Can someone do my Week 5 discussion in MHA 605 Business Intelligence?

 

• Read Chapter 9 from your textbook, An Overview of Business Intelligence, Analytics, Data Science, and AI
• Read the following articles:
  
  • Using big data and data analytics for better business decisionsLinks to an external site.
  • Big data analytics impact on business decision-making and strategyLinks to an external site.
• Review the video: Big data analytics transform how businesses make decisionsLinks to an external site.

It is important for healthcare leaders to make informed decisions for the organization and their patients. Collecting data is only part of the picture. After the information has been collected, the work begins to analyze the data for meaning or themes. The quantity of data can be overwhelming to a single researcher, but machine-based algorithms can process data that is vast in quantity (big data). In the healthcare delivery system, data scientists use big data to improve patient experiences, provide more positive healthcare outcomes, and reduce costs for the healthcare organization and patients. Additionally, big data can be used to make predictions regarding possible epidemics, improve decision-making policies, and reduce mortality-morbidity rates.

According to Delen, Sharda, and Turban (2023), “Big data and analytics instigate new ways to transform processes, organizations, entire industries, and even society altogether” (p.369). By incorporating and analyzing big data, caregivers can improve their diagnostic accuracy through the identification of treatment actions and outcomes.

In this week’s discussion address the following prompts in a minimum of 500 words:

• Discuss the importance of big data and its contribution to improving health care outcomes for patients.
• Describe how big data is changing the decision-making processes for administrative and clinical care.
• Explain stream analytics and how it differs from regular analytics.
• Describe the motivations for using stream analytics.
• Discuss three problems that can be addressed or improved with big data analytics. Examples: Regulatory Compliance, Risk Management, Improved Customer Experience, Enhanced Security Efficiency.