Explain How the lack of data standardization impedes data-driven healthcare.

Explain How the lack of data standardization impedes data-driven healthcare.

Cybersecurity Part II
The final project for this course will be created each week. Each assignment will lead to a second assignment that adds to the PPT slide deck. In this manner, you create the final project as you progress through the course. This model provides for deeper learner and a more rigorous final project.

Create PPT slides based upon assignment part I.

Include the following aspects in the assignment:

Ø Construct 3-5 PPT slides to illustrate your content and tools from part 1

Ø Add speakers notes as needed

Ø Follow the rules of good PPT slide construction

Ø Submit the slides for grading and feedback

Running head: HEALTHCARE DATA TOOLS STANDARDIZATION

2

HEALTHCARE DATA TOOLS STANDARDIZATION 2

Healthcare data tools standardization

Student’s name:

Professor:

Date:

In health care, there is a need to have standardized tools in place to ensure efficiency and effectiveness in all aspects of data. Data need to be mined, analyzed, stored and presented as per the set standards to avoid a possible violation of data. Standardizing data is a process that is used in the conversion of data to a more common first that allows the users to be able to analyze and process them (“Acknowledgement to reviewers of healthcare in 2019,” 2020). There are many sources where healthcare can access data and make good use of them, including lakes, databases, warehouses, and cloud storage. The many sources of data make I challenging to standardize them, and they need to be made uniform. Data standardization is essential for many reasons since it allows one to develop a clear, defined, and consistent attributes catalog of data. Data is usually a crucial starting point in attempts to solve and understand data. Standardizing data calls for an element of converting the data into a more precise and more uniform format (“Acknowledgement to reviewers of healthcare in 2019,” 2020). The data standardization calls for the metadata to be firmed and be created, and they form the basics of the data standardization. The perspective of the accuracy and standardization helps in the way and the format in which data access can be improved hence making reporting and analytic more accessible. In health care, data standardization usually is different from data normalization; standardization calls for the two-way method of processing, where normalization calls for the shift of the values if data to male them fall within the range of 0-1. While standardization is applied when attempting to scale and come up with the mean and the standard deviations at 0-1.

Data in healthcare is generally in accordance with the CIA triad as part of the principles to be adhered to. Eight crucial steps need to be attached to ensure that the day’s integrity is maintained and upheld. Among the steps include the aspect of the risk-based validation (“How the lack of data standardization impedes data-driven healthcare,” 2015). There is a need to first validate the performance risk-based to ensure that it is working. The other step is to select a prorate system and the best service provider for the services. The service providers need to be well established, bypassing the set criterion based on their abilities and capabilities. The third essential step is the auditing of the audit trail. It is essential to keep a trail of the auditing process and capabilities. The fourth step is the changing control .change control is critical as it allows the organization or the data users to change to and be dynamic to fit into the need of the data (“How the lack of data standardization impedes data-driven healthcare,” 2015). The other vital step is to have the IT and the system validated. The system needs to be validated, and the entire system is qualified for IT. The plan for business continuity must be put in place as another step towards ensuring data integrity. Data accuracy is also another vital step that needs to be adhered to in data integrity, and it is among the essential steps for the integrity of data. The final step is to have the data regulate archived to ensure that they dint get lost and can be easily retrieved. We need be .archived data can be used and be referred to when the dictate strikes and data may get lost or be damaged.

The AHRQ or the agency for the healthcare research has admission that ais set as part of eh objectives and the goals they aspire to meet. The mission of the AHRQ is to come up with evidence that makes healthcare typically safer and negating the risks. They also aspire to meme the objectives of ensuring that the service being offered are of higher quality. Another objective is to ensure easier services accessibility (Management Association; Information Resources, 2014). Other roles include ensuring that there is equitability and affordability. The AHRQ provides a smooth running of operations in the healthcare facilities in the US, especially in the departments of health and human services. It collaborates with other patterns to make them meet the objectives of a reset and the goals they wish to achieve.

Via the analysis in 2017 and 2018, it is true to say that there has been a significant improvement in the quality of service being offered in healthcare. There has been considerable improvement based on eh reports and the questionnaires that have been tabled. It is true to say there has been an improvement of about 18 percent improves based on the analysis (“How the lack of data standardization impedes data-driven healthcare,” 2015). Clients or the patients have shown a significant rise in eh levels of satisfaction and the safety of the treatments . They have managed to significantly decrease the levels of risks associated with low-quality treatment by 7percent. Statically it is true to say that there has been a need to have the changes brought about by the AHRQ and the implementation in place to ensure that the clients have complete trust in the healthcare, service delivery improved, and the risks negated

,

References

Acknowledgment to reviewers of healthcare in 2019. (2020). Healthcare, 8(1), 19. https://doi.org/10.3390/healthcare8010019

How the lack of data standardization impedes data-driven healthcare. (2015). Data-Driven Healthcare, 29-29. https://doi.org/10.1002/9781119205012.ch3

Management Association; Information Resources. (2014). Healthcare administration: Concepts, methodologies, tools, and applications: Concepts, methods, tools, and applications. IGI Global.

Scroll to Top