Enterprise Information Systems: Data Management in Healthcare

This report provides an overview of enterprise information systems (EIS) with a focus on their application in healthcare, specifically at Elkhorn Pediatric Medical Center. It emphasizes the importance of understanding enterprise information needs before system implementation, highlighting the necessity of stakeholder engagement and comprehensive requirement gathering. The report discusses the development and maintenance of enterprise data models (EDM), explaining their role in ensuring data quality, extensibility, and integration. It also addresses the critical aspect of data management, particularly the privacy and confidentiality of medical records, referencing relevant laws and regulations. The report concludes by reiterating the importance of detailed requirements, stakeholder involvement, and the benefits of EDM in minimizing data discrepancies and enhancing data quality and consistency.

Running head: ENTERPRISE INFORMATION SYSTEMS 1
ENTERPRISE INFORMATION SYSTEMS
Student Name
Institutional Affiliation

ENTERPRISE INFORMATION SYSTEMS 2
ENTERPRISE INFORMATION SYSTEMS
INTRODUCTION
An information system refers to collection of components that have been integrated to
facilitate data collection, storage, and processing to provide knowledge and information about a
specific phenomenon (Kerschbaum, 2012). Medical institutions and other business enterprises
depends on information systems to conduct and monitor their daily operations and further
interact with clients. Medical Institutions like Elkhorn Pediatric Medical center uses information
systems to keep medical, health, and billing records of the patients. This report will further
describe the importance of understanding enterprise information needs, development and
maintenance of enterprise data models, and data management.
UNDERSTANDING ENTERPRISE INFORMATION NEEDS
Before implementing any information system, it is important to understand the needs of
the organization and the purpose of the system. Elkhorn Pediatric Medical center needs an
information system that will be able to meet the needs of the medical institution as laid out. It is
important to continuously engage all the stakeholders to elaborate the information system
requirements extensively (Guo & Zhou, 2015). Including the teams in the requirement gathering
process make the it efficient and all the opinions and questions asked make it possible to develop
high quality requirements document for the Pediatric Medical Center.
The information system should be able to handle the patient billing and medical
information. It is necessary for the information system director to clearly understand the
objective and the final outcome that the client expects. The PCM business analysts was not able
to identify an off-the-shelf software package that could meet the critical needs. One of the critical

ENTERPRISE INFORMATION SYSTEMS 3
requirements expected for the new information system is the ability to integrate state’s
requirements for medical assistance billing into one comprehensive application (Vrana, 2012).
This requirement is essential because about 25-30% of the medical patients will be depending on
Medical Assistance. Therefore, it is necessary to develop an in-house application software that
will be able to meet these specifications.
Currently, the operations are PMC are manual and thus the need to automate these
processes is important. The expected information system is expected to be able to keep family
information using coded names to monitor the guardians and parents whose children are eligible
to get medical attention at PMC and the details of the insurance carrier and the financial head of
the family. Secondly, the system should be able to keep the record of the patients that have been
registered with the PMC (Boonstra, 2009).
DEVELOP AND MAINTAIN THE ENTERPRISE DATA MODEL
An enterprise data model (EDM) can be described as integrated view organizational data
produced and consumed internally (Sytnyk, 2016). Industry perspective have been incorporated
in such a model. An EDM gives a representation of data that is integrated and unbiased of any
application or system. It does not depend on how data is collected, processed, saved, or accessed.
EDM formalizes, unites, an represents the vital things of an institution and the governing rules.
Basically, it is a framework that integrates data architecture. The enterprise data model facilitates
PMC to identify data that is redundant or can be shared across institutional and functional
boundaries.
Having integrated data in a medical institution offers a centralized and single truth for the
medical practitioners. Data disparity, redundancy, and errors are minimized and its consistency,

ENTERPRISE INFORMATION SYSTEMS 4
quality, and accuracy are enhanced. In any medical institution, data accuracy and consistency are
key to ensuring better service delivery and prescription. The beginning point of any data systems
is the data architectural framework. It provides a visual of the framework for planning,
developing, and adopting the information system. EDM is the fundamental principle for
enterprise data initiatives. EDM enables diminishing the data silos and data integration which are
vital in legacy systems. Some of the enterprise initiative that the EDM can play in meeting the
objectives of PCM include:
Data Quality- quality of data in any medical institution is very critical and one of the
factors that contributes to poor quality of data is redundancy. Therefore, the medical information
system should ensure that there is no data redundancy (Blaha, 2018). Having enterprise data
model in place is important because it is able to detect any data discrepancies and notify the
relevant users. By mapping the information system to the EDM issues related to data quality can
be easily identified. This will ensure that the data stored in PMC data centers are complete and of
high quality.
Data system extensibility- enterprise data model has the ability to support data
architecture that is extensible. In information system extensibility is described as the ability to
stretch or scale the functionalities of the system to meet the growing and changing needs of the
user (MongoDB, 2017). As such, the system designer should factor in such aspects to ensure that
incase the needs of the Pediatric Medical Center changes or expands then the system should
facilitate extension of the functionalities without interrupting the existing functions.
Data integration- enterprise data model has the capability to integrate external view
which improves the ability of the institution to share data that is common within the operational

ENTERPRISE INFORMATION SYSTEMS 5
industry. As such, the information system should enable Elkhorn Pediatric Medical Center to
share data with other pediatric institution to get better insight on particular medical conditions.
When developing and enterprise data model, it is essential to the three decomposition
levels which include the Subject Area Model, Conceptual Model, and conceptual entity model.
As much as the models relate to one another, they have unique and specific functionality and
identity (Defourny & Nyssens, 2017). The figure below further describes the various EDM
levels.
Figure 1: Enterprise data model levels
(source: Defourny & Nyssens, 2017)
EDM ensures that any data discrepancy is identify and corrected to ensure that the
quality, integrity, consistency, and completeness of data is maintained (Blaha, 2018).

ENTERPRISE INFORMATION SYSTEMS 6

ENTERPRISE INFORMATION SYSTEMS 7
DATA MANAGEMENT
Management of data is a key aspect especially when it comes to medical records which
contains very sensitive information about individuals and should be kept private and not to be
disclosed to other parties (Mohania, Ananthanarayanan & Gupta, 2007). Sharing such
information without the knowledge of the person may cause humiliation and embarrassment and
infringe on the legal rights of ‘right to privacy’. Additionally, disclosing the information either
knowingly or unknowingly to other people could further reduce the self-esteem, damage
personal reputation, and family relationships.
Confidentiality of the medical information is a key concern to the public but legal steps
have been taken by both federal and local government to ensure privacy of personal information
(Wright, et al., 2014).Australia federal government have enacted laws and acts to regulate data
management by the hospitals. One of the acts if the Federal Privacy of medical information act
which states that the confidentiality of medical records should be maintain and no disclosure is
allowed unless by court order or request by the individual (von Dietze & Allgrove, 2014). It
further explains that the privacy of the patient is stated in the Australian Medical Association
Ethics principles. Another legislation is the Privacy Act of 1974 which states that no personal
information should be shared with other parties without the consent of the party involved (von
Dietze & Allgrove, 2014).
Privacy regulations and acts are common law, constitutional, and statutory. Medical
institutions collect patient’s information for the purpose of diagnosis and treatment and the
purpose should remained so as stated by the law. The medical record will be stored as reference
information if you have to visit the hospital once again. Personal information is process under

ENTERPRISE INFORMATION SYSTEMS 8
Article 6 of the Australian constitution and sensitive data is covered by article 9 of the
constitution. The law defines personal information as details regarding and individual who is
identifiable or can be identified such as NHS number, name, age, address etc.
Medical records are stored to provide reference for the medical professionals to provide a
patient with better medical care and to ensure it is effective and safe. The hospital has the legal
mandate to keep the patients’ information confidential. The hospital may share your medical
records with another medical institution offering you medical assistance in order to effectively
provide you with quality healthcare (Weber, 2018). The privacy Act of 1974 allows the hospital
to share medical records in cased where the safety and health of others is at risk and anybody
who receives the medical information is under the law to maintain its privacy and confidentiality.
CONCLUSION
It is important to continuously engage all the stakeholders to elaborate the information
system requirements extensively. Including the teams in the requirement gathering process make
the it efficient and all the opinions and questions asked make it possible to develop high quality
requirements document. Having detailed requirements is important because it will allow the
system designer to design a solution based on the peculiarities of the requirements. Furthermore,
comparing the results with the requirements during and after system development will visualize
the outcome and determine if the objectives have been met. Data disparity, redundancy, and
errors are minimized by EDM and enhances consistency, quality, and accuracy.

ENTERPRISE INFORMATION SYSTEMS 9
REFERENCES
Blaha, M. (2018). Enterprise Data Modeling - DATAVERSITY. Retrieved from
http://www.dataversity.net/enterprise-data-modeling/
Boonstra, A. (2009). Identifying and Managing Stakeholders in Enterprise Information System
Projects. International Journal Of Enterprise Information Systems, 5(4), 1-16. doi:
10.4018/jeis.2009090201
Defourny, J., & Nyssens, M. (2017). Mapping social enterprise models: some evidence from the
“ICSEM” project. Social Enterprise Journal, 13(4), 318-328. doi: 10.1108/sej-09-2017-
0049
Guo, B., & Zhou, S. (2015). Re-examining the role of attitude in information system acceptance:
a model from the satisfaction–dissatisfaction perspective. Enterprise Information
Systems, 10(4), 444-466. doi: 10.1080/17517575.2015.1011070
Kerschbaum, F. (2012). Building a privacy-preserving benchmarking enterprise
system. Enterprise Information Systems, 2(4), 421-441.
Mohania, M., Ananthanarayanan, R., & Gupta, A. (2007). Some issues in privacy data
management. Data & Knowledge Engineering, 63(3), 591-596. doi:
10.1016/j.datak.2007.03.003
MongoDB, M. (2017). Enterprise Data Modeling. Retrieved from
https://www.mongodb.com/scale/enterprise-data-modeling

ENTERPRISE INFORMATION SYSTEMS 10
Sytnyk, I. (2016). Updating Management Models in Terms of Enterprise Management
Intellectualization. Economics, Entrepreneuship, Management, 3(2), 57-66. doi:
10.23939/eem2016.02.057
von Dietze, A., & Allgrove, A. (2014). Australian privacy reforms--an overhauled data
protection regime for Australia. International Data Privacy Law, 4(4), 326-341. doi:
10.1093/idpl/ipu016
Vrana, I. (2012). How to approach the development of enterprise information
system. Agricultural Economics (Zemědělská Ekonomika), 50(No. 1), 41-46. doi:
10.17221/5165-agricecon
Weber, D. (2018). Privacy and Security in Big Data Management. International Journal Of
Recent Trends In Engineering And Research, 251-256. doi:
10.23883/ijrter.conf.20171201.051.rsxao
Wright, D., Wadhwa, K., Lagazio, M., Raab, C., & Charikane, E. (2014). Integrating privacy
impact assessment in risk management. International Data Privacy Law, 4(2), 155-170. doi:
10.1093/idpl/ipu001