{"id":31269,"date":"2018-11-13T08:01:00","date_gmt":"2018-11-13T13:01:00","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/sharing-data-to-advance-cancer-care-at-memorial-sloan-kettering-cancer-center\/"},"modified":"2018-11-13T08:01:00","modified_gmt":"2018-11-13T13:01:00","slug":"sharing-data-to-advance-cancer-care-at-memorial-sloan-kettering-cancer-center","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/sharing-data-to-advance-cancer-care-at-memorial-sloan-kettering-cancer-center\/","title":{"rendered":"Sharing Data to Advance Cancer Care at Memorial Sloan Kettering Cancer Center"},"content":{"rendered":"

Cancer can be a highly personal and sensitive topic, which is why significant revelations in the field tend to be exciting and noteworthy. An important development in cancer research has been the understanding that the disease is \u201cextremely heterogeneous.\u201d As a result, the concept of using precision medicine for oncology is valuable because there is not one \u201ccure\u201d for cancer; rather, each cancer patient has a different genetic profile and it would be more effective to tailor solutions to the individual patient [1]. With an incredible amount of data being generated, it is crucial to find a way to leverage this data for better healthcare outcomes. Collection of the data is only one, albeit critical, part of the equation; importantly, \u201cthe human ability to process this data without effective decision support is finite\u2026computer models are required to help clinicians organize the data, recognize patterns, interpret results, and set thresholds for actions\u201d [2]. While utilizing machine learning and big data offer big opportunities in the field of cancer, there are important issues to keep in mind as it relates to product development in the healthcare industry more generally.<\/span><\/p>\n

Memorial Sloan Kettering Cancer Center (MSK), one of the most highly respected cancer centers in the world, understands the significance of machine learning in oncology. At a conference in 2017, MSK created a competition that \u201cwas conceived because precision medicine and genetic testing are disrupting the way diseases like cancer are treated;\u201d the competition was a \u201ccall to develop a machine learning algorithm that, using MSK\u2019s database, can begin to help automatically classify actionable genetic variations\u201d [3]. While this appears to be a longer-term investment, MSK has recently made shorter-term commitments as well. Earlier this year, the hospital partnered with Paige.AI, which was \u201ca new company focused on revolutionizing clinical diagnosis and treatment in oncology through use of artificial intelligence (AI).\u201d \u00a0This specific start-up focused on using machine learning for pathology to help diagnose \u201cwith greater speed, accuracy, objectivity and reproducibility.\u201d Their ability to do this relies on having data; in turn, MSK signed an agreement that gives the start-up \u201cexclusive rights to MSK\u2019s library of 25 million pathology slides,\u201d a significant step for both sides [4].<\/span><\/p>\n

\"\"<\/a>
https:\/\/paige.ai\/about<\/figcaption><\/figure>\n

There is an important, symbiotic relationship between MSK and its collaborators, but the hospital must exercise caution with potential conflicts. To leverage machine learning, MSK needs to continue collaborating by offering its valuable data. One of the big issues faced by health data \u201cis lack of standardization, which would allow for information to be combined from multiple data centers to develop a deeper understanding of clinical outcomes\u201d [5]. Rather than working exclusively with for-profit companies, MSK should also work with other hospitals to share data; this would provide a whole new source of valuable information. When collaborating, MSK must also be careful about how it shares data, and as importantly, how the hospital is perceived. Recently, MSK\u2019s partnership with Paige.AI came under intense scrutiny. One issue was patient data; while the patient\u2019s information was de-identified, sharing images of cells or genetic variations leads to a vaguer ethical boundary. MSK must be transparent with patients about the way in which their data is being used. MSK should educate patients about machine learning and the importance of collecting data; if patients are better educated, they may be more willing and excited to share their information. Another big question that was brought to the surface in this case was how we value data, and who has the rights to that data. While certain MSK executives retained equity in Paige.AI, pathologists believed \u201cit is unfair that the founders received equity stakes in a company that relies on the pathologists\u2019 expertise and work amassed over 60 years\u201d [6]. To reduce this perception of taking advantage of others\u2019 work for personal gain, MSK should not allow specific employees to benefit individually from companies with which it collaborates. If there are any relationships, which should be encouraged, any financial benefits should be realized by the hospital more broadly.<\/span><\/p>\n

Unlike other industries, cancer care (and the healthcare industry more generally) faces different scrutiny. MSK (and other hospitals) must continue to collaborate with innovative companies, but they must have more specific policies and procedures in place as it relates to the data it shares. Creating a perception of impropriety when it comes to patients\u2019 lives can jeopardize tremendous progress. One of the most important questions to consider is how we think about the value of the data we are inputting. Who has rights to this data? Should we allow it to be used as a common good? While other industries may face similar questions about the value of data, the stakes are raised in healthcare and these questions become amplified. [785 words].<\/span><\/p>\n

 <\/p>\n

[1] Seung Ho Shin, Ann M. Bode, and Zigang Dong, \u201cPrecision medicine: the foundation of future cancer therapeutics,\u201d npj Precision Oncology <\/em>1, no. 12 (2017), https:\/\/www.nature.com\/articles\/s41698-017-0016-z<\/a>, accessed November 2018.<\/p>\n

[2] Daniel Richard Leff and Guang-Zhong Yang, \u201cBig Data for Precision Medicine,\u201d Engineering<\/em>, 1, no. 3 (September 2015): 277-279, via ScienceDirect, <\/em>accessed November 2018.<\/p>\n

[3] \u201cMemorial Sloan Kettering Advances Its AI, Machine Learning at NIPS 2017,\u201d press release, December 11, 2017, on MSK website, https:\/\/www.mskcc.org\/trending-topics\/msk-advances-its-ai-machine-learning-nips-2017<\/a>, accessed November 2018.<\/p>\n

[4] \u201cPaige.AI Created to Transform Cancer Diagnosis and Treatment by Applying Artificial Intelligence to Pathology,\u201d press release, February 5, 2018, via BusinessWire, <\/em>https:\/\/www.businesswire.com\/news\/home\/20180205005557\/en\/Paige.AI-Created-Transform-Cancer-Diagnosis-Treatment-Applying<\/a>, accessed November 2018.<\/p>\n

[5] Cary Jo R Schlick, MD, Joshua P Castle, BS, and David J. Bentrem. MD, \u201cUtilizing Big Data in Cancer Care,\u201d Surgical Oncology Clinics of North America <\/em>27, no. 4 (October 2018): 641-652, via ScienceDirect, <\/em>accessed November 2018.<\/p>\n

[6] Charles Ornstein and Katie Thomas, \u201cSloan Kettering\u2019s Cozy Deal With Start-Up Ignites a New Uproar,\u201d New York Times, <\/em>September 20, 2018, https:\/\/www.nytimes.com\/2018\/09\/20\/health\/memorial-sloan-kettering-cancer-paige-ai.html<\/a>, accessed November 2018.<\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

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