Computer-assisted healthcare: IBM Watson
IBM Watson is a technology platform using a combination of natural learning processing and machine learning to garner insights into a particular query from large amounts of unstructured data.22 IBM Watson first came to media fame in 2011 when it competed against human contestants on the American television quiz show Jeopardy! where it demonstrated an ability to answer nuanced questions in a remarkably accurate manner. In fact, the IBM Watson system quite easily defeated two of the shows most celebrated winners over a several episode competition.
In recent years, Watson has been adapted and re-invented in an effort to transform the healthcare industry, where Watson has been directed to assist physicians with both diagnosis and treatment planning for one of the leading causes of death worldwide, cancer.
The “Watson for Oncology” cognitive-computing system harnesses the power of big data to provide an evidence-based treatment plan for each patient23
Oncologists from the hospital and doctors from some of the most prominent cancer research and treatment institutions in the world continually “teach” Watson based on clinical outcomes so that Watson “learns” for future cases. First and Formemost, it provides a completely objective assessment of a patient based on their full medical and social history.
How it works
With so much information available for Watson to sort through in order to answer a specific question or give a treatment recommendation, how does it determine what the best answer to a question or solution to a problem is? A multi-faceted process is used by Watson in order to interpret information and ultimately answer a question. First, Watson determines what type of question is being asked and, more importantly, what the question is asking for by breaking down the question into parts of speech. Watson then scans its database of information, coming up with thousands of possible solutions. Where Watson excels – and differentiates itself from simple computers – is in the next step, where Watson tests hypotheses and evidence, developing both pro and con evidence for the thousands of potential solutions gathered in the previous step. In the final step, Watson ranks the possible solutions based on its hypothesis and evidence-testing as well as on previous experience, ultimately providing a percentage score of how likely that the answer provided is correct. All of this is done in a matter of minutes.
As noted earlier, the clinical utility of systems medicine, and cognitive-computing systems like IBM Watson, is exciting.
Systems like IBM’s Watson for Oncology have a number of benefits for the healthcare industry. First and foremost, it provides a completely objective assessment of a patient based on their full medical and social history. As any physician knows, there is far too much data for a physician to obtain and assess for each individual patient and new scholarly articles which the physician may have the time or access to read. Further, these systems “learn” from each patient who has a successful or failed treatment along with every article and textbook written on the disease of interest, allowing confidence that the data utilized in providing treatment recommendations is not based on static or outdated information.
From a hospital administration perceptive, these systems allow for a virtual, collaborative effort between physicians and researchers worldwide. It also serves to fill gaps that healthcare shortages can cause or in areas of the world where specialized physicians are in demand. Within treatment for cancers in the United States, for example, the latest report from the American Society of Clinical Oncology notes that while the number of cancer cases is growing, the clinical workforce is aging and exists largely in metropolitan areas, facts which may adversely impact the ability of the medical community to meet the clinical demand for care.24 Having such diagnostic and treatment recommendation technologies available through Watson or similar systems in underserved or vulnerable populations such as patients in rural, prison, or refugee settings would provide physicians who do not have the support of a large team of colleagues with the ability to obtain a more comprehensive assessment of these patients.
While the above noted benefits of systems medicine approaches propagated by programs like Watson for Oncology clearly have the potential to advance clinical practice and help patients, a number of challenges are also noteworthy.25 For example, what happens when the clinical recommendation of the treating physician or team of physicians conflicts with that of a system such as IBM Watson?26 Such systems are meant to serve as a guidance mechanism for the physician, not as the definitive solution to treatment or diagnosis. With such a powerful system providing a conflicting (suggested) treatment course, however, the physician and patient may feel conflicted about what constitutes the correct path to health. Such discussions need to be taken between the physician, patient, and family in order to determine what will have the greatest likelihood of achieving a better quality of life for the patient.
Ethics in Big Data-Assisted Healthcare
Ethical questions, in terms of the way in which data is obtained and utilized, are at the forefront of big data discussions and will likely remain until core principles of use are properly implemented throughout the healthcare industry.27,28
Acknowledging and addressing ethical questions that may arise through the use of big data and systems like IBM Watson is of the utmost urgency
In a broader sense of ethical concerns surrounding big data, the question of who “owns” the data that is gathered through the use of the Internet, mobile and wearable devices, and healthcare information poses a very real and valid ethical question.29,30 With so much data available from so many sources, this question can be difficult to answer. Can the data gathered through these devices be shared with others? Who is responsible for the data? To what extent do people have the “right to be forgotten”, or in other words, to what extent can ordinary people control the access and sharing of their data?31
Discussions surrounding the proper use of big data prompt strong opinions from citizens and policy makers, from private companies and public sector agencies, and from physicians within the healthcare industry. Further discussions will be required – and soon – in order to ensure that the massive volumes of data being obtained throughout the world are utilized in a manner which is beneficial to society, but also accords with a set of best ethical principles. This will help to ensure the greatest degree of mutual benefit for those wishing to access and use the data as well as those from whom the data is collected.
The rapid expansion of Internet access and mobile technologies worldwide provides opportunities to revolutionize healthcare in ways that were not possible 15 years ago. More people than ever use smartphones, wear smartwatches, and have regular and reliable access to the Internet. These technologies generate vast troves of data, the volume of which continues to grow each day.
It is no surprise that so-called big data is at the forefront of medicine, and cognitive-computing technologies such as those seen with IBM Watson have already started to unlock the power of this data in an effort to aid physicians in the diagnosis and treatment of patients fighting cancer. With further advancements in this and similar technologies, these systems could be expanded to combat other deleterious and complex diseases, including mental health disorders. With a growing need for healthcare services worldwide, multi-faceted collaborations between business and the healthcare industry are required in order to ensure the health of the global population. Harnessing the power of big data may help to fill gaps in care while ensuring better health and quality of life for patients throughout the world.