Digital Transformation in Medicine: From Formalized Medical Documents to Information Technologies of Digital Medicine
The purpose of the paper is to analyze the stages of digital transformation in medicine and the results of authors and their colleagues of the MIS department for the development of information technologies of digital medicine.
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irk-123456789-1504992019-04-10T01:25:39Z Digital Transformation in Medicine: From Formalized Medical Documents to Information Technologies of Digital Medicine Kozak, L.M. Kovalenko, A.S. Kryvova, O.A. Romanyuk, O.A. Медицинская и биологическая кибернетика The purpose of the paper is to analyze the stages of digital transformation in medicine and the results of authors and their colleagues of the MIS department for the development of information technologies of digital medicine. Метою статті є аналіз етапів цифрової трансформації в медицині і розробок авторів і їхніх колег відділу медичних інформаційних систем з розвитку інформаційних технологій цифрової медицини. Проанализированы этапы цифровой трансформации в медицине: І — цифровая трансформация первичной медицинской информации; ІІ — разработка систем поддержки лечебно-диагностического процесса; ІІІ — разработка технологий и систем поддержки деятельности врача с цифровой информацией; IV — мобильная медицина; V — глобализация цифровой медицины. Показан вклад разработок авторов и их коллег (отдел медицинских информационных систем) по развитию информационных технологий цифровой медицины на этих этапах. Представлены разработанные: метод определения маркеров функционального состояния ССС; ИТ поддержки процессов получения, передачи и хранения цифровых медицинских изображений; теория телемедицинских систем и результаты ее применения; базовая структура мобильного медицинского приложения и взаимодействие ее функциональных блоков с выделением задач и ограничений действий основных пользователей — врача и пациента. 2018 Article Digital Transformation in Medicine: From Formalized Medical Documents to Information Technologies of Digital Medicine / L.M. Kozak , A.S. Kovalenko, O.A. Kryvova, O.A. Romanyuk // Кибернетика и вычислительная техника. — 2018. — № 4 (194). — С. 61-78. — Бібліогр.: 25 назв. — англ. 0454-9910 DOI: https:// 10.15407/kvt194.04.061 http://dspace.nbuv.gov.ua/handle/123456789/150499 004.75+004.932.2:616 en Кибернетика и вычислительная техника Міжнародний науково-навчальний центр інформаційних технологій і систем НАН України та МОН України |
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Медицинская и биологическая кибернетика Медицинская и биологическая кибернетика |
| spellingShingle |
Медицинская и биологическая кибернетика Медицинская и биологическая кибернетика Kozak, L.M. Kovalenko, A.S. Kryvova, O.A. Romanyuk, O.A. Digital Transformation in Medicine: From Formalized Medical Documents to Information Technologies of Digital Medicine Кибернетика и вычислительная техника |
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The purpose of the paper is to analyze the stages of digital transformation in medicine and the results of authors and their colleagues of the MIS department for the development of information technologies of digital medicine. |
| format |
Article |
| author |
Kozak, L.M. Kovalenko, A.S. Kryvova, O.A. Romanyuk, O.A. |
| author_facet |
Kozak, L.M. Kovalenko, A.S. Kryvova, O.A. Romanyuk, O.A. |
| author_sort |
Kozak, L.M. |
| title |
Digital Transformation in Medicine: From Formalized Medical Documents to Information Technologies of Digital Medicine |
| title_short |
Digital Transformation in Medicine: From Formalized Medical Documents to Information Technologies of Digital Medicine |
| title_full |
Digital Transformation in Medicine: From Formalized Medical Documents to Information Technologies of Digital Medicine |
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Digital Transformation in Medicine: From Formalized Medical Documents to Information Technologies of Digital Medicine |
| title_full_unstemmed |
Digital Transformation in Medicine: From Formalized Medical Documents to Information Technologies of Digital Medicine |
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digital transformation in medicine: from formalized medical documents to information technologies of digital medicine |
| publisher |
Міжнародний науково-навчальний центр інформаційних технологій і систем НАН України та МОН України |
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2018 |
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Медицинская и биологическая кибернетика |
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http://dspace.nbuv.gov.ua/handle/123456789/150499 |
| citation_txt |
Digital Transformation in Medicine: From Formalized Medical Documents to Information Technologies of Digital Medicine / L.M. Kozak , A.S. Kovalenko, O.A. Kryvova, O.A. Romanyuk // Кибернетика и вычислительная техника. — 2018. — № 4 (194). — С. 61-78. — Бібліогр.: 25 назв. — англ. |
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ISSN 2519-2205 (Online), ISSN 0454-9910 (Print). Киб. и выч. техн. 2018. № 4 (194)
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DOI: https:// 10.15407/kvt194.04.061
UDC 004.75+004.932.2:616
KOZAK L.M., DSc (Biology), Senior Researcher,
Leading Researcher, the Medical Information Systems Department
e-mail: lmkozak52@gmail.com
KOVALENKO A.S., DSc (Medicine), Professor,
Head of the Medical Information Systems Department
e-mail: askov49@gmail.com
KRYVOVA O.A., Researcher,
the Medical Information Systems Department
e-mail: ol.kryvova@gmail.com
ROMANYUK O.A., Junior Researcher,
the Medical Information Systems Department
e-mail: ksnksn7@gmail.com
International Research and Training Center for Information Technologies
and Systems of the National Academy of Sciences of Ukraine
and Ministry of Education and Science of Ukraine,
Acad. Glushkov av., 40, Kiev, 03187, Ukraine
DIGITAL TRANSFORMATION IN MEDICINE: FROM FORMALIZED
MEDICAL DOCUMENTS TO INFORMATION TECHNOLOGIES
OF DIGITAL MEDICINE
Introduction. According to the Concept of Ukraine`s Digital Economy and Society Develop-
ment in 2018-2020, the key components of “digitalization” are the development of digital
infrastructure — broadband Internet throughout Ukraine, and the promotion of digital trans-
formations in various sectors of the economy and society, including medicine.
The purpose of the paper is to analyze the stages of digital transformation in medicine
and the results of authors and their colleagues of the MIS department for the development of
information technologies of digital medicine.
Results. A generated model of digital transformation in medicine is presented and sev-
eral main stages of this transformation are highlighted: І — digital transformation of pri-
mary medical information; ІІ — development of support systems for the diagnostic and
treatment process; ІІІ — development of technologies and systems for supporting the physi-
cians` activities with digital information; IV — mobile medicine; V — the digital medicine
© KOZAK L.M., KOVALENKO A.S., KRYVOVA O.A., ROMANYUK O.A., 2018
61
Kozak L.M., Kovalenko A.S., Kryvova O.A., Romanyuk O.A.
ISSN 2519-2205 (Online), ISSN 0454-9910 (Print). Киб. и выч. техн. 2018. № 4 (194) 62
globalization. The method of determining the markers of the functional state of the cardio-
vascular system based on mathematical models of forecasting and classification with the use
of Data Mining is proposed. The method allows detecting and determining the prognostic
values of ECG parameters of the CVS functional state for different groups of patients. The
developed IT for supporting the processes of receiving, transmitting and storing digital medi-
cal images is aimed at ensuring the effective operation of a physician with digital information
from various sources: functional diagnostic complexes, digital medical data storage and
images using Picture Archiving and Communication Systems (PACS) and cloud technologies.
The proposed telemedicine systems theory including the formulated principles of organizing
these systems, criteria and methods for analyzing digital medical data has been implemented
for elaborating and functioning the Telemedicine Center. It enables to cover the population
in more than 20 Ukraine`s regions with qualified medical assistance.
Conclusions. The digital transformation in medicine like any new process takes place
with a gradual complication of tasks, methods and means of their implementation: from formal-
ization of primary medical information to improvement of methods of its analysis, transfer and
storage to improve the quality of medical care for patients at any point of the world.
Keywords: digital transformation in medicine, formalized medical records, Data Mining, IT for
assessing human state and physiological systems` state, telemedicine, m-medicine.
INTRODUCTION
Today, Ukraine’s pace of transition to high-tech industries and efficient proc-
esses is increasing using IT technologies and communications. According to the
Concept of Ukraine`s Digital Economy and Society Development in 2018-2020,
the key components of “digitalization” are the development of digital infrastruc-
ture — broadband Internet throughout Ukraine, and the promotion of digital
transformations in various sectors of the economy and society, including medi-
cine [1]. On the way to the digital society, it is necessary for Ukraine to combine
the possibilities of domestic production with the possibility of wide use and con-
sumption of communication and digital technologies. The experience of many
world countries and the results of the implementation of products, designed by
Ukrainian specialists, demonstrate the unique opportunities that digital medicine
provides for increasing the efficiency of medical care.
More than 50 years ago, on the initiative of academicians V.M. Glushkov
and N.M. Amosov, a new direction of scientific research — biological and
medical cybernetics, was founded. During these years, scientists of the Medical
Information Systems Department of the International Research and Training
Center for Information Technologies and Systems have carried out research,
have developed and implemented methods and means of formalization and in-
formation support for diagnostic and treatment processes.
PROBLEM STATEMENT
The modern world is rushing into the process of digital transformation (DT). First of
all, this process covers the commercial activities of modern society. In their funda-
mental report, “Digital Transformation: A Roadmap for Organizations with Billion
Turnovers”, which was named as one of the top five intellectual ideas of the decade
according to Whitespace/Source.com., George Westerman, Didier Bonnet, Andrew
McAfee defined digital transformation for the sphere of production and management
as the use of modern technologies for drastically increasing the productivity and
Digital transformation in medicine: from formalized medical documents to information
ISSN 2519-2205 (Online), ISSN 0454-9910 (Print). Киб. и выч. техн. 2018. № 4 (194) 63
Object
(individual or patient)
Operational processes
Business models of the
diagnostic and treatment
process
knowledge about the object (patient information)
knowledge of the diagnostic and treatment processes
distant interaction with the object (individual / patient)
digitalization process (medical information formalizing)
increasing the possibilities of the specialist
management of medical institutions
analysis the object` state by mathematical modelling
IT distant medical consultant-patient interaction
electronic medical document platform
decision support by the physicians
Fig. 1. Digital transformation model in medicine
value of enterprises [2]. The authors identified three key areas of enterprise activity
in the digital transformation: customer experience, operational processes and busi-
ness models, described the components of these areas and concluded that digital
technologies combined with integrated information allow companies to obtain a
global synergistic effect, while retaining the ability to react sensitively to local
changes. Only some components of this model are currently implemented, the digi-
tal transformation is developing. There is a further expansion of the functions used
and components of the DT.
Digital medicine (DM) as branch of digital transformation is an extremely spe-
cific area not only according to the subject, but also to the quality of the information
analysed. Therefore, we define the main components of the digital transformation
model (DTM) in medicine, taking into account its tasks and terminology (Fig. 1).
Digital medicine is a set of methods, technologies and technical means of
computer support for the treatment and diagnostic processes, the use of which
dramatically increases the efficiency of providing medical care to a specific in-
dividual/patient, as well as to the whole population or some population groups.
We have identified several main stages in digital medicine developing:
І — digital transformation of primary medical information; ІІ — development of
support systems for the diagnostic and treatment processes; ІІІ — development
of technologies and systems for supporting the physician`s activities with digital
information; IV — mobile medicine; V — the digital medicine globalization.
These stages do not have clear boundaries and can occur simultaneously when
solving problems of different levels or with different degrees of preparedness of
digital medicine users — medical institutions, medical workers and patients.
Kozak L.M., Kovalenko A.S., Kryvova O.A., Romanyuk O.A.
ISSN 2519-2205 (Online), ISSN 0454-9910 (Print). Киб. и выч. техн. 2018. № 4 (194) 64
The purpose of the article is to analyse the stages of digital transformation
in medicine and authors’ contribution to the development of information tech-
nologies of digital medicine.
DIGITAL TRANSFORMATION OF PRIMARY MEDICAL INFORMATION
At this stage, digital medicine has been developing both technically and infor-
mationally. In accordance with the proposed DT model, the “Operational proc-
esses of the CM” (the digitalization of qualitative medical information, man-
agement of medical institutions) and the “Business-models of the medical proc-
esses” (Electronic medical document platform) are included.
As a part of the technical component, diagnostic devices giving information
about the patient’s health and certain physiological systems not in analogue (tra-
ditional recordings of cardiogram, encephalogram, electromyogram, etc.), but in
digital form were developed.
At the same time, methods for primary processing of the received informa-
tion (information component) were developed.
The beginning of the information component implementation was the stage
of formalization of medical information: medical data and records, medical
documents, the creation of a formalized medical history based on in-depth
analysis of a patient data.
These tasks, in particular, were solved in the Medical Information Systems
Department in the second half of the twentieth century. The monograph “Medi-
cal Information Systems” edited by Academician N. M. Amosov and Professor
Popov A.A. that laid the foundation for the methods of formalizing medical in-
formation was first published in 1971 and republished [3]. This monograph
raised a questions and gave the first decisions on the transformation of medical
data presentation forms, the organization of their automated processing, the crea-
tion of formalized medical cards for some nosological groups and approaches to
the development of mathematical software for medical information system.
Approaches to and methods for transforming qualitative medical informa-
tion into quantitative, digital records were formed.These methods of medical
data formalization became the basis for the development of standardized medical
documents (health passport), as well as the creation of electronic medical re-
cords. Standardized cards for various diseases and Standardized resort card and
other cards were created. Automated systems for entering, recording and storing
patient`s data were developed. Today, similar methods have been applied in
developing the standard for open EHR electronic medical records (Australia).
DEVELOPMENT OF SUPPORT SYSTEMS FOR THE DIAGNOSTIC AND TREATMENT PROCESSES
The beginning of this stage was laid down in the middle of the last century by
few developments, now this process covers all areas of medicine, work is being
carried out to create and improve DM diagnostic complexes: increasing the ac-
curacy of analysis and diagnostics, expanding the tasks, improving usability and
non-invasiveness. The development of such complexes covers a wide range of
objectives, and primarily on the components of the GT model “Object” (knowl-
edge about the object — about the patient) and “Business models of the diagnos-
Digital transformation in medicine: from formalized medical documents to information
ISSN 2519-2205 (Online), ISSN 0454-9910 (Print). Киб. и выч. техн. 2018. № 4 (194) 65
tic and therapeutic process” (using mathematical modeling methods to analyze
the state of the object).
Note the enlarged groups of tasks and the results obtained by employees of
MIS Department in each direction. In the 70–80 years of the twentieth century,
the first in the USSR models of the course of myocardial infarction were devel-
oped based on electrocardiographic and biochemical data, which made it possi-
ble to foresee its dynamics [4]. These models were implemented into the clinical
practice of the Kyiv Strazhesko Research Institute of Cardiology.
At the same time, theoretical and practical bases for the analysis of the elec-
tric field of the heart were developed using mathematical models, which made it
possible to create methods for automated analysis of ECG. Software realizing
the proposed algorithms was developed for the first time.
Of particular note are the problems of disaster medicine, a solution that was
based on long-term (since the 70s of the last century) studies, analysis and mod-
eling of the influence of external factors on the state of biosystems of different
levels by Vasilik P.V. He created a theory of the influence of heliogeomagnetic
factors on biosystems, which combines the principle of multichannel influence
of solar activity on living organisms, including human, the hypothesis about the
wave component of the gravitational field and the presence of a channel of non-
electromagnetic nature, along with the electromagnetic channel [5]. This made it
possible to predict the occurrence of epidemics, acceleration process, and cli-
mate change on Earth [6]. According to the results of the analysis of accident
data on various public infrastructure objects, periodograms of time series were
calculated and it was determined that there are rhythms in land and air transport
accidents, which will reduce the probability of emergency situations and, conse-
quently, the level of injuries [7].
Studies of changes in the state of an individuals and several physiological
systems using methods of mathematical modeling were carried out and their
results have formed the basis of decision support information technology in the
field of preventive medicine. Developed IT for assessing the psychophysiologi-
cal state of students to support the activities of psychologists in middle and high
schools [8–9], functional state models of the operators with high visual strain to
identify asthenopic disorders [10–11], IT for assessing of the population medical
and demographic state under the influence of various factors, which serve as the
basis for the formation of information support for management decision-making
in the health care system [12–15].
In recent years, we have developed the method for determining markers
of the cardiovascular system functional state, which is based on mathematical
models of communication of the ECG signs [16] and comprehensive assess-
ments of the regulation, state and reserves of the myocardium.
For prenosological diagnostics, it is important to carry out a comprehensive
assessment of the functional state (FS) of the cardiovascular system (CVS),
based both on the study of heart rate variability (HRV) [17] and on in-depth
analysis of 6, 12-channel ECG recordings [18].
A large amount of the initial data set (more than 300 ECG signs) and the need
for standardization of indicators into the interval deviation scale necessitated the
development of a multivariate method for analysis the functional state of the CVS
using Data Mining methods (DM). The peculiarity of medical data is a large num-
Kozak L.M., Kovalenko A.S., Kryvova O.A., Romanyuk O.A.
ISSN 2519-2205 (Online), ISSN 0454-9910 (Print). Киб. и выч. техн. 2018. № 4 (194) 66
ber of various interrelated parameters, often for small groups of observations. Data
Mining methods are more efficient for the selection of informative features, since,
unlike the statistical ones, there are no prerequisites for the data. Data processing
methods allow us to build a large number of prognostic models for both large and
small groups of observations. This allows us to obtain in an accessible form a new
knowledge that may be introduced into clinical practice.
The method combines the following stages.
Stage I. Data preparation. The objectives of this phase are:
• primary processing, which covers the cleaning, transformation, identifica-
tion of missing data, recoding;
• standardizing of primary indicators using an interval scale;
• defining target (dependent) variables and a set of independent indicators;
• dividing data into training and examination, testing samples in the case of
large samples (databases).
Stage II. Clustering Data. Segmentation includes the following steps.
2.1. The division of the sample of patients into typological groups is carried
out according to complex indicators (vegetative regulation state, estimates of the
myocardium and its reserves) and/or disease severity. To distribute patients into
groups, the k-Means method is used, which is implemented in the DM module.
This method makes it possible to calculate the optimal division into groups ac-
cording to the following criteria:
- Criterion for calculating cluster centres with minimization of the target
function:
min
1
2
1 →μ−μ=∑ ∑
= ∈
k
n Xx
ni
ni
F ,
where n is the number of objects to be divided into k-groups (clusters), F1 is the
sum of squares of distances between each object xi and the centre of the cluster
μn to which it belonged at each iteration;
- Criterion of the largest sum of distances between clusters:
max
1,
2
2 →μ−μ= ∑
=
k
in
niF .
2.2. In contrast to the classical k-Means method, in this method we addi-
tionally included a cross-check for n random samples, which allows minimizing
the error and choosing the optimal number of clusters. Optimization is carried
out before solving for clusters k+1, at which the error function (average distance
to cluster centres) is not more than 5% better compared with the solution of clus-
ters k. Then the solution with k clusters will be optimal and final.
2.3. Standardization of variables is carried out to convert it to the range
from 0 to 1:
minmax
max
xx
xxz i
i −
−
= .
The distance between objects and cluster centres is calculated using the
Euclidean distance.
Digital transformation in medicine: from formalized medical documents to information
ISSN 2519-2205 (Online), ISSN 0454-9910 (Print). Киб. и выч. техн. 2018. № 4 (194) 67
The results of this stage make it possible to identify and analyse the typo-
logical features of the selected clusters, taking into account such indicators as
evaluation of the CVS regulation, myocardial state (level of adaptation) and
others for the subsequent determination of predictors (the most informative
signs) on the basis of which violations of the regulation of cardiovascular system
and of myocardium state can be forecasted.
Stage III. Building models. This stage is aimed at identifying predictors and
combines several steps.
3.1. Feature Selection:
• identifying important predictors from a variety of prognostic features;
• removing unnecessary predictors.
3.2. Building models (forecast and classification) may be carried out using
several approaches: Neural Networks, Method of Classification and Regression
Trees, Boosted Trees.
3.3. Evaluation and comparison of simulation results to determine the opti-
mal model (for performance and complexity).
In this investigation, we use the Method of Classification and Regression
Trees (C & RT). The method of decision trees is a hierarchical and flexible
means of predicting the belonging of objects to a certain class or predicting the
values of quantitative variables. This method allows us to get a model, which is
a set of rules "IF (A) THEN (B)", where A is a logical condition, B is a subdivi-
sion procedure a subset into two parts, for one of which condition A is true, and
for the other, it is wrong. The results are easy to interpret because the rules are
presented in the form of a graphic structure (tree).
The construction of the tree goes from top to bottom by applying a recursive
procedure to a training sample ( size N ) using the following algorithm.
Selecting the threshold value of the variable x = A will provide “optimal
partitioning” according to a certain criterion for the target variable y.
For regression trees, the function of estimating the quality of a partition is
the sum of squared deviations or the mean square error:
2
1
)(1 yy
N
MSE
N
i∑ −= .
For classification trees (target variable is categorical), the Gini index or the
statistical criterion χ2 can be chosen as such a criterion:
d
j
k
ji
d
i ppdGini ∑
≠
−=1)( ,
where pi is the classification probability at node d as i or j, Gini (d) is the degree
of uncertainty reduction at node d.
Separation of data into subsets is applied for each subset (internal node).
Thus, the algorithm for constructing decision trees allows us to define a set
of characteristic values (attributes) that separate one data category from another.
This process is called segmentation.
The depth of the tree (its size) depends on the amount of data. The more
branches a tree has, the better results of its testing on a training sample will be,
but less successful they will be on an examination sample. Therefore, the con-
Kozak L.M., Kovalenko A.S., Kryvova O.A., Romanyuk O.A.
ISSN 2519-2205 (Online), ISSN 0454-9910 (Print). Киб. и выч. техн. 2018. № 4 (194) 68
structed model should also be optimal in size, that is, it should contain informa-
tion that improves the quality of recognition, and should ignore the information
that does not improve it. To do this, tree pruning is done.
A peculiarity of the C & RT algorithm is the choice of the optimal tree size
using cross-validation.
Stage IV. Verification of the classification model.
At this stage, the selected model with the optimal set of predictors is com-
pared with the previously obtained division into typological groups (clusters of
the II stage) or with the patient's state (severity of the disease). The confusion
matrix is calculated on typological groups obtained at the II and III stages.
Thus, the proposed method allows the construction of predictive and classi-
fication models of the relationship of complex indicators of the cardiac activity
regulation, assessments of the myocardium state and its reserves with ECG indi-
cators, the analysis of these models makes it possible to study the peculiarites of
the cardiac activity regulation.
The method was used to analyze the functional state of the CVS of children with
rheumatic diseases according to the signs system of a 6-channel ECG. The results of
the clinical and instrumental examination of children (41 children with rheumatic
diseases) who were hospitalized at the Institute of Pediatrics, Obstetrics and Gynecol-
ogy of the Academy of Medical Sciences of Ukraine were the basis for developing
models of the relationship of the studied parameters according to the proposed
method. ECGs were recorded and analyzed using the “Cardio Plus P” software and
hardware complex with the Cardio ORAKUL software. The “Cardio-plus P” registers
a large number of amplitude-time parameters, frequency indicators, characteristics of
the in-depth analysis of the ECG, and with the help of computer programs it calculates
a multi-level system of ECG estimates. The analysis is carried out according to the
hierarchical system of ECG assessments proposed in [18]. The system under study
identifyes four blocks of indicators.
Block of heart rate variability (HRV). HRV indices reflect the work of the car-
diovascular system and the mechanisms of regulation of the whole organism. The
HRV method is widely used in functional diagnostics, mass prenosological surveys,
for rapid diagnosis. These are indicators of temporal, spectral, geometric analysis, as
well as measures of nonlinear analysis of the heart rhythm complex dynamics. On
their basis, two secondary indicators are formed — operational control of regulation
and the state of regulation reserves, the third generalizing indicator — a comprehen-
sive regulation assessment, is formed from them [16, 18].
The block of amplitude-time ECG indicators has more than 130 signs. It is
known that a complex of ECG amplitude-time indicators may be the markers of the
risk of adverse cardiovascular events (sudden death, myocardial infarction, heart
failure). This complex characterizes the regulation of the heart (operative control of
the myocardium state and reserves), and the degree of compliance of these indica-
tors with the norm is a measure of the functional reserve.
Based on the primary features of these two blocks, comprehensive assess-
ments of the HRV regulation, myocardial conditions and indicators of in-depth
ECG analysis are formed.
The following blocks combine ECG signs of cardiac arrhythmias and psycho-
emotional indices. The final assessment is an integral indicator of the FS of the CVS.
It is formed as a linear convolution of complex indicators and other ECG signs.
Digital transformation in medicine: from formalized medical documents to information
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The algorithm for calculating the complex indicators system as a method of
electrocardiogram universal score evaluation was proposed and described in
detail in [20]. According to the specified system of indicators, forecast and clas-
sification models were constructed. The model in the form of a decision tree
clearly represents the rules for classifying observations, and the regression tree
shows the dependence of the target variable on the predictors. Each classifying
rule reflects a certain regularity that is hidden in empirical data.
Let us give a solution to one of the research problems. Figure 2 shows the
regression tree calculated by the C & RT algorithm for a complex indicator (as-
sessment) of the myocardium state. This tree obtained by cross-validation (10-
fold cross-validation), is optimal both in size and in the number of predictors.
Teaching data set — 41 children. The predictors are 5 signs:
1) the integral indicator of the form STT (lead II) integral form indicator
STT (lead II);
2) ECG wave amplitude index (lead AvF) ECG wave amplitude index
(V AvF));
3) T wave amplitude (lead II);
4) ST segment offset 0.08 sec after point J (lead II) ST-segment depression
at 80 ms after the J-point (V II)
5) angle αT in the frontal plane (lead II).
Tree 10 graph for КП СМ
Num. of non-terminal nodes: 4, Num. of terminal nodes: 5
ID=1 N=41
Mu=57,682927
Var=72,411660
ID=2 N=28
Mu=54,000000
Var=43,357143
ID=5 N=26
Mu=55,000000
Var=32,000000
ID=3 N=13
Mu=65,615385
Var=42,852071
ID=4 N=2
Mu=41,000000
Var=9,000000
ID=6 N=14
Mu=51,714286
Var=31,489796
ID=7 N=12
Mu=58,833333
Var=5,305556
ID=22 N=6
Mu=60,500000
Var=17,250000
ID=23 N=7
Mu=70,000000
Var=23,142857
Смещение сегмента ST через 0,08 сек после точки J (отведение II)
<= 0,050500 > 0,050500
Интегральный показатель формы STT (отведение I)
<= 44,500000 > 44,500000
Индекс амплитуд зубцов ЭКГ (отведение AvF)
<= 41,000000 > 41,000000
Интегральный показатель формы STT (отведение I)
<= 60,500000 > 60,500000
Fig. 2. Regression tree for a complex indicator (assessment) of the tate of the myocardium
Kozak L.M., Kovalenko A.S., Kryvova O.A., Romanyuk O.A.
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The root node indicates the average value of the complex indicator of the
myocardium state (CIMS) of a group of children with rheumatic diseases
(CIMS = 57.6%). The value of the indicator “ST segment displacement after
0.08 s after point J” equal to 0.05 mV determines the division into two main
groups (with low and higher myocardial scores).
The method allows to indicate the ECG predictors of the cardiovascular system
functional state according to estimates of vegetative regulation, the state of the myo-
cardium and its reserves, to determine the boundary values of these predictors for
different groups of patients, as well as to identify different functional classes.
DEVELOPMENT OF INFORMATION TECHNOLOGIES AND SYSTEMS
TO SUPPORT THE PHYSICIAN`S ACTIVITIES WITH DIGITAL MEDICAL DATA
The development of this stage corresponds to the components of the DTM
model — “Object: patient” (knowledge of the treatment and diagnostic process)
and “Operational processes” (increasing the possibilities of a specialist; manag-
ing the medical institutions activities).
1. The level of medical institutions
Over the past 30 years, a large number of complex medical systems (CMS)
have been developed. The implementation effectiveness of these CMS depends
on their compliance with the real needs of the medical institution, so CMS func-
tional content must be analyzed at the pre-project stage [19]. Recently the main
efforts have been made to ensure the effective information exchange between
different systems and modules. It is the reliability of this exchange that will en-
able the physician to use the necessary set of various digital medical data for the
diagnosis and treatment of patients.
Digital medical data includes, in addition to clinical and laboratory data,
such large groups as digital medical signals and digital medical images (DMI). A
large amount of unique medical information comes to the physician in the medi-
cal images form. It should be emphasized that such information will be sufficient
for analysis only if there is metadata that links the images with complete patient
data, time and means of obtaining these images. The necessary conditions for the
diagnostic process are the unification of medical data, convenient storage and
data losless transmission both across the hospital's local network and between
different medical institutions using the standard for regulating the creation, stor-
age, transmission and visualization of medical images and documents — Digital
Imaging and Communications in Medicine (DICOM) [20].
Solving various problems of information support of providing medical care to
patients of the Hospital for Scientists of the National Academy of Sciences of
Ukraine has been the subject of our research and development for more than 20
years. A Hospital information network combining a medical information system
(MIS), diagnostic digital devices of various modalities with a DICOM prefix and a
medical image storage module has been developed. To ensure the interaction of
old-style equipment (without using the DICOM standard), a module has been de-
veloped for the transmission and conversion to digital medical images, which al-
lows communication with the Conquest DICOM Server and supporting the neces-
sary functions of operating with data and digital medical images.
Digital transformation in medicine: from formalized medical documents to information
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The main task of the developed information technology for supporting the
processes of receiving, transmitting and storing digital medical information is to
ensure effective work of the physicians with digital information from various
sources: diagnostics complexes, digital medical data and image storage using
Picture Archiving and Communication Systems (PACS) and cloud technologies.
The use of this technology provides the organization of long-term storage of
digital medical images obtained from diagnostic systems, and the ability to use
this medical information by the physician at his workplace in the current treat-
ment and diagnostic process [21, 22].
2. Telemedicine — interregional level
The theory of telemedicine (TM) systems developed by us includes the formu-
lated principles of TM systems organization (principles of hierarchy, adaptability,
fractality and scaling), criteria and methods for analyzing digital medical data [23, 24].
The principle of hierarchical construction of the TM network allows to co-
ordinate its structure with the organization of the health care system, in the in-
formation environment of which the TM network functions. The principle of
adaptability provides the opportunity to develop the network using new techno-
logical platforms to expand the target space and increase the efficiency of medi-
cal care by upgrading the information and communication basis of TM tech-
nologies. The principle of fractality provides a “vertical” similarity to different
levels of the structure of the MT network and determines the flexible process of
preparing and exchanging medical data by implementing the similarity function.
According to the principle of scaling, the “horizontal” organization of the MT
network is carried out, ensuring the possibility of replication of software prod-
ucts at the regional level and at the level of individual medical institutions.
There are several levels of telemedicine institutions that are interconnected
technically, informationally and documentally. The first level of the TM network
includes telemedicine centres or nodes located in different districts and regions
of the country (Counseling Objects). Counseling Subjects are medical institu-
tions that provide consulting services and have a staff of highly qualified medi-
cal specialists in various fields of medicine, as well as appropriate equipment for
remote consultations, medical diagnostic procedures and organization of training
for network users. This is the second level of the MT network and organization
can be both objects and subjects of counseling. At the third level, there is the
Telemedicine Center of the Ministry of Health of Ukraine, which includes a
dispatch center and also carries out scientific and methodological activities.
The introduction of this IT into the work of the Telemedicine Center of the
Ministry of Health of Ukraine enabled to provide the population of more than 20
regions of Ukraine with qualified medical assistance.
3. Harmonization of medical informatics standards
To integrate MISs into a single network and to enter the international infor-
mation space, it is necessary to ensure the standardization of information carriers
and the transmission of medical images. On the basis of international standards
Health Level 7 and DICOM, we harmonized standards in the field of medical
statistics and health informatics. Harmonized standards are focused on defining
data types for information exchange, defining requirements for the general struc-
ture of biometric data exchange formats, presenting units of measure for data
exchange between computer applications, requirements for drugs dictionary
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systems for healthcare and to electronic prescriptions. Such standards are essen-
tial for the development of mobile medicine.
MOBILE MEDICINE
At the present stage, the development of mobile intelligent information tech-
nologies (IIT) for digital medicine is underway. The basic structure of any mo-
bile application consists of a kernel (platform based on Android, IOS or Win-
dows Mobile) and functional blocks that are formed taking into account the spe-
cific tasks of this mobile application for a specific group of users (Fig. 3) [25].
When developing medical mobile applications, we focus on two main types of
users — the physician and the patient. They differ one from other intheirs pos-
sibabilities and restrictions on access to certain information. Applications can be
used in full or partial mode.
Methods and means of IIT based on the use of mobile devices provide in-
creased efficiency of medical care to the population by preventing chronic dis-
ease, increasing the duration of remission, reducing the recurrence of the dis-
ease. When using such IT for medical care to patients, there is also a decrease in
the cost of treating and rehabilitating patients, increasing the efficiency of stor-
ing and transmitting medical data with accelerating the exchange of digital
medical information.
Patient
Decision Support
Module
Statistics
FB
(Hospitalization)
FB (Patients)
FB (Laboratory
tests)FB (Drugs)
FB (Instrumental
studies)
Physician
Li
m
ite
d
ac
ce
ss
to
sp
ec
ifi
c
da
ta
ba
se
s a
nd
fu
nc
tio
na
l b
lo
ck
s
Models for DSM
Kernel
(platform based on
Android, IOS or
Windows Mobile)
Cloud‐based mobile platform
Fig. 3. Interconnection of mobile applications targeting a physician and patient
Digital transformation in medicine: from formalized medical documents to information
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THE DIGITAL MEDICINE GLOBALIZATION
This is the stage of the future development of digital interstate medicine. Tele-
medicine and the use of mobile applications are only two facets of this process.
It is necessary to create global knowledge bases that include detailed information
not only about standard pathological cases, but also about deviations in the
course of pathological processes and the corresponding medical means, about
risk groups etc. As any interstate process, globalization of digital medicine re-
quires serious analysis and development of legal foundations taking into account
the principles of insurance medicine in different countries.
But we should not forget the ambiguous moments of the DM spread. The indis-
putable relevance and significance of DM induces a proposals flurry from the fields of
engineering and technology, much of which are “quickly baked”, not based on the
principles of evidence-based medicine, and on a rigorous analysis of the preliminary
studies results. There are cases of development of automated decision support systems
by physicians, in which the physician is practically excluded from the technological
chain. One of the mostly developing areas of DM is the creation of a variety of sensors
to collect information about the patient's state. But only a part of these proposals was
accepted by physicians for practical use.
Thus, the digital transformation in medicine, like any new process, under-
goes the stages of gradual complication of tasks, methods and means for their
implementation: from formalization of primary medical information to improv-
ing methods for its analysis, transmission and storage to improve the quality of
medical care for patients any time and at any point of the world.
CONCLUSION
The developed model of digital transformation in medicine includes such com-
ponents: Object, Operational processes and Business models of the treatment
and diagnostic processes, for which functions and tasks are selected taking into
account the specifics of the subject area according to object and quality of the
analyzed information.
There are several main stages in the digital medicine development:
І — digital transformation of primary medical information; ІІ — development of
support systems for the diagnostic and treatment process; ІІІ — development of
technologies and systems for supporting the physician’s activities with digital
information; IV — mobile medicine; V — the digital medicine globalization.
The beginning of the digital transformation of primary medical information laid
the formation of methodological foundations for the creation of formalized
medical records and standardized documents.
The proposed method for determining markers of functional status of the
CVS, based on mathematical models of forecasting and classification using Data
Mining, allows to determine the boundary values of these predictors by the iden-
tified ECG predictors of the CVS functional status (estimated vegetative regula-
tion, myocardial state and its reserves) for different groups of patients, as well as
to define different functional classes.
The development at the stage of IT support for a physician’s activity with
digital medical data ensures the implementation of such functions of digital
Kozak L.M., Kovalenko A.S., Kryvova O.A., Romanyuk O.A.
ISSN 2519-2205 (Online), ISSN 0454-9910 (Print). Киб. и выч. техн. 2018. № 4 (194) 74
transformation model in medicine: the acquisition of knowledge of the diagnos-
tic and treatment processes, enhancement of the specialist’s possibilities, man-
agement of medical institutions. These functions are carried out both at the level
of a medical institution and at the interregional level using international stan-
dards of presentation and transmission digital medical data.
The basic structure of any mobile application consists of a kernel (platform
based on Android, IOS or Windows Mobile) and functional blocks that are formed
taking into account the specific tasks of this mobile application. The development of
medical mobile applications is focused on at least two main types of users — the
physician and the patient, mobile applications for them are distinguished by a set of
opportunities and restrictions on access to certain information.
Medicine is already faced with the squall of information, which is being
formed through the use of new IT sources: large functional diagnostics com-
plexes, digital clinical laboratories, mobile data sensors of patients' health in real
time regime and others. This necessitates the creation of large information net-
works using cloud technologies for storing information and intelligent informa-
tion technologies to provide the necessary level for analysing this huge amount
of information and supporting decision-making by the physicians at all the
stages of the diagnostic and treatment processes.
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стем. Управляющие системы и машины. 2018. №4. С. 57–69.
Отримано 29.08.2018
Digital transformation in medicine: from formalized medical documents to information
ISSN 2519-2205 (Online), ISSN 0454-9910 (Print). Киб. и выч. техн. 2018. № 4 (194) 77
Козак Л.М., д-р біол. наук, старш. наук. співроб.,
пров. наук. співроб. відд. медичних інформаційних систем
e-mail: lmkozak52@gmail.com
Коваленко О.С., д-р мед. наук, проф.,
зав. відд. медичних інформаційних систем
e-mail: askov49@gmail.com
Кривова О.А., наук. співроб.
відд. медичних інформаційних систем
e-mail: ol.kryvova@gmail.com
Романюк О.О., молодш. наук. співроб.
відд. медичних інформаційних систем
e-mail: ksnksn7@gmail.com
Міжнародний науково-навчальний центр інформаційних технологій
та систем НАН України та МОН України,
пр. Акад. Глушкова, 40, м. Київ, 03187, Україна
ЦИФРОВА ТРАНСФОРМАЦІЯ В МЕДИЦИНІ: ВІД ФОРМАЛІЗОВАНИХ
МЕДИЧНИХ ДОКУМЕНТІВ ДО ІНФОРМАЦІЙНИХ ТЕХНОЛОГІЙ
ЦИФРОВОЇ МЕДИЦИНИ
Вступ. Відповідно до Концепції розвитку цифрової економіки і суспільства України на
2018-2020, прийнятої у січні 2018 року, серед ключових складників «цифровізації» є
розвиток цифрової інфраструктури — широкосмуговий Інтернет по всій території
України, і стимулювання цифрових перетворень у різних галузях економіки і суспільс-
тва, зокрема у медицині.
Метою статті є аналіз етапів цифрової трансформації в медицині і розробок авто-
рів і їхніх колег відділу медичних інформаційних систем з розвитку інформаційних
технологій цифрової медицини.
Результати. Надано сформовану модель цифрової трансформації в медицині та виділе-
но декілька основних етапів розвитку цифрової медицини: І — цифрова трансформація пер-
винної медичної інформації; ІІ — розроблення систем підтримки лікувально-діагностичного
процесу; ІІІ — розроблення технологій і систем підтримки діяльності лікаря з цифровою
інформацією; IV — мобільна медицина; V — глобалізація цифрової медицини.
Запропоновано метод визначення маркерів функціонального стану серцево-судинної
системи, в основу якого покладено математичні моделі прогнозу та класифікації із застосу-
ванням Data Mining, що дає змогу виявляти та визначати граничні значення ЕКГ предикто-
рів функціонального стану серцево-судинної системи для різних груп пацієнтів. Відзначено
інформаційну технологію підтримки процесів отримання, передачі та зберігання цифрових
медичних зображень, яку спрямовано на забезпечення ефективної роботи лікаря з цифровю
інформацією з різних джерел: комплекси функціональної діагностики, сховища цифрових
медичних даних і зображень з використанням PACS і хмарних технологій. Застосування в
роботі Центру телемедицини запропонованої теорії телемедичних систем, яка включає
сформульовані принципи організації цих систем, критерії та методи аналізу цифрових
медичних даних, дало можливість охопити кваліфікованою медичною допомогою населен-
ня більше 20-ти областей України.
Висновки. Розвиток цифрової трансформації у медицині проходить стадії з по-
ступовим ускладненням завдань, методів і засобів їх реалізації: від формалізації пер-
винної медичної інформації до удосконалення методів її аналізу, передачі і зберігання
для підвищення якості медичної допомоги пацієнтам в будь-який час та у будь-якій
точці країн світу.
Ключові слова: цифрова трансформація у медицині, формалізовані медичні записи,
інформаційні технології оцінювання стану людини та фізіологічних систем організму,
телемедицина, мобільні застосунки.
Kozak L.M., Kovalenko A.S., Kryvova O.A., Romanyuk O.A.
ISSN 2519-2205 (Online), ISSN 0454-9910 (Print). Киб. и выч. техн. 2018. № 4 (194) 78
Козак Л.M., д-р биол. наук, старш. науч. сотр.,вед. науч. сотр.
отд. медицинских информационных систем
e-mail: lmkozak52@gmail.com
Коваленко А.С., д-р мед. наук, проф.,
зав. отд. медицинских информационных систем
e-mail: askov49@gmail.com
Кривова О.А., науч. сотр.
отд. медицинских информационных систем
e-mail: ol.kryvova@gmail.com
Романюк А.А., младш. науч. сотр. отд. медицинских информационных систем
e-mail: ksnksn7@gmail.com
Международный научно-учебный центр информационных технологий и систем
НАН Украины и МОН Украины, пр. Акад. Глушкова, 40,
м. Киев, 03187, Украина
ЦИФРОВАЯ ТРАНСФОРМАЦИЯ В МЕДИЦИНЕ: ОТ ФОРМАЛИЗОВАННЫХ
МЕДИЦИНСКИХ ДОКУМЕНТОВ К ИНФОРМАЦИОННЫМ
ТЕХНОЛОГИЯМ ЦИФРОВОЙ МЕДИЦИНЫ
Проанализированы этапы цифровой трансформации в медицине: І — цифровая транс-
формация первичной медицинской информации; ІІ — разработка систем поддержки
лечебно-диагностического процесса; ІІІ — разработка технологий и систем поддержки
деятельности врача с цифровой информацией; IV — мобильная медицина; V — глоба-
лизация цифровой медицины. Показан вклад разработок авторов и их коллег (отдел
медицинских информационных систем) по развитию информационных технологий
цифровой медицины на этих этапах. Представлены разработанные: метод определения
маркеров функционального состояния ССС; ИТ поддержки процессов получения, пе-
редачи и хранения цифровых медицинских изображений; теория телемедицинских
систем и результаты ее применения; базовая структура мобильного медицинского
приложения и взаимодействие ее функциональных блоков с выделением задач и огра-
ничений действий основных пользователей — врача и пациента.
Ключевые слова: цифровая трансформация в медицине, формализованные медицинс-
кие записи, Data Mining, информационные технологии оценки состояния человека и
физиологических систем организма, телемедицина, мобильные приложения.
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/JPN <FEFF9ad854c18cea51fa529b7528002000410064006f0062006500200050004400460020658766f8306e4f5c6210306b4f7f75283057307e30593002537052376642306e753b8cea3092670059279650306b4fdd306430533068304c3067304d307e3059300230c730b930af30c830c330d730d730ea30f330bf3067306e53705237307e305f306f30d730eb30fc30d57528306b9069305730663044307e305930023053306e8a2d5b9a30674f5c62103055308c305f0020005000440046002030d530a130a430eb306f3001004100630072006f0062006100740020304a30883073002000410064006f00620065002000520065006100640065007200200035002e003000204ee5964d3067958b304f30533068304c3067304d307e30593002>
/KOR <FEFFc7740020c124c815c7440020c0acc6a9d558c5ec0020b370c2a4d06cd0d10020d504b9b0d1300020bc0f0020ad50c815ae30c5d0c11c0020ace0d488c9c8b85c0020c778c1c4d560002000410064006f0062006500200050004400460020bb38c11cb97c0020c791c131d569b2c8b2e4002e0020c774b807ac8c0020c791c131b41c00200050004400460020bb38c11cb2940020004100630072006f0062006100740020bc0f002000410064006f00620065002000520065006100640065007200200035002e00300020c774c0c1c5d0c11c0020c5f40020c2180020c788c2b5b2c8b2e4002e>
/NLD (Gebruik deze instellingen om Adobe PDF-documenten te maken voor kwaliteitsafdrukken op desktopprinters en proofers. De gemaakte PDF-documenten kunnen worden geopend met Acrobat en Adobe Reader 5.0 en hoger.)
/NOR <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>
/PTB <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>
/SUO <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>
/SVE <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>
/ENU (Use these settings to create Adobe PDF documents for quality printing on desktop printers and proofers. Created PDF documents can be opened with Acrobat and Adobe Reader 5.0 and later.)
>>
/Namespace [
(Adobe)
(Common)
(1.0)
]
/OtherNamespaces [
<<
/AsReaderSpreads false
/CropImagesToFrames true
/ErrorControl /WarnAndContinue
/FlattenerIgnoreSpreadOverrides false
/IncludeGuidesGrids false
/IncludeNonPrinting false
/IncludeSlug false
/Namespace [
(Adobe)
(InDesign)
(4.0)
]
/OmitPlacedBitmaps false
/OmitPlacedEPS false
/OmitPlacedPDF false
/SimulateOverprint /Legacy
>>
<<
/AddBleedMarks false
/AddColorBars false
/AddCropMarks false
/AddPageInfo false
/AddRegMarks false
/ConvertColors /NoConversion
/DestinationProfileName ()
/DestinationProfileSelector /NA
/Downsample16BitImages true
/FlattenerPreset <<
/PresetSelector /MediumResolution
>>
/FormElements false
/GenerateStructure true
/IncludeBookmarks false
/IncludeHyperlinks false
/IncludeInteractive false
/IncludeLayers false
/IncludeProfiles true
/MultimediaHandling /UseObjectSettings
/Namespace [
(Adobe)
(CreativeSuite)
(2.0)
]
/PDFXOutputIntentProfileSelector /NA
/PreserveEditing true
/UntaggedCMYKHandling /LeaveUntagged
/UntaggedRGBHandling /LeaveUntagged
/UseDocumentBleed false
>>
]
>> setdistillerparams
<<
/HWResolution [2400 2400]
/PageSize [612.000 792.000]
>> setpagedevice
|