Abstract
Clinical Guidelines, medical protocols, and other healthcare indications, cover a significant slice of physicians daily routine, as they are used to support clinical choices also with relevant legal implications. On the one hand, informatics have proved to be a valuable mean for providing formalisms, methods, and approaches to extend clinical guidelines for better supporting the work performed in the healthcare domain. On the other hand, due to the different perspectives that can be considered for addressing similar problems, it lead to an undeniable fragmentation of the field. It may be argued that such fragmentation did not help to propose a practical, accepted, and extensively adopted solutions to assist physicians. As in Process Mining as a general field, Process Mining for Healthcare inherits the requirement of Conformance Checking. Conformance Checking aims to measure the adherence of a particular (discovered or known) process with a given set of data, or vice-versa. Due to the intuitive similarities in terms of challenges and problems to be faced between conformance checking and clinical guidelines, one may be tempted to expect that the fragmentation issue will naturally arise also in the conformance checking field. This position paper is a first step on the direction to embrace experience, lessons learnt, paradigms, and formalisms globally derived from the clinical guidelines challenge. We argue that such new focus, joint with the even growing notoriety and interest in PM4HC, might allow more physicians to make the big jump from user to protagonist becoming more motivated and proactive in building a strong multidisciplinary community.
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References
van der Aalst, W.M.P.: Process Mining: Discovery, Conformance and Enhancement of Business Processes, 1st edn. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-19345-3
Anani, N., et al.: Applying openEHR’s Guideline Definition Language to the SITS international stroke treatment registry: a European retrospective observational study. BMC Med. Inform. Decis. Mak. 17(1), 7 (2017)
Anselma, L., Piovesan, L., Terenziani, P.: Temporal detection and analysis of guideline interactions. Artif. Intell. Med. 76, 40–62 (2017)
Beale, T., et al.: openEHR Task Planning Specification (2017). https://specifications.openehr.org/releases/PROC/latest/task_planning.html
Bilici, E., Despotou, G., Arvanitis, T.N.: The use of computer-interpretable clinical guidelines to manage care complexities of patients with multimorbid conditions: a review. Digit Health 4 (2018)
Binder, M., et al.: On analyzing process compliance in skin cancer treatment: an experience report from the evidence-based medical compliance cluster (EBMC\(^{2}\)). In: Ralyté, J., Franch, X., Brinkkemper, S., Wrycza, S. (eds.) CAiSE 2012. LNCS, vol. 7328, pp. 398–413. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31095-9_26
Boxwala, A.A., et al.: GLIF3: a representation format for sharable computer-interpretable clinical practice guidelines. J. Biomed. Inform. 37(3), 147–161 (2004)
Buijs, J.C.A.M., van Dongen, B.F., van der Aalst, W.M.P.: On the role of fitness, precision, generalization and simplicity in process discovery. In: Meersman, R., et al. (eds.) OTM 2012. LNCS, vol. 7565, pp. 305–322. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33606-5_19
Chen, C., Chen, K., Hsu, C.Y., Chiu, W.T., Li, Y.C.J.: A guideline-based decision support for pharmacological treatment can improve the quality of hyperlipidemia management. Comput. Methods Progr. Biomed. 97(3), 280–285 (2010)
Ciccarese, P., Caffi, E., Quaglini, S., Stefanelli, M.: Architectures and tools for innovative Health Information Systems: the Guide Project. Int. J. Med. Inform. 74(7–8), 553–562 (2005)
De Bleser, L., Depreitere, R., De Waele, K., Vanhaecht, K., Vlayen, J., Sermeus, W.: Defining pathways. J. Nurs. Manag. 14(7), 553–563 (2006)
Dixon, B.E., et al.: A pilot study of distributed knowledge management and clinical decision support in the cloud. Artif. Intell. Med. 59(1), 45–53 (2013)
Fdez-Olivares, J., Onaindia, E., Castillo, L., Jordan, J., Cozar, J.: Personalized conciliation of clinical guidelines for comorbid patients through multi-agent planning. Artif. Intell. Med. 96, 167–186 (2018)
Fernández-Llatas, C., Meneu, T., Traver, V., Benedi, J.M.: Applying evidence-based medicine in telehealth: an interactive pattern recognition approximation. Int. J. Environ. Res. Public Health 10(11), 5671–5682 (2013)
Ghasemi, M., Amyot, D.: Process mining in healthcare: a systematised literature review. Int. J. Electron. Healthc. 9, 60 (2016)
Goldberg, H.S., et al.: A highly scalable, interoperable clinical decision support service. J. Am. Med. Inform. Assoc. (JAMIA) 21(e1), e55–e62 (2014)
Gonzalez-Ferrer, A., ten Teije, A., Fdez-Olivares, J., Milian, K.: Automated generation of patient-tailored electronic care pathways by translating computer-interpretable guidelines into hierarchical task networks. Artif. Intell. Med. 57(2), 91–109 (2013)
Gooch, P., Roudsari, A.: Computerization of workflows, guidelines, and care pathways: a review of implementation challenges for process-oriented health information systems. J. Am. Med. Inform. Assoc. 18(6), 738–748 (2011)
Grando, M.A., Glasspool, D., Fox, J.: A formal approach to the analysis of clinical computer-interpretable guideline modeling languages. Artif. Intell. Med. 54(1), 1–13 (2012)
Greenes, R.A., Bates, D.W., Kawamoto, K., Middleton, B., Osheroff, J., Shahar, Y.: Clinical decision support models and frameworks: seeking to address research issues underlying implementation successes and failures. J. Biomed. Inform. 78, 134–143 (2018)
Gurgen Erdogan, T., Tarhan, A.: Systematic mapping of process mining studies in healthcare. IEEE Access 6, 1 (2018)
Ibanez-Sanchez, G., et al.: Toward value-based healthcare through interactive process mining in emergency rooms: the stroke case. Int. J. Environ. Res. Public Health 16(10), 1783 (2019)
Institute of Medicine: Clinical Practice Guidelines We Can Trust. The National Academies Press, Washington, DC (2011)
Jafarpour, B., Abidi, S.R., Woensel, W.V., Abidi, S.S.R.: Execution-time integration of clinical practice guidelines to provide decision support for comorbid conditions. Artif. Intell. Med. 94, 117–137 (2019)
Johnson, O.A., Ba Dhafari, T., Kurniati, A., Fox, F., Rojas, E.: The ClearPath method for care pathway process mining and simulation. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) BPM 2018. LNBIP, vol. 342, pp. 239–250. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11641-5_19
Kawamoto, K., et al.: Multi-national, multi-institutional analysis of clinical decision support data needs to inform development of the HL7 virtual medical record standard. AMIA Annu. Symp. Proc. 2010, 377–381 (2010)
Kawamoto, K., Greenes, R.A.: The role of standards: what we can expect and when. In: Greenes, R.A. (ed.) Clinical Decision Support, chap. 21, 2nd edn, pp. 599–615. Academic Press, Oxford (2014)
Kaymak, U., Mans, R., van de Steeg, T., Dierks, M.: On process mining in health care. In: 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1859–1864 (2012)
Kurniati, A.P., Johnson, O., Hogg, D., Hall, G.: Process mining in oncology: a literature review. In: 2016 6th International Conference on Information Communication and Management (ICICM), pp. 291–297 (2016)
Lenkowicz, J., et al.: Assessing the conformity to clinical guidelines in oncology: an example for the multidisciplinary management of locally advanced colorectal cancer treatment. Manag. Decis. 56(10), 2172–2186 (2018)
Mans, R.S., van der Aalst, W.M.P., Vanwersch, R.J.B., Moleman, A.J.: Process mining in healthcare: data challenges when answering frequently posed questions. In: Lenz, R., Miksch, S., Peleg, M., Reichert, M., Riaño, D., ten Teije, A. (eds.) KR4HC/ProHealth - 2012. LNCS (LNAI), vol. 7738, pp. 140–153. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36438-9_10
Marco-Ruiz, L., Moner, D., Maldonado, J.A., Kolstrup, N., Bellika, J.G.: Archetype-based data warehouse environment to enable the reuse of electronic health record data. Int. J. Med. Inform. 84(9), 702–714 (2015)
Marcos, M., Maldonado, J.A., Martínez-Salvador, B., Boscá, D., Robles, M.: Interoperability of clinical decision-support systems and electronic health records using archetypes: a case study in clinical trial eligibility. J. Biomed. Inform. 46(4), 676–689 (2013)
Martínez-Salvador, B., Marcos, M.: Supporting the refinement of clinical process models to computer-interpretable guideline models. Bus. Inf. Syst. Eng. 58(5), 355–366 (2016)
Mulyar, N., van der Aalst, W.M., Peleg, M.: A pattern-based analysis of clinical computer-interpretable guideline modeling languages. J. Am. Med. Inform. Assoc. 14(6), 781–787 (2007)
Munoz-Gama, J.: Conformance Checking and Diagnosis in Process Mining - Comparing Observed and Modeled Processes, vol. 270. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-319-49451-7
Musen, M.A., Tu, S.W., Das, A.K., Shahar, Y.: EON: a component-based approach to automation of protocol-directed therapy. J. Am. Med. Inform. Assoc. 3(6), 367–388 (1996)
Peleg, M.: Computer-interpretable clinical guidelines: a methodological review. J. Biomed. Inform. 46, 744–763 (2013)
Peleg, M., González-Ferrer, A.: Guidelines and Workflow Models, chap. 16, 2nd edn, pp. 435–464. Academic Press, Oxford (2014)
Peleg, M., et al.: Comparing computer-interpretable guideline models: a case-study approach. J. Am. Med. Inform. Assoc. 10, 52–68 (2003)
Pesic, M., van der Aalst, W.M.P.: A declarative approach for flexible business processes management. In: Eder, J., Dustdar, S. (eds.) BPM 2006. LNCS, vol. 4103, pp. 169–180. Springer, Heidelberg (2006). https://doi.org/10.1007/11837862_18
Qu, G., Liu, Z., Cui, S., Tang, J.: Study on self-adaptive clinical pathway decision support system based on case-based reasoning. In: Li, S., Jin, Q., Jiang, X., Park, J.J.J.H. (eds.) Frontier and Future Development of Information Technology in Medicine and Education. LNEE, vol. 269, pp. 969–978. Springer, Dordrecht (2014). https://doi.org/10.1007/978-94-007-7618-0_95
Quaglini, S., Stefanelli, M., Cavallini, A., Micieli, G., Fassino, C., Mossa, C.: Guideline-based careflow systems. Artif. Intell. Med. 20(1), 5–22 (2000)
Rebuge, Á., Ferreira, D.R.: Business process analysis in healthcare environments: a methodology based on process mining. Inf. Syst. 37, 99–116 (2012)
Riaño, D., Collado, A.: Model-based combination of treatments for the management of chronic comorbid patients. In: Peek, N., Marín Morales, R., Peleg, M. (eds.) AIME 2013. LNCS (LNAI), vol. 7885, pp. 11–16. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38326-7_2
Rojas, E., Munoz-Gama, J., Sepulveda, M., Capurro, D.: Process mining in healthcare: a literature review. J. Biomed. Inform. 61, 224–236 (2016)
Rovani, M., Maggi, F.M., de Leoni, M., van der Aalst, W.M.: Declarative process mining in healthcare. Expert Syst. Appl. 42(23), 9236–9251 (2015)
dos Santos Garcia, C., et al.: Process mining techniques and applications - a systematic mapping study. Expert Syst. Appl. 133, 260–295 (2019)
Schadow, G., Russler, D.C., McDonald, C.J.: Conceptual alignment of electronic health record data with guideline and workflow knowledge. Int. J. Med. Inform. 64(2–3), 259–274 (2001)
Shabo, A., Peleg, M., Parimbelli, E., Quaglini, S., Napolitano, C.: Interplay between clinical guidelines and organizational workflow systems. Experience from the MobiGuide project. Methods Inf. Med. 55(6), 488–494 (2016)
Shahar, Y., Miksch, S., Johnson, P.: The Asgaard project: a task-specific framework for the application and critiquing of time-oriented clinical guidelines. Artif. Intell. Med. 14(1–2), 29–51 (1998)
Sordo, M., Ogunyemi, O., Boxwala, A.A., Greenes, R.A.: GELLO: an object-oriented query and expression language for clinical decision support. In: AMIA Annual Symposium Proceedings, p. 1012 (2003)
Sox, H.C.: Conflict of interest in practice guidelines panels. JAMA 317(17), 1739–1740 (2017)
Sutton, D.R., Fox, J.: The syntax and semantics of the PROforma guideline modeling language. J. Am. Med. Inform. Assoc. 10(5), 433–443 (2003)
Terenziani, P., Molino, G., Torchio, M.: A modular approach for representing and executing clinical guidelines. Artif. Intell. Med. 23(3), 249–276 (2001)
van Dongen, B.F., de Medeiros, A.K.A., Verbeek, H.M.W., Weijters, A.J.M.M., van der Aalst, W.M.P.: The ProM framework: a new era in process mining tool support. In: Ciardo, G., Darondeau, P. (eds.) ICATPN 2005. LNCS, vol. 3536, pp. 444–454. Springer, Heidelberg (2005). https://doi.org/10.1007/11494744_25
Van de Velde, S., et al.: A systematic review of trials evaluating success factors of interventions with computerised clinical decision support. Implement. Sci. 13(1), 114 (2018)
Wall, E.: Clinical practice guidelines–is “regulation” the answer? J. Am. Board Family Med. 29(6), 642–643 (2016)
Wang, Z., Norris, S.L., Bero, L.: The advantages and limitations of guideline adaptation frameworks. Implement. Sci. 13(1), 72 (2018)
Wilk, S., Michalowski, W., Michalowski, M., Farion, K., Hing, M.M., Mohapatra, S.: Mitigation of adverse interactions in pairs of clinical practice guidelines using constraint logic programming. J. Biomed. Inform. 46(2), 341–353 (2013)
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Gatta, R. et al. (2019). Clinical Guidelines: A Crossroad of Many Research Areas. Challenges and Opportunities in Process Mining for Healthcare. In: Di Francescomarino, C., Dijkman, R., Zdun, U. (eds) Business Process Management Workshops. BPM 2019. Lecture Notes in Business Information Processing, vol 362. Springer, Cham. https://doi.org/10.1007/978-3-030-37453-2_44
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