Using Process Mining in Healthcare

Martin, Niels; Wittig, Nils; Muñoz-Gama, Jorge

doi:10.1007/978-3-031-08848-3_14

Cited by 5 publications

(1 citation statement)

References 85 publications

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“…Process mining (PM) is an important approach suitable for designing processes based on machine learning (ML) decision-making engines. PM has been applied to improve industrial processes [1,2] and subsequently to design healthcare processes [3,4] regarding the cost optimization of healthcare services [5], telemedicine [6], and patient fall risk management [7]. The application of PM is important for the design of organizational models based on workflows integrating ML algorithms and supporting decisions about human resource (HR) allocation or engagement [6].…”

Section: Introductionmentioning

confidence: 99%

Process Mining Organization (PMO) Based on Machine Learning Decision Making for Prevention of Chronic Diseases

Rosa,

Massaro

2024

Eng

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This paper discusses a methodology to improve the prevention processes of chronic diseases such as diabetes and strokes. The research motivation is to find a new methodological approach to design advanced Diagnostic and Therapeutic Care Pathways (PDTAs) based on the prediction of chronic disease using telemedicine technologies and machine learning (ML) data processing techniques. The aim is to decrease health risk and avoid hospitalizations through prevention. The proposed method defines a Process Mining Organization (PMO) model, managing risks using a PDTA structured to prevent chronic risk. Specifically, the data analysis is focused on stroke risk. First, we applied and compared the Random Forest (RF) and Gradient Boosted Trees (GBT) supervised algorithms to predict stroke risk, and then, the Fuzzy c-Means unsupervised algorithm to cluster information on the predicted results. The application of the proposed approach is able to increase the efficiency of healthcare human resources and drastically decrease care costs.

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Section: Introductionmentioning

confidence: 99%

Process Mining Organization (PMO) Based on Machine Learning Decision Making for Prevention of Chronic Diseases

Rosa,

Massaro

2024

Eng

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Using Process Mining to Explore the Impact of Socio-economic Status on the Treatment of Musculoskeletal Disorders – A Case Study

Claus,

Martin,

Janssen

et al. 2024

Lecture Notes in Business Information Processing

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Organization processes and artificial intelligence (AI) for healthcare processes reorganization: a case study

Rosa,

Massaro,

Secundo

et al. 2024

BPMJ

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PurposeThis study aims to provide a methodology and tools to design new organizational processes and artificial intelligence (AI)-based scoring to optimize the resources management in healthcare units.Design/methodology/approachProcess design and process data-driven simulation: the processes are designed by the business process modeling and notation and the unified modeling language standards. Data processing is performed by Correlation matrix analysis and by Fuzzy c-Means data clustering. The matching between the two methods provides the most indicated final corrective actions of the “TO BE” organizational model.FindingsThis proposed method, experimentally applied in this work merging the lean management model (LMM), process mining (PM) and AI methods, named process mining organization (PMO) model (Rosa et al., 2023 (b)), is able to improve organizational processes of a hospitalization unit (HU) by developing three propaedeutic phases: (1) analysis of the current state of the processes (“AS IS”) by identifying the critical issues as bottlenecks of processes, (2) AI data processing able to provide additional classified and predicted information allowing the “TO BE” workflow process and (3) implementation of corrective actions suggested by the PMO in order to support strategic decision-making processes in the short, medium and long term by classifying an order of priority about the healthcare procedures/protocols to perform.Research limitations/implicationsThe main limitation of the proposed case study is in the limited number of available digital data to process. This aspect reduces the capability to interpret result. In any case, the proposed methodology is a “launch” work to define a new approach to integrate organizational processes including workflow design and AI scoring. Future work will be focused on managerial implications due to use of the discussed method: design and development of new human resource (HR) organizational protocols following data analysis to optimize costs and care services and to decrease injury compensation claims.Practical implicationsMain implications are in healthcare managerial scenarios: design and development of new HR organizational protocols following data analysis to optimize costs and care services and to decrease injury compensation claims.Social implicationsCare services optimization is addressed on HUs.Originality/valueThe design of HR organizational processes integrates AI-driven data decision-making processes. This case study examines AI-based innovation analytics addressed on resource efficiency.

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Using Process Mining in Healthcare

Cited by 5 publications

References 85 publications

Process Mining Organization (PMO) Based on Machine Learning Decision Making for Prevention of Chronic Diseases

Process Mining Organization (PMO) Based on Machine Learning Decision Making for Prevention of Chronic Diseases

Using Process Mining to Explore the Impact of Socio-economic Status on the Treatment of Musculoskeletal Disorders – A Case Study

Organization processes and artificial intelligence (AI) for healthcare processes reorganization: a case study

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