Comparative study of healthcare messaging standards for interoperability in ehealth systems

Advances in the information and communication technology have created the field of "health informatics," which amalgamates healthcare, information technology and business. The use of information systems in healthcare organisations dates back to 1960s, however the use of technology for healthcare records, referred to as Electronic Medical Records (EMR), management has surged since 1990’s (Net-Health, 2017) due to advancements the internet and web technologies. Electronic Medical Records (EMR) and sometimes referred to as Personal Health Record (PHR) contains the patient’s medical history, allergy information, immunisation status, medication, radiology images and other medically related billing information that is relevant. There are a number of benefits for healthcare industry when sharing these data recorded in EMR and PHR systems between medical institutions (AbuKhousa et al., 2012). These benefits include convenience for patients and clinicians, cost-effective healthcare solutions, high quality of care, resolving the resource shortage and collecting a large volume of data for research and educational needs. My Health Record (MyHR) is a major project funded by the Australian government, which aims to have all data relating to health of the Australian population stored in digital format, allowing clinicians to have access to patient data at the point of care. Prior to 2015, MyHR was known as Personally Controlled Electronic Health Record (PCEHR). Though the Australian government took consistent initiatives there is a significant delay (Pearce and Haikerwal, 2010) in implementing eHealth projects and related services. While this delay is caused by many factors, interoperability is identified as the main problem (Benson and Grieve, 2016c) which is resisting this project delivery. To discover the current interoperability challenges in the Australian healthcare industry, this comparative study is conducted on Health Level 7 (HL7) messaging models such as HL7 V2, V3 and FHIR (Fast Healthcare Interoperability Resources). In this study, interoperability, security and privacy are main elements compared. In addition, a case study conducted in the NSW Hospitals to understand the popularity in usage of health messaging standards was utilised to understand the extent of use of messaging standards in healthcare sector. Predominantly, the project used the comparative study method on different HL7 (Health Level Seven) messages and derived the right messaging standard which is suitable to cover the interoperability, security and privacy requirements of electronic health record. The issues related to practical implementations, change over and training requirements for healthcare professionals are also discussed

Semantic Ontologies for Complex Healthcare Structures: A Scoping Review

The healthcare environment is made up of highly complicated interactions between many technologies, activities, and people. Ensuring a solid communication between them is vital to ease the healthcare management. Semantic ontologies are knowledge representation tools that implement abstractions to fully describe a given topic in terms of subjects and relations. This scoping review aims to identify and analyse available ontologies which can depict all the available use-cases that describe the hospital environment in relation to the European project ODIN and its future expansion. The review has been conducted on the Scopus database on January 13th, 2023 using the PRISMA extensions for scoping reviews. Two reviewers screened 3,225 documents emerged from the database search. Further filtering led to a final set of 32 articles to be analysed for the results. A set of 34 ontologies extracted by the identified articles has been analysed and discussed as well. The results of this study will lead to the implementation of a common integrated ontology which could hold information about healthcare entities as well as their semantic relationships, strengthen data exchange and interconnections among people, devices and applications in an expanded scenario which include Internet of Things, robots and Artificial Intelligence

Cohort Identification Using Semantic Web Technologies: Ontologies and Triplestores as Engines for Complex Computable Phenotyping

Electronic health record (EHR)-based computable phenotypes are algorithms used to identify individuals or populations with clinical conditions or events of interest within a clinical data repository. Due to a lack of EHR data standardization, computable phenotypes can be semantically ambiguous and difficult to share across institutions. In this research, I propose a new computable phenotyping methodological framework based on semantic web technologies, specifically ontologies, the Resource Description Framework (RDF) data format, triplestores, and Web Ontology Language (OWL) reasoning. My hypothesis is that storing and analyzing clinical data using these technologies can begin to address the critical issues of semantic ambiguity and lack of interoperability in the context of computable phenotyping. To test this hypothesis, I compared the performance of two variants of two computable phenotypes (for depression and rheumatoid arthritis, respectively). The first variant of each phenotype used a list of ICD-10-CM codes to define the condition; the second variant used ontology concepts from SNOMED and the Human Phenotype Ontology (HPO). After executing each variant of each phenotype against a clinical data repository, I compared the patients matched in each case to see where the different variants overlapped and diverged. Both the ontologies and the clinical data were stored in an RDF triplestore to allow me to assess the interoperability advantages of the RDF format for clinical data. All tested methods successfully identified cohorts in the data store, with differing rates of overlap and divergence between variants. Depending on the phenotyping use case, SNOMED and HPO’s ability to more broadly define many conditions due to complex relationships between their concepts may be seen as an advantage or a disadvantage. I also found that RDF triplestores do indeed provide interoperability advantages, despite being far less commonly used in clinical data applications than relational databases. Despite the fact that these methods and technologies are not “one-size-fits-all,” the experimental results are encouraging enough for them to (1) be put into practice in combination with existing phenotyping methods or (2) be used on their own for particularly well-suited use cases.Doctor of Philosoph

Microservice chatbot architecture for chronic patient support

Chatbots are able to provide support to patients suffering from very different conditions. Patients with chronic diseases or comorbidities could benefit the most from chatbots which can keep track of their condition, provide specific information, encourage adherence to medication, etc. To perform these functions, chatbots need a suitable underlying software architecture. In this paper, we introduce a chatbot architecture for chronic patient support grounded on three pillars: scalability by means of microservices, standard data sharing models through HL7 FHIR and standard conversation modeling using AIML. We also propose an innovative automation mechanism to convert FHIR resources into AIML files, thus facilitating the interaction and data gathering of medical and personal information that ends up in patient health records. To align the way people interact with each other using messaging platforms with the chatbot architecture, we propose these very same channels for the chatbot-patient interaction, paying special attention to security and privacy issues. Finally, we present a monitored-data study performed in different chronic diseases, and we present a prototype implementation tailored for one specific chronic disease, psoriasis, showing how this new architecture allows the change, the addition or the improvement of different parts of the chatbot in a dynamic and flexible way, providing a substantial improvement in the development of chatbots used as virtual assistants for chronic patients

Using ontologies to improve semantic interoperability in health data

The present–day health data ecosystem comprises a wide array of complex heterogeneous data sources. A wide range of clinical, health care, social and other clinically relevant information are stored in these data sources. These data exist either as structured data or as free-text. These data are generally individual personbased records, but social care data are generally case based and less formal data sources may be shared by groups. The structured data may be organised in a proprietary way or be coded using one-of-many coding, classification or terminologies that have often evolved in isolation and designed to meet the needs of the context that they have been developed. This has resulted in a wide range of semantic interoperability issues that make the integration of data held on these different systems changing. We present semantic interoperability challenges and describe a classification of these. We propose a four-step process and a toolkit for those wishing to work more ontologically, progressing from the identification and specification of concepts to validating a final ontology. The four steps are: (1) the identification and specification of data sources; (2) the conceptualisation of semantic meaning; (3) defining to what extent routine data can be used as a measure of the process or outcome of care required in a particular study or audit and (4) the formalisation and validation of the final ontology. The toolkit is an extension of a previous schema created to formalise the development of ontologies related to chronic disease management. The extensions are focused on facilitating rapid building of ontologies for time-critical research studies.

Automated Injection of Curated Knowledge Into Real-Time Clinical Systems: CDS Architecture for the 21st Century

abstract: Clinical Decision Support (CDS) is primarily associated with alerts, reminders, order entry, rule-based invocation, diagnostic aids, and on-demand information retrieval. While valuable, these foci have been in production use for decades, and do not provide a broader, interoperable means of plugging structured clinical knowledge into live electronic health record (EHR) ecosystems for purposes of orchestrating the user experiences of patients and clinicians. To date, the gap between knowledge representation and user-facing EHR integration has been considered an “implementation concern” requiring unscalable manual human efforts and governance coordination. Drafting a questionnaire engineered to meet the specifications of the HL7 CDS Knowledge Artifact specification, for example, carries no reasonable expectation that it may be imported and deployed into a live system without significant burdens. Dramatic reduction of the time and effort gap in the research and application cycle could be revolutionary. Doing so, however, requires both a floor-to-ceiling precoordination of functional boundaries in the knowledge management lifecycle, as well as formalization of the human processes by which this occurs. This research introduces ARTAKA: Architecture for Real-Time Application of Knowledge Artifacts, as a concrete floor-to-ceiling technological blueprint for both provider heath IT (HIT) and vendor organizations to incrementally introduce value into existing systems dynamically. This is made possible by service-ization of curated knowledge artifacts, then injected into a highly scalable backend infrastructure by automated orchestration through public marketplaces. Supplementary examples of client app integration are also provided. Compilation of knowledge into platform-specific form has been left flexible, in so far as implementations comply with ARTAKA’s Context Event Service (CES) communication and Health Services Platform (HSP) Marketplace service packaging standards. Towards the goal of interoperable human processes, ARTAKA’s treatment of knowledge artifacts as a specialized form of software allows knowledge engineers to operate as a type of software engineering practice. Thus, nearly a century of software development processes, tools, policies, and lessons offer immediate benefit: in some cases, with remarkable parity. Analyses of experimentation is provided with guidelines in how choice aspects of software development life cycles (SDLCs) apply to knowledge artifact development in an ARTAKA environment. Portions of this culminating document have been further initiated with Standards Developing Organizations (SDOs) intended to ultimately produce normative standards, as have active relationships with other bodies.Dissertation/ThesisDoctoral Dissertation Biomedical Informatics 201

A Two-Level Identity Model To Support Interoperability of Identity Information in Electronic Health Record Systems.

The sharing and retrieval of health information for an electronic health record (EHR) across distributed systems involves a range of identified entities that are possible subjects of documentation (e.g., specimen, clinical analyser). Contemporary EHR specifications limit the types of entities that can be the subject of a record to health professionals and patients, thus limiting the use of two level models in healthcare information systems that contribute information to the EHR. The literature describes several information modelling approaches for EHRs, including so called “two level models”. These models differ in the amount of structure imposed on the information to be recorded, but they generally require the health documentation process for the EHR to focus exclusively on the patient as the subject of care and this definition is often a fixed one. In this thesis, the author introduces a new identity modelling approach to create a generalised reference model for sharing archetype-constrained identity information between diverse identity domains, models and services, while permitting reuse of published standard-based archetypes. The author evaluates its use for expressing the major types of existing demographic reference models in an extensible way, and show its application for standards-compliant two-level modelling alongside heterogeneous demographics models. This thesis demonstrates how the two-level modelling approach that is used for EHRs could be adapted and reapplied to provide a highly-flexible and expressive means for representing subjects of information in allied health settings that support the healthcare process, such as the laboratory domain. By relying on the two level modelling approach for representing identity, the proposed design facilitates cross-referencing and disambiguation of certain demographics standards and information models. The work also demonstrates how it can also be used to represent additional clinical identified entities such as specimen and order as subjects of clinical documentation

A Learning Health System for Radiation Oncology

The proposed research aims to address the challenges faced by clinical data science researchers in radiation oncology accessing, integrating, and analyzing heterogeneous data from various sources. The research presents a scalable intelligent infrastructure, called the Health Information Gateway and Exchange (HINGE), which captures and structures data from multiple sources into a knowledge base with semantically interlinked entities. This infrastructure enables researchers to mine novel associations and gather relevant knowledge for personalized clinical outcomes. The dissertation discusses the design framework and implementation of HINGE, which abstracts structured data from treatment planning systems, treatment management systems, and electronic health records. It utilizes disease-specific smart templates for capturing clinical information in a discrete manner. HINGE performs data extraction, aggregation, and quality and outcome assessment functions automatically, connecting seamlessly with local IT/medical infrastructure. Furthermore, the research presents a knowledge graph-based approach to map radiotherapy data to an ontology-based data repository using FAIR (Findable, Accessible, Interoperable, Reusable) concepts. This approach ensures that the data is easily discoverable and accessible for clinical decision support systems. The dissertation explores the ETL (Extract, Transform, Load) process, data model frameworks, ontologies, and provides a real-world clinical use case for this data mapping. To improve the efficiency of retrieving information from large clinical datasets, a search engine based on ontology-based keyword searching and synonym-based term matching tool was developed. The hierarchical nature of ontologies is leveraged to retrieve patient records based on parent and children classes. Additionally, patient similarity analysis is conducted using vector embedding models (Word2Vec, Doc2Vec, GloVe, and FastText) to identify similar patients based on text corpus creation methods. Results from the analysis using these models are presented. The implementation of a learning health system for predicting radiation pneumonitis following stereotactic body radiotherapy is also discussed. 3D convolutional neural networks (CNNs) are utilized with radiographic and dosimetric datasets to predict the likelihood of radiation pneumonitis. DenseNet-121 and ResNet-50 models are employed for this study, along with integrated gradient techniques to identify salient regions within the input 3D image dataset. The predictive performance of the 3D CNN models is evaluated based on clinical outcomes. Overall, the proposed Learning Health System provides a comprehensive solution for capturing, integrating, and analyzing heterogeneous data in a knowledge base. It offers researchers the ability to extract valuable insights and associations from diverse sources, ultimately leading to improved clinical outcomes. This work can serve as a model for implementing LHS in other medical specialties, advancing personalized and data-driven medicine

What Role Can Process Mining Play in Recurrent Clinical Guidelines Issues? A Position Paper

[EN] In the age of Evidence-Based Medicine, Clinical Guidelines (CGs) are recognized to be an indispensable tool to support physicians in their daily clinical practice. Medical Informatics is expected to play a relevant role in facilitating diffusion and adoption of CGs. However, the past pioneering approaches, often fragmented in many disciplines, did not lead to solutions that are actually exploited in hospitals. Process Mining for Healthcare (PM4HC) is an emerging discipline gaining the interest of healthcare experts, and seems able to deal with many important issues in representing CGs. In this position paper, we briefly describe the story and the state-of-the-art of CGs, and the efforts and results of the past approaches of medical informatics. Then, we describe PM4HC, and we answer questions like how can PM4HC cope with this challenge? Which role does PM4HC play and which rules should be employed for the PM4HC scientific community?Gatta, R.; Vallati, M.; Fernández Llatas, C.; Martinez-Millana, A.; Orini, S.; Sacchi, L.; Lenkowicz, J.... (2020). What Role Can Process Mining Play in Recurrent Clinical Guidelines Issues? A Position Paper. International Journal of Environmental research and Public Health (Online). 17(18):1-19. https://doi.org/10.3390/ijerph17186616S1191718Guyatt, G. (1992). Evidence-Based Medicine. JAMA, 268(17), 2420. doi:10.1001/jama.1992.03490170092032Hripcsak, G., Ludemann, P., Pryor, T. A., Wigertz, O. B., & Clayton, P. D. (1994). Rationale for the Arden Syntax. Computers and Biomedical Research, 27(4), 291-324. doi:10.1006/cbmr.1994.1023Peleg, M. (2013). Computer-interpretable clinical guidelines: A methodological review. Journal of Biomedical Informatics, 46(4), 744-763. doi:10.1016/j.jbi.2013.06.009Van de Velde, S., Heselmans, A., Delvaux, N., Brandt, L., Marco-Ruiz, L., Spitaels, D., … Flottorp, S. (2018). A systematic review of trials evaluating success factors of interventions with computerised clinical decision support. Implementation Science, 13(1). doi:10.1186/s13012-018-0790-1Rawson, T. M., Moore, L. S. P., Hernandez, B., Charani, E., Castro-Sanchez, E., Herrero, P., … Holmes, A. H. (2017). A systematic review of clinical decision support systems for antimicrobial management: are we failing to investigate these interventions appropriately? Clinical Microbiology and Infection, 23(8), 524-532. doi:10.1016/j.cmi.2017.02.028Greenes, R. A., Bates, D. W., Kawamoto, K., Middleton, B., Osheroff, J., & Shahar, Y. (2018). Clinical decision support models and frameworks: Seeking to address research issues underlying implementation successes and failures. Journal of Biomedical Informatics, 78, 134-143. doi:10.1016/j.jbi.2017.12.005Garcia, C. dos S., Meincheim, A., Faria Junior, E. R., Dallagassa, M. R., Sato, D. M. V., Carvalho, D. R., … Scalabrin, E. E. (2019). Process mining techniques and applications – A systematic mapping study. Expert Systems with Applications, 133, 260-295. doi:10.1016/j.eswa.2019.05.003Bhargava, A., Kim, T., Quine, D. B., & Hauser, R. G. (2019). A 20-Year Evaluation of LOINC in the United States’ Largest Integrated Health System. Archives of Pathology & Laboratory Medicine, 144(4), 478-484. doi:10.5858/arpa.2019-0055-oaLee, D., de Keizer, N., Lau, F., & Cornet, R. (2014). Literature review of SNOMED CT use. Journal of the American Medical Informatics Association, 21(e1), e11-e19. doi:10.1136/amiajnl-2013-001636TROTTI, A., COLEVAS, A., SETSER, A., RUSCH, V., JAQUES, D., BUDACH, V., … COLEMAN, C. (2003). CTCAE v3.0: development of a comprehensive grading system for the adverse effects of cancer treatment. Seminars in Radiation Oncology, 13(3), 176-181. doi:10.1016/s1053-4296(03)00031-6Daniel, C., & Kalra, D. (2019). Clinical Research Informatics: Contributions from 2018. Yearbook of Medical Informatics, 28(01), 203-205. doi:10.1055/s-0039-1677921Marco-Ruiz, L., Pedrinaci, C., Maldonado, J. A., Panziera, L., Chen, R., & Bellika, J. G. (2016). Publication, discovery and interoperability of Clinical Decision Support Systems: A Linked Data approach. Journal of Biomedical Informatics, 62, 243-264. doi:10.1016/j.jbi.2016.07.011Marcos, C., González-Ferrer, A., Peleg, M., & Cavero, C. (2015). Solving the interoperability challenge of a distributed complex patient guidance system: a data integrator based on HL7’s Virtual Medical Record standard. Journal of the American Medical Informatics Association, 22(3), 587-599. doi:10.1093/jamia/ocv003Wulff, A., Haarbrandt, B., Tute, E., Marschollek, M., Beerbaum, P., & Jack, T. (2018). An interoperable clinical decision-support system for early detection of SIRS in pediatric intensive care using openEHR. Artificial Intelligence in Medicine, 89, 10-23. doi:10.1016/j.artmed.2018.04.012Chen, C., Chen, K., Hsu, C.-Y., Chiu, W.-T., & Li, Y.-C. (Jack). (2010). A guideline-based decision support for pharmacological treatment can improve the quality of hyperlipidemia management. Computer Methods and Programs in Biomedicine, 97(3), 280-285. doi:10.1016/j.cmpb.2009.12.004Anani, N., Mazya, M. V., Chen, R., Prazeres Moreira, T., Bill, O., Ahmed, N., … Koch, S. (2017). Applying openEHR’s Guideline Definition Language to the SITS international stroke treatment registry: a European retrospective observational study. BMC Medical Informatics and Decision Making, 17(1). doi:10.1186/s12911-016-0401-5Eddy, D. M. (1982). Clinical Policies and the Quality of Clinical Practice. New England Journal of Medicine, 307(6), 343-347. doi:10.1056/nejm198208053070604Guyatt, G. H. (1990). Then-of-1 Randomized Controlled Trial: Clinical Usefulness. Annals of Internal Medicine, 112(4), 293. doi:10.7326/0003-4819-112-4-293CHALMERS, I. (1993). The Cochrane Collaboration: Preparing, Maintaining, and Disseminating Systematic Reviews of the Effects of Health Care. Annals of the New York Academy of Sciences, 703(1 Doing More Go), 156-165. doi:10.1111/j.1749-6632.1993.tb26345.xWoolf, S. H., Grol, R., Hutchinson, A., Eccles, M., & Grimshaw, J. (1999). Clinical guidelines: Potential benefits, limitations, and harms of clinical guidelines. BMJ, 318(7182), 527-530. doi:10.1136/bmj.318.7182.527Grading quality of evidence and strength of recommendations. (2004). BMJ, 328(7454), 1490. doi:10.1136/bmj.328.7454.1490Guyatt, G. H., Oxman, A. D., Vist, G. E., Kunz, R., Falck-Ytter, Y., Alonso-Coello, P., & Schünemann, H. J. (2008). GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ, 336(7650), 924-926. doi:10.1136/bmj.39489.470347.adHill, J., Bullock, I., & Alderson, P. (2011). A Summary of the Methods That the National Clinical Guideline Centre Uses to Produce Clinical Guidelines for the National Institute for Health and Clinical Excellence. Annals of Internal Medicine, 154(11), 752. doi:10.7326/0003-4819-154-11-201106070-00007Qaseem, A. (2012). Guidelines International Network: Toward International Standards for Clinical Practice Guidelines. Annals of Internal Medicine, 156(7), 525. doi:10.7326/0003-4819-156-7-201204030-00009Rosenfeld, R. M., Nnacheta, L. C., & Corrigan, M. D. (2015). Clinical Consensus Statement Development Manual. Otolaryngology–Head and Neck Surgery, 153(2_suppl), S1-S14. doi:10.1177/0194599815601394De Boeck, K., Castellani, C., & Elborn, J. S. (2014). Medical consensus, guidelines, and position papers: A policy for the ECFS. Journal of Cystic Fibrosis, 13(5), 495-498. doi:10.1016/j.jcf.2014.06.012Clinical Practical Guidelineshttp://www.openclinical.org/guidelines.htmlHaynes, A. B., Weiser, T. G., Berry, W. R., Lipsitz, S. R., Breizat, A.-H. S., Dellinger, E. P., … Gawande, A. A. (2009). A Surgical Safety Checklist to Reduce Morbidity and Mortality in a Global Population. New England Journal of Medicine, 360(5), 491-499. doi:10.1056/nejmsa0810119Grigg, E. (2015). Smarter Clinical Checklists. Anesthesia & Analgesia, 121(2), 570-573. doi:10.1213/ane.0000000000000352Hales, B., Terblanche, M., Fowler, R., & Sibbald, W. (2007). Development of medical checklists for improved quality of patient care. International Journal for Quality in Health Care, 20(1), 22-30. doi:10.1093/intqhc/mzm062Greenfield, S. (2017). Clinical Practice Guidelines. JAMA, 317(6), 594. doi:10.1001/jama.2016.19969Vegting, I. L., van Beneden, M., Kramer, M. H. H., Thijs, A., Kostense, P. J., & Nanayakkara, P. W. B. (2012). How to save costs by reducing unnecessary testing: Lean thinking in clinical practice. European Journal of Internal Medicine, 23(1), 70-75. doi:10.1016/j.ejim.2011.07.003Drummond, M. (2016). Clinical Guidelines: A NICE Way to Introduce Cost-Effectiveness Considerations? Value in Health, 19(5), 525-530. doi:10.1016/j.jval.2016.04.020Prior, M., Guerin, M., & Grimmer-Somers, K. (2008). The effectiveness of clinical guideline implementation strategies - a synthesis of systematic review findings. Journal of Evaluation in Clinical Practice, 14(5), 888-897. doi:10.1111/j.1365-2753.2008.01014.xWatts, C. G., Dieng, M., Morton, R. L., Mann, G. J., Menzies, S. W., & Cust, A. E. (2014). Clinical practice guidelines for identification, screening and follow-up of individuals at high risk of primary cutaneous melanoma: a systematic review. British Journal of Dermatology, 172(1), 33-47. doi:10.1111/bjd.13403Woolf, S., Schünemann, H. J., Eccles, M. P., Grimshaw, J. M., & Shekelle, P. (2012). Developing clinical practice guidelines: types of evidence and outcomes; values and economics, synthesis, grading, and presentation and deriving recommendations. Implementation Science, 7(1). doi:10.1186/1748-5908-7-61Legido-Quigley, H., Panteli, D., Brusamento, S., Knai, C., Saliba, V., Turk, E., … Busse, R. (2012). Clinical guidelines in the European Union: Mapping the regulatory basis, development, quality control, implementation and evaluation across member states. Health Policy, 107(2-3), 146-156. doi:10.1016/j.healthpol.2012.08.004Rashidian, A., Eccles, M. P., & Russell, I. (2008). Falling on stony ground? A qualitative study of implementation of clinical guidelines’ prescribing recommendations in primary care. Health Policy, 85(2), 148-161. doi:10.1016/j.healthpol.2007.07.011Yang, W.-S., & Hwang, S.-Y. (2006). A process-mining framework for the detection of healthcare fraud and abuse. Expert Systems with Applications, 31(1), 56-68. doi:10.1016/j.eswa.2005.09.003Kose, I., Gokturk, M., & Kilic, K. (2015). An interactive machine-learning-based electronic fraud and abuse detection system in healthcare insurance. Applied Soft Computing, 36, 283-299. doi:10.1016/j.asoc.2015.07.018Pryor, T. A., Gardner, R. M., Clayton, P. D., & Warner, H. R. (1983). The HELP system. Journal of Medical Systems, 7(2), 87-102. doi:10.1007/bf00995116Shahar, Y., Miksch, S., & Johnson, P. (1998). The Asgaard project: a task-specific framework for the application and critiquing of time-oriented clinical guidelines. Artificial Intelligence in Medicine, 14(1-2), 29-51. doi:10.1016/s0933-3657(98)00015-3Boxwala, A. A., Peleg, M., Tu, S., Ogunyemi, O., Zeng, Q. T., Wang, D., … Shortliffe, E. H. (2004). GLIF3: a representation format for sharable computer-interpretable clinical practice guidelines. Journal of Biomedical Informatics, 37(3), 147-161. doi:10.1016/j.jbi.2004.04.002Terenziani, P., Molino, G., & Torchio, M. (2001). A modular approach for representing and executing clinical guidelines. Artificial Intelligence in Medicine, 23(3), 249-276. doi:10.1016/s0933-3657(01)00087-2Sutton, D. R., & Fox, J. (2003). The Syntax and Semantics of the PROformaGuideline Modeling Language. Journal of the American Medical Informatics Association, 10(5), 433-443. doi:10.1197/jamia.m1264Musen, M. A., Tu, S. W., Das, A. K., & Shahar, Y. (1996). EON: A Component-Based Approach to Automation of Protocol-Directed Therapy. Journal of the American Medical Informatics Association, 3(6), 367-388. doi:10.1136/jamia.1996.97084511Ciccarese, P., Caffi, E., Quaglini, S., & Stefanelli, M. (2005). Architectures and tools for innovative Health Information Systems: The Guide Project. International Journal of Medical Informatics, 74(7-8), 553-562. doi:10.1016/j.ijmedinf.2005.02.001Shiffman, R. N., & Greenes, R. A. (1994). Improving Clinical Guidelines with Logic and Decision-table Techniques. Medical Decision Making, 14(3), 245-254. doi:10.1177/0272989x9401400306Peleg, M., Tu, S., Bury, J., Ciccarese, P., Fox, J., Greenes, R. A., … Stefanelli, M. (2003). Comparing Computer-interpretable Guideline Models: A Case-study Approach. Journal of the American Medical Informatics Association, 10(1), 52-68. doi:10.1197/jamia.m1135Karadimas, H., Ebrahiminia, V., & Lepage, E. (2018). User-defined functions in the Arden Syntax: An extension proposal. Artificial Intelligence in Medicine, 92, 103-110. doi:10.1016/j.artmed.2015.11.003Peleg, M., Keren, S., & Denekamp, Y. (2008). Mapping computerized clinical guidelines to electronic medical records: Knowledge-data ontological mapper (KDOM). Journal of Biomedical Informatics, 41(1), 180-201. doi:10.1016/j.jbi.2007.05.003German, E., Leibowitz, A., & Shahar, Y. (2009). An architecture for linking medical decision-support applications to clinical databases and its evaluation. Journal of Biomedical Informatics, 42(2), 203-218. doi:10.1016/j.jbi.2008.10.007Marcos, M., Maldonado, J. A., Martínez-Salvador, B., Boscá, D., & Robles, M. (2013). Interoperability of clinical decision-support systems and electronic health records using archetypes: A case study in clinical trial eligibility. Journal of Biomedical Informatics, 46(4), 676-689. doi:10.1016/j.jbi.2013.05.004Marco-Ruiz, L., Moner, D., Maldonado, J. A., Kolstrup, N., & Bellika, J. G. (2015). Archetype-based data warehouse environment to enable the reuse of electronic health record data. International Journal of Medical Informatics, 84(9), 702-714. doi:10.1016/j.ijmedinf.2015.05.016Gooch, P., & Roudsari, A. (2011). Computerization of workflows, guidelines, and care pathways: a review of implementation challenges for process-oriented health information systems. Journal of the American Medical Informatics Association, 18(6), 738-748. doi:10.1136/amiajnl-2010-000033Quaglini, S., Stefanelli, M., Cavallini, A., Micieli, G., Fassino, C., & Mossa, C. (2000). Guideline-based careflow systems. Artificial Intelligence in Medicine, 20(1), 5-22. doi:10.1016/s0933-3657(00)00050-6Schadow, G., Russler, D. C., & McDonald, C. J. (2001). Conceptual alignment of electronic health record data with guideline and workflow knowledge. International Journal of Medical Informatics, 64(2-3), 259-274. doi:10.1016/s1386-5056(01)00196-4González-Ferrer, A., ten Teije, A., Fdez-Olivares, J., & Milian, K. (2013). Automated generation of patient-tailored electronic care pathways by translating computer-interpretable guidelines into hierarchical task networks. Artificial Intelligence in Medicine, 57(2), 91-109. doi:10.1016/j.artmed.2012.08.008Shabo, A., Parimbelli, E., Quaglini, S., Napolitano, C., & Peleg, M. (2016). Interplay between Clinical Guidelines and Organizational Workflow Systems. Methods of Information in Medicine, 55(06), 488-494. doi:10.3414/me16-01-0006Mulyar, N., van der Aalst, W. M. P., & Peleg, M. (2007). A Pattern-based Analysis of Clinical Computer-interpretable Guideline Modeling Languages. Journal of the American Medical Informatics Association, 14(6), 781-787. doi:10.1197/jamia.m2389Grando, M. A., Glasspool, D., & Fox, J. (2012). A formal approach to the analysis of clinical computer-interpretable guideline modeling languages. Artificial Intelligence in Medicine, 54(1), 1-13. doi:10.1016/j.artmed.2011.07.001Kaiser, K., & Marcos, M. (2015). Leveraging workflow control patterns in the domain of clinical practice guidelines. BMC Medical Informatics and Decision Making, 16(1). doi:10.1186/s12911-016-0253-zMartínez-Salvador, B., & Marcos, M. (2016). Supporting the Refinement of Clinical Process Models to Computer-Interpretable Guideline Models. Business & Information Systems Engineering, 58(5), 355-366. doi:10.1007/s12599-016-0443-3Decision Model and Notation Version 1.0https://www.omg.org/spec/DMN/1.0Ghasemi, M., & Amyot, D. (2016). Process mining in healthcare: a systematised literature review. International Journal of Electronic Healthcare, 9(1), 60. doi:10.1504/ijeh.2016.078745Rebuge, Á., & Ferreira, D. R. (2012). Business process analysis in healthcare environments: A methodology based on process mining. Information Systems, 37(2), 99-116. doi:10.1016/j.is.2011.01.003Rovani, M., Maggi, F. M., de Leoni, M., & van der Aalst, W. M. P. (2015). Declarative process mining in healthcare. Expert Systems with Applications, 42(23), 9236-9251. doi:10.1016/j.eswa.2015.07.040Rojas, E., Munoz-Gama, J., Sepúlveda, M., & Capurro, D. (2016). Process mining in healthcare: A literature review. Journal of Biomedical Informatics, 61, 224-236. doi:10.1016/j.jbi.2016.04.007Lenkowicz, J., Gatta, R., Masciocchi, C., Casà, C., Cellini, F., Damiani, A., … Valentini, V. (2018). Assessing the conformity to clinical guidelines in oncology. Management Decision, 56(10), 2172-2186. doi:10.1108/md-09-2017-0906Fernández-Llatas, C., Benedi, J.-M., García-Gómez, J., & Traver, V. (2013). Process Mining for Individualized Behavior Modeling Using Wireless Tracking in Nursing Homes. Sensors, 13(11), 15434-15451. doi:10.3390/s131115434Qu, G., Liu, Z., Cui, S., & Tang, J. (2013). Study on Self-Adaptive Clinical Pathway Decision Support System Based on Case-Based Reasoning. Frontier and Future Development of Information Technology in Medicine and Education, 969-978. doi:10.1007/978-94-007-7618-0_95Van de Velde, S., Roshanov, P., Kortteisto, T., Kunnamo, I., Aertgeerts, B., Vandvik, P. O., & Flottorp, S. (2015). Tailoring implementation strategies for evidence-based recommendations using computerised clinical decision support systems: protocol for the development of the GUIDES tools. Implementation Science, 11(1). doi:10.1186/s13012-016-0393-