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IBM Watson Health in Oncology IBM Watson Health in Oncology Scientific Evidence 2020 Contents 03 Foreword 06 Key studies 06 Clinical decision support 14 Clinical trials 18 Genomics 24 Real world data 28 Bibliography Foreword Nathan Levitan, MD, MBA publications, precision oncology, and clinical We are tackling the early application of AI Chief Medical Officer trial availability. in cancer care by working closely with our IBM Watson Health Oncology & Genomics users to understand their needs and evolve IBM Watson Health has been collaborating our products. As an oncologist, I have seen first-hand with physicians across the globe to the very human toll of cancer. With the understand their needs and to leverage Throughout this journey, we recognize the rising incidence and mortality of cancer artificial intelligence-based technologies strengths of our clients and partners, viewing globally, physicians and patients around the to help them facilitate the delivery of high our progress as a continuous evolution world face unprecedented complexity in quality, cost effective care. Many of these that must be rooted in scientific data that navigating treatment while health systems solutions apply to all types of healthcare, provides insight into how our products are are strained managing care for growing including but not limited to oncology. impacting care teams today in the real world. populations of patients. Examples include secure hybrid data To-date, more than 100 studies have been platforms, large scale claims-based analytics, conducted including the evaluation of our As demand for oncology care rises, the natural language processing to ingest data products’ efficacy. Here we provide a sample global oncology workforce has not kept pace, from structured and unstructured sources, of these publications for your consideration. leading to a shortage of expertise.1 As a image analysis, drug discovery, genomics, result, physicians face formidable productivity longitudinal care management, and benefits and workflow challenges. In the US, over 40% administration. We also provide timely and of physicians experience burnout according to comprehensive resources for healthcare 01. Wilson BE et al. Lancet Oncol. 2019;20(6):769- a 2018 survey. While this is an improvement professionals pertaining to drug information 780. from previous years studied, physicians and broad-based medical practice. 02. Shanafelt TD et al. Mayo Clin Proc. remain at an increased risk for burnout.2 EMR 2019;94(9):1681-1694. Foreword documentation and payer-preauthorization Examples of capabilities that are focused requirements are noteworthy. In some specifically on cancer care include decision Key studies: developing countries resource constraints support for treatment selection, literature Clinical decision support are significant. Globally, many physicians curation, clinical trials matching, and Clinical trials are challenged by the need to stay current interpretation of next generation sequencing Genomics on the management of multiple neoplastic results. Our solutions have supported the Real world data diseases, the proliferation of research delivery of cancer care in over 15 countries. Bibliography 3 Gretchen Purcell Jackson, cancer care. Our natural language processing handful of studies have shown that use of MD, PhD, FACS, FACMI, FAMIA has been found to accurately identify relevant oncology decision support helped physicians 2 Vice President and Chief Science Officer publications from bibliographic databases. identify treatment options they did not IBM Watson Health Automated clinical trial screening technology previously consider that lead to changes has been shown to reliably exclude ineligible in therapy in 5 to 13.6% of cases12,13 and As a practicing pediatric surgeon and health patients3,4,5 and accurately determine in one study led to improved adherence informatician, I know well the important trial eligibility for breast and lung cancer to guidelines.13 At the Mayo Clinic, use of role of scientific evidence in supporting patients.4,5 In the area of cancer genome automated clinical trial screening technology clinical decisions and informing technology- variant data annotation and categorization, supported increasing patient enrollment in investment decisions. IBM Watson Health is automated tools have been shown to have breast cancer trials by 84%14 and reduced focused on combining data, analytics, and high concordance with expert molecular screening time by 50% in lung cancer artificial intelligence (AI) to create solutions tumor boards in identifying actionable trials.15 AI-enabled technology not only that can empower decision makers with mutations from next generation tumor identified actionable mutations of genes with actionable insights, with the goal of helping sequencing data.6,7 FDA-approved targeted therapies, but also them to improve health and healthcare provided additional therapeutic insights in a delivery. Watson Health is building on IBM’s Once technical performance has been third of cases surveyed.6,16 long history of leading with science, and demonstrated, the next step in evaluating we are proud to present a growing body a clinical AI tools involves conducting Finally, this compilation wouldn’t be of scientific evidence demonstrating the studies of usability and workflow in the complete without highlighting the value of performance and impact of Watson Health clinical setting. Our scientific portfolio real-world data to inform practice alongside solutions in the area of cancer care. includes studies providing evidence of user traditional prospective research. The rich and patient satisfaction8,9 and improved data assets offered by IBM Watson Health The process of validating these tools requires patient engagement with oncology decision coupled with the experience of our scientists a systematic progression of studies to support.10 A community oncology practice have delivered important insights for current demonstrate the performance, applicability, found a 78% reduction in time to screen practice and identified disparities and gaps and value. The first step in evaluating any patients for clinical trials using automated in care.17,18,19,20 health information technology is maintaining clinical trial screening technology compared the output so that it is technically accurate to manual screening,3,4 and AI-enabled We are grateful to our pioneering or correct. With oncology decision support, technology has completed annotation and collaborators who have provided Foreword a systematic review of evaluation studies categorization of sequencing data in a fraction evidence for the performance, usability, and has demonstrated that therapeutic options of the time required for manual curation.6,11 impact of IBM Watson Health oncology and Key studies: have strong agreement with decisions made genomics solutions. Clinical decision support by expert multidisciplinary tumor boards — Ultimately, IBM Watson Health strives to Clinical trials greater than with individual clinicians.1 These create tools to help clinicians as they work Genomics findings support clinical applicability and help to improve oncology care. When used in Real world data illustrate the need for decision support in multidisciplinary tumor board settings, a Bibliography 4 01. Arriga Y, Hekmat R, Darulis K, Wang S, Felix 04. Beck JT, Rammage M, Jackson GP, Preininger 09. Wang Z, Yu Z, Zhang X. Artificial intelligence-based 15. Levantakos K, Helgeson J, Mansfield AS, W, Dankwa-Mullan I, Rhee K, Jackson GP. A AM, Dankwa-Mullan I, Roebuck MC, Torres A, clinical decision-support system improves cancer Deering E, Schwecke A, Adjei A, Molina J, systematic review of concordance studies using holtzen H, Coverdill SE, Williamson MP, Cahu Q, treatment and patient satisfaction. J Clin Oncol 37, Hocum C, Halfdanarson T, Marks R, Parikh K, Watson for Oncology (WfO) to support breast Rhee K, Vinegra M. Artificial intelligence tool 2019 (suppl; abstr e18303). Pomerleau K, Coverdill S, Rammage M, Haddad cancer treatment decisions: a four-year global for optimizing eligibility screening for clinical 10. Fang J, Zhu Z, Wang H, Hu F, Liu Z, Guo X, Chen J, Li T. Implementation of artificial intelligence experience. In: Proceedings of the 2019 San trials in a large community cancer center. JCO C, Shen Y, Xu Q. The establishment of a new medical (AI) for lung cancer clinical trial matching in a Antonio Breast Cancer Symposium; 2019 Dec Clin Cancer Inform. 2020 Jan; 4:50-59. doi: model for tumor treatment combined with Watson for tertiary cancer center. Annals of Oncology. 2019; 10-14; San Antonio, TX. Philadelphia (PA): AACR; 10.1200/CCI.19.00079 Oncology, MDT and patient involvement. J Clin Oncol. 30(Supplement_2): //doi.org/10.1093/annonc/ Cancer Res 2020;80(4 Suppl):Abstract nr P4- 05. Alexander M, Solomon B, Ball DL, Sheerin M, 2018;36(suppl; abstr e18504). mdz065. 14-05. Dankwa-Mullan I, Preininger AM, Jackson GP, 11. Wrzeszczynski K, Frank M, Koyama T, Rhrissorrakrai 16. Kim M, Snowdon J, Weeararatne SD, Felix W, Lim L, 02. Suarez Saiz FJ, Sanders C, Stevens RJ, Nielson Herath DM. Evaluation of an artificial intelligence K, Robine N, Utro F, Emde A, Chen B, Arora K, Shah Dankwa-Mullan I, Lee YK, Lee E, Jeon Km Lee JS, R, Britt MW, Preininger A, Jackson G. Use of clinical trial matching system in Australian lung M, Vacic V, Norel R, Bilal E, Bergmann E, Vogel J, Zang DY, Kim HJ, Kim HY, Han B. Clinical insights machine learning to identify relevant research cancer patients. JAMIA Open, ooaa002, https:// Bruce J, Lassman A, Canoll
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