Data governance paper 2

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Data Governance

Data Governance is “enterprise authority that ensures control and

accountability for enterprise data through the establishment of decision rights and

data policies and standards that are implemented and monitored through a formal

structure of assigned roles, responsibilities, and accountabilities”. An example of

data governance is data usability, if you want your employees to use your data, it

needs to be accessible and easy to understand. Data should be stored in one

location and organized in a simple, logical way. Additionally, every employee in

your organization should understand what each piece of data means, how its

collected, and how to use it.

In some cases, data governance has an effect of poor data governance on

healthcare facilities and patient care. Lack of uniformity to name one. Here’s an

example. Let’s say a healthcare provider is trying to analyze data from two

different sources, their MPM and their EHR. Both deal with patient demographics

but might have different definitions of what constitutes their demographics. Their

age brackets might vary (one might set a limit at ages 18 to 29 and another might

draw the line at 18 to 35), which can prevent seamless integration for

demographic analysis. Though less harmful, this lack of uniformity can curtail the

ability of departments to have a common understanding and derive meaningful BI

from their shared data. Eliminating data inconsistencies or adjusting inaccuracies

are temporary fixes if only your executives are committed to making a change.

Employees across your organization need to embrace the ideals of effective data

governance if your organization is going to gain useful and accurate intelligence

from your data.

The importance of data accuracy is to make sure data within the record is

accurate across the entire record (that is, the data is valid with the appropriate test

results and placed into the proper patient record). While the accuracy of this data

is important for individual treatment planning, it is vital for the development of

statewide systems of care and assessing the type of co-occurring conditions that

incur healthcare costs and impact behavioral health issues. The importance of data

integrity is that data is being used to verify the identity of an individual to ensure

that the correct patient is receiving the appropriate care and to support billing

activity. Ensuring the integrity of healthcare data is important because providers

use them in making decisions about patient care. Which leads to the Principle of

retention: Create a process for proper retention of information based on

requirements from regulations, accrediting organizations, and company policy.

Ahima created a quality data management model to support the need for true and

accurate data.

Data accuracy helps physicians at any practice to be informed of a

patient’s history, tendencies, previous complications, current conditions and likely

responses to treatment. It also allows quick treatment for patients in the most

efficient and appropriate way possible. Because hospitals tend to have

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Name Professor College Date Data Governance Data Governance is “enterprise authority that ensures control and accountability for enterprise data through the establishment of decision rights and data policies and standards that are implemented and monitored through a formal structure of assigned roles, responsibilities, and accountabilities”. An example of data governance is data usability, if you want your employees to use your data, it needs to be accessible and easy to understand. Data should be stored in one location and organized in a simple, logical way. Additionally, every employee in your organization should understand what each piece of data means, how its collected, and how to use it. In some cases, data governance has an effect of poor data governance on healthcare facilities and patient care. Lack of uniformity to name one. Here’s an example. Let’s say a healthcare provider is trying to analyze data from two different sources, their MPM and their EHR. Both deal with patient demographics but might have different definitions of what constitutes their demographics. Their age brackets might vary (one might set a limit at ages 18 to 29 and another might draw the line at 18 to 35), which can prevent seamless integration for demographic analysis. Though less harmful, this lack of uniformity can curtail the ability of departments to have a common understanding and derive meaningful BI from their shared data. Eliminating data inconsistencies or adjusting inaccuracie ...
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