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Int. J. Environ. Res. Public Health 2019, 16(2), 199; https://doi.org/10.3390/ijerph16020199

Process Mining Dashboard in Operating Rooms: Analysis of Staff Expectations with Analytic Hierarchy Process

1
Instituto Universitario de Tecnologías de la Información y Comunicaciones, Universitat Politècnica de València, Camino de Vera S/N, 46022 Valencia, Spain
2
Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
3
MYSPHERA SL, Ronda Auguste y Louis Lumiere 23, Nave 13, Parque Tecnólogico, 46980 Paterna, Spain
4
Unidad Mixta de Reingeniería de Procesos Sociosanitarios, Instituto de Investigación Sanitaria del Hospital Universitario y Politecnico La Fe Bulevar Sur S/N, 46026 Valencia, Spain
*
Author to whom correspondence should be addressed.
Received: 22 November 2018 / Revised: 7 January 2019 / Accepted: 9 January 2019 / Published: 11 January 2019
(This article belongs to the Special Issue Process-Oriented Data Science for Healthcare 2018 (PODS4H18))
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Abstract

The widespread adoption of real-time location systems is boosting the development of software applications to track persons and assets in hospitals. Among the vast amount of applications, real-time location systems in operating rooms have the advantage of grounding advanced data analysis techniques to improve surgical processes, such as process mining. However, such applications still find entrance barriers in the clinical context. In this paper, we aim to evaluate the preferred features of a process mining-based dashboard deployed in the operating rooms of a hospital equipped with a real-time location system. The dashboard allows to discover and enhance flows of patients based on the location data of patients undergoing an intervention. Analytic hierarchy process was applied to quantify the prioritization of the dashboard features (filtering data, enhancement, node selection, statistics, etc.), distinguishing 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 showed 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. This paper is an extension of a communication presented in the Process-Oriented Data Science for Health Workshop in the Business Process Management Conference 2018.
Keywords: process mining; analytic hierarchy process; operating rooms; usability; software; co-design process mining; analytic hierarchy process; operating rooms; usability; software; co-design
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Martinez-Millana, A.; Lizondo, A.; Gatta, R.; Vera, S.; Salcedo, V.T.; Fernandez-Llatas, C. Process Mining Dashboard in Operating Rooms: Analysis of Staff Expectations with Analytic Hierarchy Process. Int. J. Environ. Res. Public Health 2019, 16, 199.

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