Multi-Perspective Process Mining Interfaces for HL7 AuditEvent Repositories: XES and OCEL.

BACKGROUND: Medical information systems frequently use event logging, but these logs are not suitable for process mining as they are not logged in a standardized format. OBJECTIVES: Our goal is to enrich medical event logs for use in process mining. METHOD: We present an approach to convert events from standards- based repositories into the XES and OCEL formats commonly used in process mining. RESULTS: We tested this approach using simulated data from the Austrian breast cancer screening program. CONCLUSION: We aim to apply it to analyze care guidelines and improve hospital processes in the future.

Extending a Data Management Maturity Model for Process Mining in Healthcare.

BACKGROUND: Many components must work together to continuously improve processes in healthcare organizations. Process mining has recently developed into a discipline that can make a significant contribution here. OBJECTIVES: We want to extend an existing management tool to assess and improve the capability of organizations in this area. METHOD: We add a dimension to the adoption readiness assessment and maturity model for sharable clinical pathways to assess and improve event data quality. RESULTS: We present different approaches for formal and checkpoint assessments and an embedding of the improvement strategy with examples. CONCLUSION: The additional dimension from the process mining domain integrates with the existing model. At all levels, links can be established between the various aspects of event data quality with existing dimensions. The model has yet to be tested in a real-world use case.

Building Process-Oriented Data Science Solutions for Real-World Healthcare.

The COVID-19 pandemic has highlighted some of the opportunities, problems and barriers facing the application of Artificial Intelligence to the medical domain. It is becoming increasingly important to determine how Artificial Intelligence will help healthcare providers understand and improve the daily practice of medicine. As a part of the Artificial Intelligence research field, the Process-Oriented Data Science community has been active in the analysis of this situation and in identifying current challenges and available solutions. We have identified a need to integrate the best efforts made by the community to ensure that promised improvements to care processes can be achieved in real healthcare. In this paper, we argue that it is necessary to provide appropriate tools to support medical experts and that frequent, interactive communication between medical experts and data miners is needed to co-create solutions. Process-Oriented Data Science, and specifically concrete techniques such as Process Mining, can offer an easy to manage set of tools for developing understandable and explainable Artificial Intelligence solutions. Process Mining offers tools, methods and a data driven approach that can involve medical experts in the process of co-discovering real-world evidence in an interactive way. It is time for Process-Oriented Data scientists to collaborate more closely with healthcare professionals to provide and build useful, understandable solutions that answer practical questions in daily practice. With a shared vision, we should be better prepared to meet the complex challenges that will shape the future of healthcare.

FHIR2BPMN: Delivering Actionable Knowledge by Transforming Between Clinical Pathways and Executable Models.

Healthcare processes have many particularities captured and described within standards for medical information exchange such as HL7 FHIR. BPMN is a widely used standard to create readily understandable processes models. We show an approach to integrate both these standards via an automated transformation mechanism. This will allow us to use the various tools available for BPMN to visualize and automate processes in the healthcare domain. In the future we plan to extend this approach to enable mining and analyzing executed processes.

Open Infrastructure for Standardization of HL7® FHIR® Implementation Guides in Austria.

BACKGROUND: HL7 Austria is a non-profit association dedicated to improving electronic data communication and interoperability in healthcare using the HL7 international standards. OBJECTIVES: We aim to provide an open infrastructure to develop, manage, and maintain HL7 FHIR implementation guides. METHODS: We utilize state-of-the-art open-source tooling developed by the FHIR community to support continuous integration. RESULTS: The implementation guides can be published as static HTML websites and maintained using GitHub. CONCLUSION: The solution supports all steps of a standard's lifecycle, from drafting and reviewing to balloting, publishing, and maintenance.

Process mining for healthcare: Characteristics and challenges.

Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.

Secure Peer Review and Feedback on Medical Findings via IHE XDS.

BACKGROUND: There is a lack of secure official communication channels for peer review and peer feedback on medical findings. OBJECTIVES: We aimed to utilize the existing Austrian eHealth infrastructure to enable review and feedback processes. METHODS: We extended the IHE XDW workflow document to enable the exchange of text messages (i.e., comments on documents or images) over an XDS infrastructure. RESULTS: The workflow enables the exchange of comments on specific sections of CDA documents or radiological images and was verified in an XDS test environment. CONCLUSION: The presented solution is a proof of concept and the potential basis for the specification of a new IHE workflow definition.

Reply to "On Clinical Utility and Systematic Reporting in Case Studies of Healthcare Process Mining. Comment on: 10.3390/ijerph17041348 'Towards the Use of Standardised Terms in Clinical Case Studies for Process Mining in Healthcare' ".

Many thanks to Dr. Mordaunt for his thoughtful Comment, which we were delighted to read with great interest[...].

Towards Process Mining in Radiology: Utilization of IHE SOLE.

BACKGROUND: Audit trails of health information systems are not useful for business analytics. OBJECTIVES: To show how recent developments can be utilized to enable process mining in radiology. METHODS: The SWIM lexicon is used to code the workflow steps. RESULTS: Seven activities with their corresponding RadLex codes. CONCLUSION: The semantic enrichment of audit trails is an important step in operationalizing and improving the workflows in radiology.

Towards the Use of Standardized Terms in Clinical Case Studies for Process Mining in Healthcare.

Process mining can provide greater insight into medical treatment processes and organizational processes in healthcare. To enhance comparability between processes, the quality of the labelled-data is essential. A literature review of the clinical case studies by Rojas et al. in 2016 identified several common aspects for comparison, which include methodologies, algorithms or techniques, medical fields, and healthcare specialty. However, clinical aspects are not reported in a uniform way and do not follow a standard clinical coding scheme. Further, technical aspects such as details of the event log data are not always described. In this paper, we identified 38 clinically-relevant case studies of process mining in healthcare published from 2016 to 2018 that described the tools, algorithms and techniques utilized, and details on the event log data. We then correlated the clinical aspects of patient encounter environment, clinical specialty and medical diagnoses using the standard clinical coding schemes SNOMED CT and ICD-10. The potential outcomes of adopting a standard approach for describing event log data and classifying medical terminology using standard clinical coding schemes are further discussed. A checklist template for the reporting of case studies is provided in the Appendix A to the article.

Automated Verification of Structured Questionnaires Using HL7.

Informed consent of patients to research studies is a cornerstone to modern healthcare, which has lead to considerable administrative effort. The purpose of this paper is to show how forms and questionnaires and their respective answers can be captured in a standardized, structured way, in order to enable automated verification. The use of the HL7 FHIR resources Questionnaire and QuestionnaireResponse is discussed with respect to the different implementation options of Extensions, POST Interceptors, FHIR Operations, and CDS Hooks. These four approaches are described and it is determined whether they produce standard-compliant results and how they can be integrated with other solutions. Since all approaches yield advantages and disadvantages, the choice amongst any option must be based on the actual use case.

Process Mining and Conformance Checking of Long Running Processes in the Context of Melanoma Surveillance.

BACKGROUND: Process mining is a relatively new discipline that helps to discover and analyze actual process executions based on log data. In this paper we apply conformance checking techniques to the process of surveillance of melanoma patients. This process consists of recurring events with time constraints between the events. OBJECTIVES: The goal of this work is to show how existing clinical data collected during melanoma surveillance can be prepared and pre-processed to be reused for process mining. METHODS: We describe an approach based on time boxing to create process models from medical guidelines and the corresponding event logs from clinical data of patient visits. RESULTS: Event logs were extracted for 1023 patients starting melanoma surveillance at the Department of Dermatology at the Medical University of Vienna between January 2010 and June 2017. Conformance checking techniques available in the ProM framework and explorative applied process mining techniques were applied. CONCLUSIONS: The presented time boxing enables the direct use of existing process mining frameworks like ProM to perform process-oriented analysis also with respect to time constraints between events.

Cross-Enterprise Communication and Data Exchange in Radiology in Austria: Technology and Use Cases.

BACKGROUND: The research project REPO (Radiology Ehealth PlatfOrm) was started in 2017 with the goal to "enable cross-enterprise collaboration in radiology using the Austrian eHealth infrastructure". OBJECTIVES: The objective of this paper was to provide an overview of the radiology IT environment - actors, use cases and technology. METHODS: We conducted semi-structured expert interviews with radiologists and hospital operators and we statistically analyzed the client database of our research project partner. RESULTS: Interviews led to a list of use cases where cross-enterprise collaboration in radiology takes place and the data analysis provided insights on the systems, networks and standards in place. CONCLUSION: The Austrian IT infrastructure in radiology is a heterogeneous naturally grown environment. Future developments should be based on internationally accorded standards and on integration profiles provided by Integrating the Healthcare Enterprise (IHE).

Prefetching of medical imaging data across XDS affinity domains.

Prior studies as well as medical imaging data are crucial for a radiologist to diagnose a patient. In this paper the radiological workflow is analyzed from a patient's perspective in order to gain knowledge on how possible existing prefetching strategies still can be applied in connection with a standardized distributed health information system conforming to architectures defined by IHE and ELGA. As a result an adaption to such architectures is proposed and further evaluated in a testing environment. Although the approach presented works in terms of prefetching relevant prior studies together with medical imaging data, additional research has to be carried out on how to apply intelligent search strategies in order to narrow retrieved results concerning their possible utilization for a specific diagnosis.

From IHE Audit Trails to XES Event Logs Facilitating Process Mining.

Recently Business Intelligence approaches like process mining are applied to the healthcare domain. The goal of process mining is to gain process knowledge, compliance and room for improvement by investigating recorded event data. Previous approaches focused on process discovery by event data from various specific systems. IHE, as a globally recognized basis for healthcare information systems, defines in its ATNA profile how real-world events must be recorded in centralized event logs. The following approach presents how audit trails collected by the means of ATNA can be transformed to enable process mining. Using the standardized audit trails provides the ability to apply these methods to all IHE based information systems.

Defining and reconstructing clinical processes based on IHE and BPMN 2.0.

This paper describes the current status and the results of our process management system for defining and reconstructing clinical care processes, which contributes to compare, analyze and evaluate clinical processes and further to identify high cost tasks or stays. The system is founded on IHE, which guarantees standardized interfaces and interoperability between clinical information systems. At the heart of the system there is BPMN, a modeling notation and specification language, which allows the definition and execution of clinical processes. The system provides functionality to define healthcare information system independent clinical core processes and to execute the processes in a workflow engine. Furthermore, the reconstruction of clinical processes is done by evaluating an IHE audit log database, which records patient movements within a health care facility. The main goal of the system is to assist hospital operators and clinical process managers to detect discrepancies between defined and actual clinical processes and as well to identify main causes of high medical costs. Beyond that, the system can potentially contribute to reconstruct and improve clinical processes and enhance cost control and patient care quality.