University Of Phoenix Material Databases Worksheet Write A50

University Of Phoenix Materialdatabases Worksheetwrite A50 To 150 Wor

What is the difference between database types and capacities? How do data inaccuracies affect patient care and reimbursement? Review the databases below and explain the relationship between each of the databases and their impact on the medical records system.

Paper For Above instruction

The distinction between database types lies in their structure and functionality, such as relational, hierarchical, and object-oriented databases. Relational databases organize data into tables with relationships, facilitating efficient data retrieval and updates. In contrast, hierarchical databases structure data in a tree-like format, suitable for straightforward, predictable data like patient records. Database capacity refers to the volume of data a database can store, which varies based on hardware and software limitations. Larger capacities enable handling extensive medical records, which is crucial for comprehensive patient histories.

Data inaccuracies can significantly impact patient care and reimbursement processes. Erroneous information may lead to improper diagnoses, medication errors, or delayed treatments, jeopardizing patient safety. Inaccurate billing data can result in denied reimbursements and increased administrative costs.

In the medical records system, databases like Electronic Health Records (EHR), Laboratory Information Systems (LIS), and Radiology Information Systems (RIS) are interconnected. EHR databases store comprehensive patient data, which supports clinical decision-making and billing accuracy. LIS and RIS streamline diagnostic processes, ensuring timely and precise results. These databases collectively enhance care quality, but inaccuracies in any system can compromise patient safety and financial reimbursement, emphasizing the need for meticulous data management.

References

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  2. Miliard, M. (2019). The Impact of Data Accuracy on Healthcare Outcomes. Healthcare IT News, 24(3), 18-23.
  3. Shankar, S., & Singh, P. (2020). Electronic Health Records and Data Quality Challenges. International Journal of Medical Informatics, 139, 104157.
  4. Hannan, T. J. (2021). Database Technologies in Healthcare: A Review. Medical Informatics and Decision Making, 21, 5.
  5. Ohno-Machado, L., et al. (2020). Data Integration in Healthcare: Challenges and Opportunities. Journal of Biomedical Informatics, 109, 103515.
  6. Rahman, M., et al. (2018). Ensuring Data Accuracy and Integrity in Healthcare Databases. Journal of Healthcare Information Management, 32(2), 45-52.
  7. Jiang, F., et al. (2021). The Evolution of Healthcare Databases. Journal of Clinical Medicine, 10(10), 2092.
  8. Lee, S., & Yoon, M. (2019). The Role of Data Capacity in Healthcare Data Management. Data & Knowledge Engineering, 125, 33-44.
  9. Vallee, M., et al. (2020). Impact of Data Quality on Healthcare Reimbursements. Health Policy and Technology, 9(4), 421-429.
  10. Yen, P. Y., & Bakken, S. (2019). Review of Data Quality Frameworks and Methodologies in Healthcare. Journal of the American Medical Informatics Association, 26(8-9), 736–744.

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