Document Automation Architectures: Updated Survey in Light of Large Language Models

This paper surveys the current state of the art in document automation (DA). The objective of DA is to reduce the manual effort during the generation of documents by automatically creating and integrating input from different sources and assembling documents conforming to defined templates. There have been reviews of commercial solutions of DA, particularly in the legal domain, but to date there has been no comprehensive review of the academic research on DA architectures and technologies. The current survey of DA reviews the academic literature and provides a clearer definition and characterization of DA and its features, identifies state-of-the-art DA architectures and technologies in academic research, and provides ideas that can lead to new research opportunities within the DA field in light of recent advances in generative AI and large language models.Comment: The current paper is the updated version of an earlier survey on document automation [Ahmadi Achachlouei et al. 2021]. Updates in the current paper are as follows: We shortened almost all sections to reduce the size of the main paper (without references) from 28 pages to 10 pages, added a review of selected papers on large language models, removed certain sections and most of diagrams. arXiv admin note: substantial text overlap with arXiv:2109.1160

Development and implementation of clinical guidelines : an artificial intelligence perspective

Clinical practice guidelines in paper format are still the preferred form of delivery of medical knowledge and recommendations to healthcare professionals. Their current support and development process have well identified limitations to which the healthcare community has been continuously searching solutions. Artificial intelligence may create the conditions and provide the tools to address many, if not all, of these limitations.. This paper presents a comprehensive and up to date review of computer-interpretable guideline approaches, namely Arden Syntax, GLIF, PROforma, Asbru, GLARE and SAGE. It also provides an assessment of how well these approaches respond to the challenges posed by paper-based guidelines and addresses topics of Artificial intelligence that could provide a solution to the shortcomings of clinical guidelines. Among the topics addressed by this paper are expert systems, case-based reasoning, medical ontologies and reasoning under uncertainty, with a special focus on methodologies for assessing quality of information when managing incomplete information. Finally, an analysis is made of the fundamental requirements of a guideline model and the importance that standard terminologies and models for clinical data have in the semantic and syntactic interoperability between a guideline execution engine and the software tools used in clinical settings. It is also proposed a line of research that includes the development of an ontology for clinical practice guidelines and a decision model for a guideline-based expert system that manages non-compliance with clinical guidelines and uncertainty.This work is funded by national funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project PEst-OE/EEI/UI0752/2011"

Emerging Standards for Medical Logic

Conference PaperBiomedical Informatic

The Morningside Initiative: Collaborative Development of a Knowledge Repository to Accelerate Adoption of Clinical Decision Support

The Morningside Initiative is a public-private activity that has evolved from an August, 2007, meeting at the Morningside Inn, in Frederick, MD, sponsored by the Telemedicine and Advanced Technology Research Center (TATRC) of the US Army Medical Research Materiel Command. Participants were subject matter experts in clinical decision support (CDS) and included representatives from the Department of Defense, Veterans Health Administration, Kaiser Permanente, Partners Healthcare System, Henry Ford Health System, Arizona State University, and the American Medical Informatics Association (AMIA). The Morningside Initiative was convened in response to the AMIA Roadmap for National Action on Clinical Decision Support and on the basis of other considerations and experiences of the participants. Its formation was the unanimous recommendation of participants at the 2007 meeting which called for creating a shared repository of executable knowledge for diverse health care organizations and practices, as well as health care system vendors. The rationale is based on the recognition that sharing of clinical knowledge needed for CDS across organizations is currently virtually non-existent, and that, given the considerable investment needed for creating, maintaining and updating authoritative knowledge, which only larger organizations have been able to undertake, this is an impediment to widespread adoption and use of CDS. The Morningside Initiative intends to develop and refine (1) an organizational framework, (2) a technical approach, and (3) CDS content acquisition and management processes for sharing CDS knowledge content, tools, and experience that will scale with growing numbers of participants and can be expanded in scope of content and capabilities. Intermountain Healthcare joined the initial set of participants shortly after its formation. The efforts of the Morningside Initiative are intended to serve as the basis for a series of next steps in a national agenda for CDS. It is based on the belief that sharing of knowledge can be highly effective as is the case in other competitive domains such as genomics. Participants in the Morningside Initiative believe that a coordinated effort between the private and public sectors is needed to accomplish this goal and that a small number of highly visible and respected health care organizations in the public and private sector can lead by example. Ultimately, a future collaborative knowledge sharing organization must have a sustainable long-term business model for financial support

Guideline-based decision support in medicine : modeling guidelines for the development and application of clinical decision support systems

Guideline-based Decision Support in Medicine Modeling Guidelines for the Development and Application of Clinical Decision Support Systems The number and use of decision support systems that incorporate guidelines with the goal of improving care is rapidly increasing. Although developing systems that are both effective in supporting clinicians and accepted by them has proven to be a difficult task, of the systems that were evaluated by a controlled trial, the majority showed impact. The work, described in this thesis, aims at developing a methodology and framework that facilitates all stages in the guideline development process, ranging from the definition of models that represent guidelines to the implementation of run-time systems that provide decision support, based on the guidelines that were developed during the previous stages. The framework consists of 1) a guideline representation formalism that uses the concepts of primitives, Problem-Solving Methods (PSMs) and ontologies to represent guidelines of various complexity and granularity and different application domains, 2) a guideline authoring environment that enables guideline authors to define guidelines, based on the newly developed guideline representation formalism, and 3) a guideline execution environment that translates defined guidelines into a more efficient symbol-level representation, which can be read in and processed by an execution-time engine. The described methodology and framework were used to develop and validate a number of guidelines and decision support systems in various clinical domains such as Intensive Care, Family Practice, Psychiatry and the areas of Diabetes and Hypertension control

A comprehensive clinical guideline model and a reasoning mechanism for AAL systems

The progressive ageing of population combined with the need for comfort in situations of disease and disability are pushing healthcare organizations and governments to find new solutions to enable people to live longer in their preferred environment, while having access to quality healthcare services. iGenda is an Ambient Assisted Living platform that provides constant monitoring to people with this type of needs. The use of a Computer-Interpretable Guideline model for decision making is one of the features of this platform. The model used to represent Clinical Practice Guidelines gathers a set of features that make guidelines more dynamic and easily implementable. The model is defined using Ontology Web Language, profiting from the existing constructors provided by this language. It is based on a set of primitive tasks, namely Plans, Actions, Questions and Decisions. Focusing on decision support, a method for dealing with incomplete information about the clinical parameters of a health record is presented. The objective is to keep a continuous flow of information through the decision process and assuring that an outcome is always achieved. The usefulness of the integration of guideline recommendations with a reason mechanism capable of handling incomplete information is demonstrated through a case study about the diagnosis of metabolic syndrome.(undefined

Computerization of workflows, guidelines and care pathways: a review of implementation challenges for process-oriented health information systems

There is a need to integrate the various theoretical frameworks and formalisms for modeling clinical guidelines, workflows, and pathways, in order to move beyond providing support for individual clinical decisions and toward the provision of process-oriented, patient-centered, health information systems (HIS). In this review, we analyze the challenges in developing process-oriented HIS that formally model guidelines, workflows, and care pathways. A qualitative meta-synthesis was performed on studies published in English between 1995 and 2010 that addressed the modeling process and reported the exposition of a new methodology, model, system implementation, or system architecture. Thematic analysis, principal component analysis (PCA) and data visualisation techniques were used to identify and cluster the underlying implementation ‘challenge’ themes. One hundred and eight relevant studies were selected for review. Twenty-five underlying ‘challenge’ themes were identified. These were clustered into 10 distinct groups, from which a conceptual model of the implementation process was developed. We found that the development of systems supporting individual clinical decisions is evolving toward the implementation of adaptable care pathways on the semantic web, incorporating formal, clinical, and organizational ontologies, and the use of workflow management systems. These architectures now need to be implemented and evaluated on a wider scale within clinical settings

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-

Information Systems and Healthcare XXXIV: Clinical Knowledge Management Systems—Literature Review and Research Issues for Information Systems

Knowledge Management (KM) has emerged as a possible solution to many of the challenges facing U.S. and international healthcare systems. These challenges include concerns regarding the safety and quality of patient care, critical inefficiency, disparate technologies and information standards, rapidly rising costs and clinical information overload. In this paper, we focus on clinical knowledge management systems (CKMS) research. The objectives of the paper are to evaluate the current state of knowledge management systems diffusion in the clinical setting, assess the present status and focus of CKMS research efforts, and identify research gaps and opportunities for future work across the medical informatics and information systems disciplines. The study analyzes the literature along two dimensions: (1) the knowledge management processes of creation, capture, transfer, and application, and (2) the clinical processes of diagnosis, treatment, monitoring and prognosis. The study reveals that the vast majority of CKMS research has been conducted by the medical and health informatics communities. Information systems (IS) researchers have played a limited role in past CKMS research. Overall, the results indicate that there is considerable potential for IS researchers to contribute their expertise to the improvement of clinical process through technology-based KM approaches

A standards-based ICT framework to enable a service-oriented approach to clinical decision support

This research provides evidence that standards based Clinical Decision Support (CDS) at the point of care is an essential ingredient of electronic healthcare service delivery. A Service Oriented Architecture (SOA) based solution is explored, that serves as a task management system to coordinate complex distributed and disparate IT systems, processes and resources (human and computer) to provide standards based CDS. This research offers a solution to the challenges in implementing computerised CDS such as integration with heterogeneous legacy systems. Reuse of components and services to reduce costs and save time. The benefits of a sharable CDS service that can be reused by different healthcare practitioners to provide collaborative patient care is demonstrated. This solution provides orchestration among different services by extracting data from sources like patient databases, clinical knowledge bases and evidence-based clinical guidelines (CGs) in order to facilitate multiple CDS requests coming from different healthcare settings. This architecture aims to aid users at different levels of Healthcare Delivery Organizations (HCOs) to maintain a CDS repository, along with monitoring and managing services, thus enabling transparency. The research employs the Design Science research methodology (DSRM) combined with The Open Group Architecture Framework (TOGAF), an open source group initiative for Enterprise Architecture Framework (EAF). DSRM’s iterative capability addresses the rapidly evolving nature of workflows in healthcare. This SOA based solution uses standards-based open source technologies and platforms, the latest healthcare standards by HL7 and OMG, Decision Support Service (DSS) and Retrieve, Update Locate Service (RLUS) standard. Combining business process management (BPM) technologies, business rules with SOA ensures the HCO’s capability to manage its processes. This architectural solution is evaluated by successfully implementing evidence based CGs at the point of care in areas such as; a) Diagnostics (Chronic Obstructive Disease), b) Urgent Referral (Lung Cancer), c) Genome testing and integration with CDS in screening (Lynch’s syndrome). In addition to medical care, the CDS solution can benefit organizational processes for collaborative care delivery by connecting patients, physicians and other associated members. This framework facilitates integration of different types of CDS ideal for the different healthcare processes, enabling sharable CDS capabilities within and across organizations