Imagine storing your personal savings under your bed. Most people with access to capital would prefer to earn interest in a bank account or invest their wealth to generate returns.<\/p>\n
For years, the American healthcare system has kept medical data stashed under the bed. Rather than growing the value of medical data, most of its potential remains untapped. Because patient data is difficult to access, poorly organized, and timely to mine, the ability to generate insights from it has been relatively limited.<\/p>\n
Machine learning offers hope for better utilization of data and images stored by provider networks to improve the accuracy and efficiency of medical decision making. Medical decision making often requires analysis of many data points\u2014including symptoms, comorbidities, risk factors, imaging, blood tests, and biopsied tissue\u2014by several specialists. Yet, data interpretation may be limited by the expertise and experience of providers, as well as the time required for analysis and care coordination.<\/p>\n
In 2016, Partners Healthcare established the Clinical Center for Data Science (CCDS) to develop machine learning technologies to improve diagnosis and data interpretation in healthcare. As the largest hospital system in New England, Partners stores an enormous amount of patient data in its electronic health records system. [1] As of 2017, Partners had access to two billion medical images (ie. radiograph, CT, MRI, PET, etc.) that the CCDS could use to build and validate algorithms. [2]<\/p>\n
By April 2017, the CCDS had initiated over 20 machine learning projects. [3] In May 2017, the CCDS initiated a ten-year partnership with General Electric (GE) Healthcare to accelerate the development of machine learning applications for patient care, recognizing the need for multidisciplinary collaboration among physicians, researchers, data scientists, and developers. The shared vision is for Partners, GE Healthcare, and third parties to develop algorithms on an open platform and disseminate advances to other hospitals using the GE Health Cloud. [4,5] Additional collaborators include Nvidia, whose supercomputer and graphics processing units (GPUs) offer the CCDS significant computing power, and Nuance, who developed the first open platform for artificial intelligence innovations in medical imaging. [6,7]<\/p>\n
The initial focus for CCDS has been improving accuracy and increasing efficiency of diagnosis using medical images. For example, by pointing a physician\u2019s attention towards particular components of a patient\u2019s image or providing a rating based on the likelihood that an image contains an anomaly, machine learning could allow for quicker decision making in emergent scenarios where diagnosis is the bottleneck before sending a patient to the operating room. [5] Other applications in radiology include early detection of strokes, injuries, and cancer, allowing for more judicious use of physicians\u2019 time and diagnostic interventions such as biopsies.<\/p>\n
The diagnostic medical specialties that require interpretation of images, such as radiology and pathology, are likely to be the first specialties where machine learning will augment and expand clinical capacity. However, these specialties may serve as a pilot for others, as artificial intelligence begins to increase the speed and accuracy of diagnosis and management across diseases and patient populations. In the coming years, Partners aims to develop algorithms that can augment medical specialties beyond radiology, as well as organizational operations. For example, with an eye towards cancer treatment, CCDS is considering how this technology can help track tumor response to therapy. [5]<\/p>\n
In developing future machine learning applications, Partners should consider other rich data sources beyond images, such as the patient notes that are written by providers after each clinical encounter. Moving forward, Partners will need to determine how to organize its wealth of data to select appropriate inputs and adequately validate outputs to derive actionable insights. Another challenge will be smoothly integrating these technologies into clinic flow to augment rather than disrupt patient care. Partners should also consider how to help community hospitals and clinics with fewer resources deploy these technologies to minimize disparities.<\/p>\n
A number of other vital questions remain. How will these new technologies garner trust among providers and patients, as well as adoption among healthcare organizations? How will providers be educated on how the outputs are derived, so as to appropriately interpret the results? What safety measures and patient privacy regulations will need to be instituted? How can these approaches be distributed to improve global access to care, and how fast should they be scaled? Will these technologies improve access to care and lower costs, or like other approaches to automated image interpretation, will they fail to significantly move the needle?<\/p>\n
Pending resolution of these uncertainties, machine learning technology will hopefully offer deeper insight into high risk or at-risk patients, increase efficiency and productivity, allow for better allocation of human capital, increase opportunities for global access to care, and decrease errors by creating a safety net in medical decision making.<\/p>\n
<\/p>\n
Word count: 784<\/p>\n
[1] “About Partners Healthcare.” https:\/\/innovation.partners.org\/about\/about-partners-healthcare, accessed November 2018.<\/p>\n
[2] Tomsho R. “Artificial Intelligence Expert Sees Healthcare Impact.” Mass General Magazine, 2017. https:\/\/giving.massgeneral.org\/artificial-intelligence-healthcare-impact\/, accessed November 2018.<\/p>\n
[3] “Bringing the latest advances in artificial intelligence to patient care.” Mass General News, April 7, 2017. https:\/\/www.massgeneral.org\/News\/newsarticle.aspx?id=6264, accessed November 2018.<\/p>\n
[4] “A.I. and GE \u2013 The disruption accelerates.” Partners Healthcare Innovation, News: Summer 2017, July 11, 2017. https:\/\/innovation.partners.org\/summer-2017\/ge-disruption-accelerates, accessed November 2018.<\/p>\n
[5] “The team behind the future of AI in healthcare.” GE Healthcare: The Pulse on Health, Science & Tech, May 18, 2017. http:\/\/newsroom.gehealthcare.com\/the-team-behind-the-future-of-ai-in-healthcare\/, accessed November 2018.<\/p>\n
[6] Davenport TH and Bean R. “Revolutionizing Radiology with Deep Learning at Partners Healthcare \u2013 and Many Others.” Forbes, November 5, 2017. https:\/\/www.forbes.com\/sites\/tomdavenport\/2017\/11\/05\/revolutionizing-radiology-with-deep-learning-at-partners-healthcare-and-many-others\/#6e3d04255e13, accessed November 2018.<\/p>\n
[7] “Nuance and Partners HealthCare Collaborate to Accelerate Widespread Development, Deployment and Adoption of AI Applications for Diagnostic Imaging.” Globe Newswire, March 5, 2018. https:\/\/globenewswire.com\/news-release\/2018\/03\/05\/1415228\/0\/en\/Nuance-and-Partners-HealthCare-Collaborate-to-Accelerate-Widespread-Development-Deployment-and-Adoption-of-AI-Applications-for-Diagnostic-Imaging.html, accessed November 2018.<\/p>\n","protected":false},"excerpt":{"rendered":"
How can hospitals better utilize patient data by applying machine learning technologies to augment diagnosis and clinical decision making? Partners Healthcare has launched the Clinical Center for Data Science to lead the charge.<\/p>\n","protected":false},"author":11836,"featured_media":35657,"comment_status":"open","ping_status":"closed","template":"","categories":[729,41,346,4399],"class_list":["post-35656","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-health-delivery","category-healthcare","category-machine-learning","category-radiology","hck-taxonomy-organization-partners-healthcare","hck-taxonomy-industry-health","hck-taxonomy-country-united-states"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"\n