COVID-19 Treatment Process Identification: A Case Study in

Russian Hospital for Cardiology

Liubov Elkhovskaya

, Sergey Kovalchuk

1,2 b

, Ilya Potapov

and Dmitrii Ovchinnikov

ITMO University, 49 Kronverksky Prospect, 197101 St. Petersburg, Russia

Almazov National Medical Research Centre, 2 Akkuratova Street, 197341 St. Petersburg, Russia

Keywords: Process Mining, Process Discovery, Clinical Pathway, COVID-19, Healthcare.

Abstract: The COVID-19 pandemic has caused significant strain on medical facilities. The race between global

pandemic spread and medical practices progression produced a plethora of clinical guidelines. In Russia, more

than ten official versions of such guidelines have been developed since the start of the pandemic. Thus,

treatment processes have undergone several changes. Additionally, organizational schemes of patient care

delivery were affected by the availability of hospital resources. In our study, we identified the characteristics

of COVID-19 treatment processes at a large multidisciplinary hospital, that was adapted for treating COVID-

19 several times during disease outbreaks. For this task, we used a process mining technique. Given the

peculiarities of the hospital information system, we developed an approach for analysing treatment flow. Then,

we compared clinical pathways in different pandemic periods and verified compliance with the official

guidelines.

1 INTRODUCTION

The new coronavirus (SARS-CoV-2) infection

emerged in the Chinese province of Hubei at the end

of 2019. Since then, it has spread throughout the

world and has led to ongoing pandemic. COVID-19,

a potentially severe respiratory disease caused by the

coronavirus, imposed harsh conditions on all

countries’ healthcare systems. The growing spread of

coronavirus has caused significant strain on medical

facilities. Most of them were prepared poorly for

increased patient flows: hospitals lacked sufficient

bed capacity, medications, and staff resources. Thus,

World Health Organization (WHO) developed

guidance

on treating

COVID-19

to provide clinicians

with an efficient and safe patient care strategy. Based

on these recommendations, many governments

proposed their own guidelines to support healthcare

systems according to the current situation within a

country.

In Russia over more than 1,5 years of fighting

COVID-19, The Ministry of Health has developed

https://orcid.org/0000-0002-3121-8577

https://orcid.org/0000-0001-8828-4615

https://orcid.org/0000-0003-1756-6951

https://orcid.org/0000-0001-9430-7561

more than ten versions of clinical practice guidelines

for COVID-19 prevention, diagnosis, and treatment.

So, healthcare processes were changed several times

during the pandemic. Availability of hospital

resources and morbidity “waves” (spikes in cases)

also affected the organizational schemes of patient

care delivery. Many medical facilities changed their

specialty and were adapted, allocating some or all of

their bed capacity,

for treating COVID-19

. Almazov

National Medical Research Centre (Almazov

NMRC)

, a major scientific contributor and

healthcare provider specialized in cardiology in

Russia, was no exception. It provided resources

(beds, staff, etc.) several times when morbidity

reached its peaks in Saint Petersburg, where the

pandemic situation was one of the most intense.

Discovery of clinical pathways or treatment

processes aims at indicating current as well as best

clinical practices. A better understanding of real-life

clinical pathways through process mining can

contribute to care and data quality assurance by

analysing information system peculiarities, identifying

www.who.int/emergencies/diseases/novel-coronavirus-

2019/technical-guidance/patient-management

minzdrav.gov.ru/ministry/med_covid19 (in Russian)

www.almazovcentre.ru/?lang=en

Elkhovskaya, L., Kovalchuk, S., Potapov, I. and Ovchinnikov, D.

COVID-19 Treatment Process Identiﬁcation: A Case Study in Russian Hospital for Cardiology.

DOI: 10.5220/0010970400003123

In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF, pages 755-765

ISBN: 978-989-758-552-4; ISSN: 2184-4305

755

unmet needs, and improving patient care and

outcomes. In our study, we identified COVID-19

treatment processes in Russian hospital for cardiology

during different pandemic periods using a process

mining technique. Also, we aimed to see how clinical

guidelines that were developed gradually and “bottom-

up” (from local facilities practices to global ones),

affected or were influenced by best practices. Let us

first familiarize a reader with process mining in brief.

2 PROCESS MINING

Process Mining is an emerging discipline adopting a

data-driven approach and a classical model-based

process analysis. It has been actively developing

since there is still a demand for better insight into

what is happening at an institution. Process mining is

a promising approach to reveal and analyse the real

processes existing in all companies today. There are

three types of process mining: process discovery,

conformance checking, and process enhancement

(W. van der Aalst, 2016). With discovery algorithms,

one can automatically obtain a (business) process

model from routinely recorded data. This type of

process mining is a research topic of most interest

(Garcia et al., 2019). The results of process discovery

techniques can be used further in conformance

checking and enhancement. A priori process model

(discovered from the data or elaborated “by hand”) is

evaluated on its compliance with data by

conformance checking techniques, and its

enhancement can be proposed after an analysis of

process performance measures. In this study, we

perfrom analysis using process discovery techniques.

It is necessary to provide basic definitions and a

general view of process discovery. Every process-

aware information system that records run-time

behavior has an event log. An event log is a file that

contains information about process execution. Each

record is an event with associated data: timestamp of

its start and completion, an activity and resource that

executes this activity, and a process case id (instance)

the record belongs to. These are the minimal items for

compiling a log. However, if activities are considered

to be atomic, i.e., have no duration, the last item is

needed only for defining the order of them and can be

skipped if we a priori know data is stored according

to a timeline. We group an ordered set of events

containing only activity names into cases, that

represent single process runs. This “flat” event log is

used as an input for process mining in our discovery

algorithm. While an event log is an input, the

algorithm’s output is a (business) process model, or a

process map. In our case, a process model represents

a formal graphical description of the actual process

flow, i.e., the precedence of events, where nodes are

activities and edges are ordered relationships between

them.

As we briefly introduced process mining, we

further provide a literature review on the problem we

concern and how data and process mining techniques

address it.

3 RELATED WORKS

Processes in the healthcare sector are examples of

highly varying and distributed processes since they

are ad-hoc and healthcare information systems

usually are not process-aware (Batista & Solanas,

2019). That is why healthcare is the most researched

application domain of process discovery techniques

(Erdogan & Tarhan, 2018; Garcia et al., 2019). For

example, clinical pathways were derived from

different clusters of patient flow in facility

departments using a genetic algorithm (Funkner et al.,

2017). In study (Baker et al., 2017), the authors

pointed out that only little percentage of patients

completed the planned six cycles of chemotherapy

without unplanned hospital contacts. Information

extracted by the process mining pipeline can be also

used in prognosis, e.g., to estimate patient recovery

time (Kempa-Liehr et al., 2020).

During the review of existing studies, we figured

out that only a few works dedicated to process mining

application in COVID-19 case has been published.

The most of studies share experience in the COVID-

19 management, where retrospective data was

analysed and some conclusions about resources and

treatment process were made. Such works like

(Demirhan, 2020) are undoubtedly important in the

best clinical practices sharing, and the next researches

should use data driven approaches for better

analysing real-life clinical pathways. In study (Meng

et al., 2020), the authors designed a clinical pathway

for pre-operative COVID-19 screening in traumatic

fracture patients and assessed surgery waiting times.

Safety of medical activities were assured at the cost

of increased surgery delays by 2-4 days. The COVID

pandemic effects on waiting times of diagnosis and

treatment of nasopharyngeal carcinoma were also

studied in (Yang et al., 2020). Another retrospective

study (Thai et al., 2020) investigated factors, that

influenced length of stay (LoS) in Vietnam hospital

during this second phase of the COVID-19 pandemic.

Age group, region of residence and source of

infection were demonstrated to be associated with

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Figure 1: Bed arrangement periods (red areas) and number of infection cases in Saint Petersburg. Clinical guidelines versions

are shown in verticals.

longer hospital stay. The most interesting fact here is

that the median duration of hospital stay in Vietnam

and China was longer than in the United States and

several European countries. This can be explained by

different process organization structures. Process-

oriented data science techniques could study all the

direct and indirect problems mentioned above: process

model elaborating or discovery and its conformance

checking, LoS assessment, bottleneck identification,

etc. However, it requires process-aware information

system. Current lack of relevant studies may be caused

by lack of data on COVID-19 treatment process.

A work directly related to our study was done in

(Pegoraro et al., 2021). The authors reconstructed a

treatment model for COVID patients in intensive care

unit using data from the Uniklinik Aachen hospital

covering two first waves between February 2020 and

December 2020. Their preliminary results are

essential: besides the revealed structure and main

flow of the process, the authors calculated the rate of

utilization of ventilation machines and average case

duration with respect to different waves. Such

operational knowledge is vital in a case of resource

constraints and may help hospital managers to

efficiently allocate them.

4 CASE STUDY

In this section, we describe our experiments on

process discovery from not process-aware hospital

information system (HIS). Our colleagues from

Data from yandex.ru/covid19/stat

Almazov National Medical Research Centre provided

us anonymized database with patient electronic health

records (EHR) covering COVID-19 treatment cases

in their facility. Almazov Centre arranged pandemic

patient beds three times when patient inflows were

drastically increased during disease waves

(Fig. 1).

As it turns out, treatment processes in different

pandemic periods were not the same, since clinicians

gained more experience in disease management and

new government recommendations were stated.

Below we describe data and its issues, how we

addressed the challenges to prepare an event log, a

tool we used to discover a process model, and what

insights we got from results obtained.

4.1 Data Description

We had data on COVID-19 treatment at Almazov

NMRC for different periods, during its “routine” and

“emergency mode”, from March 2020 to June 2021.

Unfortunately, the data covered only two waves in

May 2020 and in December 2020. The dataset is a

series of records from EHR. One record contains infor-

mation on patient id, event description and associated

EHR section name, event id, timestamp, specialist

name and type, department, record status, and semi-

structured text, which is, e.g., anamnesis in natural

language or supplementary system information.

Patients included in the dataset had PCR-confirmed or

not PCR-confirmed COVID-19 diagnosis (U07.1 or

U07.2 ICD-10 codes, respectively). Table 1 presents

some statistics on the dataset we had.

COVID-19 Treatment Process Identiﬁcation: A Case Study in Russian Hospital for Cardiology

757

As mentioned, the HIS is not process-aware: it

contains a collection of fragmented medical records

from patient history. However, the data can be

transformed to an event log by resolving the problems

that might be encountered. We describe event log

issues and its remedy in the following subsection.

4.2 Event Log Preparation

Here, we outline steps we performed for event log

creation from raw data source following event log

imperfection patterns (Suriadi et al., 2017).

Preliminary data pre-processing included deletion of

records with not realized events having status not

completed, can-celled, no-show, etc. This pre-step

resulted in 9,790,199 records.

Form-based Event Capture. In our case, it is a

common pattern since the data is a set of records from

EHRs. When users (clinicians, nurses, etc.) save

electronic-based forms by clicking ‘Save’ button, they

trigger the recording of the data captured by the form

with the same timestamp. The order of activities within

the form is flatten. One of the Almazov Centre’s HIS

feature is possibility to update electronic form in any

time, which additionally causes this undesirable side

effect. So, we restored date and time from semi-

structured texts where it was explicitly associated with

corresponding event (record).

Table 1: Dataset summary.

Attribute Num. of

unique

Example (if applicable)

Patient ID 3,313

Event ID 10,655,223

Event

description

2,052 First appointment with a

cardiologist

In-hospital transfer

SARS-CoV-2 IgG

antibodies test

EHR

Section

587 Patient complaints

Hospital diagnosis

Thoracic computed

tomograph

ecialist 2,201

Specialist

Type

178 Cardiologist

Infectiologist

Nurse

Department 248 Laboratory

Infectious disease ward

Cardiovascular sur

unit

Status 9 Completed

Cancelled

Transferre

github.com/Siella/ProFIT

Distorted Label. As seen from Table 1, Event

description column has more than 2,000 unique

entities, which can spoil discovery of a main process.

A plethora of labs and tests causes such diversity.

Moreover, typo or different spelling exacerbates the

problem. So, we decided to use EHR Section column

as an event attribute since it has a higher level of

abstraction but enough information to understand

actions taken. Here, EHR Section is a “category” for

events. For example, “Biomaterial sampling” (EHR

Section) covers test types, which refer to Event

description attribute.

Collateral Events. It is a case when multiple events

essentially refer to one particular process step. We

partially resolved this problem in previous step, but it

also could be done within EHR Section level. We

aggregated high-level events by case (patient id) and

timestamp, since some of them were fragmented in the

system because of different supplementary informa-

tion. We thus had a dataset with 1,035,118 entries.

Homonymous Label. The repeated activities,

which actually have different meanings, are grouped

into one leading to “overloaded” nodes in the model.

Transferring to a higher level of abstraction caused

such problem. For example, ‘Biomaterial sampling’

or ‘Test results view’ incorporates a range of tests. In

this regard, we preserved only events on a lower level,

which are explicitly associated with COVID-19

treatment within tests and nursing. Events

corresponding results viewing and patient monitoring

routine additionally were aggregated by date but not

timestamp as previously.

After these steps, we got an event log where

process case is defined by patient id. The final data

sorted by a timestamp and event id (to maintain

system recording order) contained 307,610 entries.

Next, we divided the log into periods of disease

growths and declines, which correspond to

restructured and routine work of the hospital,

respectively. It is important to note that we did not

exclude incomplete cases, since we had enough

instances to capture the main paths. The reasons for

this decision are two-fold: (1) we cannot identify

clearly whether a case is complete or not; (2) we want

to show the ability of the tool to recover the main

process execution from a “slice” of data.

4.3 Process Discovery

We use the ideas of Fuzzy Miner (Günther & van der

Aalst, 2007) to develop a tool

for log analysis as a

Python package. The reasons for the algorithm choice

are two-fold: (i) the algorithm is suitable for

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unstructured and complex processes, which exist in

healthcare, due to con-structing a model at different

levels of details; (ii) a directly-follows graph (DFG)

as an algorithm output permits cycles, which are

crucial in a concept of meta-states (Elkhovskaya &

Kovalchuk, 2021), despite the DFG limitations (W.

M. P. van der Aalst, 2019). In healthcare, a cyclic

behaviour of the process may represent a routine

complex of procedures or repeated medical events,

i.e., a patient is at some treatment stage, or a meta-

state. We assume a cycle in the model to be a meta-

state if the estimated probability of the repeating

behaviour in the log exceeds the specified threshold.

We did not use this feature in the current study, but it

is one of the possible directions of a future work.

The main idea of frequency-based miners is to

find the most probable events and precedence

relationships over them. Here, the fundamental metric

is a significance that can be determined for event

classes (i.e., activities) and binary precedence

relations over them (i.e., transitions). Significance is

the absolute or case frequency of activities or

transitions that are occurred in the event flow. We

decide which elements to remain by evaluating their

significance and filtering them: more frequently

observed events and transitions are deemed more

significant and therefore included in the model.

Fuzzy logic does not guarantee a reachable graph

which is desired to see the complete behaviours of

process traces. So, we modify model construction by

performing the depth-first search to check whether

each node of the DFG is a descendant of the initial

state and a predecessor of the terminal state. If the

model does not match these conditions, we add edges

with respect to their significance to the model until

we get a reachable graph. This way, we overcome the

possibility of discovering an unsound model (without

the option to complete the process).

Within the used visual notation, the green vertex

(“start”) indicates the beginning of the process and

shows the total number of cases presenting in the log,

and the red vertex (“end”) is related to a terminal

state. The graph’s internal vertices and edges show

the absolute frequencies of events and transitions,

respectively: more value, darker or thicker element.

5 RESULTS

In this section, all process maps shown are obtained

by the tool described previously. They were adjusted

manually with activity and transition rates of 70% and

0%, respectively, which mean that only activities and

transitions with significance more than or equal to 0.3

and 1.0 are included in the model. In other words, we

aim to see only the main paths with some event

variations. Below we present a clinicians’ opinion

and interpretation of the results we obtained.

5.1 Non-COVID (“routine”) Mode

The difference in sizes of process models

immediately catches eye. The models of hospital’s

normal mode (Fig. 2) are smaller than clinical

pathways during adaptation to COVID-19 treatment

(Fig. 3-4). In addition, from these graphs, one can see

the increased number of patient inflows in the

pandemic waves.

It is very natural, that patients admitted for

COVID-19 treatment follow a far more elaborate

path. Curiously enough, these patients, in fact, have

far more similar (or uniform, even) treatment course

in general, in comparison to the patients, who were

treated for any other condition at Almazov Centre,

when they presented with COVID-19 symptoms and

had to undergo treatment for that too. Since these

“any other condition” type of patients have different

diagnoses that should be treated differently, it is only

natural, that they have less in common in terms of

clinical pathways.

5.2 COVID-19 (“emergency”) Mode

The processes identified in the periods of infection

cases declining (Fig. 2) are pretty like, apart from the

fact that number of patients increased after the first

wave. So, analysis of treatment processes during

COVID-19 outbreaks is of greater interest. As one

can see, patients were initially screened by a nurse

and after that admitted to the hospital. Next,

paperwork was followed by the first examination by

a doctor. Here, medical staff gathered information

about patients and evaluated their health state. One of

the mandatory steps in COVID-19 diagnostics and

treatment are PCR or SARS-CoV-2 antibody tests

and electrocardiogram (ECG), which completely

meet the official recommendations. The fact that

ECG appeared in almost all cases is remarkable. The

ECG is recommended in all versions of federal

clinical practice guidelines, because it is not only a

part of standard cardiological screening test, but also

an important tool of COVID-19 treatment’s adverse

effects prevention. Any viral infection or pneumonia

can increase the risk of development abnormal heart

rhythms and acute coronary syndrome, which can

impact the prognosis very severely, if not detected in

a timely manner. Moreover, some types of

medication, used for treatment of patients with

COVID-19 Treatment Process Identiﬁcation: A Case Study in Russian Hospital for Cardiology

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COVID-19, are known to cause cardiac

cardiotoxicity, which can be detected by screening for

QT interval prolongation in a series of ECGs. “Botkin

Hospital notes”, which are present in both periods, is

a historical name for thermometry records and other

nursing care events.

Some important differences between the first and

the second “emergency mode” periods should be

noted (Fig. 3-4).

Firstly, there are three additional events in the

second period: Morse Fall Scale risk assessment,

thromboembolic complication risk assessment, and

Figure 2: Clinical pathways for 2020/03/01-2020/05/12 (left) and 2020/08/01-2020/12/08 (right).

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Figure 3: Clinical pathway for 2020/05/13-2020/07/31 (1

wave).

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Waterloo pressure ulcer risk assessment. These

assessments were introduced by the federal control

institutions between the first COVID-19 wave and the

second one, which explains their absence during the

first COVID-19 period; but if we compare second

COVID-19 period with periods just right before and

after it, it is clear that these assessments were a far

more common thing in COVID-19 treatment process.

This is probably due to far more severe course of the

disease compared to any other conditions that patients

might have been admitted to Almazov Centre with.

So, this increased attention to prevention of typical

inpatient risks can be viewed as a sign of concerns

about COVID-19 complications being more

prominent during the second COVID-19 period.

Secondly, it seems that during this period more

patients had to stay in a hospital (maybe after being

transferred to another ward) after the end date of

second COVID-19 period, since there are clearly a lot

more cases in which the last point in patient’s

pathway is “Lab results viewing” event. This could

have no particular clinical meaning, since lab test

results can be added to EHR after the documents for

the discharge have been prepared. However, since

some other clinical processes directly precede this

event (such as various risk assessments), there is also

a very high possibility, that it is caused by the fact that

during the second COVID-19 period there was a

larger proportion of patients, whose condition was

somewhat (or significantly) severe. Our calculations

support the fact that an average hospital stay was 2.5

days longer the second time, if we consider the event

“New discharge letter” as finalising the treatment

process. Although overall the reasons of this feature

have to be investigated further.

Finally, a minor change of adding the “Inventory

of personal property and goods” event should be

noted. Of course, it is a standard procedure for any

kind of medical institution, but the fact that it starts to

appear in HIS records is a sign of the uptrend for

digitalization in health care as well.

6 DISCUSSION

As was assessed from the previous section, the

official guidelines were almost fully met in both

COVID-19 waves. We say “almost” because we

analyse treatment process with high-level abstraction

and there are aspects which should be considered

more granularly.

First, one is interested in examining the

composition and amount of laboratory research since

there is a separate section in the clinical guidelines

Figure 4: Clinical pathway for 2020/12/09-2021/03/15

wave). Main changes are highlighted in red.

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Figure 5: Amount of laboratory test types assigned

to patients in Covid-19 waves.

dedicated to tests. As seen in Figure 5, the ratio of lab

test types is nearly the same in both periods.

Biochemical screen prevails other testing types.

According to the guidelines, biochemical screening as

well as general clinical research should be run once in

a mild (outpatient) case, every second or third day in a

moderate (hospitalization) case, and every day in a

severe (intensive care) case; other lab measures should

be assessed mostly once and then for medical reasons.

That is why biochemical tests are a half of all labs

amount. However, general clinical research has the

same suggested frequency but not the same performing

in reality. This deviation can be explained by purposes

of the tests: general clinical research aims to assess

overall health, while biochemical screen helps to

specify a cause and which internal organs are targeted.

So, the last test type is performed more often to monitor

target organs health during the treatment.

Coagulation testing includes assessments of

measurements such as a d-dimer, activated partial

thromboplastin time, prothrombin ratio, etc.;

microbiology and immunology testing determine the

presence of SARS-CoV-2 and its antibodies,

respectively; hematologic study extends general

clinical research by platelet level analysis. This range

of tests covers the recommended scope of labs.

However, one measurement was not evaluated

contrary to the recommendations. It is NT-proBNP,

the N-terminal prohormone of brain natriuretic

peptide. The main reason is that this test, although

being very specific, is quite costly and, realistically,

cannot be performed routinely for every patient.

Moreover, there are various other methods of

diagnosing cardiac failure and cardiac toxicity.

Second, we can see that ECGs were performed

almost twice as often in the second wave as in the first

wave (Fig. 6). This fact is explained by the

differences in treatment schemes. During the first

wave, the medication guidelines included drugs with

proven cardiac toxicity, such as hydroxychloroquine.

So, there were a need in monitoring heart condition

more carefully (QT interval prolongation screen in

the first admission and then on every fifth day for

target patients). To the second wave, the treatment

was revised and alternative medications were

suggested. Here, ECGs were done in a case of target

groups or in a small number of cases, where the

previous treatment scheme was chosen for some

reasons.

Figure 6: The difference between event frequencies in the 2

and 1

Covid-19 waves adjusted by the number of patients.

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A new CT protocol, dedicated to COVID-19

diagnosis, was developed since the start of the 1

wave, so we can see a lot more of the specific

“Thoracic computed tomography – COVID-19

protocol” events. Thermometry was performed more

frequently, as it is implied by an increased amount of

“Botkin hospital notes” events. Meanwhile,

frequencies of performing CT (standard protocol) and

lab tests decreased (Fig. 6).

Finally, new treatment recommendations,

developed until and during the second wave, stated

that virus elimination period was shorter than in case

of usage of previous drug combinations. As we

revealed in the previous subsection, the length of stay

was longer the second time. The second “emergency”

mode in Almazov Centre was 1 month longer than the

first one (Fig. 1). At the same time, there were more

of the completed episodes of care (event “Episode

closure” in Fig. 3-4) but less arranged beds during the

second wave. Since the number of patients involved

in the process (depicted in the green vertex) is less

than completed episodes, we can refer to readmission

cases or patient transferring. Nevertheless, there are

still questions and paradoxes regarding in-hospital

times.

7 CONCLUSIONS

Some limitations present in our study and we have to

mention them. First, the variation in patient treatment

processes due to different patient models was not

taken into account. It could affect resulting process

models and possibly make them less general as was

in case of periods between COVID-19 waves (Fig. 2).

For example, patients admitted with a high-tech

medical care voucher usually need a surgery, while

many patients with other funding sources do not.

Also, clinical pathways differ for medical specialties.

The similarities and dissimilarities of these kinds of

clinical pathways provide a promising substrate for

further research. Second, in order to clearly trace the

impact of changes in clinical practice guidelines on

clinical pathways, the latter should be investigated in

more detail, because these changes often were minor

and could not possibly affect the key elements of

patient’s trajectory, such as necessary laboratory tests

or CT scans. Third, data structure we had included

static and dynamic elements. For example, “Hospital

diagnosis” could be changed several times, and the

system recorded it as a new instance. The same can

be stated about laboratory tests, ECGs, and other

procedures, that were usually performed several to

plenty of times during one treatment course. We

partially addressed this issue in Section 3.2, but it still

could be reflected in the model as most frequent

events. Finally, we did not consider other

methodologies to address the problem. Clinical

pathways can be analysed through modelling, e.g.,

simulation or other probabilistic models. However,

we also aimed to see the capacity of the emerging

discipline to model complex and ad-hoc processes.

Nevertheless, this work provides a promising

insight into how patient pathways can be modelled.

As it turned out, process mining has the potential for

addressing this problem. It demonstrates that the

more standard is this pathway, the easier it is to see it

in full detail, which raises a question of designing

proper patient models for any kind of treatment

processes research. In this paper, we identified

COVID-19 clinical pathways in Russian hospital for

cardiology during different pandemic periods using

process mining. Given peculiarities of the hospital

information system, we developed an approach for

analysing treatment flow. We confirmed clinical

practices compliance with the official guidelines,

which evolved while accumulating experience in

disease management.

ACKNOWLEDGEMENTS

This work was supported financially by the Ministry

of Science and Higher Education of the Russian

Federation, Agreement No. 075-15-2021-1013

(08.10.2021) (Internal project number

13.2251.21.0067). The authors also wish to thank the

colleagues from Almazov National Medical Research

Centre for the data provided and valuable

cooperation.

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