Data Resource Management Report: Healthcare Data Analysis at PMC

This report provides an in-depth analysis of data resource management within the context of a Pediatric Medical Center (PMC). It begins by outlining the enterprise information needs and the structure of an enterprise data model, emphasizing their importance in defining data management functions, establishing standards, and guiding the overall data strategy. The report then delves into the uniqueness of the healthcare sector in terms of data management, highlighting the influence of government regulations, business considerations such as affordability and portability, and the critical importance of addressing patients' legitimate needs, including social determinants of health. References to relevant literature support the arguments, providing a comprehensive overview of data management challenges and opportunities in healthcare. The report underscores how effective data management can improve patient care and operational efficiency within a healthcare setting.

Running head: DATA RESOURCE MANAGEMENT
Data Resource Management
(Pediatric Medical Center)
Name of the student:
Name of the university:
Author Note

1DATA RESOURCE MANAGEMENT
An analysis of data resource management:
Answer to question number 1: Understanding enterprise information needs and
enterprise data model:
The well-built undertakings of enterprise information needs as per DAMA-DMBOK include
various tasks. Firstly, they are expected to build up a consensus for the common application
perspective of the functions of data management. They have put forward the standard definitions for
the secondhand data management deliverables, functions and additional terminologies. They have
been useful to make assured of the guidance regarding data management. They have been
overviewing the accepted practices, broadly adopted techniques and methods. This also implicates the
notable alternative approaches instead of any reference to particular technology vendors and the
products. Besides, they have been briefly pointing out cultural and organizational complications.
Lastly, they are appending in the boundaries and scopes of data management (Lefebvre,
Schermerhorn & Spruit, 2018). For Pediatric Medical Center, the enterprise needs have been
informing a wide range of audiences regarding the importance and nature of data management. They
have been useful to create consensus under the community of data management. They have been
useful for data stewards and the professionals have understood the liabilities (Dahlberg & Nokkala,
2015). Further, they have been providing the basis for breakdown of data management efficiencies
and maturity. Then they involve the guiding efforts for implementing and developing the functions of
data management. They have been pointing the readers with extra sources of data management
knowledge. They are also useful for data management professionals to formulate CDMP or Certified
Data Management Professional exams. Finally, they are able to assist the Pediatric Medical Center
under its strategy of enterprise data.

2DATA RESOURCE MANAGEMENT
The enterprise model, has been encompassed of numerous subject area models. This is
effectively used to pen down different process and various data for the organization, along with the
business or enterprise. It has been carrying out, in order to make effective as the planning. This also
includes the integration for overall management of information systems. Moreover, the enterprise
data model has been providing the entire picture of the data. It has been needed by the business. This
must be done at a significant level of detail as far as the support of operations and decisions are
considered. Apart from this, the entire enterprise process model has been granting the important
processes of the business (Houston, 2018). Having the overall exception of the level of detail, the
techniques has been efficiently put into service. This are helpful to develop enterprise models. These
are the same as those elements that are utilized for constructing application data and various process
models. Apart from this, the development and maintaining of the overall enterprise data model are
aiding to coordinate various efforts of different maintenance and applications teams with smart
methods of communications (Zhang et al., 2017). At every level, the active managerial supports, the
activities of modeling have been assuring the communication to flourish. For Pediatric Medical
Center, the model delivers the framework for maintenance and development efforts. The model is
helpful for them to control the process and data effectually and efficiently. Thus they are able to live
up to the primary principle for Pediatric Medical Center.
Answer to question number 2: Uniqueness of healthcare sector in terms of data
management:
The various aspects of the uniqueness can be understood from the light of the following elements.
Government elements:
The regulations have been playing an essential role in the industry and insurance coverage of
health care. In this case, different regulatory bodies have been procuring the public from plentiful

3DATA RESOURCE MANAGEMENT
health risks and conveying programs for welfare and public healthcare. Along with these the
regulatory agencies have been protecting and regulating the health at each level. These regulations are
created and deployed at every platform of the government like local, state and federal and also by
different private companies also (Yue et al., 2016). The PBS or Pharmaceutical Benefits Schemes and
MBS or Medicare Benefits Schedules are required to assure the compliances and deliver secured
healthcare for all the people who have been accessing the overall system. These regulatory agencies
have been in turn monitoring various facilities and practitioners. Thus they have been providing the
data regarding changes in the industries promote security and assure the legal compliances and
service of worthy quality (Baro et al., 2015).
Business concerns:
The first issue is regarding affordability. Here, the employer-sponsored health insurance of
Australia has been making that possible as per “Competition and Consumer Act. Australian
standards”. They have been making that conceivable for affording the coverage. However, the plans
of the group have been less costly than distinct plans. Again regarding portability, most of the healthy
insurable coverage has been found to be tied to Competition and Consumer Act. Australian standards.
However, the group diplomacies have been less costly than distinct plans (Hu, Perer & Wang, 2016).
Lastly, it must be reminded that the foremost cause that people of Australia has been devoid of the
health insurance has been due to the cost. This is because of the pre-existing medical conditions. The
health insurances have been negated and the bills have been left unpaid because of the injury and
illness. Within the present Pediatric Medical Center, the insurance companies are found to be profit
driven. As any applicant is seen to be having greater risk, they are able to refuse to sell that to the
policy of the health insurances.

4DATA RESOURCE MANAGEMENT
Legitimate needs:
The patients’ prerequisites are to be understood in terms of domestic violence, transportation,
utilities, housing and food security. The Australian parliament has been complying that in terms of
been assessments, risks of health and patient intakes. The data are cooperative to fetch the unmet
social necessities that affect health. The systems of healthcare have been deploying the strategies for
effectively addressing the necessities (Gui et al., 2016). Thus one can cognize what patients have
actually safeguarded, regarding their healthy food and triangulate social necessities with various data
usage, outcomes and claims.

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References:
Baro, E., Degoul, S., Beuscart, R., & Chazard, E. (2015). Toward a literature-driven definition of big
data in healthcare. BioMed research international, 2015.
Dahlberg, T., & Nokkala, T. (2015). A framework for the corporate governance of data–theoretical
background and empirical evidence. Business, Management and Education, 13(1), 25-45.
Gui, H., Zheng, R., Ma, C., Fan, H., & Xu, L. (2016, November). An architecture for healthcare big
data management and analysis. In International Conference on Health Information Science
(pp. 154-160). Springer, Cham.
Houston, M. L. (2018). Defining and Developing a Generic Framework for Monitoring Data Quality
in Clinical Research. In AMIA Annual Symposium Proceedings (Vol. 2018, p. 1300).
American Medical Informatics Association.
Hu, J., Perer, A., & Wang, F. (2016). Data driven analytics for personalized healthcare. In Healthcare
Information Management Systems (pp. 529-554). Springer, Cham.
Lefebvre, A. E. J., Schermerhorn, E., & Spruit, M. R. (2018, June). HOW RESEARCH DATA
MANAGEMENT CAN CONTRIBUTE TO EFFICIENT AND RELIABLE SCIENCE. In
ECIS 2018 Proceedings Collections. AIS Electronic Library (AISeL).
Yue, X., Wang, H., Jin, D., Li, M., & Jiang, W. (2016). Healthcare data gateways: found healthcare
intelligence on blockchain with novel privacy risk control. Journal of medical systems,
40(10), 218.
Zhang, Y., Qiu, M., Tsai, C. W., Hassan, M. M., & Alamri, A. (2017). Health-CPS: Healthcare cyber-
physical system assisted by cloud and big data. IEEE Systems Journal, 11(1), 88-95.