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23.08.2022 | Original Paper

Applying process mining in health technology assessment

verfasst von: Marcelo Rosano Dallagassa, Franciele Iachecen, Luiz Henrique Picolo Furlan, Sérgio Ossamu Ioshii, Deborah Ribeiro Carvalho

Erschienen in: Health and Technology | Ausgabe 5/2022

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Abstract

Objective

Propose a process mining-based method for Health Technology Assessment.

Methods

Articles dealing with prior studies in Health Technology Assessment using Process Mining were identified. Five research questions were defined to investigate these studies and present important points and desirable characteristics to be addressed in a proposal. The was defined method with five steps and was submitted to a case study for evaluation.

Results

The Literature search identified six main characteristics. As a result, the five-step method proposed was applied in the radical prostatectomy surgical procedure between the robot assisted technique and laparoscopy.

Conclusion

It was demonstrated in this article the creation of the proposal of an efficient method with its replication for other health technologies, coupled with the good interpretation of the specialists in terms of comprehensibility of the discovered patterns and their correlation with clinical protocols and guidelines.

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Guimarães R. Technological incorporation in the Unified Health System (SUS): the problem and ensuing challenges. Cien Saude Colet. 2014;19:4899–908. https://doi.org/10.1590/1413-812320141912.04642014.CrossRef

Pichon-Riviere A, Garcia-Marti S, Oortwijn W, Augustovski F, Sampietro-Colom L. Defining the Value of Health Technologies in Latin America: Developments in Value Frameworks to Inform the Allocation of Healthcare Resources. Int J Technol Assess Health Care. 2019;35:64–8. https://doi.org/10.1017/s0266462319000072.CrossRef

Velasco-Garrido M, Busse R, Health technology assessment An introduction to objectives, role of evidence, and structure in Europe, in: Eur. Obs. Heal. Syst. Policies, 2005: pp. 1–24. http://www.euro.who.int/__data/assets/pdf_file/0018/90432/E87866.pdf?ua=1.

Kristensen FB, Sigmund H. Health Technology Assessment Handbook. 1st ed. Denmark: National Board of Health; 2008.

Lewis JRR, Kerridge I, Lipworth W. Use of Real-World Data for the Research, Development, and Evaluation of Oncology Precision Medicines, JCO Precis Oncol 1 (2017) 1–11. https://doi.org/10.1200/po.17.00157.

Vieira F. Economic evidence of health interventions under the perspective of the Brazilian public health system: why and what for can we produce it and use it? J Bras Econ Saúde. 2017;9:229–36. https://doi.org/10.21115/jbes.v9.n2.p229-36.CrossRef

van Gool K, Gallego G, Haas M, Viney R, Hall J, Ward R. Economic Evidence at the Local Level. Pharmacoeconomics. 2007;25:1055–62. https://doi.org/10.2165/00019053-200725120-00006.CrossRef

Wang SV, Schneeweiss S, Berger ML, Brown J, de Vries F, Douglas I, Gagne JJ, Gini R, Klungel O, Mullins CD, Nguyen MD, Rassen JA, Smeeth L, Sturkenboom M. Reporting to Improve Reproducibility and Facilitate Validity Assessment for Healthcare Database Studies V1.0., Pharmacoepidemiol. Drug Saf. 2017;26:1018–32. https://doi.org/10.1002/pds.4295.CrossRef

Tugwell P, Knottnerus JA. Is the “Evidence-Pyramid” now dead? J Clin Epidemiol. 2015;68:1247–50. https://doi.org/10.1016/j.jclinepi.2015.10.001.CrossRef

10.

Sherman RE, Anderson SA, Dal Pan GJ, Gray GW, Gross T, Hunter NL, LaVange L, Marinac-Dabic D, Marks PW, Robb MA, Shuren J, Temple R, Woodcock J, Yue LQ, Califf RM. Real-World Evidence - What Is It and What Can It Tell Us?, N Engl J Med 375 (2016) 2293–7. https://doi.org/10.1056/NEJMsb1609216.CrossRef

11.

Guerra-Júnior AA, Pires De Lemos LL, Godman B, Bennie M, Osorio-De-Castro CGS, Alvares J, Heaney A, Vassallo CA, Wettermark B, Benguria-Arrate G, Gutierrez-Ibarluzea I, Santos VCC, Petramale CA, Acurcio FDA. Health technology performance assessment: Real-world evidence for public healthcare sustainability. Int J Technol Assess Health Care. 2017;33:279–87. https://doi.org/10.1017/S0266462317000423.CrossRef

12.

Dang A, Vallish B. Real world evidence: An Indian perspective. Perspect Clin Res. 2016;7:156. https://doi.org/10.4103/2229-3485.192030.CrossRef

13.

Van der Aalst W. Process mining: Data science in action, Process Min. Data Sci Action (2016) 1–467. https://doi.org/10.1007/978-3-662-49851-4.

14.

Mans RS, Van Der Aalst WMP, Vanwersch RJB. Process Mining in Healthcare Evaluating and Exploiting Operational Healthcare Processes, Alexandria Tue Nl (2015) 99. https://doi.org/10.1007/978-3-319-16071-9.

15.

dos Santos Garcia C, Meincheim A, [Faria ER, Junior] MR, Dallagassa DMV, Sato DR, Carvalho EAP, Santos EE, Scalabrin. Process mining techniques and applications – A systematic mapping study. Expert Syst Appl. 2019;133:260–95. https://doi.org/10.1016/j.eswa.2019.05.003.CrossRef

16.

Munoz-Gama J, Process mining for healthcare: Characteristics and challenges. J Biomed Inform. 2022 Mar;127:103994. doi: https://doi.org/10.1016/j.jbi.2022.103994. Epub 2022 Jan 29. PMID: 35104641.

17.

Kusuma GP. Process Mining of Disease Trajectories: A Literature Review. Stud Health Technol Inform. 2021 May 27;281:457–461. doi: https://doi.org/10.3233/SHTI210200. PMID: 34042785.

18.

Mascii L, Luschi A, Iadanza E. Sentiment Analysis for Performance Evaluation of Maintenance in Healthcare. In International Conference on Medical and Biological Engineering. 2021: pp. 359–367. Springer, Cham.

19.

Polisena J, Andellini M, Salerno P, Borsci S, Pecchia L, Iadanza E. Case Studies on the Use of Sentiment Analysis to Assess the Effectiveness and Safety of Health Technologies: A Scoping Review. IEEE Access. 2021: pp. 66043–66051.

20.

de Lusignan S, Crawford L, Munro N. Creating and using real-world evidence to answer questions about clinical effectiveness. J Innov Heal Informatics. 2015;22:368–73. https://doi.org/10.14236/jhi.v22i3.177.CrossRef

21.

Maissenhaelter BE, Woolmore AL, Schlag PM. Real-world evidence research based on big data. Der Onkol. 2018;24:91–8. https://doi.org/10.1007/s00761-018-0358-3.CrossRef

22.

Spitzer E, Cannon CP, Serruys PW. Should real-world evidence be incorporated into regulatory approvals?, Expert Opin. Drug Saf. 2018;17:1155–9. https://doi.org/10.1080/14740338.2018.1546842.CrossRef

23.

Hampson G, Towse A, Dreitlein WB, Henshall C, Pearson SD. Real-world evidence for coverage decisions: opportunities and challenges. J Comp Eff Res. 2018;7:1133–43. https://doi.org/10.2217/cer-2018-0066.CrossRef

24.

Rebuge Á, Ferreira DR. Business process analysis in healthcare environments: A methodology based on process mining. Inf Syst. 2012;37:99–116. https://doi.org/10.1016/j.is.2011.01.003.CrossRef

25.

Santos Garcia Cd, Pina Cabral Silva RV, Meincheim A, do Valle AM, Ramos MP, Scalabrin EE. Applying Process Mining in a Multilevel Variants Analysis on Collaborative Sales to Cash Activities, in: 2019 IEEE 23rd Int. Conf Comput Support Coop Work Des, 2019: 176–81. https://doi.org/10.1109/CSCWD.2019.8791902.

26.

Munoz-Gama J, Echizen I, Insuring Sensitive Processes through Process Mining, in: 2012 9th Int. Conf. Ubiquitous Intell. Comput. 9th Int. Conf. Auton. Trust. Comput., Fukuoka, 2012: pp. 447–454. https://doi.org/10.1109/UIC-ATC.2012.83.

27.

Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;6:e1000097. https://doi.org/10.1371/journal.pmed.1000097.CrossRef

28.

Mans R, Reijers H, Wismeijer D, [van Genuchten] M. A process-oriented methodology for evaluating the impact of IT: A proposal and an application in healthcare. Inf Syst. 2013;38:1097–115. https://doi.org/10.1016/j.is.2013.06.005.CrossRef

29.

Huang Z, Dong W, Ji L, Gan C, Lu X, Duan H. Discovery of clinical pathway patterns from event logs using probabilistic topic models. J Biomed Inform. 2014;47:39–57. https://doi.org/10.1016/j.jbi.2013.09.003.CrossRef

30.

Cho M, Song M, Comuzzi M, Yoo S. Evaluating the effect of best practices for business process redesign: An evidence-based approach based on process mining techniques. Decis Support Syst. 2017;104:92–103. https://doi.org/10.1016/j.dss.2017.10.004.CrossRef

31.

Dunkl R, Fröschl KA, Grossmann W, Rinderle-Ma S. Assessing Medical Treatment Compliance Based on Formal Process Modeling, Inf. Qual. e-Health, Springer, Berlin, 2011. https://doi.org/10.1007/978-3-642-25364-5_37.CrossRef

32.

Baumgärtel P, Tenschert J, Lenz R. A Query Language for Workflow Instance Data, New Trends Databases Inf. Syst., Springer, Cham, 2014. https://doi.org/10.1007/978-3-319-01863-8_9.CrossRef

33.

Rovani M. Strumenti per la modellazione e l’analisi dei processi sanitari, Universita’ Degli Studi Di Napoli “Federico II,” n.d.

34.

Huang X, Lin J, Demner-Fushman D. Evaluation of PICO as a Knowledge Representation for Clinical Questions, in: AMIA Annu Symp Proc, 2006: pp. 359–663.

35.

36.

Dongen B, Medeiros A, Verbeek H, Weijters A, Aalst W, The ProM Framework: A New Era in Process Mining Tool Support, in: Lect. Notes Comput. Sci., Springer-Verlag Berlin Heidelberg, 2005: pp. 444–454. https://doi.org/10.1007/11494744_25.

37.

Günther CW, Van der Aalst W. Fuzzy Mining – Adaptive Process Simplification Based on Multi-perspective Metrics, Bus. Process Manag., Springer, Berlin, 2007. https://doi.org/10.1007/978-3-540-75183-0_24.CrossRef

38.

Vanni T, Luz PM, Ribeiro RA, Novaes HMD, Polanczyk CA. Avaliação econômica em saúde: aplicações em doenças infecciosas, Cad. Saude Publica. 2009;25:2543–52. https://doi.org/10.1590/s0102-311x2009001200002.CrossRef

39.

Chisholm D, Evans DB. Economic evaluation in health: Saving money or improving care? J Med Econ. 2007;10:325–37. https://doi.org/10.3111/13696990701605235.CrossRef

40.

Drummond MF, Sculpher MJ, Torrance GW, O’Brien J, Stoddart GL. Methods for the economic evaluation of healthcare programmes. New York: Oxford University Press; 2005.

41.

Chisholm D, Evans DB. Economic evaluation in health: Saving money or improving care? J Med Econ. 2007;10:325–37. https://doi.org/10.3111/13696990701605235.CrossRef

Titel: Applying process mining in health technology assessment
verfasst von: Marcelo Rosano Dallagassa
Franciele Iachecen
Luiz Henrique Picolo Furlan
Sérgio Ossamu Ioshii
Deborah Ribeiro Carvalho
Publikationsdatum: 23.08.2022
Verlag: Springer Berlin Heidelberg
Erschienen in: Health and Technology / Ausgabe 5/2022
Print ISSN: 2190-7188
Elektronische ISSN: 2190-7196
DOI: https://doi.org/10.1007/s12553-022-00692-5

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Abstract

Objective

Methods

Results

Conclusion

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