Modeling control objectives for business process compliance

Business process design is primarily driven by process improvement objectives. However, the role of control objectives stemming from regulations and standards is becoming increasingly important for businesses in light of recent events that led to some of the largest scandals in corporate history. As organizations strive to meet compliance agendas, there is an evident need to provide systematic approaches that assist in the understanding of the interplay between (often conflicting) business and control objectives during business process design. In this paper, our objective is twofold. We will firstly present a research agenda in the space of business process compliance, identifying major technical and organizational challenges. We then tackle a part of the overall problem space, which deals with the effective modeling of control objectives and subsequently their propagation onto business process models. Control objective modeling is proposed through a specialized modal logic based on normative systems theory, and the visualization of control objectives on business process models is achieved procedurally. The proposed approach is demonstrated in the context of a purchase-to-pay scenario

Electric Scooter Injuries and Hospital Admissions in the United States, 2014-2018.

This study investigates trends of injury and hospital admission associated with electric scooter use

Model-Driven Management of Internal Controls for Business Process Compliance

The thesis tackles the problem of high effort for achieving business process compliance to regulations in the area of Enterprise Risk Management. Common to these regulations are requirements on the presence of effective internal controls in companies. The level of automation with regard to translating compliance requirements into a set of internal controls and assuring the effectiveness of these controls during execution of business processes is raised thorugh a novel model-driven approach

Quantitative characterization of viscoelastic behavior in tissue-mimicking phantoms and ex vivo animal tissues.

Viscoelasticity of soft tissue is often related to pathology, and therefore, has become an important diagnostic indicator in the clinical assessment of suspect tissue. Surgeons, particularly within head and neck subsites, typically use palpation techniques for intra-operative tumor detection. This detection method, however, is highly subjective and often fails to detect small or deep abnormalities. Vibroacoustography (VA) and similar methods have previously been used to distinguish tissue with high-contrast, but a firm understanding of the main contrast mechanism has yet to be verified. The contributions of tissue mechanical properties in VA images have been difficult to verify given the limited literature on viscoelastic properties of various normal and diseased tissue. This paper aims to investigate viscoelasticity theory and present a detailed description of viscoelastic experimental results obtained in tissue-mimicking phantoms (TMPs) and ex vivo tissues to verify the main contrast mechanism in VA and similar imaging modalities. A spherical-tip micro-indentation technique was employed with the Hertzian model to acquire absolute, quantitative, point measurements of the elastic modulus (E), long term shear modulus (η), and time constant (τ) in homogeneous TMPs and ex vivo tissue in rat liver and porcine liver and gallbladder. Viscoelastic differences observed between porcine liver and gallbladder tissue suggest that imaging modalities which utilize the mechanical properties of tissue as a primary contrast mechanism can potentially be used to quantitatively differentiate between proximate organs in a clinical setting. These results may facilitate more accurate tissue modeling and add information not currently available to the field of systems characterization and biomedical research

Phase 1 human trial of autologous bone marrow-hematopoietic stem cell transplantation in patients with decompensated cirrhosis

Aim: To evaluate safety and feasibility of autologous bone marrow-enriched CD34+ hematopoietic stem cell Tx through the hepatic artery in patients with decompensated cirrhosis. Methods: Four patients with decompensated cirrhosis were included. Approximately 200 mL of the bone marrow of the patients was aspirated, and CD34+ stem cells were selected. Between 3 to 10 million CD34+ cells were isolated. The cells were slowly infused through the hepatic artery of the patients. Results: Patient 1 showed marginal improvement in serum albumin and no significant changes in other test results. In patient 2 prothrombin time was decreased; however, her total bilirubin, serum creatinine, and Model of End-Stage Liver Disease (MELD) score worsened at the end of follow up. In patient 3 there was improvement in serum albumin, porthrombin time (PT), and MELD score. Patient 4 developed radiocontrast nephropathy after the procedure, and progressed to type 1 hepatorenal syndrome and died of liver failure a few days later. Because of the major side effects seen in the last patient, the trial was prematurely stopped. Conclusion: Infusion of CD34+ stem cells through the hepatic artery is not safe in decompensated cirrhosis. Radiocontrast nephropathy and hepatorenal syndrome could be major side effects. However, this study does not preclude infusion of CD34+ stem cells through other routes. © 2007 The WJG Press. All rights reserved

Efficient Compliance Checking Using BPMN-Q and Temporal Logic

Abstract. Compliance rules describe regulations, policies and quality constraints business processes must adhere to. Given the large number of rules and their frequency of change, manual compliance checking can be-come a time-consuming task. Automated compliance checking of process activities and their ordering is an alternative whenever business pro-cesses and compliance rules are described in a formal way. This paper introduces an approach for automated compliance checking. Compliance rules are translated into temporal logic formulae that serve as input to model checkers which in turn verify whether a process model satisfies the requested compliance rule. To address the problem of state-space explo-sion we employ a set of reduction rules. The approach is prototypically realized and evaluated.

Towards Compliance of Cross-Organizational Processes and their Changes

Businesses require the ability to rapidly implement new processes and to quickly adapt existing ones to environmental changes including the optimization of their interactions with partners and customers. However, changes of either intra- or cross-organizational processes must not be done in an uncontrolled manner. In particular, processes are increasingly subject to compliance rules that usually stem from security constraints, corporate guidelines, standards, and laws. These compliance rules have to be considered when modeling business processes and changing existing ones. While change and compliance have been extensively discussed for intra-organizational business processes, albeit only in an isolated manner, their combination in the context of cross-organizational processes remains an open issue. In this paper, we discuss requirements and challenges to be tackled in order to ensure that changes of cross-organizational business processes preserve compliance with imposed regulations, standards and laws

Design and Verification of Instantiable Compliance Rule Graphs in Process-Aware Information Systems

Abstract. For enterprises it has become crucial to check compliance of their business processes with certain rules such as medical guidelines or financial regulations. When automating compliance checks on pro-cess models, existing approaches have mainly addressed process-specific compliance rules so far, i.e., rules that correspond to a particular pro-cess model. However, in practice, we will rather find process-independent compliance rules that are nevertheless to be checked over process models. Thus, in this paper, we present an approach that enables the instantiation and verification of process-independent compliance rules over process models using domain models. For this, we provide an intuitive visualiza-tion of compliance rules and compliance rule instances at user level and show how rules and instances can be formalized and verified at system level. The overall approach is validated by a pattern-based comparison to existing approaches and by means of a prototypical implementation.

The KNee OsteoArthritis Prediction (KNOAP2020) challenge:An image analysis challenge to predict incident symptomatic radiographic knee osteoarthritis from MRI and X-ray images

Objectives: The KNee OsteoArthritis Prediction (KNOAP2020) challenge was organized to objectively compare methods for the prediction of incident symptomatic radiographic knee osteoarthritis within 78 months on a test set with blinded ground truth. Design: The challenge participants were free to use any available data sources to train their models. A test set of 423 knees from the Prevention of Knee Osteoarthritis in Overweight Females (PROOF) study consisting of magnetic resonance imaging (MRI) and X-ray image data along with clinical risk factors at baseline was made available to all challenge participants. The ground truth outcomes, i.e., which knees developed incident symptomatic radiographic knee osteoarthritis (according to the combined ACR criteria) within 78 months, were not provided to the participants. To assess the performance of the submitted models, we used the area under the receiver operating characteristic curve (ROCAUC) and balanced accuracy (BACC). Results: Seven teams submitted 23 entries in total. A majority of the algorithms were trained on data from the Osteoarthritis Initiative. The model with the highest ROCAUC (0.64 (95% confidence interval (CI): 0.57–0.70)) used deep learning to extract information from X-ray images combined with clinical variables. The model with the highest BACC (0.59 (95% CI: 0.52–0.65)) ensembled three different models that used automatically extracted X-ray and MRI features along with clinical variables. Conclusion: The KNOAP2020 challenge established a benchmark for predicting incident symptomatic radiographic knee osteoarthritis. Accurate prediction of incident symptomatic radiographic knee osteoarthritis is a complex and still unsolved problem requiring additional investigation.</p