Process-Oriented e-Learning System for Training Healthcare
Professionals on Big Data Usage
Vassiliki Karabetsou and Flora Malamateniou
Department of Digital Systems, University of Piraeus, 80 Karaoli & Dimitriou Str., 18534 Piraeus, Greece
Keywords: Big Data, Healthcare, Process-Oriented, Workflow, Learning Analytics, e-Learning.
Abstract: Big data technology promises to transform the way in which medical care is delivered and help the
healthcare industry to address problems related to variability in healthcare quality and escalating healthcare
costs. However, integrating Big Data use in healthcare professionals’ daily practice seems to be a
challenging task as they are accustomed to making treatment decisions independently, using their own
clinical judgement, rather than relying on protocols based on big data. Taking medical decisions based on
Big Data - combined with physicians’ valuable clinical knowledge and experience - can lead them to safer
and more accurate diagnosis and focused treatments. In order to support this transformation in medical
practice healthcare professionals (e.g. physicians, nurses, pharmacists) will need to be trained in the
collection, integration and analysis of large data sets. To this end, this paper presents a process-oriented e-
learning system which aims at making healthcare professionals understand how to use big data tools and
giving them the necessary skills to improve operations. The system uses workflow technology and Learning
Analytics which has been specifically planned for learners’ custom needs.
Big Data in healthcare have given great propulsion
to research and to medical care as well. They have
contributed to predicting diseases by studying genes
while their use leads physicians to quickly targeted
and effective diagnosis and treatments (Groves et al.,
2013). They can stimulate innovation by identifying
new treatments and approaches for the provision of
medical care using data coming from clinical trials.
They also promise to cut down the cost of the health
system through the decrease of medical errors.
Correct diagnosis will reduce unnecessary exams
and limit wrong treatment.
However, healthcare organizations still have
difficulties in fully taking advantage of big data's
capabilities. This is because of the fact that they
don't know where to «start from». Actually,
everything begins from data. The use of big data in
healthcare requires a set of many heterogeneous
sums of data. The bigger the sum, the more the
chances of finding a correct answer on the questions
about medical practice. The use of the World Wide
Web has made possible the collection of clinical
data and the observation of epidemics on a global
scale. The ever-increasing number of data will
gradually lead to finding useful data, in better
decisions and more effective attempts (Murdoch and
Detsky, 2013).
Unfortunately, physicians don't have the totality
of data related to patient healthcare (e.g. clinical
data, electronic patient records’ data, sensor data,
emergency care data) which makes up Big Data
(Raghupathi and Raghupathi, 2014) concentrated
somewhere in order to access all of them. In
addition, physicians are accustomed to making
decisions based only on their judgment and not on
data. The result is that one of five medical decisions
is wrong or imperfect and the third reason for most
fatalities is medical errors that occur due to unlucky
medical decisions (Groves et al., 2013; Sun and
Reddy, 2013; IBM, 2015). However, taking the fact
that medical knowledge doubles every three years
into consideration, it will be impossible for
healthcare professionals to read all that information
to be informed (Kohn, 2012).
Some organizations and academic medical
centers have already started estimating capabilities
that big data can offer in clinical practice and
research and have adopted the use of big data
analytics systems. Unfortunately, professors in
medical schools have not been trained in order to
Karabetsou, V. and Malamateniou, F.
Process-Oriented e-Learning System for Training Healthcare Professionals on Big Data Usage.
DOI: 10.5220/0005828205230528
In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - Volume 5: HEALTHINF, pages 523-528
ISBN: 978-989-758-170-0
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
manage and analyze data. Training in this sector is
considered to require acquiring technological skills
beyond a doctor's training capabilities. As a
consequence, physicians cannot make use of big
data analytics software and take advantage of their
use (Moskowitz et al., 2015; Waxer, 2014).
Process oriented e-learning systems have
recently received much attention, as they utilize
workflow technology, to support highly structured
teaching/learning processes. Furthermore, they allow
not only to design and evaluate the effectiveness of
processes, but also to easily redesign and improve
them. Especially, when training healthcare
professionals, redesigning of processes is required
because of the constantly increase of medical
knowledge (Lenz and Reichert, 2006).
In this paper a process-oriented e-learning
system for training physicians on big data usage is
presented. In particular, the prototype system uses
workflow technology and aims at training physicians
on how to search, gather, visualize and store medical
data from their daily practice. Furthermore, Learning
Analytics are incorporated into the system in order,
not only for physicians to monitor their progress, but
for the whole learning process to be evaluated.
The need to support teaching/learning processes,
rather than simple tasks, in recent times has resulted
in a new type of process-oriented, educational
technology. By using workflow-based e-learning
systems, it is possible to introduce flexible start and
finish times for each task (unit of content) based on
user’s needs and progress. Technically, this is made
possible by the coordination mechanism used by
workflow technology. Furthermore, it is possible to
increase flexibility of the curriculum. This means the
introduction of flexible learning pathways so
students can progress through the content in a
variety of ways based on their needs and preferences
(Marjanovic, 2007).
The use of digital technology in learning
processes though, leads to the collection of more and
more data. Data create awe with their breadth and
heterogeneity but they can contribute to education
development with the help of Learning Analytics.
Learning Analytics can collect and process data with
volume, variety and velocity, they can process “Big
Data”. The question is which educational data
should be gathered and analyzed? Who should
choose them? Which are the suitable criteria for this
purpose? Ellaway et al., (2014) believe that
professional medical education should use analytics’
techniques which are proper for the temperaments
and special needs of the health sector. Another
question that someone should take in consideration
is if learners must be aware of the whole procedure
of gathering and analyzing educational data and if
this awareness can affect the training outcome.
Finally, it is important to examine how analysis’
results should be used in order to identify best
practices and to provide educational process success.
As far as physicians’ special characteristics are
concerned, someone could end up with the following
Despite technology’s extensive growth and the
fact that almost everyone owns modern devices,
physicians are not quite familiar with all these.
As a result, they still prefer studying printed
documents (medical articles and books) to get
informed. They also prefer storing data on paper
(Raghupathi and Raghupathi, 2014).
Their demanding schedules don’t allow them to
get informed and to digitize the printed data they
own. Moreover, the lack of spare time makes it
impossible for them to attend educational
programmes that take place in traditional classes.
During their daily clinical practice a lot of
questions come up. These questions are about
medicine of course, not only older but also new
medical knowledge, and sometimes they are
relative to other specialties. In all of these cases,
physicians turn to their colleagues to ask for a
piece of advice. What is more, there are asked to
offer their services to e-patients, who have
already searched for information online and have
a lot of questions to ask.
They use the internet to a limited extent, because
they don’t feel quite safe when they use it and
they don’t actually trust it. For this reason,
although they use to sign in social networks, they
don’t use to search for medical information.
A lot of physicians would choose distance
learning to get educated, but they can’t always
find the proper e-learning program made for this
purpose (Cortelyou-Ward et al., 2013; Ellaway et
al., 2014).
As regards to the way of their training, they need to:
Have support, guidance, confirmation and
feedback during the whole process
Have flexibility of space and time
Attend a program that allows them to follow
their own learning pace
Make their own selections in order to draw their
own learning path
HEALTHINF 2016 - 9th International Conference on Health Informatics
Get educated quickly, easily and economically.
As regards to the subject of their training, they need
Get more familiar with new technology
Use internet safely
Have easy and quick access to the data they need
in order to make decisions regarding medical
diagnosis and treatment
Get informed of Big Data usage benefits
Use Big Data in their everyday practice, in order
to improve medical care provided to patients
Meet healthcare system’s needs for reducing
medical errors, unnecessary tests and cost
Get informed about changes and improvements
in medical field
Share best practises with the medical
A lot of e-learning systems for training healthcare
professional have been implemented. Some of them
are based on Workflow technology. For example,
Puustjärvi and Puustjärvi’s (2010) proposal for
physicians distance learning. They suggest a solution
that provides physicians’ daily duties coordination
and the necessary learning material as well. Another
example is the complete framework that Chodos et
al., (2009) suggested for healthcare professionals
training by video and virtual world simulations.
University of California, Davis (UC Davis, 2015)
promises that applying clinical Analytics can
“improve health care, manage risks and improve
patient outcomes”. UC Davis Extension’s online
Healthcare Analytics Certificate Program designed
for working clinicians and IT professionals aims to
help them acquire comprehensive understanding of
the use and implementation of healthcare analytics.
A lot of Universities (University of Central
Florida, Rio Salado Community College, Northern
Arizona University, Purdue University, Ball State
University, University of Michigan, University of
Maryland, Graduate School of Medicine, University
of Wollongong) use Learning Analytics in order to
monitor and engage their students, provide them
support when they need it and enhance the learning
experience. In healthcare education though “learning
analytics is in its infancy” (Dietz-Uhler and Hurn,
Elsevier, a world-leading provider of information
solutions, which provides web-based courses for
nursing and health professions students, will use
Knewton’s infrastructure “to power personalized
digital solutions”. Elsevier offers the educational
content when Knewton provides analytics and
technology (Knewton, 2014).
Recognizing the significance of gaming and
learning analytics, the Stanford School of Medicine
developed two educational games, Septris and
SICKO, for medical students. The games were very
well received and this proved that the application
of gaming and the collection of learning analytics
data offer many potential opportunities in
education(Tsui et al., 2014).
A high-level model of the learning process
considered, which is based on constructivism
learning theory, is shown in Figure 1. The learning
process has been specifically designed for healthcare
professionals to enable them realize the benefits
from using healthcare analytics at their workplace.
The model consists of the following activities:
Introduction. Learners watch a video
presentation with ultimate goal to help them
digitize printed data, collect medical data online,
organize, store and visualize them and to
evaluate data usage in decision-making process
according to diagnosis and treatment.
Assignment Subject Selection. Learners are
asked to choose among ten different subjects.
They can also suggest their own subject. The free
selection is necessary, because it will give them
the possibility to collect data that they really
need and can use in their medical practice. The
fact that they find the subject interesting, will
motivate them and get them engaged. Teacher is
informed about their choice in order to be able to
support them when needed.
File Creation. Learners create the files in which
they will store their data. They will store all the
data they will collect about their subject in these
files. The main file will be named after the
subject and a number of sub-files will be also
created. In the sub-files information about the
subject’s definition, symptoms, aetiology,
diagnosis, prevention, treatment, medications
and personal notes will be stored. This is a very
simple way for anyone to organize data that
doesn’t demand any programming skills.
Process-Oriented e-Learning System for Training Healthcare Professionals on Big Data Usage
Figure 1. High-level BPM Studio model of the learning process.
1st Assignment Assessment. Learners are
informed about their performance. If they get
grade under 50%, they will watch a simulation
video in order to correct their assignment.
Otherwise, they are addressed to the next
Data Collection. Learners gather all the data
they could find and then store them in the files
writing down useful metadata as the link, the
date they found them and a short description of
the content. Finding, collecting, organizing data
and creating metadata about them, is a simple
stage of analysis that can make data ready to be
used and easily updated. Data collection
constitutes a process activity consisting of three
o Printed Material Management and
Digitation. Learners check first if the
material (e.g. articles, images, diagrams,
video, medical books) already exists. If it
doesn’t exist, they will digitize their printed
material. In any case, they should store data.
o Guided Research. Learners collect data
using suggested links of medical blogs and
o Free Research Online. Learners search for
data using suggested combinations of words
and directions to use internet in a safe way.
Learners are informed about the way their
assignments will be evaluated. The
assessment depends on the volume of the data
that they collect, the relevance of the data to
the subject and the way that these data are
Assignments’ Assessment. Teacher assesses
each assignment separately. The average grade is
automatically calculated and presented to
him/her. If the average grade is less than 50%,
teacher makes suggestions in order to help
learners to complete their collection and submit
it again. Otherwise, they are driven to the next
Data Collection’s Completion and Evaluation.
Learners are informed not only about the way
they could use their data collection, but also
about the way world medical community uses
Big Data. They are informed about the medical
databases that are created and furthermore about
the Big Data analytics systems that are adopted
by physicians, their advantages and their
conditions, way and scope of use. They could
collect and use Big Data alone or they could
adopt a system that provides them all of the
useful data they need in order to answer
complicated questions.
Learning Process’ Evaluation. Learners are
asked to evaluate the learning process by
completing an evaluation rubric. The data that
will come up from this procedure will enable the
quality control of the process and will contribute
to it’s improvement through redesign.
The proposed system was implemented with
Business Process Management Oracle BPM Studio
10g Release 3 (10.3.0). BPM Studio was used both
for designing and implementing the learning process
model and for implementing learning analytics.
As shown in Figure 2, learners are able to choose
the subject of their assignment. Ten different
subjects are proposed to them. They can choose one
of them or they can submit their own subject. In
addition, it offers educational designers the ability to
review the workflow, when needed. For example, if
HEALTHINF 2016 - 9th International Conference on Health Informatics
they notice that learners don’t submit an assignment
on time, they suspect that this assignment is too
difficult and they can modify it properly.
Figure 2: Assignment subject selection.
Learning Analytics is used to keep track of the
learners’ progress and intervene in cases when it is
needed. As shown in Table 1, Learning Analytics
are being used in two different ways, “Micro
interventions” during the whole learning process and
educational data gathering for future use. As “Micro
interventions” are concerned, Chris Brooks’ opinion
was adopted which suggests giving support to
learners at the right moment (Diaz and Brown,
2012). The proposed model includes a dashboard
which presents the elapsed time between activities
and another one where trainer can watch the average
engagement time for participants to each activity.
There are also two charts for students where they
monitor the learning process progress. At the same
time, learners’ privacy is being protected, because
information is displayed anonymously. Teacher’s
intervention is immediate in order for learners not to
abandon the process. Also, frequent assessment is
applied for the teacher to will be able to detect
problems and intervene immediately.
During the learning process lots of educational
data are collected that are used to extract
conclusions. These data include learners’
assignments, dashboards references, assignments
comments and grades, learning process evaluation.
The material that is gathered consists of texts,
images, graphs, questionnaires and e-mail. Data
volume and velocity depend on learners’ number.
While the use of big data shows exciting promise for
improving health outcomes and controlling costs,
healthcare professionals should increase their
reliance on big data technology for triage, diagnosis
and decision-making. To this end, training
Table 1: Learning Analytics Implementation.
Goal Actions
Prediction of
o 1st Assignment
o Learning pace
Early detection
of learners’
o Date of assignments’ delivery
o Elapsed time between
assignments (Dashboard)
o Automated reminder of
deadlines and detection of
when needed
o Support learners providing
extra material individually
o Evaluate assignments using
comments to guide and support
o Support and encourage learners
through e-mails
o Graphs presenting active work
items in one activity and in the
whole process
o Inform learners about their
assignments’ evaluation criteria
Helping and
o Dashboard that visualize the
average time that learners spend
in each activity
o Teacher’s inform every time
that a learner uploads an
assignment with e-mail
o Provided support evaluation
o Completion of an assessment
healthcare professionals on big data usage
constitutes a rather challenging task. In this paper a
process-oriented e-learning system was implemented
with the objective to train physicians on big data
usage. The system uses Learning analytics for
monitoring learners’ progress and for, improving the
learning process.
With today’s huge patient loads, treating patients
sooner saves both lives and healthcare costs. To this
end, the proposed system aims at helping healthcare
professionals to deliver much more precise and
personalized care by quickly making informed
medical decisions.
System evaluation is a task to be undertaken in
the near future aiming at determining the system
usability. Thus, its potential weaknesses may be
revealed suggesting alterations in the system design.
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