What Role Can Process Mining Play in Recurrent Clinical Guidelines Issues? A Position Paper

Gatta, Roberto; Vallati, Mauro; Fernández-Llatas, Carlos; Martínez-Millana, Antonio; Orini, Stefania; Sacchi, Lucia; Lenkowicz, Jacopo; Marcos, Mar; Muñoz-Gama, Jorge; Cuendet, Michel A.; Bari, Berardino De; Marco-Ruiz, Luis; Stefanini, Alessandro; Valero-Ramón, Zoe; Michielin, Olivier; Lapinskas, Tomas; Montvila, Antanas; Martin, Niels; Tavazzi, Erica; Castellano, Maurizio

doi:10.3390/ijerph17186616

Cited by 15 publications

(8 citation statements)

References 94 publications

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“…In the case of healthcare, the specific requirements related to this application domain required the definition of a new perimeter of the discipline called Process Mining for Healthcare (PM4HC) ( 20 ). PM4HC adopts the original standpoint of the healthcare domain by taking into account, for example, clinical guidelines ( 21 ), or the heterogeneity and the complexity of the data stored in Electronic Health Records (EHR) ( 22 ), even if a minimum of data standardisation and quality is needed to warrant the validity of the analysis. Of note, PM/PM4HC are not limited to PD: in the so called Conformance Checking (CC) approach, a reference process, such as clinical guidelines, is given together with the empirical patient EL.…”

Section: Introductionmentioning

confidence: 99%

A differential process mining analysis of COVID-19 management for cancer patients

et al. 2022

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During the acute phase of the COVID-19 pandemic, hospitals faced a challenge to manage patients, especially those with other comorbidities and medical needs, such as cancer patients. Here, we use Process Mining to analyze real-world therapeutic pathways in a cohort of 1182 cancer patients of the Lausanne University Hospital following COVID-19 infection. The algorithm builds trees representing sequences of coarse-grained events such as Home, Hospitalization, Intensive Care and Death. The same trees can also show probability of death or time-to-event statistics in each node. We introduce a new tool, called Differential Process Mining, which enables comparison of two patient strata in each node of the tree, in terms of hits and death rate, together with a statistical significance test. We thus compare management of COVID-19 patients with an active cancer in the first vs. second COVID-19 waves to quantify hospital adaptation to the pandemic. We also compare patients having undergone systemic therapy within 1 year to the rest of the cohort to understand the impact of an active cancer and/or its treatment on COVID-19 outcome. This study demonstrates the value of Process Mining to analyze complex event-based real-world data and generate hypotheses on hospital resource management or on clinical patient care.

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Section: Introductionmentioning

confidence: 99%

A differential process mining analysis of COVID-19 management for cancer patients

et al. 2022

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“…Within the field of medicine, various clinical practice guidelines and protocols are available, which build upon evidencebased information on a certain topic [79,87]. This implies that, for clinical processes, reference processes are often available, which does not hold in many other domains [35]. This opens opportunities for process mining to, e.g., analyse the adherence to these guidelines and protocols [34,59].…”

Section: Exhibit Significantmentioning

confidence: 99%

Using Process Mining in Healthcare

Martin

Wittig²,

Muñoz-Gama

2022

Lecture Notes in Business Information Processing

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This chapter introduces a specific application domain of process mining: healthcare. Healthcare is a very promising domain for process mining given the significant societal value that can be generated by supporting process improvement in a data-driven way. Within a healthcare organisation, a wide variety of processes is being executed, many of them being highly complex due to their loosely-structured and knowledge-intensive nature. Consequently, performing process mining in healthcare is challenging, but can generate significant societal impact. To provide more insights in process mining in healthcare, this chapter first provides an overview of healthcare processes and healthcare process data, as well as their particularities compared to other domains. Afterwards, an overview of common use cases in process mining in healthcare research is presented, as well as insights from a real-life case study. Subsequently, an overview of open challenges to ensure a widespread adoption of process mining in healthcare is provided. By tackling these challenges, process mining will become able to fully play its role to support evidence-based process improvement in healthcare and, hence, contribute to shaping the best possible care for patients in a way that is sustainable in the long run.

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“…Discrepancies between model and log indicate that the model needs to be improved to capture reality better. Recently a growing interest in these techniques has gained ground in the healthcare domain with collaborative workshops and initiatives such as in (Gatta et al, 2017;Gatta et al, 2020). One of the most interesting applications in healthcare is to compare the real clinical pathway of a patient with a process model to check how much a specific healthcare provider is adhering to a clinical guideline in order to assess the quality of care and improve daily practice accordingly (Lenkowicz et al, 2018).…”

Section: Conformance Checkingmentioning

confidence: 99%

Building an Artificial Intelligence Laboratory Based on Real World Data: The Experience of Gemelli Generator

Damiani

Masciocchi

Lenkowicz

et al. 2021

Front. Comput. Sci.

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The problem of transforming Real World Data into Real World Evidence is becoming increasingly important in the frameworks of Digital Health and Personalized Medicine, especially with the availability of modern algorithms of Artificial Intelligence high computing power, and large storage facilities.Even where Real World Data are well maintained in a hospital data warehouse and are made available for research purposes, many aspects need to be addressed to build an effective architecture enabling researchers to extract knowledge from data.We describe the first year of activity at Gemelli Generator RWD, the challenges we faced and the solutions we put in place to build a Real World Data laboratory at the service of patients and health researchers. Three classes of services are available today: retrospective analysis of existing patient data for descriptive and clustering purposes; automation of knowledge extraction, ranging from text mining, patient selection for trials, to generation of new research hypotheses; and finally the creation of Decision Support Systems, with the integration of data from the hospital data warehouse, apps, and Internet of Things.

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What Role Can Process Mining Play in Recurrent Clinical Guidelines Issues? A Position Paper

Cited by 15 publications

References 94 publications

A differential process mining analysis of COVID-19 management for cancer patients

A differential process mining analysis of COVID-19 management for cancer patients

Using Process Mining in Healthcare

Building an Artificial Intelligence Laboratory Based on Real World Data: The Experience of Gemelli Generator

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