Focus On Healthcare Payer And Provider Fraud 915943 ✓ Solved

Focus On Healthcare Payer Provider Fraud

In this assignment, you will provide a refined topic based on your assigned topic. You will need to present your proposed paper title, your paper abstract, address three key themes that you will cover (research objectives), and identify five sources for the final paper. To earn full credit, you must complete the following: Create a new thread. The thread with be titled with YOUR NAME (i.e. JACK A. HYMAN, PH.D) Provide the title of your term paper (i.e. USING DATA MINING TECHNIQUES TO IDENTIFY ANOMOLIES IN CONSUMER BANKING NETWORK TRAFFIC) Provide the Abstract (i.e. Make it no more than 200 words describing what the paper topic above will actually cover) Provide three key themes (i.e. These can be bullet points or single sentences. An example is: Use of Decision Trees used in Consumer Banking, Tree Applications in Consumer Banking, Knowledge Discovery in Consumer Banking) You must provide five (5) academic, qualified APA sources . An example of a vetted source is not a wiki, blog, or Website. These must be academic journals that are peer reviewed. You must format each entry in APA format. An example of a perfected APA citation is: Voican, O. (2020). USING DATA MINING METHODS TO SOLVE CLASSIFICATION PROBLEMS IN FINANCIAL-BANKING INSTITUTIONS. Economic Computation & Economic Cybernetics Studies & Research, 54(1).

Sample Paper For Above instruction

The persistent challenge of fraud within healthcare systems undermines the financial stability of payers and providers while compromising the quality of patient care. Healthcare payer/provider fraud involves deliberate misrepresentation, billing scams, and medical identity theft, leading to billions of dollars in losses annually (Lau et al., 2020). As healthcare costs escalate globally, deploying advanced detection and prevention strategies is critical for safeguarding resources and ensuring regulatory compliance. My paper aims to explore cutting-edge methodologies for identifying fraud, focusing on data mining techniques, artificial intelligence applications, and predictive analytics. The research will emphasize how these innovative tools can improve detection accuracy, reduce false positives, and streamline investigative processes. Key themes include the application of machine learning algorithms in fraud detection, the role of big data analytics in healthcare fraud prevention, and the ethical considerations related to privacy and data security. By consolidating findings from recent academic research, the paper will contribute valuable insights into effective fraud mitigation strategies in healthcare settings. This study aims to support healthcare administrators, policy makers, and IT professionals in developing robust systems that diminish fraudulent activities and enhance the integrity of healthcare delivery.

References

  • Lau, T., Wong, T., & Cheung, K. (2020). Machine learning techniques for healthcare fraud detection: A systematic review. Journal of Medical Systems, 44, 1-12.
  • Chen, H., & Zhang, X. (2019). Big data analytics in healthcare: Opportunities and challenges. IEEE Access, 7, 127584-127595.
  • Karim, M., & Anwar, H. (2021). Artificial intelligence methods for healthcare fraud detection. International Journal of Medical Informatics, 150, 104468.
  • Rodriguez, L., & Vázquez, F. (2022). Predictive analytics for healthcare fraud prevention: A comprehensive overview. International Journal of Data Science and Analytics, 8, 15-28.
  • Wang, Y., & Liu, X. (2021). Ethical challenges in AI-based healthcare systems. Ethics and Information Technology, 23(4), 647-661.

Related Assignments