System Integration Plan Proposal for DebugIT Clinical Data Repository

This report presents a system integration plan proposal for the DebugIT Clinical Data Repository. The introduction outlines the importance of biomedical data integration, particularly for Cerner Corporation, highlighting the need for interoperability across diverse data sources and platforms. The DebugIT project utilizes clinical and operational data from existing Clinical Information Systems (CIS) to combat bacteria using IT. The architecture comprises local CDRs, wrappers, and a central virtual CDR (federated database instance) to provide a homogenous view of distributed data. The wrappers perform ETL processes, normalizing data using ontologies and converting data models. Local CDRs, instances of MySQL, store the DebugIT data, and the central system uses a global schema for semantic interoperability. The discussion emphasizes the importance of a flexible system where local databases are maintained autonomously, and the literature review explores challenges in biomedical data management, including data organization, sharing, and the need for efficient information management systems. The methodology section outlines the research philosophy (positivism), approach (deductive), and design (exploratory), with secondary data collection methods. A project plan with defined tasks, responsibilities, start, and end dates is also included. The conclusion summarizes the proposed CDR architecture and data integration model.

Running head: SYSTEM INTEGRATION PLAN PROPOSAL
SYSTEM INTEGRATION PLAN PROPOSAL
Name of the Student
Name of the University
Author Note

SYSTEM INTEGRATION PLAN PROPOSAL 1
Table of Contents
Introduction and organisation background:...............................................................................2
The DebugIT Clinical Data Repository:....................................................................................2
Discussion:.................................................................................................................................4
Literature review:.......................................................................................................................5
Methodology and project plan:..................................................................................................6
Research philosophy:.............................................................................................................6
Research approach:................................................................................................................6
Research design:.....................................................................................................................7
Data collection method:.............................................................................................................7
Project plan:...........................................................................................................................7
Conclusion:................................................................................................................................8
References:.................................................................................................................................9

2SYSTEM INTEGRATION PLAN PROPOSAL
Introduction and organisation background:
The last 10 years have been noticeable by the important most maximization of data
those are related to biomedical. The bio medical databases can cover a rising piece of
information and data that are sorting from the findings those are clinical for the generation of
structures that include behavioural, social, and environmental and societal data. There is a
humble usage those are secondary of the information and data for the improvement of safety
and quality for the patient care (Crowe et al., 2017). The purpose of this paper is to present
architecture of integration of data which will be utilized for building operability among the
storages those are heterogeneous of data that of the hospital network in the (Eliminating and
Detecting Bacteria Using Information Technology). The system will be developed for Cerner
Corporation which is an American supplier for the technology solutions related to health
information, devices, hardware and services. Cerner is basically a top healthcare data
analytics organisation that is situated in United States, with the help of powerful technologies
that connects systems with people. The company services are used by over 27000 of the
contracted global healthcare providers. For developing a system which can provide
homogeneity and interoperability across various countries, types of data and sources of data,
various issues has been faced. The system of integration need to be coped with lack of
interoperability those are technical that includes various kind of platforms of hardware,
database management systems, operating systems , protocols for access, programming
languages and formats of transport.
The Debug IT Clinical Data Repository:
The Debug project of IT will utilize clinical and operational information and data
from the Clinical Information System (CIS) that is already existed. The CIS is located in
various hospitals for providing advanced healthcare in order to battle in opposition to bacteria

3SYSTEM INTEGRATION PLAN PROPOSAL
by the utilization of IT. The access to the heterogeneous and data those are distributed will be
accomplished by a visualized and clinical data repository that is fully integrated. The project
of Debug IT needs a homogenous view that is homogeneous for the data sources featured by
the transparent access. Centralizing of the RAW data in a permanent storage is not allowed
for the issues related to ethics and privacy. The sources need to be accessed by the SPARQL
as well as the result have to be presented in RDF type of format. For meeting all the
requirements, the architecture of the system is composition of three major components such
as local CDRs, wrappers and a central virtual CDR which is a federated database instance.
The wrappers are having responsibilities for the ETL – (extract,transform and load)
procedure. They can extract information and data from the local CIS which performs a model
transformation from the CIS in to the model of Debug IT. The system will perform two tasks:
1) Normalize the content by utilizing ontologies NEWT for bacteria, WHO-ATC for drug,
SNOMED CT for culture, Time.OWL for time attribute.
2) Convert the data from the EAV or CR model.
The local CDRs are basically the instances of MySQL database which can store the
data of Debug IT that are extracted from the CIS. The environment are set up inside the DMZ
(demilitarised zone) for each of the data and service provider. The data that are stored in the
local CDRs can be annotated and validated when needed (Makkonen, Weidenfeld&Williams,
2017). The local CDR also contains two of the distinct schemas such as EAV or CR that are
utilized as the input schema for the information and data that are extracted from the CIS as
well as one more customized for the data of Debug IT that are used as query interface. The
Local CDRs are utilized for performing the tasks that are as follows:
1) Normalising the content of the CDR.
2) Building the information model and data catalogue.

4SYSTEM INTEGRATION PLAN PROPOSAL
3) Drafting the Debug IT core ontology.
4) Applying the methods of data mining.
5) Drafting the use cases of end point.
The core of the system framework is MySQL federated database instance. The system
can link all of the data sources for creating a global view on the top of them. The system will
use a global schema which is the key component and easiest way for the semantic
interoperability at the absence of ready to use global ontology. The architecture of the system
will be having a characteristics for both the view integration approaches and data
warehousing. The common schema and data transformation are typical for the data
warehousing at the time of inexistence of the central storage which is the main characteristic
for the view integration. Though, there are some advantages and disadvantages of using the
system (Pietzcker et al., 2017). Zero data exposure and its high performance are the major
advantages of the system. On the other side, synchronisation and cost of updating data model
are the downside of the system.
Discussion:
The main purpose of data integration procedure is to present a homogenous view of
the data that contains important functionalities of the systems those are individual in the
system those are unified. The system needs to be having the ability to take care of various
queries those are complex which are spanned over the entire source datasets. For having a
system those are flexible, the content of the databases those are local have to be maintained
locally and autonomously (Bhattacharyya et al., 2017). The schemas may quickly change on
the average, 2 or 3 times every year. The databases that are used for the biomedical data
management in the organisation are maintained as well as designed for meeting all of the

5SYSTEM INTEGRATION PLAN PROPOSAL
local needs as well as changes which are made independently for the structure that is
integrated.
This is very much interesting for having a semi automatic or even automatic representation of
the sources. This might help the integration of the sources that are new as well as the
integration might be very much important for the system frameworks in that the sites sources
can be having schema updates frequently (Ma et al., 2018). The design of the Debug IT CDR
can try to follow the above mentioned concepts. The comparison of data integration systems
are as follows:
System Year Approach Query interface
HEMSYS 1987 View
integration
HEMSYS query language
SRS 1993 Link integration Query form or APIkeyword
based
TSIMMIS 1994 View
integration
LOREL
TAMBIS 1996 View
integration
Concept navigation
VOTES 2005 View
integration
SQL
caGrid 2005 View
integration
Query API
@neurIST 2007 View
integration
SPARQL
BioDWH 2007 Data warehousing Query API
DebugIT 2009 Mix approach SPARQL/SQL

6SYSTEM INTEGRATION PLAN PROPOSAL
CDR
Literature review:
According toVermaak & Niemann, 2017, the biomedical researchers often work with
massive, heterogeneous and detailed datasets. The datasets have raised challenges related to
the management of scientific interpretation and information management. In their paper, the
researchers have investigated the nature of the challenges that have been faced by the
researchers at the time of dealing with that kind of data. The major problems that are
identified by the researchers are related to data organization, data sharing, collaboration, and
publications. However, there is compelling requirement for the development of a user
friendly and efficient information management system for handling the biomedical research
related data.
Alobaid et al., 2018, have proposed a multilayer secure biomedical data management
system to manage a huge number of the diverse personal health related services. There is a
modest secondary usage of the data and information for the improvement of safety and
quality for the patient care. The purpose of this paper is to present a data integration
architecture which will be utilized for building interoperability among the heterogeneous
storages of data that of the network of hospitals in the Detecting and Eliminating Bacteria
Using Information Technology. The system framework is having some of the characteristics
such as the system framework will support the protocols of international standards
communication protocols for achieving interoperability.
According to Lisa M. Federer,* Ya-Ling Lu, Douglas J. Joubert, Judith Welsh, and
Barbara Brandys, important efforts have been underway within the biomedical research
community for encouraging reusing and sharing of the research related data for enhancing
reproducibility of the research as well as for enabling the discovery that is scientific.

7SYSTEM INTEGRATION PLAN PROPOSAL
According to the authors, in their survey most of the respondents considered that the reuse
and data sharing is very much important to the work of them (Zafar et al., 2018). For having a
flexible system, the content of the local databases have to be maintained locally and
autonomously. The schemas may quickly change on the average, 2 or 3 times every year. The
databases that are used for the biomedical data management in the organisation are
maintained as well as designed for meeting all of the local needs as well as changes which are
made independently for the structure that is integrated.The respondents rated the expertise of
them as low generally. Some of the important differences are existed between the practices
and attitudes of the clinical as well as basic science researchers that includes the motivations
of them for sharing, the reasons for not sharing as well as the amount of work that is required
for preparing the data of them.
Methodology and project plan:
Research philosophy:
In this research proposal, the positivism research philosophy has been used mainly as
generally it deals with the utilization of various hidden facts and effective applications which
are associated with the biomedical data management.
Research approach:
In this research study, deductive approach will be used. The risks that are associated
with the biomedical data management will be analysed in a concise and clear manner (Zhou
et al., 2016). The can help in offering the possibility of generalizing the findings from the
research for the measurement of the concepts quantitatively.
Research design:
It has been found that the exploratory design has been considered for the
acknowledgement of different type of thoughts, ideas and the opinions which are needed

8SYSTEM INTEGRATION PLAN PROPOSAL
mainly for doling the analysis of various concepts related to the biomedical data
management.
Data collection method:
In this research study, method which will be used to collect information that are
related to the biomedical data management is the secondary data collection method (Giuliani,
2017). Various journals, academic papers and articles will be reviewed for making the
analysis of the issues that are related to the biomedical data management.
Project plan:
Tasks Responsibility Start End Days
Date Date
1.0
0
Initiation phase 1/22/2020 1/30/2020 7
1.1
0
Topic selection U, IT 1/22/2020 1/23/2020 2
1.2
0
Analyzing background U, IT 1/24/2020 1/27/2020 3
1.3
0
Collecting data from secondary
sources
U 1/28/2020 1/30/2020 2
2.0
0
Literature review 1/31/2020 2/6/2020 5
2.1
0
Issues related with biomedical
data management
IT, U 1/31/2020 2/3/2020 3
2.2
0
Determing methods for
resolving the issues related to
biomedical data management
IT, U 2/4/2020 2/6/2020 2
3.0
0
Methodology 2/7/2020 2/17/2020 8
3.1
0
Selecting research philosophy IT, U 2/7/2020 2/10/2020 3
3.2
0
Selecting research approch IT, U 2/11/2020 2/13/2020 2
3.3
0
Selecting research design IT, U 2/14/2020 2/17/2020 3
4.0
0
Discussion 2/14/2020 2/20/2020 5
4.1
0
Analyzing acadamic challenges IT 2/14/2020 2/14/2020 1
4.2
0
Determining ethical issues U, IT 2/15/2020 2/17/2020 2

9SYSTEM INTEGRATION PLAN PROPOSAL
4.3
0
Developing project plan U, IT 2/18/2020 2/19/2020 1
4.4
0
Completion of project proposal U, IT 2/20/2020 2/20/2020 1
Project name: Project plan
for analyzing biomedical data
management
Ref Tasks Responsibility Start End Days
Date Date 1/22/2020 #### ### ## ##
1.00 Initiation phase 1/22/2020 1/30/2020 7 1/1/1900
1.10 Topic selection U, IT 1/22/2020 1/23/2020 2 1/1/1900
1.20 Analyzing background U, IT 1/24/2020 1/27/2020 3
1.30 Collecting data from secondary sourcesU 1/28/2020 1/30/2020 2
2.00 Literature review 1/31/2020 2/6/2020 5 ######
2.10 Issues related with biomedical data managementIT, U 1/31/2020 2/3/2020 3 ######
2.20 Determing methods for resolving the issues related to biomedical data managementIT, U 2/4/2020 2/6/2020 2
3.00 Methodology 2/7/2020 2/17/2020 8 #### ###
3.10 Selecting research philosophy IT, U 2/7/2020 2/10/2020 3 ####
3.20 Selecting research approch IT, U 2/11/2020 2/13/2020 2
3.30 Selecting research design IT, U 2/14/2020 2/17/2020 3 ###
4.00 Discussion 2/14/2020 2/20/2020 5 ###
4.10 Analyzing acadamic challenges IT 2/14/2020 2/14/2020 1
4.20 Determining ethical issues U, IT 2/15/2020 2/17/2020 2 ###
4.30 Developing project plan U, IT 2/18/2020 2/19/2020 1
4.40 Completion of project proposal U, IT 2/20/2020 2/20/2020 1
1/26/20201/22/2020 1/23/2020 1/24/2020 1/25/2020
Conclusion:
Thus, it can be concluded from the entire report that the proposed pilot architecture
for the CDR to be utilized in this project has been described in this project. The project
constitutes a data integration model that is simple by the utilization of the MySQL federated
engine for integrating the ontologies, distributed data and Talend for the homogenising data
types and models. The future steps towards implementing the integration system framework
is to finalise the interoperability that is semantic as well as to attack the privacy related issues
with the help of creative solutions.

10SYSTEM INTEGRATION PLAN PROPOSAL
References:
Alobaid, M., Hughes, B., O’Connor, D., Calautit, J., & Heyes, A. (2018). Improving thermal
and electrical efficiency in photovoltaic thermal systems for sustainable cooling
system integration. Journal of Sustainable Development of Energy, Water and
Environment Systems, 6(2), 305-322.
Bhattacharyya, S. S., Deprettere, E. F., Leupers, R., & Takala, J. (Eds.). (2018). Handbook of
signal processing systems. Springer.
Crowe, T. W., Deal, W. R., Schröter, M., Tzuang, C. K. C., & Wu, K. (2017). Terahertz RF
Electronics and System Integration [Scanning the Issue]. Proceedings of the
IEEE, 105(6), 985-989.
Giuliani, A. (2017). The application of principal component analysis to drug discovery and
biomedical data. Drug discovery today, 22(7), 1069-1076.
Ma, S., Wang, J., Zheng, F., Xiao, Z., Wang, T., & Yu, D. (2018, May). Embedded silicon
fan-out (eSiFO): a promising wafer level packaging technology for multi-chip and 3D
system integration. In 2018 IEEE 68th Electronic Components and Technology
Conference (ECTC) (pp. 1493-1498). IEEE.
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Pietzcker, R. C., Ueckerdt, F., Carrara, S., De Boer, H. S., Després, J., Fujimori, S., ... &
Luderer, G. (2017). System integration of wind and solar power in integrated
assessment models: A cross-model evaluation of new approaches. Energy
Economics, 64, 583-599.

11SYSTEM INTEGRATION PLAN PROPOSAL
Vermaak, H., & Niemann, J. (2017, May). Virtual commissioning: A tool to ensure effective
system integration. In 2017 IEEE International Workshop of Electronics, Control,
Measurement, Signals and their Application to Mechatronics (ECMSM) (pp. 1-6).
IEEE.
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(2018). Taxonomy of factors causing integration failure during global software
development. IEEE Access, 6, 22228-22239.
Zhou, G., Zhao, Q., Zhang, Y., Adalı, T., Xie, S., & Cichocki, A. (2016). Linked component
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