POWERPOINT NOTES updated

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Western Governors University *

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C803

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Health Science

Date

Feb 20, 2024

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docx

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2

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When it comes to standardized terminologies and classification system they have a huge impact upon improving data quality in Health information. Standardized terminologies ensure that everyone is unity when it comes to the same language. It also helps enhance consistency in the healthcare setting. Standard classification allows you to compare and contract data across a hospital setting. This also you to identify outbreaks and track diseases. Terminologies system can improve better communication within healthcare providers, HR, and healthcare personnel. This will also help with better patient care and efficient operations. Standardized data can be more interpreted and analyzed. This helps with decision making and research efforts. Data requirements may be included in organizational policies by the following; You should implement a data quality management tool check the process. This tool would identify any inconsistency and duplicates and correct errors. Regular Data Audits can identify when there is a area that is of concern by taking corrective action. This audit process would be within organization policy and procedures. Training and education will help ensure that all staff understands how important data quality is will be trained on the procedures that are related the data management. This will be achieved with the help of training session and workshops. The CDI makes sure that all clinical information that are recorded by healthcare providers are accurate. High quality data leads to accurate documentation. Compliance and standardization ensure consistency and comparability data across difference health facility. Decision making supports clinical decision, health outcomes analysis and strategic planning. Revenue cycle management plays a important role in CDI . When you have complete and accurate information this can lead to correct reimbursement for different service rendered at the hospital Data quality is critical to gathering statistical data because high quality data can ensure that your results of your statistical data are accurate. Poor data can lead to incorrect results. Reliability data is very consistent if the data quality is poor than the results are compromised. Validity data created accurately represents the phenomenon that under study. Reproducibility allows other researchers to reproduce your results. An than Efficiency high quality can be processed more efficiently, when you have poor quality, it sometimes require more cleaning which ultimately can be more costly and consuming. You need to check for empty fields in your dataset. This can be achieved by using a programming language like Python. Inconsistent data that doesn’t conform to a specific format. Their can be duplicate data so search for records that would appear more than once in your database. Trying to find incorrect data can be a little tricky, try to look for data that doesn’t make sense in the text. The root causes of data quality often include mistakes made during data entry, system errors in the system that is used to collect and store data. Lack of standardization which result in
inconsistent application. And integration issues that happens when merging data from a different source Once you have identified the problem you can then begin to take the necessary steps to manage it by data cleaning. This typically involves correcting and removing inaccurate data from the database. You than want to implement data validation to make sure that the data entered meets the criteria. Data standardization enforces consistency. Data governance implement a strategy to manage the availability and security of the data.
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