Knowledge Management Theory Creation in Healthcare
Environment
Erja Mustonen-Ollila
1
, Jukka Heikkonen
2
, Antti Valpas
3
and Helvi Nyerwanire
1
1
Department of Innovation & Software, Lappeenranta University of Technology, Finland
2
European Commission, Joint Research Centre, Unit JRC.G1,
Scientific Support to Financial Analysis, Italy
3
Department of Obstetrics and Gynaecology,
South Karelia Social and Health Care District, Finland
Abstract. In this research project, the research environment is the central
hospital of the South Karelia Social and Healthcare District, Finland. This study
is qualitative research project in which the data is collected by semi-conducted
interviews in order to create a knowledge management theory in a healthcare
environment. The theory consists of conceptual frameworks and their categories
and the relationships between the categories. The categories and their
relationships are discovered by using the Grounded Theory approach. When
discovering a new theory the approach needs both qualitative and quantitative
methods. Therefore, this research project will utilize a new methodological
approach where both qualitative and quantitative research approaches are
applied. The quantitative data will be analyzed with the novel intelligent
computing methods which are used to find out relationships of the data and to
give deeper understanding of the research domain and its conceptual
dependencies.
1 Introduction
The primary goal of this research project is to create a knowledge management theory
in healthcare environment based on empirical findings. Health and healthcare are
important components for physical and mental well-being and life satisfaction. Only
part of the healthcare is medicine where the goal is to retrieve the lost homelike
being-in-the-world experience of the patient e.g., by reducing pain and suffering via
preventing and curing diseases. Healthcare also consists of nursing and rehabilitation
which are lacking from the medicine [1]. In healthcare, the patients are aware of their
own health including mental, physical, and social dimensions [2], and transparency,
integrated care platform, consumer engagement, patient centeredness, evidence-based
medicine, and doctor-patient relationship are needed to be implemented [3], [4], [2],
[5].
Healthcare is a knowledge rich sector which experiences rapid growth in order to
understand the different diseases and their treatments combined with the ability to
apply and access the up-to-date and relevant information. Knowledge itself has many
Nyerwanire H., Valpas A., Mustonen-Ollila E. and Heikkonen J.
Knowledge Management Theory Creation in Healthcare Environment.
DOI: 10.5220/0006156700610076
In European Project Space on Computational Intelligence, Knowledge Discovery and Systems Engineering for Health and Sports (EPS Rome 2014), pages 61-76
ISBN: 978-989-758-154-0
Copyright
c
 2014 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
61

conceptualizations depending on disciplines [6]. For instance, accountants measure it
on the balance sheet; information technologists want to codify it on systems;
sociologists want to balance power with it; psychologists want to develop minds
because of it; human resource managers calculate a return of investment on it; and
training and development officers want to make sure that they can build it. It has been
also claimed that knowledge can be divided into creation or construction process,
transfer process, storage and retrieval process, and application process [7], [8], [1],
[9]. These processes control organizational knowledge in the means of knowledge
dissemination, organizational learning, and data management [8], [10].
Among healthcare practitioners, knowledge is captured in a social interaction, e.g.
when physicians and nurses meet patients. Physicians transfer their knowledge and
expertise in meetings and consultations sessions, and they can express and interpret
diagnosis reports, create new expert knowledge by reading or learning in traineeship,
and having discussions [7], [8]. Collective knowledge exists in the organizational
networks and knowledge is internally ingrained in people [8]. People learn new
knowledge through participation with each other, e.g. cardiologists can belong to a
community of practice transferring and receiving knowledge on best practices [11].
Knowledge can be made useful to support a specific task or make a decision [12],
personalized information [8], awareness, experience, skills, and learning [13], [14],
tacit knowledge [15], [16], explicit knowledge [17], and clinical and medical
knowledge of the physicians and nurses [18], [19].
Different types of knowledge concepts have been identified and integrated into
existing and emerging healthcare information management practices [8]. Knowledge
concepts are even expanded to cover clinical medical expert knowledge which is
bounded to physicians' medical knowledge and expertise both in practice and theory
[18], [19], [20]. Furthermore, collective knowledge is expanded to cover
organizational learning from internal and external sources of organizations and sub-
networks [20] and embedded knowledge of the members, tools, and tasks of the
organization [21]. Mostly healthcare organizations utilize combination of the different
types of knowledge and healthcare practitioners are able to apply it properly and
efficiently in their work and tasks [20], [21].
Healthcare sector is heavily dependent on knowledge and it needs efficient
knowledge management in order to achieve high standards in patient care quality and
patent safety and centeredness, but also in cost-effectiveness. Therefore, knowledge
management needs information communication technology (ICT) systems support to
be applied in knowledge creation, as well as in the capturing, organization, access,
and use of knowledge [22], [8]. Based on the ICT systems support, knowledge
management includes knowledge acquisition, creation, transfer, storage, and
application processes [8], and organizational learning, unlearning, and internal
learning processes [23], [24], [25]. It has been stated that evidence-based medicine is
a form of organizational learning in the knowledge management context [20]. The
British Medical Informatics Society states that the goal of medical informatics, also
defined as the health informatics, is to share and promote healthcare through the
information technology (IT) with proper skills, knowledge and tools [26]. In
healthcare, there exist different types of healthcare information systems, such as
electronic health records, electronic medical records, and electronic patient records
[26], [8]. These records based information systems contain data about the patient
diagnosis, drugs and electronic prescriptions of medical laboratory examinations. The
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functionalities and goals of these three types of systems, however, are different from
each other and dependent on the hospital or clinic they are used [27], [22], [28].
The physicians’ and nurses’ tacit, expertise, medical and nursing knowledge,
however, cannot be found from the healthcare information systems. This is an
unfortunate omission because without such knowledge the other healthcare
professionals are not able to carry out the similar diagnoses and nursing decisions in a
similar patient care situation. The important role of information and communication
technologies (ICT) in healthcare is also ignored due to its problematic nature [26],
[29], [30], [31]. Furthermore, studies of knowledge management theory creation in
healthcare have been neglected, and past studies have rather focused on knowledge
management theory creation in IT context [32], [33].
At our research site practice, external and tacit knowledge is captured and
transferred by conducting lectures, and special training sessions to young physicians,
and learning by experience. The current healthcare information systems are updated
regularly and it causes stress for the physicians and nurses because they need to learn
parallel the issues of new healthcare information systems and new medicine and
nursing practices. The physicians in the central hospital even claim that the healthcare
information systems do not support but on the contrary often disturb and complicate
their clinical work. Therefore, there is immense need for information systems
modernization, as well as restructuration of knowledge management processes. This
is a challenging task because the nationwide medical and nursing practices have to be
updated regularly and new guidelines should be easily retrieved from the information
systems. In addition, the changes in Finnish and European Union (EU) legislation,
national guidelines for management practices, and economic and political situation
have to be found from the healthcare information systems [34].
Due to these several important omissions in the past studies, and on the other hand
a simultaneous need to modernize the information systems in practice in the research
site because of the new guidelines and legislation, this research project is important
and carried out in South Karelia Social and Healthcare District, Finland. In this
district’s central hospital we choose a department as a unit of analysis in order to
create a knowledge management theory in a healthcare environment. The theory also
includes the healthcare information systems, information systems integrations’, and
cloud computing adoption’s impact to knowledge management. This research project
is very significant nationally, because the Finnish healthcare districts are under a great
pressure of the growing healthcare costs and organizational changes due to need of
specialized medical and nursing care. In the future, the hospitals and social and
healthcare districts in Finland have to decide their main quality of service, because the
level of service must be balanced with the money and resources available. In Finland,
there are nationwide guidelines and operational processes of how to take care of a
patient, and the goal of these processes is to provide the same patient care nationally.
It should be noted that similar problems have been faced in very many other EU
countries making this research also significant internationally.
The rest of this paper is organized as follows. In section two the objectives and
research problems are outlined. In section three, we develop the conceptual
framework of knowledge management. In section four, we outline research
methodology consisting of data collection and categorization, and qualitative data
analysis with the grounded theory and quantitative data analysis with the novel
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intelligent computing approach. Finally, in section five we communicate the major
research findings of the project.
2 Objectives and Research Problems
The goal of this research project is to create a theory in healthcare environment by
using both the Grounded Theory (GT) qualitative research approach [35] and novel
intelligent computing methods based on general framework called the Cross Industry
Standard Process for Data Mining (CRISP-DM) [36]. This research project is planned
to take a total of 9 years (2012-2020) in which the output will be five doctoral theses.
Furthermore, at least 20 conference and 10 journal articles in high quality conferences
and journals will be published.
Table 1. Research areas, research problems in healthcare, and related theories.
Research area Research problems in healthcare
Related theory
Knowledge concepts What are the knowledge concepts? [8], [1], [12]
Internal and external knowledge
acquisition mechanisms
How do the nurses and physicians acquire
knowledge?
[45], [9], [46], [32],
[20]
Knowledge use
and application
What practical, clinical, medical, and
nursing knowledge the physicians
and nurses use and apply in
patient care situation?
[37], [47], [8], [1],
[48], [12], [45], [20],
[7]
Knowledge creation How the physicians and
nurses construct knowledge?
[49], [37], [47], [8],
[1], [48], [12], [45],
[20], [7]
Knowledge transfer mechanisms
and
transfer problems
How knowledge is transferred?
What are the knowledge
transfer problems?
[37], [50], [9], [51],
[52], [53], [9], [20],
[54], [55]
Organizational learning
mechanisms, unlearning and
internal learning mechanisms
What are the organizational learning,
unlearning and internal
learning mechanisms?
[45], [37], [9], [32],
[56], [23], [24], [25]
Role of information and
communication technology
What is the role of information
and communication technology?
[43]
Information systems’ integration How to integrate information
systems together?
[26]
Information systems’ integration
approaches
and mechanisms
What are the information systems’
integration approaches
and mechanisms?
[57]
Information systems’ standards
and technologies
What are the information systems’
standards and technologies?
[58]
Cloud computing adoption How to adopt cloud computing? [44]
Healthcare services
in cloud computing
What healthcare services are available
when using cloud computing?
[29]
Data security and privacy issues
in cloud computing
What data security and privacy issues must
be taken into account
in cloud computing?
[29], [59]
Cloud computing risks What are the risks associated
with cloud computing?
[29]
The first primary objective of this study is to discover and conceptualize
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knowledge concepts and knowledge management processes in a hospital
environment by combining together information systems science, knowledge
management science, medical science, nursing science, sociology of knowledge,
management science, and computational intelligence [15], [7], [37], [8], [9], [38],
[39], [32], [40], [41], [13], [14], [2], [42]. The second primary objective is concerned
to discover healthcare information systems’ impact to knowledge management in
healthcare [43]. The third and fourth primary objectives are to study cloud computing
adoption’s impact [44], [29], [31], and information systems’ integration impact [30],
[26] to knowledge management in healthcare. The research questions, research
problems in healthcare, and related theories are presented in Table 1.
3 Conceptual Framework of Knowledge Management
The research site is the South Karelia Social and Healthcare District, and the unit of
analysis is a department at district’s central hospital. As a whole, the study covers
currently at least six departments. As shown in Figure 1, knowledge management
processes are knowledge acquisition (1), knowledge creation and construction (2),
knowledge transfer (3), knowledge storage (4), and knowledge application (5). Their
interactions with each other are represented as solid arrows. Internal learning,
organizational learning, and organizational unlearning have an impact to knowledge
creation and construction process. Their impact is represented as dashed arrows.
Cloud computing adoption, information systems’ integration, and healthcare
information systems’ impacts to knowledge management processes, department,
central hospital and healthcare district are seen as solid arrows.
Fig. 1. The conceptual framework of the study.
In healthcare environment the knowledge is ingrained in the practitioners, and the
knowledge exists in the medical practice and is stored to healthcare information
CentralHospital
Department
Cloud
computing
adoption

Healthcare
information
s
y
stems
Inform ationsystems’
integration

HealthcareDistrict
Organizational
learning
Organizational
unlearning
Internal
learnin
g
KnowledgeManagementProcesses
Knowledge
application(5)
Knowledge
ac
q
uisition
(
1
)

Knowledgecreati on&
construction(2)
Knowledge
stora
g
e
(
4
)
Knowledge
transfer(3)
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systems [1], [60]. In healthcare environment the amount of medical and nursing
knowledge accumulates overtime, and the information collected from patients must be
stored and updated to the information systems [13] in a way that supports medical
practice needs 8], [61].
Knowledge acquisition involves searching for valuable knowledge, and external
knowledge may be acquired by importing knowledge components directly or by
depending on intermediaries [45], [32]. It has been argued that an organizational
gatekeeper is the key individual who connects the organizational members to the
external sources of information, and the organizational members are kept up-to-date
with the outside information by communicating with the gatekeepers [46].
Learning influences knowledge creation, and knowledge provided by evidence-
based medical guidelines and drug information databases help physicians to learn new
issues [14]. The electronic patient records enable creation of organizational
knowledge, and they are a useful tool to survive in everyday work in primary care
[13], [14]. In decision-making and clinical practice, knowledge is transferrable
through individual learning for example by observation [9], [62]. It is also possible
that knowledge transfer can occur without the individual being aware of it happening
[9]. Learning in groups occurs through discussions, meetings and lecture sessions in
which people share their experiences [9].
It has been stated that knowledge transfer is the ability to apply knowledge gained
in one situation in another similar situation, or to use metacognitive strategies to act in
a novel situation [50]. New knowledge is generated by the influx of information into
an individual’s mind, combined with the existing knowledge of this individual, and
then communicated further and made explicit [63]. After knowledge creation, it needs
to be transferred throughout the healthcare organization. The knowledge that is
relevant and right to be transferred needs to be determined as well [9]. The formal
communities of practice include meetings, and the informal communities of practice
will include discussion groups, study groups and online communities [53]. When
more knowledge is shared between individuals, the more opportunities there are for
knowledge creation [9]. Due to the reason that communities are formed with different
ways of working and adoption of different vocabularies, they may not understand
each other [9]. For example, human actors in IT and the business domain often speak
different technical and procedural languages [63]. In addition one domain can
articulate requirements, goals and constraints that another domain can think of as
being unreasonable and uncooperative [63].
Knowledge storage can be defined as the organization’s memory which comprises
the knowledge and information that the people working in the organization possess
through their skills and experiences. The collective memory of the organization in the
organizational culture is expressed through the routines and attitudes inhabiting in
groups and networks [48]. Organizational memory can be mental abilities and issues
inside the organizational members, but also the information possible to retrieve, such
as copies of memos, letters, spreadsheets, and data stored in computers constitute
organizational memory [48]. It has been stated that knowledge management systems
are a supporting class of systems to the organizational processes of knowledge
management and knowledge storage [8]. The organization’s computer-based
communication and information system applications contain databases, repositories,
directories, and networks [8].
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Knowledge application is the ability to use the learned material in new and
concrete situations by applying rules, methods, concepts, principles, laws, and
theories [9]. The use of external knowledge will create new knowledge [45].
Internal learning has two modes of knowledge: tacit and explicit knowledge. Tacit
knowledge is gained through clinical and practical experience [9]. It has been argued
that the use of healthcare information systems has enhanced individual learning and
group learning, and the physicians can achieve and create new knowledge by using
information systems [60]. A physician will require also other knowledge than medical
and clinical knowledge, such as technical skills, academic knowledge, a hospital’s
and healthcare organization’s cultural knowledge, management know-how, and
administrative skills. Healthcare organizations’ knowledge can be transferred to an
individual, a group or a system [51]. A patient relationship management system is
affected by the impact of the knowledge work performed by the physicians in a
hospital, and the use of the case system seems to enhance knowledge creation [13].
The knowledge transfer barriers are lowered between the physicians and the patients
by enhancing communication through a follow-up system [13].
In organizational learning, knowledge is stored in databases in documents, and the
learning entities are both the individual and organization [20]. This kind of
organizational learning is an ‘old organizational learning’. On the other hand, new
organizational learning means discovering new theories, practices and innovation and
then distributing or transferring that new knowledge to the organization [32], [20].
In organizational unlearning, the old organizational knowledge is disregarded. The
knowledge considered for elimination is the same knowledge that led the organization
to its previous success [64], and there is a need to remove or reject previously used
practice from the organization [32]. Therefore, change and learning theories are
relevant and should be included in a framework in order to draw a comprehensive
image of processes at work in the changing organizations [64], [25].
It has been claimed that security and confidentiality issue has slowed down the
cloud computing adoption in knowledge management systems in healthcare [31].
When using commercial cloud computing it is not known where the data is physically
stored and how it is secured. This issue is problematic especially if the data is
confidential [29], [59]. Of course, by creating own cloud computing environment this
problematic issue related to the physical location of data is solved, but the security
issues still remain. The communication between the healthcare actors and awareness
of their relationships with each other is important in information systems’ cloud
computing adoption [44], [31]. Healthcare personnel must understand how the cloud
computing adoption to information systems affects to the processes and how adoption
on the other hand is affected by the relationship between personnel and processes
[44], [31]. It has been further claimed that cloud computing adoption in its best would
minimize costs of healthcare personnel’s information retrieval because they can use
laptops and mobile phones everywhere when using cloud services remotely, and thus
cloud computing helps to simplify the management and access of patients’ data [31].
Healthcare information systems’ and information systems’ integration both need
integration requirements gathering in order to evaluate different approaches and
industrial integration standards [30]. The goal of information systems’ integration is
to offer services to disease management [22], to promote and prevent healthcare to
identify cause of illnesses, to help in medication and therapy, to offer rehabilitation
services, offer long term care, to provide clinical healthcare, and to offer information
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communication technologies (ICTs) in order to support management, administrative
activities and logistic services [22], [30]. ICTs cover electronic patient records,
electronic medical records, picture archiving communication systems, physicians’ e-
Prescriptions and e-Referrals, and portals with healthcare information and health
cards [30]. Healthcare information systems and information systems’ integration
should also offer infrastructure, research, education, collaboration, healthcare
knowledge infrastructure, clinical trials, medical education, local and international
platforms and collaborations, efficient IT infrastructure and security, deployment of e-
Health with efficient IT infrastructure, establish physical networks which allows
connectivity supporting interoperability of various technologies and various systems,
data integrity and security, and frame work of security and confidentiality [22], [30].
Thus, cloud computing adoption, information systems’ integration, and healthcare
information systems all impact to knowledge management in healthcare.
Our conceptual framework in healthcare has many dimensions and due to its
complexity and lack of solid and matured knowledge management theories in
healthcare it has a lot of challenges both for the academic studies, but also for the
practical implementations. As already outlined healthcare is a knowledge driven
process and hence knowledge management when properly studied and formalized and
implemented provides an opportunity to improve the healthcare performance at its all
levels.
4 Research Methodology
4.1 Data Collection
This study is a qualitative inquiry based on a case study approach [65], [66], [67] on
the empirical data collected by interviews [16]. This method is best suited for social
sciences, as it allows the researcher to interact with the society through interviews and
observations for the purpose of acquiring the desired data, such as in our case for
creating a knowledge management theory in healthcare. The researcher will be able to
combine various data sources such as archival records, interviews, observations, audio
recording, and even quantitative data for the analysis without restricting the data
formats [68]. The Grounded Theory (GT) approach [35], [69] is used in data
collection and analysis.
The individuals considered for the interviews need to have participated in the
process or action, and they must be given the time and place to be interviewed [67].
Our study is in line with this, because the central hospital arranges the place and time
for the interviews and the research coordinator arranges the interview timetables. It
has also been highlighted the importance of type of sampling and the number of
interviews needed [67]. Before the interview, permission is asked from the
interviewee to use the tape recorder. Audio-recorded unstructured and semi-structured
recordings of the interviews will be transcribed.
In our study a department in a central hospital is the unit of analysis and the study
covers several departments in order to generalize the results to have a wider impact
for the healthcare. A hypothesis was made that via a deep understanding of the
selected case departments and the identification of their knowledge management
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processes, categories, relationships between the categories, healthcare information
systems’ impact, cloud computing adoptions’ impact, and information systems’
integration impact to knowledge management in healthcare would provide a solid
background for the generalization of the results. The primary data sources are both
open-ended and structured interviews which create the possibility for individuals or
groups to express themselves freely in a relaxing atmosphere. Furthermore, archival
records are used as secondary data sources, and the background context data includes
the annual and financial reports, and press releases of the South Karelia Social and
Healthcare District [70], [71].
At first a pilot study was carried out in the Obstetrics and Gynaecology
department in January - March 2013 including 10 interviews. The interview questions
were predesigned and they were sent to the interviewees in advance [67]. The
interviewees were highly motivated, and they were asked to describe how they use
their own knowledge in medical and patient care. After this first pilot study, the
interview questions were improved based on the received feedback. This
reformulation of the research questions was needed in order to match better each of
the different department’s knowledge management. The chief physician of the
Obstetrics and Gynaecology department, who acts also as the research site
coordinator, has arranged the research permission and interviews. This is because in
the hospitals and healthcare districts in Finland the national laws and regulations are
very strict, and the interviews also need a specific time table not to affect the patient
care work.
After the pilot study and reformulation of the interview questions, five other
departments were included in the research, and new interview rounds have been and
will be carried out in the following order (number inside parenthesis gives the number
of interviews): in January - April 2014, a second round of interviews was carried out
in the Obstetrics and Genecology department (10); in March - May 2014 the first
interview round was carried out in the Paediatric department (5); in March - June
2014 the first interview round was carried out in the Paediatric Neurology department
(4); in May 2014-June 2014 the first interview round was carried out in the
Anaesthesia and Surgery department (10), and finally in May - June 2015 the first
interview round will be carried out in the Surgical department (10). Furthermore,
more interviews will be carried out in December 2015 - December 2018, including,
e.g., hospital IT administration department (10).
The ethical issues related to the data are guaranteed both in the case of the primary
data (interviews) and secondary data (the archival material). One of the risks is the
difficulty to build up a theory from the empirical case studies. In addition, it may turn
out that a single theory is not sufficient to cover all aspects in a required and selected
detail level. The validity and reliability of the research can be although affected by the
research design, data collection and methods, quality of data, analyses of data,
presentation of the results and making the conclusions [72], but simultaneous
triangulation from different data sources, such as archival material will improve the
validity and reliability of data, and the data is stored and protected according to the
Finnish laws. The legal permission to use central hospital as the research site was
granted in December 2012 by the Social and Healthcare District’s Service Director,
because every research which needs attendance and interviews of the staff must be
approved by the service director. Each of the interviewees has been and will be asked
in the future a permission to use the interviewee material as the data in the study and
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their anonymity is guaranteed. The collected data material is not allowed to be taken
abroad and hence it will stay in Finland.
4.2 Data Categorization and Analysis with the Grounded Theory
The pre-classified data will be analyzed first with grounded theory (GT) approach
[35] which allows the researcher to interact with the society through the interviews
and observations for the purpose of acquiring the desired data. This methodology will
allow the researcher to combine the various data sources such as interviews, and
observations, and even quantitative data for the analysis without restricting the data
formats [68].
The knowledge management research problems are the basis for interviews and
data collection. The research problems are presented to the interviewees, and they are
chosen because their role is to use, create and transfer healthcare-related medical and
ICT information, and translate it to knowledge relevant to the healthcare situation at
hand. We will use fragmentation and reassembling based on the researchers’ own
intuition and knowledge in order to categorize our data into thematic categories by
trying to capture a broader social system of ideas from the experience of the social
actors working in the Social and Health Care District [35], [69]. After the categories
have been found, we determine the properties of the categories and propositions
(hypotheses) for how the categories were related. The constant comparison between
the data and concepts in the past studies in order to accumulate evidence convergence
on simple and well-defined categories will led us to a higher level of abstraction of
statements about the relationships between the categories. This theorizing is in line
with GT approach suggestions in creating a theory [69], [35]. Finally, in the future we
will develop several conceptual frameworks of the discovered categories, and their
relationships between each other [35], [69]. Furthermore, in our in-depth case studies,
we must take carefully take into consideration beforehand who to interview, what to
do next, what group to look for, and what additional data we should collect in order to
develop a grand theory from the emerging data.
4.3 Quantitative Data Analysis
As this is research is also quantitative in its nature, the collected qualitative data will
be converted to quantitative form in order to carry out the needed statistical and
computational analyses. The exploratory data analysis approach is needed for
generating hypothesis due to weaker assumptions and prior knowledge about the data
and the domain. Data mining techniques are tools for exploratory data analysis [36].
The goal of data mining is to find unsuspected relationships and to summarize the
data in novel ways that are both understandable and useful for the goals of the project.
This includes visualization, projection methods, clustering, regression, classification,
and association analysis such as association rule mining techniques. Especially data
visualization methods, such as the Self-Organizing Maps [73], [74], Bayesian
networks [75], and multidimensional scaling and hierarchical clustering [36], are
needed for the deeper understanding of domain and variable dependencies.
Quantitative analysis covers both linear and non-linear methodologies combined with
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variable selection and uncertainty analysis but also classical hypothesis testing to
reject or accept hypotheses whenever available.
As a general framework (Figure 2) for quantitative data analysis, the Cross
Industry Standard Process for Data Mining (CRISP-DM) [36] approach is followed to
guarantee that no important steps in data analysis are missed.
Fig. 2. CRISP-DM framework for data analysis.
5 Conclusions and Discussion
The primary theoretical and scientific outcome and contribution of the project is to
create a theory of knowledge management in healthcare in specifically in the hospital
environment based on empirical findings. Here the term theory should be understood
as 1) a new conceptual framework of the knowledge management processes, and 2)
new knowledge management categories, and the relationships between the categories
in healthcare environments. Furthermore, the healthcare information systems’ impact,
cloud computing adoption’s impact, and information systems’ integration impact to
knowledge management in healthcare are also studied. In this research project the
research environment is the central hospital of the South Karelia Social and
Healthcare District, Finland.
In general, when discovering a new theory, a multiple case study approach should
be applied [66], [72]. In our study a department in the central hospital is our unit of
analysis. The sample, however, has not been limited to one department, but several
departments, because the goal of the study is to achieve a deep understanding of the
selected case departments and to identify their knowledge management categories,
relationships and processes. Theory creation should also combine multiple data
Project understanding
Data understanding
Data preparation
Modelling
Data suitable?
Technical quality
improvable?
Evaluation
Deployment
Objective achieved?
yes
yes
unlikely
no
no
p
artiall
y
partially
Close
p
ro
j
ect
Cancel
p
ro
j
ect
revise objective
revise ob
j
ective
Problem definition, expected benefits
What would be the solution
A priori knowledge about the domain
What data are available
Relevancy and validity of the data
Data quality, quantity and recency
Which data to concentrate on
Data transformations needed
Is it possible to improve the data quality
Models to be used
Selection of modelling approaches
Models’ technical performance
Evaluation approaches
How good is the model in terms of
objectives
What has been learned from the project
Best deployment strategy for the model
How to quarantee the validity of the model
in the future
likely
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collection methods due to the triangulation in order to provide stronger substantiation
of categories. Collecting different types of data by different methods from different
sources produces a wider scope of coverage may result in a fuller picture of the
phenomena under study. Thus, both quantitative and qualitative data are used in this
study [72]. The flexibility given by Grounded Theory (GT) on the other hand gives
respondents an ability to express their views and opinions easily and freely [66], [72].
Therefore, the methodological scientific contribution of this study is to utilize a new
methodological approach where both diverse qualitative research methods such as
Grounded Theory (GT) [35] and quantitative research analyzing approaches are
applied. As the quantitative research approach we use novel intelligent computing and
analyzing methods, and as a general framework, the Cross Industry Standard Process
for Data Mining (CRISP-DM) [36] approach has been selected. Constant comparison
between the data and concepts will be made so that accumulating evidence converges
on simple and well defined constructs. The boundary conditions of the theory,
however, have to be taken into account, because the phenomenon is so atypical that it
holds only in this specific contextual healthcare environment.
The practical and managerial contributions of this study are as follows. First, to
help physicians and nurses to understand their own valuable knowledge capital and
practice, to understand knowledge management better, and to get familiar with
knowledge management practices in the hospital. Second, to develop knowledge
transfer from the physicians to nurses, and vice versa. Third, with the discovered
knowledge new and user friendly knowledge management processes could be
remodeled. Fourth, the hospital based knowledge could be used later to implement
more user-friendly healthcare information systems. Of course, it may also turn out
that the data will not contain enough information to derive valid and solid knowledge
management categories and therefore a very careful analysis has to be carried out to
find out if the categories discovered in the data are the correct ones.
The results of this project are expected to gain a lot of interest in other Finnish
social and healthcare districts and most probably the results are applicable to many
hospitals due to their similarity. Internationally this study can offer guidelines and
good practices to follow up in the hospitals abroad, and also to improve their ability to
better and safety patient care. The research project will have several collaboration
partners which have special knowledge in medical science, nursing science,
sociology, intelligent computing and systems, information systems, and software
engineering. The collaboration partners include both other Finnish Social and
Healthcare Districts and international cooperation.
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