urgent please

Section 1 Read the following session and answer the questions The healthcare industry is deluged with big data, including patient histories, clinical records, charts, and test results. Medical information is now doubling every 3 years and will be doubling every 73 days by 2020. How can healthcare professionals keep up with the knowledge in their field, and how can they use this knowledge to make more informed decisions about treatment options and managing healthcare costs when there is way too much data for humans to easily analyze and absorb? One of the many health care organizations struggling with this problem is Akershus University Hospital (Ahus), a Norwegian public university hospital serving approximately 500,000 inhabitants around Oslo, Norway, and employing 9,500 people. Ahus had amassed huge volumes of data on patients and treatments, but much of this information was in unstructured, textual reports that made it extremely difficult and time-consuming to extract meaningful information. Combing through thousands of complex clinical documents was impossible to complete manually. Working with Capgemini consultants, Ahus is trying to solve this problem by using artificial intelligence technology in IBM Watson Explorer. IBM Watson Explorer is a cognitive computing platform that can analyze structured and unstructured data to uncover trends and patterns that would be difficult, if not impossible, for humans to discern. It uses natural language processing to search data expressed in everyday language like ordinary speech and machine learning algorithms to improve search results. Natural language processing technology makes it possible for a machine to understand, analyze, and derive meaning from human language. Machine learning software can identify patterns in very large databases without explicit programming, although with significant human training. IBM Watson Explorer is able to rapidly mine large volumes of data, interpret speech and text, pick up on nuances of meaning and context, answer questions, draw conclusions, and learn from its experience. It can make inferences and correlations about the content it ingests and rank potential responses for a user to select. The hospital's image diagnostic department wanted to improve the use of CT examinations in emergencies. Ahus used IBM Watson Explorer to analyze when its CT scans performed on pediatric patients in emergency situations fell within recommended guidelines. CT scans can be life-saving in critical circumstances but the radiation can also be potentially harmful, so CT scans should not be overused. A large amount of Ahus's CT scan data was in text format. Ahus used Watson Explorer to gather unstructured data from more than 5,000 anonymous CT examination reports and apply machine learning and natural language processing techniques to learn how often CT scans were undertaken and the findings of those scans 1. What is the business problem and the business solution defined here? (20 points) 2. Please list all the changes that happened at Akershus University Hospital (management, organizational and technology)? (20 points) 704