ECOSYSTEM FOR HEALTHCARE
SERVICES AND MANAGEMENT SYSTEM
Azana Hazah Mohd Aman
Senior Lecturer, Faculty of
Information Science and Technology,
National University of Malaysia.
Selangor (Malaysia).
E-mail: azana@ukm.edu.my
Syed Abdul Mutalib Al Junid
Senior Lecturer, Faculty of Electrical
Engineering,
Universiti Teknologi MARA, Shah
Alam, Selangor (Malaysia).
E-mail: samaljunid@uitm.edu.my
Hasimi Salehudin
Senior Lecturer, Faculty of
Information Science and Technology,
National University of Malaysia.
Selangor (Malaysia).
E-mail: hasimi@ukm.edu.my
Adil Hidayat
Director, My6 Initiative Berhad. Cyberjaya
(Malaysia).
E-mail: adil@my6.my
Rosilah Hassan
Associate Professor, Faculty of Information
Science and Technology, National University
of Malaysia. Selangor (Malaysia).
E-mail: rosilah@ukm.edu.my
Syed Mohamed Aljunid
Professor, Faculty of Medicine, National
University of Malaysia. Kuala Lumpur
(Malaysia).
Faculty of Public Health, Dept of Health
Policy and Management,
Kuwait University (Kuwait).
E-mail: smohamed@ppukm.ukm.edu.my
Recepción: 29/07/2019 Aceptación: 19/09/2019 Publicación: 06/11/2019
Citación sugerida:
Aman, A.H.M., Al Junid, S.A.M., Salehudin, H., Hidayat, A., Hassan, R. y Aljunid,
S.M. (2019). Ecosystem for healthcare services and management system. 3C Tecnología.
Glosas de innovación aplicadas a la pyme. Edición Especial, Noviembre 2019, 121-131. doi: http://
dx.doi.org/10.17993/3ctecno.2019.specialissue3.121-131
Suggested citation:
Aman, A.H.M., Al Junid, S.A.M., Salehudin, H., Hidayat, A., Hassan, R. & Aljunid,
S.M. (2019). Ecosystem for healthcare services and management system. 3C Tecnología.
Glosas de innovación aplicadas a la pyme. Speciaal Issue, November 2019, 121-131. doi: http://
dx.doi.org/10.17993/3ctecno.2019.specialissue3.121-131
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ABSTRACT
Dengue fever is one of the neglected tropical diseases (NTDs) in the Southeast
Asian Countries (ASEAN), almost 70 million cases of dengue fever occur annually.
This infection is now one of the most economically important NTDs in the
region. Hence, there are urgent needs to spread public awareness on NTDs, the
prevention, the treatment and the clinical cost involved. An innovation in health
services and management system is needed to cater this issue. Intelligent Ecosystem
for Healthcare Service and Management (IEHSM) is an integration of healthcare
management, health knowledge base and data reference, control and elimination
tools, and clinical costing. The main objective of this research is to provide a model
for the improvement of fundamental understanding of public health especially NTDs
and at the same time improve the existing healthcare services and management
system. IEHSM adapts the optimization of prevention emphasizing on therapeutic
approaches through Big Data Analytics, Articial Intelligent, Cloud Computing,
Machine Learning and Information Centric Networking. The IEHSM framework is
based on Casemix system, a system that aggregates information about patients and
associated procedures based on the type and mix of patients’ treatment.
KEYWORDS
Intelligent Healthcare Ecosystem, Articial Intelligence, Cloud Computing, Big
Data.
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1. INTRODUCTION
The Southeast Asian Countries (ASEAN) are still facing issues of neglected tropical
diseases (NTDs). Nearly 650 million people live in ASEAN countries, about 200
million come from the low or lower middle-income countries and many of them
are aected by at least one NTD (World Health Organization, 2019). However,
NTDs also aect upper middle-income ASEAN countries such as Malaysia. In
Malaysia as reported by World Health Organization (WHO), during week 26,
2019, a total of 2,806 dengue cases including one death was reported, bringing the
cumulative number as of 29 June 2019 to 62,421 cases including 93 deaths. This is
higher compared to 32,425 cases with 53 deaths reported during the same period
last year (World Health Organization, 2019). Weakness in public awareness and
understanding, shows that the health systems need to be improved. An urgency to
eectively spread public awareness on NTDs prevention (Asat, 2018), treatment and
clinical cost (Ibrahim, 2019) involved. Obviously current health services are unable
to attract the public interest and awareness, despite having heterogenous technology
and excellent infrastructure, these services are not interesting, not approachable or
not user friendly to the public.
Intelligent Ecosystem for Healthcare Service and Management (IEHSM) is an
integration of healthcare management (doctors, medical experts and patients),
health knowledge base and data reference, control and elimination tools, and clinical
costing. One of the goals of this research is to help the public by educating them about
their medical status and keeping them health-aware as the saying goes ‘prevention
is better than cure’. The main objective of this research is to provide a model for
the improvement of fundamental understanding of public health especially NTDs
and at the same time improve the existing healthcare services and management
system. IEHSM also empowers public to self-manage information, an emphasis on
improving the quality of interesting and deliverable health awareness information.
IEHSM helps in utilizing available current health system resources to their maximum
potential. It also aids remote monitoring of patients and helps in reducing the cost
of treatment via medical practitioners’ support that extend their services without any
geographical barriers.
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IEHSM adapts the optimization of prevention emphasizing on therapeutic
approaches through Big Data Analytics (Wikipedia, 2019), Articial Intelligent
(AI) (Wikipedia, 2019), Machine Learning (Wikipedia, 2019), Cloud Computing
(Wikipedia, 2019) and Information Centric Networking (ICN) (Aman, 2019).
Machine learning is the scientic study of algorithms and statistical models that
computer systems use in order to perform a specic task eectively without using
explicit instructions, relying on patterns and inference instead. It is seen as a subset
of articial intelligence. In computer science, articial intelligence, sometimes called
machine intelligence, is intelligence demonstrated by machines, in contrast to the
natural intelligence displayed by humans. While Big data is a eld that treats ways to
analyze, systematically extract information from, or otherwise deal with data sets that
are too large or complex to be dealt with by traditional data-processing application.
Cloud computing (Al Junid et al., 2012) is the on-demand availability of computer
system resources, especially data storage and computing power that is available to
many users over the Internet. Information Centric Networking is an approach to
evolve the current Internet infrastructure to content based distribution. It is hoped
that, with the integration of these technologies the new proposed IEHSM will help
to produce better healthcare ecosystem.
2. RESEARCH METHODOLOGY
The IEHSM framework is based on Casemix system (Casemix, 2019), a health
system that aggregates information about patients and associated procedures based
on the type and mix of patients’ treatment. The current Casemix grouper uses
International Classication of Disease, Tenth Revision (ICD-10), as for this research
ICD-10 will be upgraded, modied and improved based on the new IEHSM model.
The rst stage of the research methodology will involve investigation of research
problem followed by framework design stage and nally validation of the proposed
model. The details of the stages are explained in Table 1.
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Table 1. Methodology Stages.
Stages Description
Stage 1
Problems, solutions and methodology identication phase. The target
scopes are NTDs, Casemix, IEHSM, Therapeutic approaches, Big Data
Analytics, Articial Intelligent, Machine Learning, Cloud Computing and
Information Centric Networking. Hence this phase focuses on identifying
research problems and research gap for NTDs and Casemix system
environment. Also, to nd the possible solutions and methodology offered by
Big Data Analytics, Articial Intelligent, Machine Learning, Cloud Computing
and Information Centric Networking that satises the improvement of public
health especially NDTs. Additionally, other critical system information and
system parameters that affect the functionality of the IEHSM system will also
be identied.
Stage 2
Framework design and implementation phase. This phase focuses on how to
initialize solution and satisfy the information requirement to develop IEHSM
environment. In order to accomplish this goal, by using stage 1 results the
baseline model for the proposed IEHSM will be logically drawn. Based on
the baseline design, the pseudo code/owchart, communication process,
mathematical models and algorithm will then be developed.
Stage 3
Evaluation and results analysis phase. The proposed IEHSM will be
analyzed through user acceptance test. The performance will be evaluated
and benchmark with existing system.
This research will produce preliminary analysis for IEHSM in order to adapt new
ICD-11 and therapeutic prevention approaches. As per above research background,
the following research questions and objectives mapping are formulated and
presented in Table 2.
Table 2. Research Questions and Objectives.
Research Questions Research Objectives
How to intelligently adapt prevention
approaches in IEHSM?
To intelligently adapt prevention
approaches in IEHSM
How to employ Big Data Analytics,
Articial Intelligent, Machine Learning,
Cloud Computing and Information Centric
Networking for an effective IEHSM
environment?
To employ Big Data Analytics, Articial
Intelligent, Machine Learning and Cloud
Computing for an effective IEHSM
environment.
How to efciently provide approachable,
interesting, deliverable and public friendly
IEHSM system?
To efciently provide approachable,
interesting, deliverable and public
friendly IEHSM system.
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3. PRELIMINARY ANALYSIS
Casemix is one of the available health systems in ASEAN. Currently Casemix system
solutions consist of Grouper, DataTool, Code Assist, Clinical Cost, Casemix Cloud
and Casemix Hospital Information System (HIS). Grouper (Aljunid et al., 2011) is a
universal and unied patient grouping to convert diagnosis and procedures done by
clinician into a single Casemix code called UNU-CBG. While the DataTool assigns
diagnosis and procedure codes, it simplies the coding process and help to improve
coding quality. Clinical Cost Modeling consists of CCM module, Casemix Module,
and Hospital Tari module. CCM module is used to calculate cost per patient per
stay by ward and cost per patient per visit by clinic. Meanwhile, Casemix module is
used to impute cost per UNU-CBG group. Lastly, Hospital Tari module is used to
calculate the UNU-CBG group tari per patient. Casemix is also accessible via web
based in cloud computing. Casemix has a centralized Hospital Information System
(HIS). Casemix systems are shown in Graphic 1, Graphic 2, Graphic 3 and Graphic
4.
Graphic 1. Grouper. Source: (Casemix Solutions, 2019).
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Graphic 2. Data Tool. Source: (Casemix Solutions, 2019).
Graphic 3. Code Assist. Source: (Casemix Solutions, 2019).
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Graphic 4. Clinical Cost. Source: (Casemix Solutions, 2019).
Despite all these solutions Casemix still needs to be improved and upgraded for
future changes in diagnosis and procedure classications as well as features such as
prevention of NTDs using therapeutic approaches. An innovation in health services
and management to improve health system deliverable and ecient public health
awareness system is highly needed. Table 3 shows the preliminary analysis between
casemix and IEHSM.
Table 3. Preliminary Analysis.
Parameter Casemix IEHSM
Healthcare management Yes Yes
Clinical costing Yes Yes
Prevention approaches No Yes
Health knowledge base Yes Yes
Interactive communication No Yes
Public friendly No Yes
Smart technologies Cloud Computing
Big Data, AI, Machine
Learning, Cloud Computing
and ICN.
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4. ACKNOWLEDGEMENT
The authors are grateful to Faculty of Information Science and Technology, National
University of Malaysia. This research is also funded by research grant DIP-2018-
040.
5. CONCLUSIONS
IEHSM preliminary analysis based on Casemix framework is necessary in order to
knows the enhancement needed. It is the initial stage of the IEHSM development
phase. As for future work, investigation will be done for other scope areas which are
NTDs, Therapeutic approaches, Big Data Analytics, Articial Intelligent, Machine
Learning, Cloud Computing and Information Centric Networking. It is hoped
that the completed research will empower smart and intelligent health services and
management system to be sustainable globally and greater public health engagement.
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