Picture this. You visit your doctor, who is 20 minutes behind schedule. You eventually see them for 15 minutes, of which they spend 50% of the time typing notes, constantly clicking their mouse, and staring at their computer screen. The doctor then performs a lightning-fast exam, rapidly relays essential information, and sends you for a test. Before you know it, the visit is over and you leave the clinic feeling confused and unsatisfied. Hours later, you receive a phone call about your test result.<\/p>\n
Unfortunately, the above situation is far too common, leaving patients and physicians feeling increasingly frustrated. The culprit? Poor design and implementation of the last wave of digital health technology driven by the electronic medical record (EMR).<\/p>\n
In a recent New Yorker<\/em> piece entitled \u201cWhy Doctors Hate Their Computers,\u201d Atul Gawande, a world-renowned surgeon and CEO of Amazon\u2019s healthcare venture, wrote, \u201cA system [EMR] that promised to increase my mastery over my work has, instead, increased my work\u2019s mastery over me.\u201d[1]<\/a> These unfavorable dynamics have driven physician burnout to an all-time high, with over 50% of physicians experiencing symptoms of burnout.[2]<\/a><\/p>\n EMR technology has left a bad taste in physicians\u2019 mouths by costing them unnecessary time and mental energy. Partners HealthCare, which includes Massachusetts General Hospital (MGH) and Brigham and Women\u2019s Hospital (BWH), is developing the next wave of health care technology to reverse this trend and empower its physicians. How? By using machine learning.<\/p>\n Partners emphasized its commitment to machine learning by establishing the MGH and BWH Center for Clinical and Data Science (CCDS) in 2016.[3]<\/a> Partners has since positioned itself for long-term success by creating partnerships with key industry leaders. In 2017, the CCDS announced a 10-year collaboration with General Electric to \u201cintegrate deep learning technology into every aspect of the patient journey.\u201d[4]<\/a> More recently, CCDS has partnered with Nuance Communications, a company that specializes in open-platform artificial intelligence development. Through this agreement, CCDS will focus on translating machine learning technology into solutions for radiologists who interpret medical images.[5]<\/a>\u00a0Multi-disciplinary teams consisting of physicians, data scientists, and engineers will greatly enhance these collaborations\u2019 chances of success.<\/p>\n While Partners clearly recognizes the long-term potential of machine learning, how quickly can we expect this technology to impact patients? Remarkably, MGH researchers have already produced exciting results. In a 2018 study published in Nature<\/em>, a preeminent scientific journal, researchers enhanced medical images through a machine learning technology called AUTOMAP.[6]<\/a> In the clinical setting, this technology could allow for faster, more accurate interpretations by radiologists. Now, imagine that the test performed in the earlier scenario is a medical image that is vital for your doctor to establish a diagnosis. Instead of anxiously waiting hours for the results, AUTOMAP could allow your doctor to more quickly and confidently reveal the results to you in minutes, leaving more time for discussion of your questions and concerns.<\/p>\n Another encouraging MGH study showed that a machine learning algorithm could accurately predict which emergency department patients with bloodstream infections would need potentially life-saving medication.[7]<\/a> With this algorithm, instead of reacting to complications that arise, doctors could proactively administer the important medication and prevent complications.<\/p>\n These discoveries could improve the speed and quality of diagnoses and treatment plans. This would create more time for physicians to counsel patients and answer questions, ultimately enhancing trust and satisfaction with the patient-doctor experience.<\/p>\n As Partners strives to unleash the full potential of machine learning, it should consider the following recommendations:<\/p>\n As health care organizations grapple with this new technology, the following questions are interesting to consider:<\/p>\n While many details must be worked out, Partners HealthCare is at the cutting-edge of using machine learning to improve the patient-doctor experience and health care more broadly. The innovations discussed here are just the tip of the iceberg.<\/p>\n (798)<\/p>\n [1]<\/a> Gawande, A. (2018, November 06). Why Doctors Hate Their Computers. Retrieved from https:\/\/www.newyorker.com\/magazine\/2018\/11\/12\/why-doctors-hate-their-computers<\/p>\n [2]<\/a> Shanafelt, T. D., Hasan, O., Dyrbye, L. N., Sinsky, C., Satele, D., Sloan, J., & West, C. P. (2015). Changes in Burnout and Satisfaction With Work-Life Balance in Physicians and the General US Working Population Between 2011 and 2014. Mayo Clinic Proceedings<\/em>, 90<\/em>(12), 1600\u20131613. https:\/\/doi.org\/https:\/\/doi.org\/10.1016\/j.mayocp.2015.08.023<\/p>\n [3]<\/a> Mass General News. (2017, April 7). Bringing the latest advances in artificial intelligence to patient care. Retrieved from https:\/\/www.massgeneral.org\/News\/newsarticle.aspx?id=6264<\/p>\n [4]<\/a> Copp, R., & Sousa, K. (2017, May 17). Partners HealthCare and GE Healthcare launch 10-year collaboration to integrate Artificial Intelligence into every aspect of the patient journey. Retrieved from https:\/\/www.partners.org\/Newsroom\/Press-Releases\/Partners-GE-Healthcare-Collaboration.aspx<\/p>\n [5]<\/a> Nuance Communications, Inc. (2018, March 05). Nuance and Partners HealthCare Collaborate to Accelerate Widespread Development, Deployment and Adoption of AI Applications for Diagnostic Imaging. Retrieved from 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<\/p>\n [6]<\/a> Zhu, B., Liu, J. Z., Cauley, S. F., Rosen, B. R., & Rosen, M. S. (2018). Image reconstruction by domain-transform manifold learning. Nature<\/em>, 555<\/em>, 487. Retrieved from https:\/\/doi.org\/10.1038\/nature25988<\/p>\n [7]<\/a> Matheson, R., & MIT News Office. (2018, November 07). Machine-learning system could aid critical decisions in sepsis care. Retrieved from http:\/\/news.mit.edu\/2018\/machine-learning-sepsis-care-1107?fbclid=IwAR24dS96H6t6_7GIbH4JVv0OMyW0dzh-nCHLk1TrVoq77gXgGuCJVErvnnM<\/p>\n [8]<\/a> Fogel, A. L., & Kvedar, J. C. (2018). Artificial intelligence powers digital medicine. Npj Digital Medicine<\/em>, 1<\/em>(1), 5. https:\/\/doi.org\/10.1038\/s41746-017-0012-2<\/p>\n [9]<\/a> Verghese A, Shah NH, Harrington RA. What This Computer Needs Is a Physician: Humanism and Artificial Intelligence.\u00a0JAMA.<\/em>\u00a02018;319(1):19\u201320. doi:10.1001\/jama.2017.19198<\/p>\n [10]<\/a> Featured Photo by\u00a0Markus Spiske<\/a>\u00a0on\u00a0Unsplash<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" Many U.S. physicians feel that health care digitization has created serious headaches with limited benefits for patients. Will machine learning soon change their minds? Partners HealthCare, a health care system that oversees two world-class Boston hospitals, certainly thinks so.<\/p>\n","protected":false},"author":11160,"featured_media":30574,"comment_status":"open","ping_status":"closed","template":"","categories":[1869,1909,887,2689,41,346,4496,1685,1291],"class_list":["post-30505","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-ai","category-artificial-intelligence","category-health-care","category-health-technology","category-healthcare","category-machine-learning","category-medical-ai","category-medicine","category-partners-healthcare","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\n
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