{"id":2576,"date":"2021-11-15T08:54:39","date_gmt":"2021-11-15T08:54:39","guid":{"rendered":"https:\/\/symposium.org\/?p=2576"},"modified":"2021-12-02T07:41:29","modified_gmt":"2021-12-02T07:41:29","slug":"how-are-you-feeling-today-human","status":"publish","type":"post","link":"https:\/\/symposium.org\/how-are-you-feeling-today-human\/","title":{"rendered":"\u201cHow are you feeling today, human?\u201d"},"content":{"rendered":"\n

Data analytics have begun to transform the world of medicine. An algorithm that can scan databases of randomised controlled trials, as well as actual patient outcomes, is something pretty revolutionary, if you think about it. If the whole world used data analytics, then a doctor\u2019s visit could potentially leave you with more answers than questions and concrete treatment plans rather than the usual \u201cwe could try you on this.\u201d<\/p>\n\n\n\n

Rather than destroying healthcare jobs, in other words, AI has the potential to create the time and tools needed to make doctors more \u201chuman\u201d again. \u201cData analytics and deep learning, transfer learning, machine learning, all these algorithms, are means for me to make best use of the existing data,\u201d says Claudia S\u00fcssmuth Dyckerhoff, a member of the board of directors for Roche Pharmaceuticals who holds a PhD in business administration from the University of St. Gallen.<\/p>\n\n\n\n

Receiving a whole stream of data, including information collected by at-home wearable devices that will feed back to the hospital and track a patient\u2019s progress, means that a doctor\u2019s job will include a lot less uncertainty. \u201cIt\u2019s not about replacing jobs,\u201d S\u00fcssmuth Dyckerhoff explains, but \u201crather about using analytics to improve support.\u201d<\/p>\n\n\n\n

But here\u2019s the fear: if AI can scan databases of every treatment plan, every randomised controlled trial, every case study ever, will certain jobs in healthcare become meaningless? Can a robot do a better job of diagnosis and treatment plans than a human ever could?<\/p>\n\n\n\n

The answer is not so clear cut. Radiologists, for example, may be at risk, but according to S\u00fcssmuth Dyckerhoff radiologists are in short supply in countries like China to begin with. \u201cWhat is important is training your own people to be ready,\u201d she says. \u201cSome people need to be retrained. More data could even create jobs.\u201d<\/p>\n\n\n\n

Technology is already making significant inroads. A team at Stanford University recently introduced an AI therapist dubbed \u201cEllie.\u201d The programme uses voice recognition, image recognition, and can scan hundreds of databases to provide appropriate responses to the patient. \u201cMachine learning has been going on for years, but it\u2019s still early in terms of its use in healthcare,\u201d says Harvard University medical student Joshua Onyango. \u201cIf this is what it can do so far, in terms of providing very personalised healthcare, what can we imagine it doing in 20 or 25 years?\u201d<\/p>\n\n\n\n

Eventually, experts think robots might even allow doctors to be more human, so to speak. When it comes to the innate uncertainty that surrounds disease, treatment, side-effects, and mental health, sometimes a patient really needs a person to talk to. But physicians in today\u2019s overburdened healthcare system struggle with that task. Sometimes doctors simply just do not have the resources or time to be as holistic as we want them to be.<\/p>\n\n\n\n

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Joshua Onyango<\/figcaption><\/figure>\n\n\n\n

Orest Firsov, an entrepreneur at New Body Technology, a digital healthcare service that tracks posture for users \u2013 many who have chronic health conditions \u2013 points out the  fundamental benefit of AI in medicine. Automation that takes away the burden of \u201croutine checkups and assessments\u201d means doctors have more free time to be both more  competent and more caring. Understaffing has left healthcare workers unavoidably robotic in practice, and perhaps we need AI to relieve the burden of routine work as a way to restore humanity to medicine.<\/p>\n\n\n\n

Not to be taken lightly, this time to humanise the patient could save healthcare jobs in the age of AI. \u201cOne thing that machines are going to have a very difficult time doing,\u201d Onyango says, \u201cis understanding the cultural and emotional nuances that make us human.\u201d Tasks such as navigating social norms, symbolism, body language and empathy \u2013 all the little things that make us human and separate us from AI \u2013 will be difficult for robots to accomplish.<\/p>\n\n\n\n

According to Valeriia Kasatkina, a Leader of Tomorrow researching the connectedness of Australia\u2019s social support systems for domestic abuse victims, AI could also help  mobilise policy and care. If we have data analytics to help improve communication between the healthcare and social work sectors, then the role of the social care worker will become more efficient.<\/p>\n\n\n\n

\u201cOn the organisational level, using data collection and information sharing platforms could provide relevant information about victims,\u201d Kasatkina says. This is vitally important for issues in cases of domestic violence. \u201cVictims could have mental health issues, a child, or problems with drug addiction,\u201d Kasatkina says. \u201cDrawing connections means all of their health and social care services could be coordinated.\u201d<\/p>\n\n\n\n

The world of medicine and healthcare is changing, but for now, it seems that the changes are for the better. As long as doctors, healthcare providers and carers can fill the gaps left by machines, providing patients with the human touch, then there really is room for both man and machine in the future of healthcare. \u201cThe hospital will always be a space with a human touch,\u201d S\u00fcssmuth Dyckerhoff says. \u201cHealth is so human.\u201dImage<\/p>\n\n\n\n

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

Data analytics have begun to transform the world of medicine. An algorithm that can scan databases of randomised controlled trials, as well as actual patient outcomes, is something pretty revolutionary, if you think about it. If the whole world used data analytics, then a doctor\u2019s visit could potentially leave you with more answers than questions […]<\/p>\n","protected":false},"author":22,"featured_media":2577,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_gspb_post_css":"","footnotes":""},"categories":[13],"tags":[],"ppma_author":[102],"class_list":["post-2576","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-insights"],"blocksy_meta":{"styles_descriptor":{"styles":{"desktop":"","tablet":"","mobile":""},"google_fonts":[],"version":6}},"acf":[],"featured_image_urls_v2":{"full":["https:\/\/symposium.org\/wp-content\/uploads\/2021\/11\/SYMP-49-Magazine-Web-Article-L-Dyckerhoff.jpg",1110,758,false],"thumbnail":["https:\/\/symposium.org\/wp-content\/uploads\/2021\/11\/SYMP-49-Magazine-Web-Article-L-Dyckerhoff-150x150.jpg",150,150,true],"medium":["https:\/\/symposium.org\/wp-content\/uploads\/2021\/11\/SYMP-49-Magazine-Web-Article-L-Dyckerhoff-300x205.jpg",300,205,true],"medium_large":["https:\/\/symposium.org\/wp-content\/uploads\/2021\/11\/SYMP-49-Magazine-Web-Article-L-Dyckerhoff-768x524.jpg",768,524,true],"large":["https:\/\/symposium.org\/wp-content\/uploads\/2021\/11\/SYMP-49-Magazine-Web-Article-L-Dyckerhoff-1024x699.jpg",1024,699,true],"xl":["https:\/\/symposium.org\/wp-content\/uploads\/2021\/11\/SYMP-49-Magazine-Web-Article-L-Dyckerhoff.jpg",1110,758,false],"xxl":["https:\/\/symposium.org\/wp-content\/uploads\/2021\/11\/SYMP-49-Magazine-Web-Article-L-Dyckerhoff.jpg",1110,758,false],"xxxl":["https:\/\/symposium.org\/wp-content\/uploads\/2021\/11\/SYMP-49-Magazine-Web-Article-L-Dyckerhoff.jpg",1110,758,false],"xxxxl":["https:\/\/symposium.org\/wp-content\/uploads\/2021\/11\/SYMP-49-Magazine-Web-Article-L-Dyckerhoff.jpg",1110,758,false],"xxxxxl":["https:\/\/symposium.org\/wp-content\/uploads\/2021\/11\/SYMP-49-Magazine-Web-Article-L-Dyckerhoff.jpg",1110,758,false],"1536x1536":["https:\/\/symposium.org\/wp-content\/uploads\/2021\/11\/SYMP-49-Magazine-Web-Article-L-Dyckerhoff.jpg",1110,758,false],"2048x2048":["https:\/\/symposium.org\/wp-content\/uploads\/2021\/11\/SYMP-49-Magazine-Web-Article-L-Dyckerhoff.jpg",1110,758,false]},"post_excerpt_stackable_v2":"

Data analytics have begun to transform the world of medicine. An algorithm that can scan databases of randomised controlled trials, as well as actual patient outcomes, is something pretty revolutionary, if you think about it. If the whole world used data analytics, then a doctor\u2019s visit could potentially leave you with more answers than questions and concrete treatment plans rather than the usual \u201cwe could try you on this.\u201d Rather than destroying healthcare jobs, in other words, AI has the potential to create the time and tools needed to make doctors more \u201chuman\u201d again. \u201cData analytics and deep learning, transfer…<\/p>\n","category_list_v2":"INSIGHTS<\/a>","author_info_v2":{"name":"wordpress@weitblick-online.ch","url":"https:\/\/symposium.org\/author\/wordpressweitblick-online-ch\/"},"comments_num_v2":"0 comments","authors":[{"term_id":102,"user_id":0,"is_guest":1,"slug":"kizzy-bray","display_name":"Kizzy Bray","avatar_url":"https:\/\/symposium.org\/wp-content\/uploads\/gravatars\/762b22de4bf1bf3924204e9b02554eaa","0":null,"1":"","2":"","3":"","4":"","5":"","6":"","7":"","8":""}],"_links":{"self":[{"href":"https:\/\/symposium.org\/wp-json\/wp\/v2\/posts\/2576","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/symposium.org\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/symposium.org\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/symposium.org\/wp-json\/wp\/v2\/users\/22"}],"replies":[{"embeddable":true,"href":"https:\/\/symposium.org\/wp-json\/wp\/v2\/comments?post=2576"}],"version-history":[{"count":2,"href":"https:\/\/symposium.org\/wp-json\/wp\/v2\/posts\/2576\/revisions"}],"predecessor-version":[{"id":3015,"href":"https:\/\/symposium.org\/wp-json\/wp\/v2\/posts\/2576\/revisions\/3015"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/symposium.org\/wp-json\/wp\/v2\/media\/2577"}],"wp:attachment":[{"href":"https:\/\/symposium.org\/wp-json\/wp\/v2\/media?parent=2576"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/symposium.org\/wp-json\/wp\/v2\/categories?post=2576"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/symposium.org\/wp-json\/wp\/v2\/tags?post=2576"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/symposium.org\/wp-json\/wp\/v2\/ppma_author?post=2576"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}