Average Performance Rate Research Articles

Analysis of User Satisfaction in Banten with Shopeepay Mobile Payment Based on E-Service Quality

The rapid development of technology has led to innovations in various areas, including mobile payments. Mobile payments are one of the innovations in financial technology designed to facilitate online transactions. In Indonesia, there are numerous mobile payment service providers, making competition increasingly fierce. One of the popular e-wallets in Indonesia is ShopeePay, a service created by Shopee. This study aims to analyze the importance and performance levels of various electronic service quality factors and assess user satisfaction with ShopeePay. This research is a survey-based study with a sample of 100 respondents. The analysis was conducted using Importance Performance Analysis (IPA) and Customer Satisfaction Index (CSI) to measure the importance and performance of each ShopeePay service quality factor and the satisfaction of its users. The results show that out of 24 ShopeePay service quality factors, 13 were considered important by respondents. The average importance rating was 4.07, while the average performance rating was 3.94. This results in a harmony between importance and performance of 97%. The Customer Satisfaction Index (CSI) for ShopeePay services in the Banten area was 78.77%, placing it in the "satisfying" category. This study has several limitations, including its focus on the Banten area, a limited and homogeneous respondent group, potential bias from online surveys, and not accounting for external factors or long-term changes in user perceptions of ShopeePay.

Job Satisfaction and Performance of Faculty Members in Apayao State College

To evaluate the job satisfaction and performance of faculty members in Apayao State College, it sought answers about the demographic and socio-economic profile, performance rating, level of job satisfaction, significant difference in the level of job satisfaction to profile, significant relationship between the level of job satisfaction to performance and the problems encountered and the degree of seriousness. The study made use of the descriptive correlational research design. Respondents of the study were the sixty-six (66) permanent faculty members. A modified questionnaire was used in the study. Before the questionnaires were administered, the researcher sought approval from the College officials – the College President, Campus Academic Dean, and Human Resource Management Officer. Frequency and percentage, weighted mean, T-test, One-way ANOVA, and Pearson-r coefficient of correlation were used to analyze data. The performance rating of faculty was determined through averaging. Findings revealed that the majority of the faculty members were adults, females, married, well-experienced teachers who hold master’s degrees have moderate to high monthly incomes, receive low take-home pay, and have an average of 2-3 children. The computed average performance rating of the respondents was very satisfactory. The level of job satisfaction was highly satisfied. There is no significant difference in the level of job satisfaction in respondents’ profiles. There is no significant relationship between the level of job satisfaction and faculty performance. However, the problems encountered were moderately serious. The study concluded that the job satisfaction and performance of faculty members at Apayao State College were high.

DEVELOPMENT OF AN RPG MAKER MV-BASED INTERACTIVE GAME AS LEARNING MEDIA ON VIRUS MATERIALS FOR GRADE X STUDENTS AT NURUL ISLAM JEMBER SENIOR HIGH SCHOOL

The limited accessibility of digital educational game resources on the topic of viruses in the public domain poses a significant challenge that must be addressed. The primary objective of this research is to determine the validity of the RPG maker MV based game, followed by an assessment of its practicality. The research used Lee & Owens as the development model. The result of this study is the produced RPG maker MV-based game, focused on virus material. The validity of the game has been confirmed through product validation conducted by experts in the discipline, achieving an average percentage of 96.63% (highly valid) from material experts, 93.07% (highly valid) from media experts, and 80.71% (valid) from biology teacher practitioners. The practicality of the RPG maker MV-based game was then demonstrated by small-scale trials, yielding an average performance rate of 93% (an exceedingly captivating product), and large-scale trials, yielding an average performance rate of 94.3% (an exceedingly captivating product).

Analisis Employee Performance Pada Kantor Badan Pertahanan Nasional Kabupaten Muaro Jambi

The research of this study is to compare the performance of permanent and temporary workers. 113 workers at the National Land Agency Office in the Muaro Jambi Regency comprise the study's sample. There is a difference in the results between permanent employees (numbering 33) and non-permanent employees (numbering 80). The employee performance results show agreement with the employee's performance. For permanent personnel, the average performance rating is 3.72, while for non-permanent/contractual staff, it is 3.80. Based on the findings of an independent sample t-test, there is no significant difference in the performance of permanent and non-permanent personnel. The significance value (sig) of 0.937, which is less than 0.05, indicates this.

Reproductive ability and milk productivity of mares of different genealogical lines of Argaleisky type of Transbaikal breed

Productive herd horse breeding in Transbaikal region is one of the traditional branches of animal husbandry. The problem of preserving and improving local (aboriginal) horse breeds in our country is extremely important for their eff ective economic use and maintaining the diversity of the original gene pool of farm animals. Therefore, line breeding is an important aspect of horse breeding. The purpose of the research was to study the reproductive ability and milk productivity of mares of diff erent lines of Argaley type of Transbaikal breed. Research was carried out at the APC “Plemzavod imeni Kalinina” in the Aginsky district of the Trans-Baikal Territory. The objects of the study were mares of Transbaikal breed of Argaley type of diff erent genealogical lines origin, the age of the mares was 8–9 years after 4–5 foalings. The conformation and constitutional features and live weight of mares depending on their genealogical lines origin have been presented in the article. The average fertility performance rate for mares of different lines was 93,7 %, with foaling being 98,8 %. The greatest live weight was in mares belonging to the line Argali. It exceeded the indicators of herdmates from the lines Premier, Chetkiy and Kagor by 20,0; 15,0, 10,0 kg, respectively. During 5 months of lactation, mares of the line Argali produced 1854,0 kg of milk with an average daily milk yield of 12,36 kg. Mares of the line Argali over 5 months of lactation were superior to herdmates of other lines in terms of milk yield by 459,0; 336,0 and 223,5 kg and average daily milk yield by 3,06, 2,24 and 1,49 kg, respectively. It has been established that the physicochemical composition and environmental safety of milk from mares of the different lines of Argaley type of Transbaikal breed indicate its fairly high nutritional value.

Design and Implementation of Smart Control Elevator Based on Biometric Recognition System for People with Special Needs

The elevators have been developed to serve the requested passenger’s floors with efficient vertical transportation. However, it presents permanent difficulties for people with special needs. They can’t manipulate easily those elevators and usually make them feel uncomfortable. Many researches propose an efficient solution for those peoples based on biometric technology. The main purpose of this research is to investigate the effectiveness and accuracy of the biometric recognition technology in the development of a smart elevator for people with special needs and how to help them to use elevators easily based on multimodal biometric systems? In this paper, we developed a prototype of an efficient three floors smart elevator through programmable electronic devices such as microcontroller and programmable logic controller. The proposed smart elevator allow handicapped peoples to control the elevator easily and comfortably, especially people with speechless, visual, hearing, and physical impairments that have daily challenges with elevator. An average performance rate more than 98% has been stored with an efficient control of the proposed smart elevator. The proposed smart elevator permits to facilitate the daily life of people with special needs making them more independent by using biometric recognition modalities.

Objective structured clinical examination as a competency assessment tool of students’ readiness for advanced pharmacy practice experiences in South Korea: a pilot study

BackgroundThe assessment of pharmacy students’ readiness to begin the education of an advanced pharmacy practice experience (APPE) in clinical pharmacy settings continues to gain increasing attention. This study aimed to develop an objective structured clinical examination (OSCE) in the core domains acquired through an introductory pharmacy practice experience (IPPE), for evaluating its appropriateness as a tool of assessing clinical pharmacist competency for APPEs in Korean pharmacy students throughout a pilot study.MethodsOSCE’s core competency domains and case scenarios were developed through a literature review, ideation by researchers, and external experts’ consensus by a Delphi method. A prospective single-arm pilot test was conducted to implement the OSCE for Korean pharmacy students who completed a 60-h course of in-class simulation IPPE. Their competencies were assessed by four assessors in each OSCE station with a pass-fail grading system accompanied by a scoring rubric.ResultsOSCE competency areas including patient counseling, provision of drug information, over-the-counter (OTC) counseling, and pharmaceutical care services were developed with four interactive and one non-interactive cases. Twenty pharmacy students participated in the OSCE pilot test, and their competencies were evaluated by 20 assessors. The performance rate was the lowest in the area of patient counseling for a respiratory inhaler (32.1%) and the highest (79.7%) in OTC counseling for constipation. The students had an average performance rate of 60.4% in their communication skills. Most participants agreed on the appropriateness, necessity, and effectiveness of the OSCE in evaluating pharmacy students’ clinical performance and communication skills.ConclusionsThe OSCE model can be used to assess pharmacy students’ readiness for off-campus clinical pharmacy practice experience. Our pilot study suggests the necessity of conducting an OSCE domain-based adjustment of difficulty levels, and strengthening simulation-based IPPE education.

Successive Light Interference Cancellation and Allocation (SLICA) Algorithm for Indoor VLC System: A Backbone for 6G Network

This paper considers a successive light interference cancellation and allocation algorithm (SLICA) practicing interference cancellation for an indoor visible light communication (VLC) system based on successive interference cancellation (SIC) technique. The interference among users is cancelled with the help of the proposed approach to reduce computational complexity. VLC has become the cornerstone of intelligence in the current scenario. The data services allocated to the users are demand based and rely on their channel conditions. The data is allocated to user by modulating light waves via illuminated LEDs. The receiving end has photodiode installed in it to recover back the data. Better performance rate is achieved with lesser interference with the proposed approach. To guarantee good (quality-of service) QoS, considerable simulations are carried out considering real- time scenario which further demonstrates the high level performance of the proposed approach over conventional approach. Based on the analysis, the SINR and interference level is demonstrated for the conventional and the proposed approach and there is a hike in the performance rate of SINR and interference level of about 20 – 30 % with the applied proposed approach. The prior motto of this study is to enhance the system throughput and improve per user average performance rate and reducing the complexity of the system. This contributes to the total efficiency of the optical indoor VLC network. Our proposed algorithm will provide a strong backbone for the 6G deployment.

A Longitudinal Study Identifying the Characteristics and Content Knowledge of Those Seeking Certification to Teach Secondary Biology in the United States.

Teacher content knowledge has been identified as a key prerequisite to effective instruction, and current educational policies require measurement of teacher content knowledge to assess candidacy for licensure. The primary instruments used in the United States are the Praxis Subject Assessment exams, which are designed to measure the subject-specific content knowledge needed to be a teacher. The Praxis Biology Subject Assessment exam, used by 42 U.S. states in the past decade, is the most common national measure used to determine biology content knowledge for teacher certification. Demographic and performance data from examinees (N = 43,798) who took the Praxis Biology Subject Assessment from 2006 to 2015 were compared to present a much-needed picture of who is seeking certification to teach biology, how different groups of aspiring biology teachers have performed, and how demographic makeup of prospective biology teachers compares with reports in previous studies describing the composition of the biology teacher workforce. Results indicate the majority of students self-reported as White (76%), female (66%), having undergraduate grade point averages (GPAs) at or above a 3.0 (76%) and majoring in biology (45%). Additionally, the demographic data were included in a linear regression model to determine the factors that explained the most variance in performance of the examinee. The model revealed substantial differences in average performance and pass rates between examinees of different genders, races, undergraduate majors, undergraduate GPAs, and census regions. This suggests that if the examinee is a White science, technology, engineering, and mathematics major, man with a 3.5 or higher undergraduate GPA, resides in the western United States, or plans to teach in a suburban school, the examinee will on average outperform their counterparts on the exam. From our analyses, we suggest several measures for the improvement of the biology teaching workforce and establish potential issues in the teacher pipeline that may impact the quality and diversity of U.S. biology teachers.

Improving the quality of HIV rapid testing in Ghana using the dried tube specimen-based proficiency testing program.

BackgroundThe introduction of human immunodeficiency virus (HIV) antibody rapid testing (RT) in resource-limited settings has proven to be a successful intervention to increase access to prevention measures and improve timely linkage to care. However, the quality of testing has not always kept pace with the scale-up of this testing strategy. To monitor the accuracy of HIV RT test results, a national proficiency testing (PT) program was rolled out at selected testing sites in Ghana using the dried tube specimen (DTS) approach.MethodsBetween 2015 and 2018, 635 HIV testing sites, located in five regions and supported by the U.S. President’s Emergency Plan for AIDS Relief (PEPFAR), were enrolled in the HIV PT program of the Ghana Health Service National AIDS/STI Control Programme. These sites offered various services: HIV Testing and Counselling (HTC), prevention of mother-to-child transmission (PMTCT) and Antiretroviral Treatment (ART). The PT panels, composed of six DTS, were prepared by two regional laboratories, using fully characterized plasma obtained from the regional blood banks and distributed to the testing sites. The results were scored by the PT providers according to the predefined acceptable performance criteria which was set at ≥ 95%.ResultsSeven rounds of PT panels were completed successfully over three years. The number of sites enrolled increased from 205 in round 1 (June 2015) to 635 in round 7 (December 2018), with a noticeable increase in Greater Accra and Eastern regions. The average participation rates of enrolled sites ranged from 88.0% to 98.0% across the PT rounds. By round 7, HTC (257/635 (40.5%)) and PMTCT (237/635 (37.3%)) had a larger number of sites that participated in the PT program than laboratory (106/635 (16.7%)) and ART (12/635 (1.9%)) sites. The average testing performance rate improved significantly from 27% in round 1 to 80% in round 7 (p < 0.001). The highest performance rate was observed for ART (100%), HTC (92%), ANC/PMTCT (90%) and Laboratory (89%) in round 5.ConclusionThe DTS PT program showed a significant increase in the participation and performance rates during this period. Sub-optimal performances observed was attributed to non-compliance to the national testing algorithm and testing technique. However, the implementation of review meetings, peer-initiated corrective action, supportive supervisory training, and mentorship proved impactful. The decentralized approach to preparing the PT panels ensured ownership by the region and districts.

Hippocampus Segmentation Using U-Net Convolutional Network from Brain Magnetic Resonance Imaging (MRI).

Hippocampus is a part of the limbic system in human brain that plays an important role in forming memories and dealing with intellectual abilities. In most of the neurological disorders related to dementia, such as, Alzheimer's disease, hippocampus is one of the earliest affected regions. Because there are no effective dementia drugs, an ambient assisted living approach may help to prevent or slow the progression of dementia. By segmenting and analyzing the size/shape of hippocampus, it may be possible to classify the early dementia stages. Because of complex structure, traditional image segmentation techniques can't segment hippocampus accurately. Machine learning (ML) is a well known tool in medical image processing that can predict and deliver the outcomes accurately by learning from it's previous results. Convolutional Neural Networks (CNN) is one of the most popular ML algorithms. In this work, a U-Net Convolutional Network based approach is used for hippocampus segmentation from 2D brain images. It is observed that, the original U-Net architecture can segment hippocampus with an average performance rate of 93.6%, which outperforms all other discussed state-of-arts. By using a filter size of [Formula: see text], the original U-Net architecture performs a sequence of convolutional processes. We tweaked the architecture further to extract more relevant features by replacing all [Formula: see text] kernels with three alternative kernels of sizes [Formula: see text], [Formula: see text], and [Formula: see text]. It is observed that, the modified architecture achieved an average performance rate of 96.5%, which outperforms the original U-Net model convincingly.

Proficiency and Interlaboratory Variability in the Determination of Phthalate and DINCH Biomarkers in Human Urine: Results from the HBM4EU Project.

A quality assurance/quality control program was implemented in the framework of the EU project HBM4EU to assess and improve the comparability of biomarker analysis and to build a network of competent laboratories. Four rounds of proficiency tests were organized for 15 phthalate and two DINCH urinary biomarkers (0.2–138 ng/mL) over a period of 18 months, with the involvement of 28 laboratories. A substantial improvement in performance was observed after the first round in particular, and by the end of the program, an average satisfactory performance rate of 90% was achieved. The interlaboratory reproducibility as derived from the participants’ results varied for the various biomarkers and rounds, with an average of 24% for the biomarkers of eight single-isomer phthalates (e.g., DnBP and DEHP) and 43% for the more challenging biomarkers of the mixed-isomer phthalates (DiNP, DiDP) and DINCH. When the reproducibility was based only on the laboratories that consistently achieved a satisfactory performance, this improved to 17% and 26%, respectively, clearly demonstrating the success of the QA/QC efforts. The program thus aided in building capacity and the establishment of a network of competent laboratories able to generate comparable and accurate HBM data for phthalate and DINCH biomarkers in 14 EU countries. In addition, global comparability was ensured by including external expert laboratories.

A Deep Convolutional Neural Networks Based Approach for Alzheimer’s Disease and Mild Cognitive Impairment Classification Using Brain Images

Alzheimer’s disease (AD) is a hazardous neurological disorder of people aged in the early 60s. The main symptoms of AD is significant memory loss. Mild Cognitive Impairment (MCI) is a state of dementia in which a patient exhibits the early symptoms of AD. Since brain is the most impacted region, the disorders can be classified by analyzing factors from brain tissues in different subjects. Machine Learning (ML) is a widely utilised concept that aids in the decision-making process. Deep Convolutional Neural Network (DNN) is a type of ML techniques that uses artificially connected neurons to mimic the human brain. In this work, we have proposed a novel DNN-based model for distinguishing AD and MCI patients from Cognitively Normal individuals. Inspired by the original VGG-19, we have created 19 deep layers in the network. In Back Propagation, deeper models suffer from the problem of vanishing gradient and information loss. As a solution, we borrowed the Dense-Block notion from the original DenseNet architecture, which provides a path of information exchange amongst all the layers. Furthermore, we have implemented depth-wise convolutional procedures to make the model computationally faster. Outcome of the proposed model is compared with some prominent DNN models and observed that, the proposed approach performs most convincingly with an average performance rate of 95.39%.

An experimental analysis of different Deep Learning based Models for Alzheimer’s Disease classification using Brain Magnetic Resonance Images

Classification of Alzheimer’s disease (AD) is one of the most challenging issues for neurologists. Manual methods are time consuming and may not be accurate all the time. Since, brain is the most affected region in AD, a proper classification framework using brain images may provide more accurate results. Deep Learning (DL) is a popular representation of machine learning techniques, that emulate the functionalities of a human brain to process information and creates patterns that help in making complex decisions. The ability to absorb information, even from the unstructured and unlabeled data, makes DL one of the first choices by researchers. In this paper, some of the most popular DL models are discussed along with their implementation results for AD classification. All brain Magnetic Resonance (MR) images are acquired from the online data-set, “Alzheimer’s Disease Neuroimaging Initiative (ADNI)”. From the performance comparison amongst all the discussed models, it is observed that the DenseNet-121 model achieves a convincing result with an average performance rate of 88.78%. But one limitation of the DenseNet model is that it uses lots of convolutional operations that make the model computationally slower than many of the discussed models. Depth-wise convolution is a popular way to make a convolutional operation faster and better. Hence, to improve the execution time, we have proposed replacing the convolution layers in the original DenseNet-121 architecture with depth-wise convolution layers. The new architecture also improved the performance of the model with an average rate of 90.22%.

A Survey on Relatedness between Creation of Urban Parks and Services of Public Servants from Green Space Department(PSGSD)

Purpose: The purpose of this study is to conduct a survey on the service performance rate of creation of ur-ban parks so that the problems in local governments arising out of that service by the public servants from oth-er department than the ones from green space department will be solved. Method: In this study, we conducted a survey on local government in the nation over the performance rate of the public servants from green space department(the “PSGSD”) for the stages of urban park duties: making decisions on creation of urban park; establishment of park creation plan; carrying out construction of park; and park management. Result: In the stage of making decisions of urban park creation, the work performance rate of public ser-vants from green space department in all the local government of the nation is 0% and it is analyzed that no PSGSD has performed the duties to make those decisions. In analyzing average work performance rate of PSGSD for the duties of park creation plan, construction of park creation, and park management out of the du-ties of urban park creation by local governments, while the average work performance rate of public servants from green space department in Seoul, Jeju Province and other 5 metropolitan cities except for Sejong Special Self-governing city and Gwangju metropolitan city is 100%, the one in Gyeongsangbuk province is 27.8%, the lowest one. Conclusion: Through this study, we can identify the work system of PSGSD the work performance rate of the duties of urban park creation by local governments in the nation. In addition, in this study, we suggest policy plan making the public servants other than PSGSD to carry out the duties of urban park creation so that this study may be utilized as a material of ground for enhancing PSGSD’s work performance rate for the duties of urban park creation.

Design and simulation of a 1-GWp solar photovoltaic power station in Sudan

Abstract Developing nations have a critical need to increase electricity supply. Sudan has much unrealized potential for generating solar energy, particularly in the northern region. This research study focuses on designing a 1-GW solar power station in northern Sudan using the PVsyst7.0 software program. To determine the appropriate location for the solar-energy station, 14 criteria were evaluated. This process is generic and suitable for use in any other country. The method for conducting cash-flow estimates and return on investment is illustrated in the economic evaluation. The city of Dongola, the capital of the northern state, was selected because of its high annual irradiance on a horizontal surface at ~2333.2 kWh/m2. The simulation results show that the annual optimum tilt angle of inclination for photovoltaic (PV) modules is 30°, the energy production is 1 979 259 MWh/yr and the average annual performance rate is 0.810. In addition, the electric power consumption per capita in Sudan is 269 kWh/yr, so the proposed solar power plant with 1 979 259 MWh/yr can provide energy to 7.4 million people per year annually and reduce carbon emissions by ~18 million tons of carbon dioxide per year. Economic calculations show that the levelized cost of electricity (LCOE) is $0.06/kWh, the discounted payback period is ~11 years and the net present value is $635 291 000. As a result, the proposed grid-connected PV solar plant is considered economically, technically and environmentally feasible in Sudan.

An Improved LeNet-Deep Neural Network Model for Alzheimer’s Disease Classification Using Brain Magnetic Resonance Images

Alzheimer’s Disease (AD) is a psychological disorder in elderly people which causes severe intellectual disabilities. Proper processing of neuro-images can provide differences in brain tissues which may help in diagnosing the disease more effectively. But, due to the complex structures, this is a challenge in differentiating the brain tissues and classifying AD using traditional classification mechanisms. Deep Neural Network (DNN) is a machine learning technique that has the ability to absorb the most important information for classifying an object accurately. LeNet is a popular DNN based model with a simple and effective architecture that also consumes very less implementation time. As like most of the DNN models, LeNet also uses MaxPooling layer for dimensionality reduction by eliminating the information of minimum valued elements. In brain images low intensity valued pixels also may contain very important features. To keep the minimum valued elements too in the network, we have created a separate layer that performs Min-Pooling operation. MinPooling and MaxPooling layers are then concatenated together. Finally, we have replaced all MaxPooling Layers in LeNet by the concatenated layers. We have analysed and compared the performances of modified LeNet model with 20 other most commonly used DNN models, and some of the related works. It is observed that, the modified LeNet model achieved the highest performances. It is also observed that, original LeNet model can classify AD with a performance rate of 80%, whereas, the proposed modified LeNet model achieved an average performance rate of 96.64%.

A machine learning-based forecasting system of perishable cargo flow in maritime transport

The uncertainty cargo flow problem establishes a limitation in ports management where decision-making processes need accurate information of the future values. This work aims at predicting the future values of Ro-Ro perishable cargo flow at the Port of Algeciras Bay using a machine learning-based forecasting system. Two datasets consisting of daily records of fresh fruits and vegetables between 2010 to 2017 were analyzed. Additionally, these two--time series were pre-processed applying an exponential moving average method to obtain a smoothed version of the original ones. Multiple Linear Regression, Support Vector Machines, Long Short-Term Memory networks and an ensemble approach have been used to build a forecasting system and obtain the future values of the perishable cargo. The results of the analysis showed how this machine learning-based system achieved 14.83% better performance rate than a baseline persistence model in terms of root mean squared error in the fresh fruits dataset and 11.3% better in the vegetables one. In general, models’ average performance rates are better using the smoothed version of the times series rather than the original ones.

TrustE-VC: Trustworthy Evaluation Framework for Industrial Connected Vehicles in the Cloud

The integration between cloud computing and vehicular ad hoc networks, namely, vehicular clouds (VCs), has become a significant research area. This integration was proposed to accelerate the adoption of intelligent transportation systems. The trustworthiness in VCs is expected to carry more computing capabilities that manage large-scale collected data. This trend requires a security evaluation framework that ensures data privacy protection, integrity of information, and availability of resources. To the best of our knowledge, this is the first study that proposes a robust trustworthiness evaluation of vehicular cloud for security criteria evaluation and selection. This article proposes three-level security features in order to develop effectiveness and trustworthiness in VCs. To assess and evaluate these security features, our evaluation framework consists of three main interconnected components: 1) an aggregation of the security evaluation values of the security criteria for each level; 2) a fuzzy multicriteria decision-making algorithm; and 3) a simple additive weight associated with the importance-performance analysis and performance rate to visualize the framework findings. The evaluation results of the security criteria based on the average performance rate and global weight suggest that data residency, data privacy, and data ownership are the most pressing challenges in assessing data protection in a VC environment. Overall, this article paves the way for a secure VC using an evaluation of effective security features and underscores directions and challenges facing the VC community. This article sheds light on the importance of security by design, emphasizing multiple layers of security when implementing industrial VCs.

Radiologist Group Performance Reporting: Power in Numbers

PurposeRadiologists have historically participated as individuals in CMS pay-for-performance programs, but little is known about how radiologists perform under increasingly available group participation. We aimed to assess radiologists’ relative national performance on CMS quality metrics using group versus individual participation. MethodsRadiologists’ group- and individual-level 2016 performance on Physician Quality Reporting System (PQRS) and non-PQRS Qualified Clinical Data Registry (QCDR) measures were obtained from the CMS national Physician Compare database and compared. ResultsRadiology groups reported an average 4.6 ± 2.0 quality measures; individual radiologists reported 2.3 ± 1.2 (P < .001). At least six measures were reported by 31.5% of groups versus 1.0% of individuals. Only one measure was reported by 5.4% of groups versus 33.0% of individuals. Groups reported 21 unique measures (20 via registries and one via QCDR). For 8 of the 11 measures reported by 20 or more groups, the average group performance rate was 3% or better than the average performance rate among radiologists participating as individuals (maximum 14% improvement with group participation versus individual participation for any individual measure). Group and individual performance were similar for the remaining three such measures. For measures reported by 20 or more groups in which a higher score indicates better performance, average group performance rates ranged from 86.2% to 98.9%. ConclusionCompared with individual participation in CMS quality performance programs, radiologists participating as a group reported larger numbers of quality measures and achieved higher performance rates on those measures. Radiology practices seeking success under Medicare’s new Quality Payment Program should carefully explore group participation.