Improved Organizational Design of the Diagnostic and Therapeutic

Process in Ovarian and Lung Cancer

Ewa Okoń-Horodyńska

, Tomasz Sierotowicz

and Agnieszka Sznyk

Jagiellonian University, Faculty of Management and Social Communication,

Institute of Economics, Finance and Management, Department of Economics and Innovation,

Prof. Lojasiewicza 4, 30-348, Krakow, Poland

Institute of Innovation and Responsible Development, INNOWO, Warsaw, Poland

Keywords: Health Care and Well-being, Improvement of Diagnostic Process, Modeling and Simulation of Diagnostic

Processes, Use ICT in Improved Diagnostic Process.

Abstract: A disease in each single patient can be one of a kind and an individual approach is needed. Tumors are in this

case particularly risky. The main research problem, as a case study, was to demonstrate the length and com-

position of the current Polish diagnostic process for two most risky conditions: ovarian carcinoma and lung

cancer, as well as to determine the causes for this. The main goal was to create a new, innovative model for

time-improved diagnostic processes of ovarian and lung cancer. In order to achieve aims of this research, the

BPMN and CPM were used. The main result of this study is a model of treatment processes in the indicated

cases with organizational changes, encompassing all possible types of contact between the patient and the

healthcare system. The entire diagnostic time for lung and ovarian cancer was shortened about 50%, which

significantly increases the chances of quick diagnosis and effective treatment. The newly designed and pre-

sented in the text model is an example of organizational innovation where implementation of ICT solutions,

allows to achieve and even improve an optimal path leading to the development of personalized medicine.

1 INTRODUCTION

A disease in each single patient can be one of a kind

and an individual approach is therefore needed. The

proper treatment is undertaken with considerable de-

lay. This delay can be divided into three types. The

first concerns delay in diagnosis caused by too late

first visit at the primary care physician (PCP) or in-

sufficient knowledge of the PCP to notice certain

symptoms. The second group of delay results from

lengthy and rigid workup procedures (overlapping

delay loop) and introduction of appropriate treatment

due to accurate decisions. Finally, the third type of

delay is associated with the reaction of the organism

to therapy. The economic dimension of these types of

delay and barriers in treatment cannot be left unno-

ticed. Damage to health caused by neoplasms renders

the vast human potential, mainly the professional one,

unexploited, and the costs of treatment and care over

patients leave a mark in economies, which might

https://orcid.org/0000-0002-2548-254X

https://orcid.org/0000-0002-1462-8267

threaten the future stability of public finances and

cause a decrease in Gross National Product (GNP),

not only in Poland. According to the Polish National

Cancer Registry (Didkowska et al., 2015), there were

156.5 thousand new cases of malignancies and 94

thousand deaths due to such diseases in 2013. The in-

cidence had increased by approximately 4 thousand.

Malignant tumors constitute the second cause of mor-

tality in Poland. In 2013, these diseases accounted for

26% of deaths among males and almost 23% among

females. Malignant tumors are the principal cause of

premature death (before the age of 65). This phenom-

enon is particularly evident in the female population:

33% of death in the group of young women and nearly

49% among middle-aged ones (Didkowska et al.,

2015). In Poland, lung cancer (ICD10: C34) accounts

for 1/3 of all deaths due to malignant cancers among

males and 15% among females. Ovarian carcinoma

(ICD10: C56), in turn, is a cause of 6.2% of all deaths

due to malignant cancers among Polish women. In

154

Oko

n-Horody

nska, E., Sierotowicz, T. and Sznyk, A.

Improved Organizational Design of the Diagnostic and Therapeutic Process in Ovarian and Lung Cancer.

DOI: 10.5220/0007950801540163

In Proceedings of the 16th International Joint Conference on e-Business and Telecommunications (ICETE 2019), pages 154-163

ISBN: 978-989-758-378-0

2000–2002, one-year survival rates reached 71.9%.

This value remained similar in 2003–2005 (70.8%) as

though global innovations could not reach Poland.

According to the EUROCARE-5 study (Eurocare,

2014), Poland, with 5-year survival among ovarian

cancer patients of 34.5%, falls below the European

value, i.e. 37.6%. Is this not the highest time for the

implementation of personalized model of treatment in

these cases? However, in order to do this properly and

effectively, it is necessary to first review the existing

process paths in the diagnosis and treatment of these

diseases. The critical point in the treatment of these

diseases is the moment of initiation of proper inpa-

tient treatment as well as selection and application of

individualized therapies. In order to perfect the entire

process, one must first improve the diagnostic process

because the moment of initiation and duration of

proper treatment depends upon the efficiency and ef-

ficacy of diagnosis. This paper, describes a design of

a processes, starting with the first contact up to the

conclusion of personalized therapy and a return to so-

cial and economic functioning. The process includes

the main scheme up to a detailed and complex hospi-

tal treatment. The main research problem was to

demonstrate the length and composition of the current

Polish diagnostic process for two most risky condi-

tions, i.e. ovarian carcinoma and lung cancer, as well

as to determine the causes for this. The results of this

application study are models of the treatment process

in the indicated cases with organizational changes,

encompassing all possible types of contact between

the patient and the healthcare system. These models

are a pilot undertaking that ought to be popularized if

the results described in the conclusions, which depict

an optimal development path for Polish personalized

medicine, are desired.

2 LITERATURE REVIEW

An optimal diagnostic process, i.e. a process which is

as short as possible and leads to establishing an accu-

rate diagnosis, is an important factor, not only for the

time of recovery and re-initiation of professional ac-

tivity, which lowers losses for the economy, but also

for the efficacy of subsequent treatment (Rivera et al.,

2013). Diagnosis, proper inpatient treatment and in-

dividualized post-hospital therapy are subprocesses

that comprise one diagnostic and therapeutic process

of a given disease, occurring in certain specified con-

ditions of the healthcare system in a given country.

The diversification of processes in terms of their du-

ration results from both the type of disease and its

stage of progression. That is why optimization must

be conducted for a given process that encompasses

the subprocesses of diagnosis and treatment proper

for a given disease. Diseases that pose the greatest

challenge in establishing a rapid diagnosis include

ovarian carcinoma and lung cancer. In these cases,

improvement of time needed to make a diagnosis

seems to be of fundamental significance due to the

fact that initial symptoms are not necessarily specific,

and the efficacy of the proper treatment strongly de-

pends on the time of its initiation (Havrileskya et al.,

2009). The issue of optimizing the diagnostic process

in ovarian and lung cancers is not new (Evans et al.,

2007; Collins et al., 2007). Good practice concerning

optimal organization of the diagnostic process in

terms of its duration and establishing an accurate di-

agnosis vary depending on a country. This results

from the diversity of healthcare systems. They can be

helpful in identifying the major barriers on the way to

proper diagnosis and treatment. Obviously, they can-

not eliminate all barriers and are not ready instru-

ments for optimal solutions in other healthcare sys-

tems (Evans et. al., 2007).

3 MATERIALS AND METHODS

In real environments, it is common to deal with pro-

cess characterized by incremental development, fol-

lowed by agile approaches and time improved meth-

ods such as Critical Path Method (CPM). For this rea-

son, the processes under research have to be consid-

ered to be continuous (Kunze et al., 2016). This iter-

ative approach to the process re-thinking along with

organizational change of both the structure and the

outcomes of a process is often referred to as process

re-engineering. In the presented solution, the process

will be change in order to time-improvement all steps

of diagnostic part of process. As a result, the treat-

ment can be start at earliest opportunity with using the

same medical assets.

3.1 Conceptual Framework and

Assumptions

Improvement of the diagnostic and therapeutic pro-

cess requires an individual approach to the process

that consists of various consecutive events. This study

is an attempt to identify and design organizational

changes aiming at shortening the duration of diagnos-

ing ovarian and lung cancers using existing material

and non-material resources, on the basis of the opti-

mization of current treatment processes of these dis-

eases. Therapy in ovarian and lung cancers consists

of three subprocesses: diagnosis, hospital treatment

Improved Organizational Design of the Diagnostic and Therapeutic Process in Ovarian and Lung Cancer

155

and individual post-hospital therapy. Since the main

aim of the study is to optimize the duration of the di-

agnostic subprocess, it was necessary to obtain de-

tailed data concerning this subprocess, which is a part

of the patient treatment process.

3.2 Data

In the Polish healthcare system, the therapeutic pro-

cess is different for each patient (which considerably

prolongs the diagnostic process and treatment proper)

instead of creating certain specific disease-and pa-

tient-based groups. Thus, data collected for the study

were collected from two sources: the Polish

healthcare system and patients participating in the

therapeutic process for ovarian and lung cancers.

These data allow to identify all possible events that

could occur depending on: the type of physician

whom patients consult initially, symptoms that are

identified and the number of specialists that can take

part in the diagnostic process. At the same time, the

minimum and maximum time periods associated with

awaiting individual actions were distinguished.

3.3 Methods

Management sciences currently have at their disposal

computer tools for process management. These make

use of standards enabling description of various pro-

cesses characterized by variable complexity (Ouyang

et al., 2008; Grosskopf et al., 2009). The application

of these tools is not limited to business processes

only. In particular, the Business Process Management

Notation (BPMN) standard can be used for descrip-

tion, simulation and optimization of diagnostic and

therapeutic processes (Allweyer, 2008; White, Bock,

2011). Process optimization and its associated modi-

fication not only allow to identify bottlenecks, but al-

lows the entire process to be accelerated, but also give

opportunities to obtain clues aiming at process recon-

figuration in accordance with the value-driven ap-

proach (Thomas et al., 2010; Bollen, 2016, Combi et

al., 2017). The application of this method entailed the

performance of the following actions in successive

sequences. 1. Based on the data obtained, three ther-

apeutic processes were modeled in the dedicated soft-

ware: for ovarian carcinoma treatment if the patient

initially consulted a PCP; for ovarian carcinoma treat-

ment if the patient initially consulted a gynecologist;

and for lung cancer treatment when the patient re-

ported to a PCP. The models consisted of all events

and time delays of their performance in the diagnostic

subprocess. The remaining subprocesses (treatment

proper and individualized post-hospital therapy) were

presented in the models in the global form, as envis-

aged by the standard adopted. 2. Each process was

then simulated in each possible variant according to

minimum and maximum time that lapse from the ini-

tial patient appointment up to the moment of referring

to hospital for treatment, which is equivalent to spec-

ifying a point for establishing a diagnosis. 3. The di-

agnostic subprocess was improved for each model of

the treatment process. 4. The model of improved di-

agnosis for all three treatment processes was designed

and verified. The obtained models of treatment pro-

cesses and results of conducted optimization served

for forming final conclusions and indicating the need

for further research.

4 RESULTS

Each of the models encompasses all possible types of

patient-healthcare system contact, organized in the

form of events in accordance with the BPMN stand-

ard in swimlane diagrams (Ryan, et al., 2009; White

et al., 2011; Scheuerlein et at., 2012, Combi et al.,

2017), starting with the initial visit up to the conclu-

sion of post-hospital individual treatment and a return

to effective professional activity. The following sub-

sections present individual models for three processes

dedicated to treatment of ovarian and lung cancers.

4.1 Model of Ovarian Cancer

Treatment Process – The First Visit

at a PCP Office

The first model (Figure 1) presents possible paths of

contact between an ovarian cancer patient and the

healthcare system assuming that her first visit in the

diagnostic subprocess took place in a PCP office.

Solid lines denote a typical course of events (connec-

tion between events). Dashed lines, however, show

the flow of information concerning test results. After

each medical consultation with any type of doctor, al-

ternative decisions that the doctor could make are pre-

sented. The remaining subprocesses, being a part of

the treatment process but not the subject of investiga-

tion, are marked and described as follows:

1. Treatment of other diseases – both the PCP and

any specialist (apart from geneticist) can diagnose

diseases other than ovarian cancer and initiate a

subprocess of treatment at any stage of the pro-

cess.

2. Inpatient treatment – each of the doctors partici-

pating in the diagnostic process (apart from

geneticist) can diagnose ovarian cancer based on

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156

Figure 1: Model of ovarian cancer treatment process – the

first visit at a PCP office.

data achieved or obtained in examinations and refer

patients to hospital for proper inpatient treatment. 3.

Post-hospital personal therapy – it is a subprocess of

individual post-hospital treatment after the conclu-

sion of the inpatient ovarian cancer treatment. The in-

dividual stages of the diagnostic process involve time

delay, marked as D. As the model suggests, 33 maxi-

mum delays, which may negatively affect effective

treatment, were identified. The model contains the

maximum number of possible events in the diagnostic

subprocess. The stages of specialist consultations can

be repeated and their number depends on the number

of specialists engaged in the diagnostic process.

Events in the form of specialist appointments as con-

secutively referred by a PCP are therefore possible.

Moreover, each specialist can repeatedly refer the pa-

tient for specialist tests proper for that doctor’s spe-

cialty, according to their qualifications. This entails

multiple events in terms of laboratory testing. A re-

ferral for specialist examinations entails another spe-

cialist visit upon receipt of test results. If there are no

signs of a disease belonging to the field of expertise

of a given specialist, the patient is again referred to a

PCP who, in turn, refers her for further examinations

(in the case of issuing a referral to hospital, all exam-

inations are conducted there). The delay in this model

strictly depends on the events in the process and con-

cern the periods of awaiting: appointments with indi-

vidual physicians; performance of examinations; ob-

taining test results and admission to hospital. The

model (Figure 1) contains a description of actions

conducted by four specialists to whom the patient

may be referred by a PCP: geneticist, gastrologist, on-

cologist and gynecologist. Minimum and maximum

delay values, based on obtained data and simulations,

are presented in Table 1.

Table 1: Delay in waiting for doctor appointments- ovarian

cancer treatment - first visit at a PCP office.

Specialist

Wait time

for visit-

min (days)

Wait time

for visit-

max (days)

Delay time

PCP 1 7

D6, D7,

D11, D12,

D19, D20,

D29, D30

Gastrologist 90 120 D9

Geneticist 30 180 D3

Oncologist 14 90 D18

Gynecologist 2 14 D28

As indicated by the data and calculations presented in

Table 1, the shortest period of awaiting a visit con-

cerns a PCP – 1 day. The longest period of waiting

for an appointment with a specialist among the mini-

mum waiting periods concerns a gastrologist: 90

days. The longest possible time of awaiting consulta-

tion concerns a geneticist and may reach even 180

days. The shortest and the fastest treatment path in

terms of the number of events occurs when the patient

is referred to hospital during the first PCP appoint-

ment. Such situations are nearly non-existent in prac-

tice since this disease does not produce specific signs

in early stages. The longest treatment path involves

subsequent referrals to all specialists engaged in the

treatment process. If the patient is referred by a spe-

cialist for further examinations, the second appoint-

ment with this specialist is delayed at least by the time

of awaiting test results. Specialist examinations are

another source of delay. This concerns both the date

of their performance and awaiting results. Table 2

presents delay periods for each patient’s appointment.

Improved Organizational Design of the Diagnostic and Therapeutic Process in Ovarian and Lung Cancer

157

Table 2: Delay in awaiting tests and results in the model of

ovarian cancer diagnosis and treatment – the first visit at a

PCP office.

Referring physician appoint-

ment

Result –

min (days)

Result –

max

(days)

PCP or Gynecologist 1 2

Oncologist or Gynecologist 7 14

PCP or Gynecologist 14 28

PCP or Gynecologist 1 2

PCP or Gynecologist or On-

cologist

5 10

PCP or Gynecologist 1 7

Oncologist or Gynecologist 30 120

Oncologist or Gynecologist 30 180

Gastrologist or Oncologist 30 180

Geneticist 180 180

The delay time contains time delay points marked as

“D” included in the first model (Figure 1) for duration

of waiting for examinations and test results, respec-

tively. The duration of delays ends with the duration

of waiting for hospital admission. Since a referral to

hospital can be issued by any physician, this delay can

occur at various points during the diagnostic subpro-

cess. They are identified as: D1, D10, D15, D16, D17,

D23, D24, D25, D26, D27, D33. These delays will

certainly occur only once in the diagnostic process.

The time of awaiting hospital admission ranges from

1 to 14 days. Moreover, during each visit a PCP,

based on test results, may decide that: 1) there are no

signs of a severe disease that would require further

processes in the healthcare system; 2) there is a need

to initiate a different treatment subprocess; 3) there is

a need for hospital treatment.

4.2 Model of Ovarian Cancer

Treatment Process – The First Visit

at a Gynecologist Office

Figure 2 presents a model of ovarian cancer diagnos-

tic and therapeutic process when the patient first re-

ports to a gynecologist. The second model (Figure 2)

was drawn up based on the approach adopted in Fig-

ure 1. As the model suggests, if the patient reports

first to a gynecologist, the number of specialists to

whom she can be referred is lower. This also means

that the number of possible laboratory examinations

is lower, but the number of tests remains unchanged.

In the entire diagnostic subprocess, 25 delays were

noted. This number is lower by 8 compared with the

previous model. Table 3 presents delay in awaiting

doctor appointment in the model of ovarian cancer di-

agnosis and treatment when the first visit took place

at a gynecologist office. The longest period of waiting

for an appointment among the cases with the lowest

time periods concerns a gastrologist: 90 days.

Table 3: Delay in waiting for doctor appointments- ovarian

cancer treatment - the first visit at a gynecologist office.

Specialist

Wait time

for visit-

min (days)

Wait time

for visit-

max (days)

Delay time

Gynecologist 2 14

D6, D7,

D11, D12,

D19, D20

Gastrologist 90 120 D9

Geneticist 30 180 D3

Oncologist 14 90 D18

As indicated by the data and calculations presented in

Table 3, the shortest period of awaiting a visit con-

cerns a gynecologist: 2 days.

Figure 2: Model of ovarian cancer treatment process – the

first visit at a gynecologist office.

The longest possible time of awaiting consultation

concerns a geneticist and may reach even 180 days.

Compared with the model shown in Figure 1 (the first

visit – PCP), the range of delay did not change con-

siderably, but the entire diagnostic subprocess is

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158

shorter by one gynecological consultation, which

saves 2 to 180 days. This results from the possibility

of being referred for specialist examinations as soon

as during the very first visit (by a gynecologist). The

remaining delays in other segments of this process

still lower chances for effective treatment. The short-

est path in terms of the number of events and, at the

same time, the fastest treatment route takes place

when a gynecologist refers the patient to hospital at

the very first visit. Such situations are rare in practice

because symptoms of ovarian cancer would then in-

dicate advanced disease, the treatment of which is

poorly effective. The longest treatment path involves

subsequent referrals to all specialists engaged in the

treatment process. As in the previous model, if the pa-

tient is referred for further examinations by a special-

ist, the second appointment with this specialist is de-

layed at least by the time of awaiting test results. Spe-

cialist examinations are another source of delay. This

concerns both the date of their performance and

awaiting results. Table 4 presents delay periods for

each patient’s appointment. The delay time contains

time delay points marked as “D” included in the sec-

ond model (Figure 2) for duration of waiting for ex-

aminations and test results, respectively. The total de-

lay ends with the duration of waiting for hospital ad-

mission. Since a referral to hospital can be issued by

any physician, this delay can occur at various points

during the diagnostic subprocess. They are marked

as: D1, D10, D15, D16, D17, D23, D24, D25.

Table 4: Delay in awaiting tests and results in the model of

ovarian cancer diagnosis and treatment – the first visit at a

gynecologist office.

Referring physician appoint-

ment

Result-

min (days)

Result-

max

(days)

Gynecologist 1 2

Gynecologist or Oncologist 7 14

Gynecologist 14 28

Gynecologist 1 2

Gynecologist or Oncologist 5 10

Gynecologist 1 7

Gynecologist or Oncologist 30 120

Gynecologist or Oncologist 30 180

Gastrologist or Oncologist 30 180

Geneticist 180 180

These delays will certainly occur only once in the di-

agnostic process. As in the previous model, the time

of awaiting hospital admission ranges from 1 to 14

days. Moreover, during each visit a gynecologist,

based on test results, may decide that there are no

signs of a severe disease that would require further

processes in the healthcare system, there is a need to

initiate a different treatment subprocess or that there

is a need for hospital treatment.

4.3 Model of Lung Cancer Treatment

Process

Figure 3 presents a model of lung cancer diagnostic

and therapeutic process. The third model (Figure 3) is

also based on Figure 1 (first visit at a PCP office).

Figure 3: Model of lung cancer diagnostic and therapeutic

process.

It is characterized by a lower number of specialists to

whom patients may be referred. This also means that

the number of possible laboratory examinations is

lower, but the number of tests remains unchanged. In

the entire diagnostic process, 25 delays were noted.

This number is lower by 8 compared with the previ-

ous model (Figure 1). Table 5 presents delay in await-

ing doctor appointment in the model of lung cancer

diagnosis and treatment. As indicated by the data and

calculations presented in Table 5, the shortest period

of awaiting a visit concerns a PCP: 1 day. The shortest

and the fastest treatment path in terms of the number

Improved Organizational Design of the Diagnostic and Therapeutic Process in Ovarian and Lung Cancer

159

of events occurs when the patient is referred to hospi-

tal during the first PCP appointment. Such situations

are rare in practice because first symptoms of lung

cancer are non-specific and require additional, some-

times numerous, examinations.

Table 5: Delay in awaiting doctor appointment in the model

of lung cancer diagnosis and treatment.

Specialist

Wait time

for visit-

min (days)

Wait time

for visit-

max (days)

Delay time

PCP 1 7

D6, D7,

D11, D12,

D19, D20

Geneticist 30 180 D3

Oncologist 14 90 D9

Pulmonologist 14 120 D18

The longest period of waiting for an appointment

among the events with the shortest time periods con-

cerns a geneticist: 30 days. The longest possible time

of awaiting consultation also concerns a geneticist

and may reach even 180 days. The longest treatment

path involves subsequent referrals to all specialists

engaged in the treatment process. As in the previous

models, if the patient is referred for further examina-

tions by a specialist, the second appointment with this

specialist is delayed at least by the time of awaiting

test results. Specialist examinations are another

source of delays. This concerns both the date of their

performance and awaiting results. Table 6 presents

types of examinations and delay periods that they en-

tail.

Table 6: Delay in awaiting tests and results in the model of

lung cancer diagnosis and treatment.

Referring physician appoint-

ment

Result –

min

(days)

Result –

max

(days)

PCP 1 2

PCP 14 28

PCP or Oncologist 1 7

PCP or Oncologist or Pul-

monologist

30 120

Oncologist or Pulmonologist 1 7

Oncologist or Pulmonologist 31 62

Pulmonologist 30 180

Pulmonologist 7 14

Geneticist 180 180

The delay time contains time delay points marked as

“D” included in the second model (Figure 3) for du-

ration of waiting for the examination and test results,

respectively. The delay ends with the duration of

waiting for hospital admission. They are marked as:

D1, D10, D15, D16, D17, D23, D24, D25. The time

of awaiting hospital admission occurs only once and

ranges from 1 to 14 days. During each visit, a PCP,

based on test results, may decide that there is a need

to initiate a different treatment subprocess or that

there is a need for hospital treatment. The PCP can

also diagnose a minor disease or prescribe certain

medications, after which the patient leaves the

healthcare system.

4.4 Improved Model of Ovarian and

Lung Cancer Treatment

The model simulations yielded results presented in

Table 7. It was assumed that all necessary examina-

tions will be conducted in an optimal way, i.e. without

extending the waiting period associated with their

performance and receiving results. That is why the

delay ranges from 1 to 180 days in each of the ana-

lyzed models. Also, delay associated with the initial

PCP or gynecological visit are not taken into consid-

eration in the simulation since, in this case, the patient

makes decisions irrespective of the system in which

treatment is conducted. As simulations suggest (Table

7), the longest delay is generated by repeatable loops

of appointments with specialists. A question arises:

Can the duration of the diagnostic process in ovarian

and lung cancer be shortened significantly using the

present resources of the healthcare system? The sim-

ulations indicate that two organizational changes may

be performed: 1. Broadening the scope of PCP quali-

fications in terms of referring patients for specialist

examinations, involving oncological, gynecologic

and gastrological tests. 2. Organizing online medical

conferences with specialists that would have access to

all data that a PCP has obtained from the patient and

to results of tests from within their fields of expertise.

Following such a conference, a PCP can inform the

patient about the results of the conference and refer

them to hospital for treatment even during the second

visit. These two organizational changes served for de-

signing an improved model of ovarian and lung can-

cer treatment (Figure 4). First of all, the scope of PCP

and gynecologist qualifications must be broadened so

that patients could be referred for examinations dur-

ing the first appointments. The range of tests should

include the most significant tests conducted by an on-

cologist, gastrologist, geneticist and pulmonologist.

The PCP should have the power to refer patients for

gynecological examinations if symptoms suggest

ovarian cancer. When all tests have been conducted

in accordance with the initial diagnosis of ovarian or

lung cancer it is necessary to organize an online med-

ical conference without the need for the patient pres-

ence. Physicians that must take part in the conference

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160

Table 7: Simulations of ovarian and lung cancer treatment model.

Model Event

Wait time for

visit- min (days)

Wait time for visit -

max (days)

Designation

Model of ovar-

ian cancer treat-

ment process –

the first visit at

a PCP office

4 PCP visits 4 28

Delay concerning visits

is presented in Table 1

visits to 4 specialists 136 404

Admission to hospital 1 14

Total 277 940

Perform and obtain test results 1 180

Delay concerning tests

is presented in Table 2

Total visits and examinations 278 1120

N/A

Model of ovar-

ian cancer treat-

ment process –

the first visit at

a gynecologist

office

4 visits to the gynecologist 8 72

Delay concerning visits

is presented in Table 3

visits to 3 specialists 134 390

Admission to hospital 1 14

Total 267 866

Perform and obtain test results 1 180

Delay concerning tests

is presented in Table 4

Total visits and examinations 268 1046

N/A

Model of lung

cancer treat-

ment process –

the first visit al-

ways takes

place at a PCP

office

4 PCP visits 4 28

Delay concerning visits

is presented in Table 5

visits to 3 specialists 58 390

Admission to hospital 1 14

Total 121 822

Perform and obtain test results 1 180

Delay concerning tests

is presented in Table 6

Total visits and examinations 122 1002

N/A

include: a PCP, oncologist and, depending on the sus-

pected disease: gynecologist and gastrologist for

ovarian cancer, or pulmonologist and thoracic sur-

geon for lung cancer

Figure 4: Improved model of ovarian and lung cancer diag-

nostic and therapeutic process.

Doctors taking part in such an online medical confer-

ence have access to all data collected by the PCP dur-

ing the first visit and to all test results. The conference

can be organized using standard and generally avail-

able tools for a video conference. Eventually, such

conferences will be held regularly as the work on

computerization of the health system progresses will

continue with application of appropriate security

measures. Doctors can make the following decisions:

1. The patient is healthy; 2. Admission to hospital; 3.

Depending on the diagnosis, initiation of treatment

for diseases other than ovarian or lung cancer. The

application of the proposed solution to the treatment

process of lung and ovarian cancer will reduce the

time of awaiting appointments with specialists. The

simulations of the optimal model of ovarian and lung

cancer diagnostic and therapeutic process yielded re-

sults presented in Table 8. For the simulation condi-

tions to be identical to those in the previous models,

it was assumed that all necessary examinations will

be conducted in the optimal way, i.e. without extend-

ing the waiting period associated with their perfor-

mance and receiving results. That is why the delay

ranges from 1 to 180 days in each of the analyzed

models. Delay associated with the initial PCP visit are

Improved Organizational Design of the Diagnostic and Therapeutic Process in Ovarian and Lung Cancer

161

Table 8: Results of simulations of the optimal model of ovarian and lung cancer diagnosis and treatment.

Disease Event

Wait time for

visit-min (days)

Wait time for visit-

max (days)

Designation

Improved model of

ovarian cancer treat-

ment process – the

first visit at a PCP

office

One PCP visit 1 7 D9

One visit with a geneticist 30 180 D3

Medical conference 106 224 D6, D7

Admission to hospital 1 14 D1, D10

Total 138 425

N/A

Perform and receive test results 1 180

D2, D4, D5,

Total visits and examinations 139 605

N/A

Improved model of

ovarian cancer treat-

ment process – the

first visit at a gyne-

cologist office

One visit to a gynecologist 2 14 D9

One visit to a geneticist 30 180 D3

Medical conference 104 210 D6, D7

Admission to hospital 1 14 D1, D10

Total 137 404

N/A

Perform and receive test results 1 180

D2, D4, D5,

Total visits and examinations 138 584

N/A

Improved model of

lung cancer treatment

process – the first

visit always takes

place at a PCP office

One PCA visit 1 7 D9

One visit to a geneticist 30 180 D3

Medical conference 28 210 D6, D7

Admission to hospital 1 14 D1, D10

Total 60 411

N/A

Perform and receive test results 1 180

D2, D4, D5,

Total visits and examinations 61 591

N/A

not taken into consideration in the simulation since,

in this case, the patient makes decisions irrespective

of the system in which treatment is conducted. When

conducting the simulation, all data concerning delay

calculated in all models for doctor appointments, per-

formance of examinations and obtaining test results

as well as admission to hospital were used.

5 DISCUSSION AND

CONCLUSIONS

The work presented in this paper demonstrates that

organizational changes could reduce the number of

delays in the diagnostic and therapeutic process of

ovarian and lung cancer from 33 and 25, respectively,

to 10 in both cases with using the same number of

specialists and other medical staff. Moreover, it al-

lowed the application of one standardized diagnostic

subprocess where the difference between diseases

only involves different sets of specialist examinations

and a different team of physicians taking part in the

medical conference. Upon the introduction of

changes, the improved model preserves the shortest

possible diagnostic path by the possibility of issuing

a referral to hospital even during the first visit. The

simulation of the improved model for ovarian cancer

diagnosis and treatment with the first visit at a PCP

office indicates that the minimum time was reduced

from 278 to 139 days, and the longest possible time

of staying within the healthcare system – from 1120

to 605 days. This means reductions in the time from

the first PCP visit to hospital admission by 50% and

46%, respectively. In the case of the first patient visit

at a gynecologist office, the minimum time was re-

duced from 268 to 138 days, and the maximum time

– from 1046 to 584 days. This means reductions in

the time from the first appointment with a gynecol-

ogist to hospital admission by 48.5% and 44.17%, re-

spectively. As for the simulation of the optimal model

for lung cancer diagnosis and treatment, the minimum

time was reduced from 122 to 61 days, and the longest

possible time – from 1002 to 591 days. This means

reductions in the time from the first PCP visit to hos-

pital admission by 50% and 41%, respectively. Con-

sidering the results reported above, it can be stated

that introducing organizational changes (for now

without making investments in the fixed assets and

equipment), in the form of broadening the scope of

PCP and gynecologist qualifications in terms of issu-

ing referrals for specialist examinations as well as or-

ICE-B 2019 - 16th International Conference on e-Business

162

ganizing regular medical conferences online depend-

ing on the type of symptoms and initial PCP diagno-

sis, helps save approximately 50% of time in the sub-

process of ovarian and lung cancer diagnosis. These

results enable earlier decisions about referring pa-

tients for proper personalized treatment. Organizing

online medical conferences without the need for the

patient presence will reduce lines to specialists since

in many cases, decisions will be made during the con-

ference. Reductions in the time that would otherwise

be devoted to awaiting appointments with specialists

is another long-term effect of the aforementioned or-

ganizational innovation. Essential investments must

be made within the Information and Communication

Technologies (ICT) (rapid communication, online

conferences). It is an example of how the ICT can be

used in the process of diagnostic and treatment in or-

der to reduce time for diagnostic and start personal-

ized therapy as soon as possible. Without additional

investments in specialists and medical staff, a sub-

stantial time-optimization of process brings about

50% of diagnostic time reduction.

6 FUTURE RESEARCH

The subsequent stage of improvement management

should involve the performance of preliminary re-

search and making a selection of facilities that per-

form specialist laboratory tests in order to shorten

procedures and examinations, as well as reduce the

time of waiting for test results (they should be avail-

able instantaneously). Another challenge for the pre-

sent healthcare system is the introduction of an inte-

grated database in which patient data and their entire

medical histories could be stored. The issues pre-

sented above should constitute new research and im-

plementation projects that need to be undertaken as

soon as possible.

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