Instructions:

1. Read the attached document containing a choice of four (4) group case study questions.
2. Choose (1) case study.
3. Decide upon the best course of action to complete the required four (4) questions (you may consider drafting an outline) being 1) identify the primary issues prevalent in the case study, 2) identify any ethical implications, 3) provide prevention solutions and remedies, and 4) provide your personal perspective of the case study.
4. Conduct research on the Internet and find materials supporting your chosen stance (across questions).
5. Construct one (1) initial post with a minimum of 600 words (approximately 150 words per section).
6. Use the following initial post headings (centered and bolded (and in the following order)): Primary Issues, Ethical Implications, Remedies and Solutions, and Personal Perspective.
7. You may take any direction you desire (in answering the above questions), noting your initial post (and corresponding viewpoints) must have a logical order and flow. Further, all answers must be fully supported and justified.
8. You may consider the following when compiling your initial post: 1) make sure you have a grasp of all the facts available (be sure you are familiar with all the facts), 2) list any information you would like to have, but dont, and what assumptions you would have to make, if any, in resolving the dilemma (this is where you would likely conduct Internet research), 3) take any person involved in the dilemma and list the concerns they face or might have (be sure to consider the impact on those not specifically mentioned in the case), 4) develop a list of resolutions for the problem, 5) make a list of personal perspectives.
9. Note: You are to pick only one Case Study from either of the two below question sets.
10. Note: Please list the number of your Case Study at the very top of your initial post. Example: Question Set #2: Question #3.

Question #3

IBM WatsonNot Yet Ready for Prime Time?

The University of Texas MD Anderson Cancer Center is one of the worlds most respected medical centersdevoted exclusively to cancer patient care, research, education, and prevention. Its 21,000 employees are focused on one mission: to end cancer. Each year, the center treats over 100,000 people, and it is currently running more than 1,200 clinical trials designed to test various types of cancer treatments.

In 2012, the center launched an ambitious project in collaboration with IBM to create the Oncology Expert Adviser (OEA), a learning system employing the IBM Watson cognitive computing program. OEA was designed to read and learn from MD Andersons vast database of electronic medical records, academic literature, research data, and treatment options. In theory, OEA would glean patterns from all this data and use that knowledge to make suggestions to improve individual patient cancer care. In addition, OEA was intended to match cancer patients to appropriate clinical trials to offer patients an opportunity to fight their cancers by participating in trials of new therapies.

Unfortunately, after five years of effort (at a cost of $62 million), the center has not achieved its desired results, according to an assessment performed by the University of Texas System Audit Office. Incorporating machine learning software into complex healthcare settings is extremely challenging, and according to the audit report, the system never reached a development phase in which it could be used in a clinical setting. The audit uncovered project management failings as well as limitations in the programs ability to integrate with other hospital systems.

Initially, the OEA pilot project was focused on leukemia, but that effort was suspended mid-project because of lack of progress, and the project was refocused on lung cancer. The lung-cancer system was tested as a pilot project in 2015 and was able to suggest the same treatment plan as MD Anderson physicians in 90 percent of select cases. However, in order for the system to be useful in making complex healthcare-related recommendations, medical personal need to know how OEA reached a conclusion and what data and logic was used. A visualization tool called WatsonPaths attempted to fill this need; however, that tool was also complex, requiring a high level of technical sophistication to interpret how Watson arrived at its conclusion.

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Another issue noted in the audit of the system is that the lung-cancer pilot was conducted using data from the hospitals old electronic records system, which was replaced in 2016, and the project team has been unable to integrate the pilot program with the hospitals current electronic health records. In addition, clinical trial and drug-protocol data in the OEA system are outdated. Before the program could be tested further, major rework would be required to first convert data from the old system to make it compatible with the new system, and then retrain OEA on all the data in the new records system.

Another goal of the project was to make OEA widely available to physicians at partner hospitals outside MD Anderson. However, the audit found that the program was never piloted with partner hospitals. The audit cited cybersecurity concerns and lack of engagement or interest by partner hospitals as factors that prevented the testing of the technology outside MD Anderson.

Defining a successful treatment plan for lung cancer is certainly a challenging task, more so than many other problems to which the computing power of Watson has been applied. OEA did not prove that vast amounts of patient data and knowledge of various courses of cancer treatment are as significant as advocates of artificial intelligence make them out to be. MD Anderson and the OEA project still needs to provide evidence that the technology could be developed to a level at which it would reliably improve patient outcomes, lower costs, or provide some other benefit.