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ZOOM
HIT2500 Week 2 Assignment #2 Bigger than MPI
Bigger than the MPI
In 2012, Value City Hospital initiated an organizational realignment when it added a
third hospital to its healthcare system. Situated in one county, the health system also
included four physician practices and two urgent care centers. Value City knew that
the integration and management of master data would be key to the success of the
organization. The master data subject areas considered the core of success for the
program included patient, supplier, employee, and provider master data. In 2013, a
master data management program was created that included a data governance
council made up of senior-level stakeholders. The mission of the data governance
council team was to align and consolidate people, processes, and technologies. This
was followed by the establishment of data stewardship committees for each of the
core master data areas with one central team managing master data delivery across
all information technology projects. The first task was for each data steward
committee to evaluate the way its core master data were created, read, updated,
deleted, and searched (CRUDS). For example, patient master data are created at
the time of a patient’s first visit to any of the health system’s facilities; it is read
based on the contextualized views which are based on the role of the viewer;
updates can occur for name, address, and phone number; patient master data are
never destroyed; and the master data are searched by the R-ADT system and
clinical and financial management information systems. After conducting the
CRUDS analysis, each set of master data was evaluated in terms of its metadata.
This included attribute names, data types, allowed values, constraints, default
values, definition, and data sources. During this process, the teams found that there
were multiple data sources that needed to be reconciled and that the metadata from
these various sources were not the same. For example, the three clinics and two
urgent care centers had different master patient indexes than the two hospitals. To
reconcile this problem, each data steward team developed a model for its master
data. This included attributes in use, their data type, allowed values, and so on. The
source systems were then mapped to the data model. Once the master data
attributes were agreed upon and source systems mapped, the next step was to
create a master list by cleansing, transforming, and merging the source data. Once
the teams were assured that a clean and consistent master data list was achieved,
the master data could be uploaded and maintained in the architecture solution
decided on by the data governance council, which in this case was a transaction hub
implementation.
Discussion Questions
1. What benefit did development of a CRUDS evaluation provide?
The teams found multiple data sources that needed to be reconciled and that
the metadata from these sources was not the same. The two urgent care centers
and three clinics had different master patient indexes than the two hospitals did.
Each data steward team developed a model for its master data to help reconcile this
problem. This included attributes in use, allowed values, data types, etc. The source
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systems were subsequently mapped to the data model.
2. What is the benefit of a data model for master data? Would this data model be
different than the logical data model for a relational database?
All applications rely on master data; therefore, any business process can
benefit from a master data model. Master data drives everything from customer
information to financial reports. When data is properly managed with an effective
master data model, the organization can achieve many rewards, including improved
customer satisfaction, higher profits and revenue, improved decision making, and
being more effective. Models are created to visually represent the proposed
database so that business requirements can be easily associated with database
objects. This ensures that requirements are completely and accurately gathered.
Data modeling is a critical part of documenting the requirements of the user, it
(Data Architecture Management; Master Data Management, 2015)organizes the
data logically and is not concerned with how the data is created, stored, or
manipulated. The purpose of data modeling is to describe the things about which
the organization wishes to collect data and share the meaning to two audiences.
End-users are the first audience, and they use the data models to verify that they
meet the actual needs of the system. The second audience are the system
designers and technical staff. They use the data models to execute the business
rules represented in the data model. They use this information to create and
construct the actual system.
The logical data model also includes primary keys and foreign keys and
undergoes the process of normalization. This is a formal process applied to the
database design to determine how to group variables in a table which decreases the
amount of data redundancy. This model is more descriptive of the data needs
3. How do you suppose agreement on the final master data attributes was
achieved?
Each data steward team developed a model for their master data. Data type,
allowed values, and attributes in use were included in the model. Next, the source
systems were mapped to the data model. Once a consistent master data list was
achieved, it could be uploaded and maintained in the solution which the data
governance council agreed upon. In this case, it was a transaction hub
configuration.
4. Why do you think the organization choose a transaction hub configuration?
In a transaction hub implementation, master data are stored and used as the
authoritative system of record (SOR). It supports data access and updates
transactions in the master data model. The data is cleansed, augmented, and
matched to ensure the quality of the master data. Changes can be made in real
time. This helps ensure that the data is complete, consistent, and correct.
5. What was the role of the data steward committees after consolidation of the
master data?
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After the master data was consolidated, the team stewards would assist in
applying data governance. They would assist in developing and maintaining the
master data model identify the users and sources of the data, assist with
maintenance of the data, help identify the data requirements. They could also assist
in developing polices, procedures, and standards to help ensure the security and
quality of the data, they could also assist in making sure that compliance and
regulatory standards are met.
Data Architecture Management; Master Data Management. (2015). In M. L. Johns, Enterprise Health
Information Management and Data Governance (pp. 132-133;171-184). Chicago: AHIMA Press.

Unformatted Attachment Preview

ZOOM HIT2500 Week 2 Assignment #2 – Bigger than MPI Bigger than the MPI In 2012, Value City Hospital initiated an organizational realignment when it added a third hospital to its healthcare system. Situated in one county, the health system also included four physician practices and two urgent care centers. Value City knew that the integration and management of master data would be key to the success of the organization. The master data subject areas considered the core of success for the program included patient, supplier, employee, and provider master data. In 2013, a master data management program was created that included a data governance council made up of senior-level stakeholders. The mission of the data governance council team was to align and consolidate people, processes, and technologies. This was followed by the establishment of data stewardship committees for each of the core master data areas with one central team managing master data delivery across all information technology projects. The first task was for each data steward committee to evaluate the way its core master data were created, read, updated, deleted, and searched (CRUDS). For example, patient master data are created at the time of a patient’s first visit to any of the health system’s facilities; it is read based on the contextualized views which are based on the role of the viewer; updates can occur for name, address, and phone number; patient master data are never destroyed; and the master data are searched by the R-ADT system and clinical and financial management information systems. After conducting the CRUDS analysis, each set of master data was evaluated in terms of its metadata. This included attribute names, data types, allowed values, constraints, default values, definition, and data sources. During this process, the teams found that there were multiple data sources that needed to be reconciled and that the metadata from these various sources were not the same. For example, the three clinics and two urgent care centers had different master patient indexes than the two hospitals. To reconcile this problem, each data steward team developed a model for its master data. This included attributes in use, their data type, allowed values, and so on. The source systems were then mapped to the data model. Once the master data attributes were agreed upon and source systems mapped, the next step was to create a master list by cleansing, transforming, and merging the source data. Once the teams were assured that a clean and consistent master data list was achieved, the master data could be uploaded and maintained in the architecture solution decided on by the data governance council, which in this case was a transaction hub implementation. Discussion Questions 1. What benefit did development of a CRUDS evaluation provide? The teams found multiple data sources that needed to be reconciled and that the metadata from these sources was not the same. The two urgent care centers and three clinics had different master patient indexes than the two hospitals did. Each data steward team developed a model for its master data to help reconcile this problem. This included attributes in use, allowed values, data types, etc. The source systems were subsequently mapped to the data model. 2. What is the benefit of a data model for master data? Would this data model be different than the logical data model for a relational database? All applications rely on master data; therefore, any business process can benefit from a master data model. Master data drives everything from customer information to financial reports. When data is properly managed with an effective master data model, the organization can achieve many rewards, including improved customer satisfaction, higher profits and revenue, improved decision making, and being more effective. Models are created to visually represent the proposed database so that business requirements can be easily associated with database objects. This ensures that requirements are completely and accurately gathered. Data modeling is a critical part of documenting the requirements of the user, it (Data Architecture Management; Master Data Management, 2015)organizes the data logically and is not concerned with how the data is created, stored, or manipulated. The purpose of data modeling is to describe the things about which the organization wishes to collect data and share the meaning to two audiences. End-users are the first audience, and they use the data models to verify that they meet the actual needs of the system. The second audience are the system designers and technical staff. They use the data models to execute the business rules represented in the data model. They use this information to create and construct the actual system. The logical data model also includes primary keys and foreign keys and undergoes the process of normalization. This is a formal process applied to the database design to determine how to group variables in a table which decreases the amount of data redundancy. This model is more descriptive of the data needs 3. How do you suppose agreement on the final master data attributes was achieved? Each data steward team developed a model for their master data. Data type, allowed values, and attributes in use were included in the model. Next, the source systems were mapped to the data model. Once a consistent master data list was achieved, it could be uploaded and maintained in the solution which the data governance council agreed upon. In this case, it was a transaction hub configuration. 4. Why do you think the organization choose a transaction hub configuration? In a transaction hub implementation, master data are stored and used as the authoritative system of record (SOR). It supports data access and updates transactions in the master data model. The data is cleansed, augmented, and matched to ensure the quality of the master data. Changes can be made in real time. This helps ensure that the data is complete, consistent, and correct. 5. What was the role of the data steward committees after consolidation of the master data? After the master data was consolidated, the team stewards would assist in applying data governance. They would assist in developing and maintaining the master data model identify the users and sources of the data, assist with maintenance of the data, help identify the data requirements. They could also assist in developing polices, procedures, and standards to help ensure the security and quality of the data, they could also assist in making sure that compliance and regulatory standards are met. Data Architecture Management; Master Data Management. (2015). In M. L. Johns, Enterprise Health Information Management and Data Governance (pp. 132-133;171-184). Chicago: AHIMA Press. Name: Description: ...
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