 10,015 ultrasound, CT, MRI, x-ray, and nuclear medicine examinations that were interpreted by all 34 radiologists. Use of the tool lead to a decrease in related quality reviews (from 8.0% to 0.0%, P < .007). Use of the system increased from 1.7% of examinations in the early implementation phase to 3.1% (+82%, P < .00001) in the postimplementation phase. Each radiologist used the tool on an average of 294.6 unique examinations (SD 404.8). Overall, radiologists currently use the tool approximately 4,000 times per year. DiscussionRadiologists frequently used a finding tracking management system to ensure effective communication and raise awareness of the importance of recommended future follow-up studies. Use of this system was associated with a decrease in the rate of quality review requests in this domain.