Advertisement

Expectations from a Process Mining Dashboard in Operating Rooms with Analytic Hierarchy Process

  • Antonio Martinez-MillanaEmail author
  • Aroa Lizondo
  • Roberto Gatta
  • Vicente Traver
  • Carlos Fernandez-Llatas
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 342)

Abstract

The wide-spread adoption of real-time location system is boosting the development of software applications to track persons and assets on real-time and perform analytics. Among the vast amount of data analysis techniques, process mining allows to conform work-flows with heterogeneous multivariate data, enhancing the model understandability and usefulness in clinical environments. However, such applications still find entrance barriers in the clinical context. In this paper we have identified the preferred features of a process mining based dashboard deployed in the operating rooms of a hospital equipped with a real-time location system. Work-flows are inferred and enhanced using process discovery on location data of patients undergoing an intervention, drawing nodes (states in the process) and transitions across the entire process. Analytic Hierarchy Process has been applied to quantify the prioritization of the features contained in the process mining dashboard (filtering data, enhancement, node selection, statistics, etc..), distinguishing on the priorities that each of the different roles in the operating room service assigned to each feature. The staff in the operating rooms (N=10) was classified into three groups: Technical, Clinical and Managerial staff according to their responsibilities. Results show different weights for the features in the process mining dashboard for each group, suggesting that a flexible process mining dashboard is needed to boost its potential in the management of clinical interventions in operating rooms.

References

  1. 1.
    Agnoletti, V., et al.: Operating room data management: improving efficiency and safety in a surgical block. BMC Surg., 13(1), 7 (2013)Google Scholar
  2. 2.
    Marques, I., Captivo, M.E., Pato, M.V.: An integer programming approach to elective surgery scheduling. OR Spectr., 34(2), 407–427 (2011)CrossRefGoogle Scholar
  3. 3.
    Haynes, A.B., et al.: A surgical safety checklist to reduce morbidity and mortality in a global population. N. Engl. J. Med., 360(5), 491–499 (2009)Google Scholar
  4. 4.
    Dexter, F., Epstein, R.H., Traub, R.D., Xiao, Y.: Making management decisions on the day of surgery based on operating room efficiency and patient waiting times. J. Am. Soc. Anesth. 101(6), 1444–1453 (2004)Google Scholar
  5. 5.
    Smith, S.F.: Reactive scheduling systems. In: Intelligent scheduling systems. Springer, Boston, pp. 155–192 (1995).  https://doi.org/10.1007/978-1-4615-2263-8_7CrossRefGoogle Scholar
  6. 6.
    Raheja, A.S., Subramaniam, V.: Reactive schedule repair of job shops. Int. J. Adv. Manuf. Technol., 19(10), 756-763 (2002)Google Scholar
  7. 7.
    Tanimizu, Y., Komatsu, Y., Ozawa, C., Iwamura, K., Sugimura, N.: Co-evolutionary genetic algorithms for reactive scheduling. J. Adv. Mech. Des. Syst. Manuf. 4(3), 569–577 (2010)CrossRefGoogle Scholar
  8. 8.
    Redondi, A., Chirico, M., Borsani, L., Cesana, M., Tagliasacchi, M.: An integrated system based on wireless sensor networks for patient monitoring, localization and tracking. Ad Hoc Netw. 11(1), 39–53 (2013)CrossRefGoogle Scholar
  9. 9.
    de Vries, E.N., Ramrattan, M.A., Smorenburg, S.M., Gouma, D.J., Boermeester, M.A.: The incidence and nature of in-hospital adverse events: a systematic review. Qual. Saf. Health Care 17(3), 216–223 (2008)CrossRefGoogle Scholar
  10. 10.
    González-Arévalo, A., Gómez-Arnau, J., García del Valle, S.: Coordinación y gestión de las áreas quirúrgicas. Tratado de Anestesia y Reanimación. Torres. Arán Ediciones, 1, 221–238 (2001)Google Scholar
  11. 11.
    Fernandez-Llatas, C., Lizondo, A., Monton, E., Benedi, J.M., Traver, V.: Process mining methodology for health process tracking using real-time indoor location systems. Sensors, 15(12), 29821–29840 (2015)CrossRefGoogle Scholar
  12. 12.
    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)CrossRefGoogle Scholar
  13. 13.
    Westbrook, J.I., Braithwaite, J.: Will information and communication technology disrupt the health system and deliver on its promise? Med. J. Aust. 193(7), 399 (2010)Google Scholar
  14. 14.
    Drazen, E., Rhoads, J.: Using tracking tools to improve patient flow in hospitals, issue brief. Calif. HealthCare Found., 4(1) (2011)Google Scholar
  15. 15.
    Miclo, R., Fontanili, F., Marquès, G., Bomert, P., Lauras, M.: RTLS-based process mining: towards an automatic process diagnosis in healthcare. In: IEEE International Conference on Automation Science and Engineering (CASE), pp. 1397–1402. IEEE (2015)Google Scholar
  16. 16.
    Fisher, J.A., Monahan, T.: Evaluation of real-time location systems in their hospital contexts. Int. J. Med. Inform. 81(10), 705–712 (2012)CrossRefGoogle Scholar
  17. 17.
    Pecchia, L., et al.: Analytic hierarchy process (AHP) for examining healthcare professionals assessments of risk factors. Methods Inf. Med. 50(5), 435–444 (2011)CrossRefGoogle Scholar
  18. 18.
    Sloane, E.B., Liberatore, M.J., Nydick, R.L., Luo, W., Chung, Q.: Using the analytic hierarchy process as a clinical engineering tool to facilitate an iterative, multidisciplinary, microeconomic health technology assessment. Comput. Oper. Res. 30(10), 1447–1465 (2003)CrossRefGoogle Scholar
  19. 19.
    Saaty, T.L.: A scaling method for priorities in hierarchical structures. J. Math. Psychol. 15(3), 234–281 (1977)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Bridges, J.F., et al.: Conjoint analysis applications in health–a checklist: a report of the ISPOR good research practices for conjoint analysis task force. Value Health 14(4), 403–413 (2011)CrossRefGoogle Scholar
  21. 21.
    World Health Organization: Safe Surgery Saves Lives (2009)Google Scholar
  22. 22.
    Saaty, T.: How to structure and make choices in complex problems. Hum. Syst. Manag. 3(4), 255–261 (1982)Google Scholar
  23. 23.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Antonio Martinez-Millana
    • 1
    Email author
  • Aroa Lizondo
    • 1
  • Roberto Gatta
    • 2
  • Vicente Traver
    • 1
    • 3
  • Carlos Fernandez-Llatas
    • 1
    • 3
  1. 1.ITACAUniversitat Politècnica de ValènciaValenciaSpain
  2. 2.Polo Scienze Oncologiche ed EmatologicheUniversit Cattolica del Sacro Cuore, Fondazione Policlinico Universitario Agostino GemelliRomeItaly
  3. 3.Unidad Mixta de Reingeniería de Procesos Sociosanitarios, Instituto de Investigación Sanitaria del Hospital Universitario y Politecnico La FeValenciaSpain

Personalised recommendations