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Seven reasons why the time has come for AI technology in cancer care

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  1. Data is proliferating: Worldwide healthcare data doubles every 24 days1—and much of that is unstructured data hidden in electronic health records (EHRs).
  2. EHRs are a key source of data—and burnout: For every hour a physician spends with a patient, they spend two hours doing EHR-based clerical work. 2 The EHR burden has pushed burnout rates up more than 10 percent since 2011.3
  3. Meanwhile, fewer doctors are practicing oncology: A shortage of more than 2,300 medical oncologists in the United States is anticipated in 2025.4 Other countries are facing shortages, as well. A recent study found that there were eight countries with no clinical oncologist available to provide care for patients with cancer. In 39 countries, a clinical oncologist would provide care for more than 500 patients with cancer. An extreme shortage of clinical oncologists, greater than 1,000 incident cancers per clinical oncologist—existed in 25 countries in Africa (78%) and two countries (11%) in Asia.5
  4. And cancer is spreading: There was an estimated 18 million cancer cases worldwide in 2018—and that number is projected to reach 29.5 million by 2040.6
  5. More care is needed: It comes as no surprise, then, that the American Society of Clinical Oncology predicts a 42 percent increase in the demand for cancer treatments over the next decade.7 And the global market for oncology therapeutic medicines will reach as much as $200 billion by 2022, averaging 10–13 percent growth over the next five years, with the U.S. market reaching as much as $100 billion by 2022, averaging 12–15 percent growth.8
  6. Clinical research keeps growing: The countries generating the most cancer research produced 88,529 publications between 2010-2014.9 This amount of research is impossible for a human to keep up with.
  7. Yet, clinical trials struggle to enroll patients. Only 3 percent of adults with cancer are enrolled in clinical trials10 and 80 percent of US clinical trials fail to meet recruitment timelines.11

More data, more research and more patients with fewer oncologists at risk for burnout. While the outlook may sound bleak, the good news is that technology is catching up with healthcare data—which could help patients and the oncologists who treat them.

Connecting patient data with potential treatment options

Artificial intelligence (AI) technologies like IBM Watson® for Oncology are helping physicians worldwide keep up-to-date with the growing body of medical literature to make connections with key insights in patients’ medical records. Using natural language processing (NLP), Watson for Oncology also consumes massive amounts of medical literature to extract potential evidence-based treatment recommendations that may be a good fit for a patient. Parsing the data quickly and perceptively, Watson for Oncology presents treatment options ranked by level of confidence and includes supporting evidence. The oncologist can then apply their own expertise to identify the most appropriate treatment options.

Matching patients to potential clinical trials

AI is also helping to match more patients with potential clinical trial opportunities. Again, massive amounts of patient and clinical trials data are consumed and analyzed using NLP and advanced cognitive algorithms. Explicit eligibility criteria are weighed against specific patient characteristics to determine a potential match. The output is an ordered list of relevant trials a patient is eligible for, as well as the trials they were excluded from. This technology is a boon for oncologists and their patients searching for treatment options and clinical trial coordinators trying to meet enrollment targets.

Cancer is the second leading cause of death globally, and is responsible for an estimated 9.6 million deaths in 2018,12 and the healthcare industry is racing to find answers. AI technology offers exciting potential and is already hard at work helping oncologists connect patients with treatment options and clinical trial opportunities, even in the face of exponentially increasing amounts of data.

1  Marconi, Katherine and Lehmann, Harold. Big Data and Health Analytics. CRC Press, 2014. Accessed at: http://bit.ly/1UjEtLL

2  NLP: Enabling The Potential of a Digital Healthcare Era. Chilmark Research. July 2018

3  NLP: Enabling The Potential of a Digital Healthcare Era. Chilmark Research. July 2018

4  Yang W, Williams JH, Hogan PF, et al: Projected supply of and demand for oncologists and radiation oncologists through 2025: An aging, better-insured population will result in shortage. J Oncol Pract 10:39-45, 2014

5  DOI: 10.1200/JGO.17.00188 Journal of Global Oncology. published online February 8, 2018. Accessed at http://ascopubs.org/doi/abs/10.1200/JGO.17.00188

6  International Agency for Research on Cancer, Cancer Today Database. Accessed at t http://gco.iarc.fr/tomorrow/ho.

7  The State of Cancer Care in America, 2014: A Report by the American Society of Clinical Oncology

8  Global Oncology Trends 2018. IQVIA Institute for Human Data Science. Accessed at https://www.iqvia.com/-/media/iqvia/pdfs/institute-reports/global-oncology-trends-2018.pdf?_=1542686813876

9  Cancer Research Current Trends & Future Directions. Elsevier, 2016.

10  Schuler, Peter and Buckley, Brendan. Re-Engineering Clinical Trials: Best Practices for Streamlining the Development Process. 2015

11  “Clinical Trial Educator Program—A Novel Approach to Accelerate Enrollment in a Phase III International Acute Coronary Syndrome Trial,” Clinical Trials, 2012.

12  World Health Organization. http://www.who.int/news-room/fact-sheets/detail/cancer

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