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The recent increase in the availability of medical data, possible through automation and digitization of medical equipment, has enabled more accurate and complete analysis on patients' medical data through many branches of data science. In particular, medical records that include timestamps showing the history of a patient have enabled the representation of medical information as sequences of events, effectively allowing to perform process mining analyses. In this paper, we will present some preliminary findings obtained with established process mining techniques in regard of the medical data of patients of the Uniklinik Aachen hospital affected by the recent epidemic of COVID-19. We show that process mining techniques are able to reconstruct a model of the ICU treatments for COVID patients.
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Analyzing Medical Data with Process Mining:
a COVID-19 Case Study
Marco Pegoraro 1, Madhavi Bangalore Shankara Narayana 1,
Elisabetta Benevento 1,3, Wil M.P. van der Aalst 1, Lukas Martin 2, and
Gernot Marx 2
1Chair of Process and Data Science (PADS), Department of Computer Science,
RWTH Aachen University, Aachen, Germany
{pegoraro, madhavi.shankar, benevento, vwdaalst}@pads.rwth-aachen.de
2Department of Intensive Care and Intermediate Care,
RWTH Aachen University Hospital, Aachen, Germany
{lmartin, gmarx}@ukaachen.de
3Department of Ener, Systems, Territory and Construction Engineering, University of Pisa, Pisa, Italy
Abstract
The recent increase in the availability of medical data, possible through automa-
tion and digitization of medical equipment, has enabled more accurate and com-
plete analysis on patients’ medical data through many branches of data science.
In particular, medical records that include timestamps showing the history of a
patient have enabled the representation of medical information as sequences of
events, efectively allowing to perform process mining analyses. In this paper, we
will present some preliminary ndings obtained with established process mining
techniques in regard of the medical data of patients of the Uniklinik Aachen hos-
pital afected by the recent epidemic of COVID-19. We show that process mining
techniques are able to reconstruct a model of the ICU treatments for COVID pa-
tients.
Keywords: Process Mining ·Healthcare ·COVID-19.
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ternational” license.
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This document is an Author Accepted Manuscript (AAM) corresponding to the following scholarly paper:
Pegoraro, Marco, Madhavi Bangalore Shankara Narayana, Elisabetta Benevento, Wil M. P. van der Aalst, Lukas Martin,
and Gernot Marx. “Analyzing Medical Data with Process Mining: a COVID-19 Case Study”. In: Business Information
Systems Workshops. Ed. by Abramowicz, Witold, S¨
oren Auer, and Milena Str´
o˙
zyna. Springer, 2022, pp. 39–44
Please, cite this document as shown above.
Publication chronology:
2021-05-01: full text submitted to the Workshopon Applications of Knowledge-Based Technologies in Business, work-in-progress track
2021-05-14: notication of acceptance
2021-05-19: camera-ready version submitted
2021-06-15: presented
2022-04-06: proceedings published
The published version referred above is ©Springer.
Correspondence to:
Marco Pegoraro, Chair of Process and Data Science (PADS), Department of Computer Science,
RWTH Aachen University, Ahornstr. 55, 52074 Aachen, Germany
Website: http://mpegoraro.net/ ·Email: pegoraro@pads.rwth-aachen.de ·ORCID: 0000-0002-8997-7517
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M. Pegoraro et al. Analyzing COVID-19 Data with Process Mining
1 Introduction
The widespread adoption of Hospital Information Systems (HISs) and Electronic Health
Records (EHRs), together with the recent Information Technology (IT) advancements,
including e.g. cloud platforms, smart technologies, and wearable sensors, are allowing
hospitals to measure and record an ever-growing volume and variety of patient- and
process-related data [7]. This trend is making the most innovative and advanced data-
driven techniques more applicable to process analysis and improvement of healthcare
organizations [5]. Particularly, process mining has emerged as a suitable approach to ana-
lyze, discover, improve and manage real-life and complex processes, by extracting knowl-
edge from event logs [1]. Indeed, healthcare processes are recognized to be complex, ex-
ible, multidisciplinary and ad-hoc, and, thus, they are dicult to manage and analyze
with traditional model-driven techniques [9]. Process mining is widely used to devise in-
sightful models describing the ow from diferent perspectives—e.g., control-ow, data,
performance, and organizational.
On the grounds of being both highly contagious and deadly, COVID-19 has been
the subject of intense research eforts of a large part of the international research com-
munity. Data scientists have partaken in this scientic work, and a great number of arti-
cles have now been published on the analysis of medical and logistic information related
to COVID-19. In terms of raw data, numerous openly accessible datasets exist. Eforts
are ongoing to catalog and unify such datasets [6]. A wealth of approaches based on
data analytics are now available for descriptive, predictive, and prescriptive analytics, in
regard to objectives such as measuring efectiveness of early response [8], inferring the
speed and extent of infections [2,10], and predicting diagnosis and prognosis [11]. How-
ever, the process perspective of datasets related to the COVID-19 pandemic has, thus far,
received little attention from the scientic community.
The aim of this work-in-progress paper is to exploit process mining techniques to
model and analyze the care process for COVID-19 patients, treated at the Intensive Care
Unit (ICU) ward of the Uniklinik Aachen hospital in Germany. In doing so, we use a
real-life dataset, extracted from the ICU information system. More in detail, we discover
the patient-ows for COVID-19 patients, we extract useful insights into resource con-
sumption, we compare the process models based on data from the two COVID waves,
and we analyze their performance. The analysis was carried out with the collaboration
of the ICU medical staf.
The remainder of the paper is structured as follows. Section 2describes the COVID-
19 event log subject of our analysis. Section 3reports insights from preliminary process
mining analysis results. Lastly, Section 4concludes the paper and describes our roadmap
for future work.
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M. Pegoraro et al. Analyzing COVID-19 Data with Process Mining
Figure 1: Dotted chart of the COVAS event log. Every dot corresponds to an event recorded in the log; the
cases with Acute Respiratory Distress Syndrom (ARDS) are colored in pink, while cases with no ARDS are
colored in green. The two “waves” of the virus are clearly distinguishable.
2 Dataset Description
The dataset subject of our study records information about COVID-19 patients moni-
tored in the context of the COVID-19 Aachen Study (COVAS). The log contains event
information regarding COVID-19 patients admitted to the Uniklinik Aachen hospital
between February 2020 and December 2020. The dataset includes 216 cases, of which
196 are complete cases (for which the patient has been discharged either dead or alive)
and 20 ongoing cases (partial process traces) under treatment in the COVID unit at the
time of exporting the data. The dataset records 1645 events in total, resulting in an aver-
age of 7.6 events recorded per each admission. The cases recorded in the log belong to
65 diferent variants, with distinct event ows. The events are labeled with the executed
activity; the log includes 14 distinct activities. Figure 1shows a dotted chart of the event
log.
3 Analysis
In this section, we illustrate the preliminary results obtained through a detailed process
mining-based analysis of the COVAS dataset. More specically, we elaborate on results
based on control-ow and performance perspectives.
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M. Pegoraro et al. Analyzing COVID-19 Data with Process Mining
Start startSymptoms Hospitalization
startOxygen
endOxygen
endSymptoms ICUadmission
startVentilation
startECMO
endECMO
endVentilation
ICUdischarge
DischDeadDischAlive
End
Figure 2: A normative Petri net that models the process related to the COVAS data.
Firstly, we present a process model extracted from the event data of the COVAS event
log. Among several process discovery algorithms in literature [1], we applied the Inter-
active Process Discovery (IPD) technique [3] to extract the patient-ows for COVAS pa-
tients, obtaining a model in the form of a Petri net (Figure 2). IPD allows to incorporate
domain knowledge into the discovery of process models, leading to improved and more
trustworthy process models. This approach is particularly useful in healthcare contexts,
where physicians have a tacit domain knowledge, which is dicult to elicit but highly
valuable for the comprehensibility of the process models.
The discovered process map allows to obtain operational knowledge about the struc-
ture of the process and the main patient-ows. Specically, the analysis reveals that
COVID-19 patients are characterized by a quite homogeneous high-level behavior, but
several variants exist due to the possibility of a ICU admission or to the diferent out-
comes of the process. More in detail, afer the hospitalization and the onset of rst symp-
toms, if present, each patient may be subject to both oxygen therapy and eventually ICU
pathway, with subsequent ventilation and ECMO activities, until the end of the symp-
toms. Once conditions improve, patients may be discharged or transferred to another
ward.
We evaluated the quality of the obtained process model through conformance check-
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M. Pegoraro et al. Analyzing COVID-19 Data with Process Mining
Figure 3: Plot showing the usage of assisted ventilation machines for COVID-19 patients in the ICU ward
of the Uniklinik Aachen. Maximum occupancy was reached on the 13th of April 2020, with 39 patients
simultaneously ventilated.
ing [1]. Specically, we measured the token-based replay tness between the Petri net
and the event log, obtaining a value of 98. This is a strong indication of both a high
level of compliance in the process (the ow of events does not deviate from the intended
behavior) and a high reliability of the methodologies employed in data recording and
extraction (very few deviations in the event log also imply very few missing events and a
low amount of noise in the dataset).
From the information stored in the event log, it is also possible to gain insights re-
garding the time performance of each activity and the resource consumption. For exam-
ple, Figure 3shows the rate of utilization of ventilation machines. This information may
help hospital managers to manage and allocate resources, especially the critical or shared
ones, more eciently.
Finally, with the aid of the process mining tool Everow [4], we investigated difer-
ent patient-ows, with respect to the rst wave (until the end of June 2020) and second
wave (from July 2020 onward) of the COVID-19 pandemic, and evaluated their perfor-
mance perspective, which is shown in Figures 4and 5respectively. The rst wave involves
133 cases with an average case duration of 33 days and 6 hours; the second wave includes
63 patients, with an average case duration of 23 days and 1 hour. The diference in average
case duration is signicant, and could have been due to the medics being more skilled and
prepared in treating COVID cases, as well as a lower amount of simultaneous admission
on average in the second wave.
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M. Pegoraro et al. Analyzing COVID-19 Data with Process Mining
Figure 4: Filtered directly-follows graph related to
the rst wave of the COVID pandemic.
Figure 5: Filtered directly-follows graph related to
the second wave of the COVID pandemic.
4 Conclusion and Future Work
In this preliminary paper, we show some techniques to inspect hospitalization event data
related to the COVID-19 pandemic. The application of process mining to COVID event
data appears to lead to insights related to the development of the disease, to the eciency
in managing the efects of the pandemic, and in the optimal usage of medical equipment
in the treatment of COVID patients in critical conditions. We show a normative model
obtained with the aid of IPD for the operations at the COVID unit of the Uniklinik
Aachen hospital, showing a high reliability of the data recording methods in the ICU
facilities.
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M. Pegoraro et al. Analyzing COVID-19 Data with Process Mining
Among the ongoing research on COVID event data, a prominent future develop-
ment certainly consists in performing comparative analyses between datasets and event
logs geographically and temporally diverse. By inspecting diferences only detectable
with process science techniques (e.g., deviations on the control-ow perspective), novel
insights can be obtained on aspects of the pandemic such as spread, efectiveness of dif-
ferent crisis responses, and long-term impact on the population.
Acknowledgements
We acknowledge the ICU4COVID project (funded by European Union’s Horizon 2020
under grant agreement n. 101016000) and the COVAS project for our research interac-
tions.
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Interactive Data-Driven Process Model Construction
  • Prabhakar M Dixit
  • H M W Verbeek
  • C A M Joos
  • Buijs
Dixit, Prabhakar M., H. M. W. Verbeek, Joos C. A. M. Buijs, et al. "Interactive Data-Driven Process Model Construction". In: Conceptual Modeling -37th International Conference, ER 2018, Xi'an, China, October 22-25, 2018, Proceedings. Ed. by Trujillo, Juan, Karen C. Davis, Xiaoyong Du, et al. Vol. 11157. Lecture Notes in Computer Science. Springer, 2018, pp. 251-265. : 10.1007/978-3-030-00847-5_19.