Costs and predictors of early readmissions in patients with Infective Endocarditis. Utilizing the Nationwide Readmission Database

BACKGROUND: There have been previous studies detailing the variables involved in readmissions in patients with a primary admission diagnosis of infective endocarditis – however those studies were done prior to the 2015 change in AHA guidelines and introduction to ICD-10 codes. OBJECTIVES: The aim of this study was to describe the frequency, causes, factors, and costs associated with infective endocarditis encounters. METHODS: Utilizing the 2017 national readmission database (NRD), we identified all patients that were admitted with infective endocarditis. These patients were evaluated for the rates, predictors, and costs of unplanned 30 days readmissions. Weighted analysis was performed to obtain nationally representative data. RESULTS: 56,357 patients were identified to have been admitted with a diagnosis of infective endocarditis of whom 13,004 patients (23%) were readmitted within 30 days of the index discharge. The most common causes of readmission were septicemia (15.1%), endocarditis and endocardial disease (10.5%), heart failure (9.5%), and complication of cardiovascular device, implant or graft, initial encounter (5.6%). Data showed that there were certain comorbidities that resulted in a higher risk of being readmitted, these include chronic kidney disease, COPD, tobacco use, and hepatic failure. Cost of readmissions per patient was approximately $22,059 (IQR $11,630 - $49,964). CONCLUSIONS: Thirty-day unplanned readmissions remain a significant issue affecting nearly 1 in 6 patients with infective endocarditis. This is associated with significant mortality and financial burden. Multi-disciplinary approach may help decrease readmissions, reduce complications, and improve overall outcomes as well as the overall quality of life of our patients.

Industrial Quality Prediction System through Data Mining Algorithm

Based on an assessment of production capabilities, manufacturing sectors' core competency is increased. The importance of product quality in this aspect cannot be overstated. Several academics have introduced Deming's 14 principles, Shewhart cycle, total quality management, and other approaches to decrease the external failure costs and enhance product yield rates. Analysis of industrial data and process monitoring is becoming increasingly important as a part of the Industry 4.0 paradigm. In order to reduce the internal failure cost and inspection overhead, quality control (QC) schemes are utilized by industries. The final product quality has an interactive and cumulative effect of various parameters like operators and equipment in multistage manufacturing processes (MMP). In other cases, the final product is inspected in a single workstation with QC. It's challenging to do a cause analysis in MMP whenever a failure occurs. Several industries are looking for the optimal quality prediction model in order to achieve flawless production. The majority of current approaches solely handles single-stage manufacturing and is inadequate in dealing with MMP quality concerns. To overcome this issue, this paper proposes an industrial quality prediction system with a combination of multiple Program Component Analysis (PCA) and Decision Stump (DS) algorithm for MMP quality prediction. A SECOM (SEmiCOnductor Manufacturing) dataset is used for verification and validation of the proposed model. Based on the findings, it is clear that this model is capable of performing accurate classification and prediction in the field of industrial quality.

Costs and predictors of early readmissions in patients with Infective Endocarditis. Utilizing the Nationwide Readmission Database.

BACKGROUND: There have been previous studies detailing the variables involved in readmissions in patients with a primary admission diagnosis of infective endocarditis, however those studies were done prior to the 2015 change in AHA guidelines and introduction to ICD10 codes. OBJECTIVES: The aim of this study was to describe the frequency, causes, factors, and costs associated with infective endocarditis encounters. METHODS: Utilizing the 2017 national readmission database (NRD), we identified all patients that were admitted with infective endocarditis. These patients were evaluated for the rates, predictors, and costs of unplanned 30 days readmissions. Weighted analysis was performed to obtain nationally representative data. RESULTS: 56,357 patients were identified to have been admitted with a diagnosis of infective endocarditis of whom 13,004 patients (23%) were readmitted within 30 days of the index discharge. The most common causes of readmission were septicemia (15.1%), endocarditis and endocardial disease (10.5%), heart failure (9.5%), and complication of cardiovascular device, implant or graft, initial encounter (5.6%). Data showed that there were certain comorbidities that resulted in a higher risk of being readmitted, these include chronic kidney disease, COPD, tobacco use, and hepatic failure. Cost of readmissions per patient was approximately $22,059 (IQR $11,630 to $49,964). CONCLUSIONS: Thirty-day unplanned readmissions remain a significant issue affecting nearly 1 in 6 patients with infective endocarditis. This is associated with significant mortality and financial burden. Multi-disciplinary approach may help decrease readmissions, reduce complications, and improve overall outcomes as well as the overall quality of life of our patients.

Rational Adaptation in Lexical Prediction: The Influence of Prediction Strength

Recent studies indicate that the processing of an unexpected word is costly when the initial, disconfirmed prediction was strong. This penalty was suggested to stem from commitment to the strongly predicted word, requiring its inhibition when disconfirmed. Additional studies show that comprehenders rationally adapt their predictions in different situations. In the current study, we hypothesized that since the disconfirmation of strong predictions incurs costs, it would also trigger adaptation mechanisms influencing the processing of subsequent (potentially) strong predictions. In two experiments (in Hebrew and English), participants made speeded congruency judgments on two-word phrases in which the first word was either highly constraining (e.g., “climate,” which strongly predicts “change”) or not (e.g., “vegetable,” which does not have any highly probable completion). We manipulated the proportion of disconfirmed predictions in highly constraining contexts between participants. The results provide additional evidence of the costs associated with the disconfirmation of strong predictions. Moreover, they show a reduction in these costs when participants experience a high proportion of disconfirmed strong predictions throughout the experiment, indicating that participants adjust the strength of their predictions when strong prediction is discouraged. We formulate a Bayesian adaptation model whereby prediction failure cost is weighted by the participant’s belief (updated on each trial) about the likelihood of encountering the expected word, and show that it accounts for the trial-by-trial data.

A Microservice-Based System for Industrial Internet of Things in Fog-Cloud Assisted Network

Nowadays, the usage of the Industrial Internet of Things (IIoT) in practical applications has increased. The primary utilization is a fog cloud network, which offers different services, such as network and remote edges, at different places. Existing studies implemented the Service-Oriented Architecture (SOA) based on the fog-cloud network to run IIoT applications, such as e-healthcare, e-agriculture, renewable energy, etc. However, due to the applications' monolithic property, issues like failures, security, and cost factors occur, e.g. the failure of one service in SOA affects monolithic applications' performance in the system. With this motivation, this study suggests a microservice-based system to deal with the cost, security, and failure risks of IIoT applications in the fog-cloud system. The study improves the existing SOA systems for e-healthcare, e-agriculture, and renewable energy and minimizes the applications' overall cost. The performance evaluation shows that the devised systems outperform the existing SOA system in terms of failure, cost, and the deadline for all applications.

Understanding the components and magnitude of the cost of quality in building construction

PurposeThough the components and concepts of cost of quality (COQ) are well understood in the domain of manufacturing, only limited data are available from the construction industry for various reasons. The present study seeks to establish a relationship between project defect score (pds), representing the quality of construction in the project, and the COQ in the building construction industry. The study also seeks to estimate the contributions of the various components to the overall COQ in the construction industry, along with their distribution and interrelationships among themselves.Design/methodology/approachA framework for estimating COQ was developed, and the data regarding prevention, appraisal and failure cost were collected from 122 projects. Various mathematical and statistical tools like Pearson's correlation, multiple linear regression (MLR) and curve fitting have been used for data analysis.FindingsThe prevention–appraisal–failure (PAF) model was found to be appropriate to estimate COQ, and the prevention, appraisal, conformance cost (CC) and failure cost were found to vary between 0.19 and 8%, 0.05 and 5%, 0.3 and 10% and 0.01 and 5%, respectively, whereas the overall COQ varied from 3.5 to 10.01% of the project cost. The correlations between various components of COQ were found to be significant. MLR suggested that appraisal cost is more impactful in reducing failure cost than prevention cost. Using curve fitting, the cubic model appropriately represented all interrelationships. The optimal overall COQ was found to be 3.86%, and the reasons for low COQ have been explored.Originality/valueThe study evaluates the applicability of available models for COQ calculations for the construction industry and presents a framework to estimate its components. The study also explores the interrelationship between the various components of COQ and presents a generalized relationship between COQ and the pds.

ANALISIS PERHITUNGAN BIAYA KUALITAS DALAM MENINGKATKAN KUALITAS PRODUK PADA PABRIK GULA MADUKISMO

ABSTRAK Tujuan penelitian ini adalah untuk menganalisis perhitungan biaya kualitas untuk meningkatkan kualitas hasil produksi pada Pabrik Gula (PG) Madukismo. Penelitian ini juga mengenai perencanaan program kualitas dan perhitungan biaya kualitas dengan metode prevention, apprisial, failure cost (P-A-F) pada PG Madukismo. Pendekatan penelitian yang digunakan adalah penelitian kualitatif dengan pendekatan analisis deskriptif. PG Madukismo merupakan objek dari penelitian ini karena satu-satunya pabrik gula yang terletak di Provinsi Daerah Istimewa Yogyakarta yang mengembang tugas untuk mensukseskan program pangan nasional dan siap dalam menhadapi persaingan di era globalisasi. Hasil penelitian ini menunjukkan bahwa PG Madukismo telah mengeluarkan biaya yang terkait dalam peningkatkan kualitas untuk produk yang mereka hasilkan. Biaya kualitas yang dikeluarkan oleh perusahaan untuk meningkatkan produk mereka ialah biaya pelatihan karyawan, biaya pemeliharaan mesin, biaya penyuluhan tebu, biaya proteksi hama, biaya pemeriksaan tebu, biaya pemeriksaan proses produksi, biaya akurasi alat, dan biaya pengerjaan kembali. Kelemahan model P-A-F hanya memaparkan biaya kualitas yang tertera langsung pada laporan keuangan tidak dapat memaparkan dari aktivitas yang sulit untuk diidentifikasi biayanya (hidden cost). Kata kunci: biaya kualitas, perusahaan gula, pencegahan, penilaian, biaya kegagalan. ABSTRACT The purpose of this study was to analyze the accounting of the cost quality to improve the quality of production in Madukismo sugar company. This study is also about quality of program planning and accounting of quality costs with prevention, appraisal, failure cost method (P-A-F) on the Madukismo sugar company. The approach used in this study is a qualitative research with descriptif analysis approach. Madukismo sugar company is the object of this study because the only sugarr mill located in the province of Yogyakarta which expands the task to make the program successful national food and ready in overcoming global competition. The results of this study indicate that Madukismo sugar company has incurred related to enhancing the quality of the products they produce. Quality costs incurred by companies to improve their products is the cost of employee training, machine maintenance costs, costs of extension cane, pest protection fees, inspection fees cane, its cost of production proces, the accuracy of the charges, and the cost of rework. The weakness of the model P-A-F only describe the cost of quality printed directly on the financial statements can not be explained from a difficult activity to be identified costs (hidden costs) Keywords: cost of quality, sugar company, prevention, apprisal, the cost of failure.