Business Process Model and Notation and openEHR Task Planning for Clinical Pathway Standards in Infections: Critical Analysis.

BACKGROUND: Clinical pathways (CPs) are usually expressed by means of workflow formalisms, providing health care personnel with an easy-to-understand, high-level conceptual model of medical steps in specific patient conditions, thereby improving overall health care process quality in clinical practice. From a standardized perspective, the business process model and notation (BPMN), a widely spread general-purpose process formalism, has been used for conceptual modeling in clinical domains, mainly because of its easy-to-use graphical notation, facilitating the common understanding and communication of the parties involved in health care. However, BPMN is not particularly oriented toward the peculiarities of complex clinical processes such as infection diagnosis and treatment, in which time plays a critical role, which is why much of the BPMN clinical-oriented research has revolved around how to extend the standard to address these special needs. The shift from an agnostic, general-purpose BPMN notation to a natively clinical-oriented notation such as openEHR Task Planning (TP) could constitute a major step toward clinical process improvement, enhancing the representation of CPs for infection treatment and other complex scenarios. OBJECTIVE: Our work aimed to analyze the suitability of a clinical-oriented formalism (TP) to successfully represent typical process patterns in infection treatment, identifying domain-specific improvements to the standard that could help enhance its modeling capabilities, thereby promoting the widespread adoption of CPs to improve medical practice and overall health care quality. METHODS: Our methodology consisted of 4 major steps: identification of key features of infection CPs through literature review, clinical guideline analysis, and BPMN extensions; analysis of the presence of key features in TP; modeling of relevant process patterns of catheter-related bloodstream infection as a case study; and analysis and proposal of extensions in view of the results. RESULTS: We were able to easily represent the same logic applied in the extended BPMN-based process models in our case study using out-of-the-box standard TP primitives. However, we identified possible improvements to the current version of TP to allow for simpler conceptual models of infection CPs and possibly of other complex clinical scenarios. CONCLUSIONS: Our study showed that the clinical-oriented TP specification is able to successfully represent the most complex catheter-related bloodstream infection process patterns depicted in our case study and identified possible extensions that can help increase its adequacy for modeling infection CPs and possibly other complex clinical conditions.

Advanced Control Systems in Industry 5.0 Enabling Process Mining.

This paper merges new research topics in Industry 5.0 using the Business Process Modeling and Notation (BPMN) approach able to integrate Artificial Intelligence (AI) in production processes. The goal is to provide an innovative approach to model production management in industry, adopting a new "proof of concept" of advanced Process Mining (PM) automatizing decisions and optimizing machine setting and maintenance interventions. Advanced electronic sensing and actuation systems, integrating supervised and unsupervised AI algorithms, are embedded in the PM model as theoretical process workflows suggested by a Decision Support System (DSS) engine enabling an intelligent decision-making procedure. The paper discusses, as examples, two theoretical models applied to specific industry sectors, such as food processing and energy production. The proposed work provides important elements of engineering management related to the digitalization of production process matching with automated control systems setting production parameters, thus enabling the self-adapting of product quality supervision and production efficiency in modern industrial systems.

Towards circular economy in the textiles and clothing value chain through blockchain technology and IoT: A review.

The textile and clothing industry sector has today a big environmental impact, not only due to the consumption of water and the use of toxic chemicals but also due to the increasing levels of textile waste. One way to reduce the problem is to circularise the, currently linear, textile and clothing value chain, by using discarded clothes as raw material for the production of new clothes, transforming it into a model of circular economy. This way, while reducing the need to produce new raw materials (e.g. cotton), the problem of textile waste produced is also reduced, thus contributing to a more sustainable industry. In this article, we review the current approaches for traceability in the textile and clothing value chain, and study a set of technologies we deem essential for promoting the circular economy in this value chain - namely, the blockchain technology - for registering activities on traceable items through the value chain, and the Internet of Things (IoT) technology, for easily identifying the traceable items' digital twins.

Live process modeling with the BPMN Sketch Miner.

BPMN Sketch Miner is a modeling environment for generating visual business process models starting from constrained natural language textual input. Its purpose is to support business process modelers who need to rapidly sketch visual BPMN models during interviews and design workshops, where participants should not only provide input but also give feedback on whether the sketched visual model represents accurately what has been described during the discussion. In this article, we present a detailed description of the BPMN Sketch Miner design decisions and list the different control flow patterns supported by the current version of its textual DSL. We also summarize the user study and survey results originally published in MODELS 2020 concerning the tool usability and learnability and present a new performance evaluation regarding the visual model generation pipeline under actual usage conditions. The goal is to determine whether it can support a rapid model editing cycle, with live synchronization between the textual description and the visual model. This study is based on a benchmark including a large number of models (1350 models) exported by users of the tool during the year 2020. The main results indicate that the performance is sufficient for a smooth live modeling user experience and that the end-to-end execution time of the text-to-model-to-visual pipeline grows linearly with the model size, up to the largest models (with 195 lines of textual description) found in the benchmark workload.

A novel approach to educate hospitalized cardiovascular disease patients about lifestyle and behavior modifications.

BACKGROUND: Cardiovascular diseases (CVDs) are always considered by healthcare specialists for different reasons, including extensive prevalence, increased costs, chronicity, and high risk of death. The control of CVDs is highly influenced by behavior and lifestyle and it seems necessary to train special abilities about lifestyle and behavior modification to improve self-care skills for patients, and their caregivers. As a result, the development of effective training systems should be considered by healthcare specialists. METHODS: Hence, in this study, a framework for improving cardiovascular patients' education processes is presented. Initially, an existing training system for cardiovascular patients is reviewed. Using field observations and targeted interviews with hospital experts, all components of its educating processes are identified, and their process maps are drawn up. After that, challenges in the training system are extracted with the aid of in-depth semi-structured interviews with experts. Due to the importance and different influence of the identified challenges, they are prioritized using a Multiple Criteria Decision-making (MCDM) method, and then their root causes were investigated. Finally, a novel framework is proposed and evaluated with hospital experts' help to improve the main challenges. RESULTS: The most important challenges included high nursing workload and shortage of time, lack of understanding of training concepts by patients, lack of attention to training, disruption of the training processes by the patients' caregivers, and patient's weakness in understanding the standard language. In identifying the root causes, learner, educator, and educational tools are the most effective in the training process; therefore, the improvement scenarios were designed accordingly in the proposed framework. CONCLUSIONS: Our study indicated that presenting a framework with applying different quantitative and qualitative methods has great potential to improve the processes of patient education for chronic diseases such as cardiovascular disease.

Information technology of verification of algorithmic of medical regulations.

OBJECTIVE: Introduction: In the medical field, using of information-analytical technologies and expert systems is becoming increasingly common. Therefore, the problem of quality of normative acts (documents), which unify the standards of the newest methods of medical activity, becomes urgent. But, unfortunately, legal experts state that there is a problem of errors in regulations of different branches of rulemaking. A rulemaking error can be recognized as inconsistency of a text or a rule of law's content regarding its purpose. There are two types of legal errors: purely textual and substantive (algorithmic). The aim: The aim of the article is to demonstrate the possibilities of using information technology based on BPMN to display algorithms and identify algorithmic errors in regulations that adjust activities of health care professionals. PATIENTS AND METHODS: Materials and methods: It is used BPMN (Business Process Model And Notation) technology in the research. With its help, a logical-analytical check of algorithm scheme for treatment of abnormal uterine bleeding, provided in the normative document of the Ministry of Health of Ukraine "Unified Clinical Protocol of Primary, Secondary (specialized) and Tertiary (highly specialized) Medical Care. Abnormal Uterine Bleeding", approved by the order of the Ministry of Health of Ukraine on 13.04.2016, No. 353. Algorithmic errors in the scheme were checked using Microsoft Visio software. According to the results of the logical-analytical examination of the mentioned normative act's text, a scheme of algorithm for treatment of abnormal uterine bleeding in BPMN was constructed. RESULTS: Results: The use of the proposed BPMN-based information technology and Microsoft Visio software allows you to control the algorithmic nature of regulated medical practice processes and to detect errors, to create visual models of schematically regulated medical practice algorithms. CONCLUSION: Conclusions: The proposed information technology, based on BPMN can be used to display algorithms and detect errors in regulatory acts that adjust activity of medical professionals.

A BPMN Based Notation for the Representation of Workflows in Hospital Protocols.

Flowcharts used for hospital protocols have a series of ambiguities and limitations in order to express some types of information. In this article, a notation proposal for flowcharts that partially avoids these problems is presented. This new notation is an adaptation of BPMNE2, an extension of the Business Process Model and Notation (BPMN), which allows direct modelling of procedures that follow the Hazard Analysis and Critical Control Points (HACCP) model. The new notation has been validated in the hospital context, specifically in the field of hazardous drugs (HDs). To measure usability from the perspective of the health staff and auditors, the System Usability Scale (SUS) was used. A total of 47 experts took part in the assessment, resulting in a SUS score of 71, that corresponds to an acceptable level of usability. The feedback provided by these participants allows us to discover benefits and drawbacks of the proposal. Also, it is noteworthy that 76.6% of professionals prefer to migrate to the new notation from the ISO 5807:1985 notation, the most commonly used model. In addition to the direct benefits of this notation from the human point of view, its machine-understandable nature provides the required support for its integration into software tools for intelligent monitoring and auditing.

A meta-model for computer executable dynamic clinical safety checklists.

BACKGROUND: Safety checklist is a type of cognitive tool enforcing short term memory of medical workers with the purpose of reducing medical errors caused by overlook and ignorance. To facilitate the daily use of safety checklists, computerized systems embedded in the clinical workflow and adapted to patient-context are increasingly developed. However, the current hard-coded approach of implementing checklists in these systems increase the cognitive efforts of clinical experts and coding efforts for informaticists. This is due to the lack of a formal representation format that is both understandable by clinical experts and executable by computer programs. METHODS: We developed a dynamic checklist meta-model with a three-step approach. Dynamic checklist modeling requirements were extracted by performing a domain analysis. Then, existing modeling approaches and tools were investigated with the purpose of reusing these languages. Finally, the meta-model was developed by eliciting domain concepts and their hierarchies. The feasibility of using the meta-model was validated by two case studies. The meta-model was mapped to specific modeling languages according to the requirements of hospitals. RESULTS: Using the proposed meta-model, a comprehensive coronary artery bypass graft peri-operative checklist set and a percutaneous coronary intervention peri-operative checklist set have been developed in a Dutch hospital and a Chinese hospital, respectively. The result shows that it is feasible to use the meta-model to facilitate the modeling and execution of dynamic checklists. CONCLUSIONS: We proposed a novel meta-model for the dynamic checklist with the purpose of facilitating creating dynamic checklists. The meta-model is a framework of reusing existing modeling languages and tools to model dynamic checklists. The feasibility of using the meta-model is validated by implementing a use case in the system.

Toward an interoperable, intraoperative situation recognition system via process modeling, execution, and control using the standards BPMN and CMMN.

PURPOSE: For the modeling, execution, and control of complex, non-standardized intraoperative processes, a modeling language is needed that reflects the variability of interventions. As the established Business Process Model and Notation (BPMN) reaches its limits in terms of flexibility, the Case Management Model and Notation (CMMN) was considered as it addresses weakly structured processes. METHODS: To analyze the suitability of the modeling languages, BPMN and CMMN models of a Robot-Assisted Minimally Invasive Esophagectomy and Cochlea Implantation were derived and integrated into a situation recognition workflow. Test cases were used to contrast the differences and compare the advantages and disadvantages of the models concerning modeling, execution, and control. Furthermore, the impact on transferability was investigated. RESULTS: Compared to BPMN, CMMN allows flexibility for modeling intraoperative processes while remaining understandable. Although more effort and process knowledge are needed for execution and control within a situation recognition system, CMMN enables better transferability of the models and therefore the system. Concluding, CMMN should be chosen as a supplement to BPMN for flexible process parts that can only be covered insufficiently by BPMN, or otherwise as a replacement for the entire process. CONCLUSION: CMMN offers the flexibility for variable, weakly structured process parts, and is thus suitable for surgical interventions. A combination of both notations could allow optimal use of their advantages and support the transferability of the situation recognition system.

Interoperability Is a Process - The Data Sharing Framework.

Common syntax and data semantics are core components of healthcare interoperability standards. However, interoperable data exchange processes are also needed to enable the integration of existing systems between organizations. While solutions for healthcare delivery processes are available and have been widely adopted, support for processes targeting bio-medical research is limited. Our Data Sharing Framework creates a platform to implement research processes like cohort size estimation, reviews and approvals of research proposals, consent checks, record linkage, pseudonymization and data sharing across organizations. The described framework implements a distributed business process engine for executing BPMN 2.0 processes with synchronization and data exchange using FHIR R4 resources. Our reference implementation has been rolled out to 38 organizations across three research consortia in Germany and is available as open source under the Apache 2.0 license.

TraumaFlow-development of a workflow-based clinical decision support system for the management of severe trauma cases.

PURPOSE: The treatment of severely injured patients in the resuscitation room of an emergency department requires numerous critical decisions, often under immense time pressure, which places very high demands on the facility and the interdisciplinary team. Computer-based cognitive aids are a valuable tool, especially in education and training of medical professionals. For the management of polytrauma cases, TraumaFlow, a workflow management-based clinical decision support system, was developed. The system supports the registration and coordination of activities in the resuscitation room and actively recommends diagnosis and treatment actions. METHODS: Based on medical guidelines, a resuscitation room algorithm was developed according to the cABCDE scheme. The algorithm was then modeled using the process description language BPMN 2.0 and implemented in a workflow management system. In addition, a web-based user interface that provides assistance functions was developed. An evaluation study was conducted with 11 final-year medical students and three residents to assess the applicability of TraumaFlow in a case-based training scenario. RESULTS: TraumaFlow significantly improved guideline-based decision-making, provided more complete therapy, and reduced treatment errors. The system was shown to be beneficial not only for the education of low- and medium-experienced users but also for the training of highly experienced physicians. 92% of the participants felt more confident with computer-aided decision support and considered TraumaFlow useful for the training of polytrauma treatment. In addition, 62% acknowledged a higher training effect. CONCLUSION: TraumaFlow enables real-time decision support for the treatment of polytrauma patients. It improves guideline-based decision-making in complex and critical situations and reduces treatment errors. Supporting functions, such as the automatic treatment documentation and the calculation of medical scores, enable the trauma team to focus on the primary task. TraumaFlow was developed to support the training of medical students and experienced professionals. Each training session is documented and can be objectively and qualitatively evaluated.

Use of Real-World FHIR Data Combined with Context-Sensitive Decision Modeling to Guide Sentinel Biopsy in Melanoma.

Background: To support clinical decision-making at the point of care, the "best next step" based on Standard Operating Procedures (SOPs) and actual accurate patient data must be provided. To do this, textual SOPs have to be transformed into operable clinical algorithms and linked to the data of the patient being treated. For this linkage, we need to know exactly which data are needed by clinicians at a certain decision point and whether these data are available. These data might be identical to the data used within the SOP or might integrate a broader view. To address these concerns, we examined if the data used by the SOP is also complete from the point of view of physicians for contextual decision-making. Methods: We selected a cohort of 67 patients with stage III melanoma who had undergone adjuvant treatment and mainly had an indication for a sentinel biopsy. First, we performed a step-by-step simulation of the patient treatment along our clinical algorithm, which is based on a hospital-specific SOP, to validate the algorithm with the given Fast Healthcare Interoperability Resources (FHIR)-based data of our cohort. Second, we presented three different decision situations within our algorithm to 10 dermatooncologists, focusing on the concrete patient data used at this decision point. The results were conducted, analyzed, and compared with those of the pure algorithmic simulation. Results: The treatment paths of patients with melanoma could be retrospectively simulated along the clinical algorithm using data from the patients' electronic health records. The subsequent evaluation by dermatooncologists showed that the data used at the three decision points had a completeness between 84.6% and 100.0% compared with the data used by the SOP. At one decision point, data on "patient age (at primary diagnosis)" and "date of first diagnosis" were missing. Conclusions: The data needed for our decision points are available in the FHIR-based dataset. Furthermore, the data used at decision points by the SOP and hence the clinical algorithm are nearly complete compared with the data required by physicians in clinical practice. This is an important precondition for further research focusing on presenting decision points within a treatment process integrated with the patient data needed.

Policy-Based Holistic Application Management with BPMN and TOSCA.

With the wide adoption of cloud computing across technology industries and research institutions, an ever-growing interest in cloud orchestration frameworks has emerged over the past few years. These orchestration frameworks enable the automated provisioning and decommissioning of cloud applications in a timely and efficient manner, but they offer limited or no support for application management. While management functionalities, such as configuring, monitoring and scaling single components, can be directly covered by cloud providers and configuration management tools, holistic management features, such as backing up, testing and updating multiple components, cannot be automated using these approaches. In this paper, we propose a concept to automatically generate executable holistic management workflows based on the TOSCA standard. The practical feasibility of the approach is validated through a prototype implementation and a case study.

No Transfer Without Validation: A Data Sharing Framework Use Case.

Availability and accessibility are important preconditions for using real-world patient data across organizations. To facilitate and enable the analysis of data collected at a large number of independent healthcare providers, syntactic- and semantic uniformity need to be achieved and verified. With this paper, we present a data transfer process implemented using the Data Sharing Framework to ensure only valid and pseudonymized data is transferred to a central research repository and feedback on success or failure is provided. Our implementation is used within the CODEX project of the German Network University Medicine to validate COVID-19 datasets at patient enrolling organizations and securely transfer them as FHIR resources to a central repository.

Artificial Intelligence Applied to a First Screening of Naevoid Melanoma: A New Use of Fast Random Forest Algorithm in Dermatopathology.

Malignant melanoma (MM) is the "great mime" of dermatopathology, and it can present such rare variants that even the most experienced pathologist might miss or misdiagnose them. Naevoid melanoma (NM), which accounts for about 1% of all MM cases, is a constant challenge, and when it is not diagnosed in a timely manner, it can even lead to death. In recent years, artificial intelligence has revolutionised much of what has been achieved in the biomedical field, and what once seemed distant is now almost incorporated into the diagnostic therapeutic flow chart. In this paper, we present the results of a machine learning approach that applies a fast random forest (FRF) algorithm to a cohort of naevoid melanomas in an attempt to understand if and how this approach could be incorporated into the business process modelling and notation (BPMN) approach. The FRF algorithm provides an innovative approach to formulating a clinical protocol oriented toward reducing the risk of NM misdiagnosis. The work provides the methodology to integrate FRF into a mapped clinical process.

A model-driven transformation approach for the modelling of processes in clinical practice guidelines.

Clinical Practice Guidelines (CPGs) include recommendations aimed at optimising patient care, informed by a review of the available clinical evidence. To achieve their potential benefits, CPG should be readily available at the point of care. This can be done by translating CPG recommendations into one of the languages for Computer-Interpretable Guidelines (CIGs). This is a difficult task for which the collaboration of clinical and technical staff is crucial. However, in general CIG languages are not accessible to non-technical staff. We propose to support the modelling of CPG processes (and hence the authoring of CIGs) based on a transformation, from a preliminary specification in a more accessible language into an implementation in a CIG language. In this paper, we approach this transformation following the Model-Driven Development (MDD) paradigm, in which models and transformations are key elements for software development. To demonstrate the approach, we implemented and tested an algorithm for the transformation from the BPMN language for business processes to the PROforma CIG language. This implementation uses transformations defined in the ATLAS Transformation Language. Additionally, we conducted a small experiment to assess the hypothesis that a language such as BPMN can facilitate the modelling of CPG processes by clinical and technical staff.

A framework for multi-perspective process mining into a BPMN process model.

Process mining is mainly focused on process discovery from control perspective. It is further applied to mine the other perspectives such as time, data, and resources by replaying the events in event logs over the initial process model. When process mining is extended far beyond discovering the control-flow models to capture additional perspectives; roles, bottlenecks, amounts of time passed, guards, and routing probabilities in the process can be identified. This is a such extensions are considered under the topic of multi-perspective process mining, which makes the discovered process model more understandable. In this study, a framework for applying multi-perspective process mining and creating a Business Process Modelling Notation (BPMN) process model as the output is introduced. The framework, which uses a recently developed application programming interface (API) for storing the BPMN Data Model which keeps what is produced from each perspective as an asset into a private blockchain in a secure and immutable way, has been developed as a plugin to the ProM tool. In doing so, it integrates a number of techniques for multi-perspective process mining in literature, for the perspectives of control-flow, data, and resource; and represents a holistic process model by combining the outputs of these in the BPMN Data Model. In this article, we explain technical details of the framework and also demonstrate its usage over a case in medical domain.

Don't overthink it: The paradoxical nature of expertise for the detection of errors in conceptual business process models.

Business process models are widely used artifacts in design activities to facilitate communication about business domains and processes. Despite being an extensively researched topic, some aspects of conceptual business modeling are yet to be fully explored and understood by academicians and practitioners alike. We study the attentional characteristics specific to experts and novices in a semantic and syntactic error detection task across 75 Business Process Model and Notation (BPMN) models. We find several intriguing results. Experts correctly identify more error-free models than novices, but also tend to find more false positive defects. Syntactic errors are diagnosed faster than semantic errors by both groups. Both groups spend more time on error-free models. Our findings regarding the ambiguous differences between experts and novices highlight the paradoxical nature of expertise and the need to further study how best to train business analysts to design and evaluate conceptual models.

Secure Multi-Party Computation Based Distributed Feasibility Queries - A HiGHmed Use Case.

The integration of routine medical care data into research endeavors promises great value. However, access to this extra-domain data is constrained by numerous technical and legal requirements. The German Medical Informatics Initiative (MII) - initiated by the Federal Ministry of Research and Education (BMBF) - is making progress in setting up Medical Data Integration Centers to consolidate data stored in clinical primary information systems. Unfortunately, for many research questions cross-organizational data sources are required, as one organization's data is insufficient, especially in rare disease research. A first step, for research projects exploring possible multi-centric study designs, is to perform a feasibility query, i.e., a cohort size calculation transcending organizational boundaries. Existing solutions for this problem, like the previously introduced feasibility process for the MII's HiGHmed consortium, perform well for most use cases. However, there exist use cases where neither centralized data repositories, nor Trusted Third Parties are acceptable for data aggregation. Based on open standards, such as BPMN 2.0 and HL7 FHIR R4, as well as the cryptographic techniques of secure Multi-Party Computation, we introduce a fully automated, decentral feasibility query process without any central component or Trusted Third Party. The open source implementation of the proposed solution is intended as a plugin process to the HiGHmed Data Sharing Framework. The process's concept and underlying algorithms can also be used independently.

Guideline-Based Context-Sensitive Decision Modeling for Melanoma Patients.

INTRODUCTION: The provision of knowledge through clinical practice guidelines and hospital-specific standard operating procedures (SOPs) is ubiquitous in the medical context and in the treatment of melanoma patients. However, these knowledge sources are only available in unstructured text form and without any contextual link to real patient data. The aim of our project is to give a modeled decision support for the next treatment step based on the actual data and position of a patient. METHODS: First, we identified passages for qualified decision-making necessary at the point of care from the SOP for melanoma. Thereby, the patient-specific contextual reference data at decision points was considered in parallel and represented by FHIR (Fast Healthcare Interoperability Resource) resources. The decision algorithm was then formalized using BPMN modeling with FHIR annotations. Validation was provided by medical experts, dermatooncologists from University Hospital Essen. RESULTS: The resulting BPMN model is presented here with the diagnostic procedure of sentinel lymph node excision as the example snippet from the whole algorithm. Each decision point is edited with FHIR resources covering the patient data and preparing the context sensitivity of the model. CONCLUSION: Modeling guideline-based information into a decision algorithm that can be presented at the point of care with contextual reference, may have the potential to support patient-specific clinical decision-making. For patients from a certain status like in the metastatic setting modeling becomes highly tailored to specific patient cases, alternative and individualized treatment options.