AFTER READING this chapter 11 case study below: Case Study:Do Spotlight on: Technology

What Happened to Watson Health? On page 417-418.

THE BOOK Essentials of MIS (Pearson+)Kenneth Laudon;Jane Laudon;Carol Traver

PLEASE HELP ANSWER THESE CASE STUDY QUESTIONS CITE SOURCE STYLE APA

USE 2 REFERENCES AND CITE SOURCE STYLE APA PLEASE AND THANK YOU

NO CHATGPT OR AI NEED TUTOR HELP

DO NOT USE the BOOK above AS REFERENCE NEED 2 EXTERNAL REFERENCES SITE SOURCE STYLE APA NO AI HELP NEED ONLY TUTOR HELP

SPOTLIGHT ON: TECHNOLOGY

What Happened to Watson Health?

Watson was an IBM computer that made history in February 2011 by handily defeating the two-most-decorated champions of the game show Jeopardy!, Ken Jennings and Brad Rutter. Watson's achievement marked a milestone in the ability of computers to process and interpret human language. The Watson version used in Jeopardy took 20 IBM engineers three years to build at an $18 million labor cost, with an estimated $1 million in equipment. Watson had to be able to register the intent of a question, search through millions of lines of text and data, pick up nuances of meaning and context, and rank potential responses for a user to select, all in less than three seconds.

Watson was able to learn from its mistakes as well as its successes. Watson analyzed both Jeopardy questions and answers to determine patterns or similarities among clues. Using these patterns, it assigned varying degrees of confidence to the answers it gave. Although Watson was initially able only to correctly answer a small fraction of the questions it was given, machine learning allowed the system to continue to improve until it reached Jeopardy champion level.

IBM viewed its investment in Watson as a stepping stone to broader commercial uses of its AI technology, including applications for healthcare, financial services, or any industry where sifting through large amounts of data (including unstructured data) to answer questions is important. Watson was expected to become more useful and powerful by learning from new sets of experts in new fields of knowledge. IBM allocated one-third of its overall research efforts to Watson and made a huge bet that Watson could transform IBM's business from an aging hardware company into an AI leader. Former IBM CEO Virginia Rometty referred to Watson as "our moonshot." At one point Watson Health had 7,000 employees.

Healthcare appeared to provide the biggest, best opportunity for demonstrating the "new" IBM. The US healthcare industry collects a tremendous amount of information every day on the care of hundreds of millions of people. However, that information is very fragmented and unable to link individuals across all the domains in which they get care to develop a holistic picture of who they are, their diseases, the best treatments, and how to ensure the best care at the lowest possible cost. There is no connectivity right now that can do that at scale. The US healthcare market is a $3 trillion business that has legacy technology infrastructure and insufficient connectivity. Tech companies such as Microsoft, Google, and IBM all want a piece of the healthcare action.

To develop Watson for the healthcare field, IBM needed massive amounts of data on which to train Watson. It obtained that data through acquisitions, eventually spending some $5 billion buying a series of health data companies such as Truven, Phytel, Explorys, and Merge. Truven had the biggest insurance database in the United States, with 300 million covered individuals. Explorys provided a clinical data set of actual electronic health records kept by health systems that represented about 50 million patients. Phytel added more and Merge had a huge imaging database. The idea was to expose Watson to all these data to find patterns that physicians and anyone else couldn't possibly find when looking at that data, given all the variables.

Armed with Watson and Big Data, Watson Health was touted as a revolutionary healthcare solution that could help diagnose patients, recommend treatment options, improve drug development, and match patients with clinical trials. But it didn't work out: After billions invested over the course of a decade and a series of setbacks, Watson Health was sold for its data and assets for only around $1 billion to private equity firm Francisco Partners in January 2022much, much less than what IBM had invested over the years.

IBM's partnership with MD Anderson Cancer Center in Texas is instructive. Participating physicians said that there weren't enough data for the program to make sound recommendations and that Watson had trouble with the complexity of patient files. The participants complained that Watson's recommendations were just not relevant. Watson might, for example, suggest a particular kind of treatment that wasn't available in the locality for which it was making the recommendation, or the recommendation did not square with the treatment protocols used at that local institution. The physicians also said that Watson was not telling them anything they didn't already know. An artificial intelligence tool exposed to data on patients who were cared for on the upper east side of Manhattan may not be able to derive any meaningful insights to treat patients in India. You need to have representative data, and the data limited to New York are not necessarily going to apply to different kinds of patients all the way across the world. The Anderson partnership was eventually audited and terminated. Other high-profile hospital partners also ended their collaborations with Watson.

IBM continued to pour money into marketing Watson Health without having proven that it could live up to the hype. The Watson Health investment was too big to fail, but it eventually did. Does this mean that using AI is too difficult for healthcare? Experts don't think so: Microsoft and a large group of hospitals have formed a coalition to promote the use of AI in healthcare by providing recommendations, tools, and best practices. The generation of AI technology used in Watson Health was nowhere near ready to accomplish what IBM had promised. Nevertheless, AI can make significant improvements in healthcare if companies learn from IBM's mistakes.

Sources: Shravani Durbhakula, "IBM Dumping Watson Is an Opportunity to Re-Evaluate Artificial Intelligence." MedCity News, March 27, 2022; Sandeep Konam, "Where Did IBM Go Wrong with Watson Health?" Quartz, March 2, 2022; Lizzie O'Leary, "How IBM's Watson Went from the Future of Health Care to Sold Off for Parts," Slate.com, January 31, 2022; Elly Yates-Roberts, "Microsoft Forms New Coalition for AI in Healthcare," Technology Record, January 17, 2022.

CASE STUDY QUESTIONS

One critic has described Watson Health as "a hammer looking for a nail" and said that it is more effective to define and understand a problem before building an AI application. Discuss.

How could IBM Watson Health have benefited from using the four-step problem-solving method introduced in Chapter 1?

To what extent was Watson Health a technology problem? A people problem? An organizational problem? Explain your answer.

How can organizations using AI in healthcare avoid the mistakes IBM made?

AFTER READING this chapter 11 case study below: Case Study:Do Spotlight on: Technology

What Happened to Watson Health? On page 417-418.

THE BOOK Essentials of MIS (Pearson+)Kenneth Laudon;Jane Laudon;Carol Traver

PLEASE HELP ANSWER THESE CASE STUDY QUESTIONS CITE SOURCE STYLE APA

USE 2 REFERENCES AND CITE SOURCE STYLE APA PLEASE AND THANK YOU

NO CHATGPT OR AI NEED TUTOR HELP

DO NOT USE the BOOK above AS REFERENCE NEED 2 EXTERNAL REFERENCES SITE SOURCE STYLE APA NO AI HELP NEED ONLY TUTOR HELP