{"id":34256,"date":"2018-11-13T17:53:59","date_gmt":"2018-11-13T22:53:59","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/can-artificial-intelligence-help-address-the-complexities-of-non-artificial-mind\/"},"modified":"2018-11-13T17:53:59","modified_gmt":"2018-11-13T22:53:59","slug":"can-artificial-intelligence-help-address-the-complexities-of-non-artificial-mind","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/can-artificial-intelligence-help-address-the-complexities-of-non-artificial-mind\/","title":{"rendered":"Can artificial intelligence help address the complexities of non-artificial mind?"},"content":{"rendered":"

Mental health: The challenge<\/strong><\/p>\n

 <\/p>\n

Mental health is a behemoth of an issue; one that not only millions around the world continuously grapple with (in fact, depression will be the #1 most common chronic condition for Americans by 2050 [1]), but that the healthcare system has been struggling to provide adequate care for. The critical steps in treating this condition are diagnosis and timely intervention, which can be a challenge in today’s world where specialist appointments can take weeks to get and episodes of the illness can land patients in the Emergency Room, for lack of alternate timely intervention options.<\/p>\n

\"\"<\/a>
Ginger.io enables a continuous view into a patient’s mental health status [4]<\/figcaption><\/figure>Ginger.io\u2019s innovative approach<\/strong><\/p>\n

 <\/p>\n

Whereas traditional mental health diagnostic tools rely on extensive self-assessed and self-reported mood metrics at discrete points in time, Ginger.io processes a continuous stream of data gathered from a patient’s smartphone through machine learning to identify the potential onset of a mental health episode and the need for intervention. This data includes phone movement data, screen usage data, text and other communication data, and self-reported mood data [2]. The algorithm learns the typical patterns within these data for each patient and uses machine learning to identify abnormalities which signal a likelihood of mood disorder. The algorithm was built by contrasting data from these sensors with responses to 1.4 million mental health assessment questionnaires, and continues to learn as users continue to complete these assessments in-app [3]. This app has been proven to identify changes in mood and signs of depression before a patient even realizes it has occurred; in several cases, up to 2 days before [4]. In response, the app can help virtually engage a coach with targeted therapy before outward symptoms even manifest.<\/p>\n

\"\"<\/a>
Types of data used by ginger.io [4]<\/figcaption><\/figure>\n

This approach is a very clever application of machine learning. The input data comes in several forms and combines several datasets containing different variables to identify patterns. Deviation from historical data and behavior, while entirely possible that it is not a sign of mental health illness, can be a very good indicator of overall mood in this case. Because the prediction of mood is not absolute, but rather suggestive to initiate outreach of a coach, this problem lends itself very well to the benefits of machine learning.<\/p>\n

 <\/p>\n

Strategic recommendations for the future<\/strong><\/p>\n

 <\/p>\n

The company began by offering its technology to hospitals, but quickly pivoted to scaling their impact by selling their service directly to consumers and integrating into platforms of medium and large self-insured employers as well as United Healthcare\u2019s insurance plans [5]. In the longer term, the company is working to offer better access, expanding its suite of therapists and coaches on staff to offer a fuller stack of service and improving training of its coaches and accuracy of its algorithm through continuous in-app feedback.<\/p>\n

 <\/p>\n

My recommendations are two-fold: 1) On the algorithm side, to integrate additional datasets, including language processing and social media data and 2) On the strategic side, to sign up more integrated delivery systems and ACOs.<\/p>\n

 <\/p>\n

On the algorithm, we input near infinite amounts of data into our phone in the form of language, from the words we use to text to the keyword searches we look up. The app could implement additional natural language processing and sentiment analysis to better predict mood. In addition, there have been several studies about the impact of social media and mental health; for example, one study found that increased use of blue and gray-toned colors in a person’s Instagram posts is correlated to depression [6]. For this reason, the algorithm could evaluate and analyze social media use and content further to distinguish additional nuances in mood.<\/p>\n

 <\/p>\n

On the strategy, the company could take even greater advantage of their value proposition to reduce healthcare costs by marketing towards payers that are taking on risk for the cost of care of populations: for example, integrated delivery systems (such as Kaiser Permenante), ACOs (such as Chen Medical), concierge medicine programs (such as One Medical), and even Medicare or Medicaid programs.<\/p>\n

 <\/p>\n

Open questions <\/strong><\/p>\n

 <\/p>\n

Although ginger.io shows promise as a potent treatment for non-serious mental illness and has proven hospitalization avoidance [4] and clinically significant improvement in depression [5], an open question remains in my mind. How much can we rely on machine learning to identify when a patient with mental illness needs help? Will an app like this make others, such as the medical care team or the personal support network of a patient, abdicate responsibility and assume that an app will alert them if and when someone may need intervention? To what degree will this impact interpersonal relationships and community as we know them today? With all the hullabaloo about technology ruining society as we know it, will this app just take us another step in that direction?<\/p>\n

 <\/p>\n

 <\/p>\n

(Word count:<\/strong> 770 words)<\/p>\n

 <\/p>\n

References: <\/strong><\/p>\n

 <\/p>\n

[1] 1. Heo M, Murphy C, Fontaine K, Bruce M, Alexopoulos G. Population projection of US adults with lifetime experience of depressive disorder by age and sex from year 2005 to 2050.\u00a0Int J Geriatr Psychiatry<\/em>. 2008;23(12):1266-1270. doi:10.1002\/gps.2061<\/p>\n

 <\/p>\n

[2] Hari Prasad, \u201cBehavioral Analytics for Healthcare\u201d, 2014. http:\/\/www.caltelehealth.org\/sites\/main\/files\/file-attachments\/hariprasad.pdf<\/a>, accessed November 2018.<\/p>\n

 <\/p>\n

[3] Sean Captain, \u201cCan Artificial Intelligence Help The Mentally Ill?\u201d, July 21, 2016. https:\/\/www.fastcompany.com\/3059995\/can-artificial-intelligence-help-the-mentally-ill<\/a>, accessed November 2018.<\/p>\n

 <\/p>\n

[4] World Economic Forum, \u201cEmpowering mental health care patients through personalization\u201d, 2018. http:\/\/reports.weforum.org\/digital-transformation\/ginger-io\/<\/a>, accessed November 2018.<\/p>\n

 <\/p>\n

[5] Ginger.io press; \u201cGinger.io to Expand Access to Emotional-Support Coaching Through Ginger.io App For BuzzFeed Employees Outside of the United States\u201d, July 27, 2018. https:\/\/globenewswire.com\/news-release\/2018\/06\/27\/1530424\/0\/en\/Ginger-io-to-Expand-Access-to-Emotional-Support-Coaching-Through-Ginger-io-App-For-BuzzFeed-Employees-Outside-of-the-United-States.html<\/a>, accessed November 2018.<\/p>\n

 <\/p>\n

[6] University of Vermont, \u201cWhen you’re blue, so are your Instagram photos: Researchers discover an early-warning system for depression in social media images\u201d, August 9, 2017. https:\/\/www.sciencedaily.com\/releases\/2017\/08\/170809083209.htm<\/a>, accessed November 2018.<\/p>\n

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

Can an app powered by machine learning be better and faster at diagnosing mental illness than humans?<\/p>\n","protected":false},"author":11577,"featured_media":34340,"comment_status":"open","ping_status":"closed","template":"","categories":[346,2600],"class_list":["post-34256","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-machine-learning","category-mental-health","hck-taxonomy-organization-gingerio","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":"\nCan artificial intelligence help address the complexities of non-artificial mind? - Technology and Operations Management<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/can-artificial-intelligence-help-address-the-complexities-of-non-artificial-mind\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Can artificial intelligence help address the complexities of non-artificial mind? - Technology and Operations Management\" \/>\n<meta property=\"og:description\" content=\"Can an app powered by machine learning be better and faster at diagnosing mental illness than humans?\" \/>\n<meta property=\"og:url\" content=\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/can-artificial-intelligence-help-address-the-complexities-of-non-artificial-mind\/\" \/>\n<meta property=\"og:site_name\" content=\"Technology and Operations Management\" \/>\n<meta property=\"og:image\" content=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/ThinkstockPhotos-466268301-1.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1920\" \/>\n\t<meta property=\"og:image:height\" content=\"1096\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"5 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/can-artificial-intelligence-help-address-the-complexities-of-non-artificial-mind\\\/\",\"url\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/can-artificial-intelligence-help-address-the-complexities-of-non-artificial-mind\\\/\",\"name\":\"Can artificial intelligence help address the complexities of non-artificial mind? - Technology and Operations Management\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/can-artificial-intelligence-help-address-the-complexities-of-non-artificial-mind\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/can-artificial-intelligence-help-address-the-complexities-of-non-artificial-mind\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/wp-content\\\/uploads\\\/sites\\\/4\\\/2018\\\/11\\\/ThinkstockPhotos-466268301-1.jpg\",\"datePublished\":\"2018-11-13T22:53:59+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/can-artificial-intelligence-help-address-the-complexities-of-non-artificial-mind\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/can-artificial-intelligence-help-address-the-complexities-of-non-artificial-mind\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/can-artificial-intelligence-help-address-the-complexities-of-non-artificial-mind\\\/#primaryimage\",\"url\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/wp-content\\\/uploads\\\/sites\\\/4\\\/2018\\\/11\\\/ThinkstockPhotos-466268301-1.jpg\",\"contentUrl\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/wp-content\\\/uploads\\\/sites\\\/4\\\/2018\\\/11\\\/ThinkstockPhotos-466268301-1.jpg\",\"width\":1920,\"height\":1096},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/can-artificial-intelligence-help-address-the-complexities-of-non-artificial-mind\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Submissions\",\"item\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Can artificial intelligence help address the complexities of non-artificial mind?\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/#website\",\"url\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/\",\"name\":\"Technology and Operations Management\",\"description\":\"MBA Student Perspectives\",\"potentialAction\":[{\"@type\":\"性视界Action\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Can artificial intelligence help address the complexities of non-artificial mind? - Technology and Operations Management","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/can-artificial-intelligence-help-address-the-complexities-of-non-artificial-mind\/","og_locale":"en_US","og_type":"article","og_title":"Can artificial intelligence help address the complexities of non-artificial mind? - Technology and Operations Management","og_description":"Can an app powered by machine learning be better and faster at diagnosing mental illness than humans?","og_url":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/can-artificial-intelligence-help-address-the-complexities-of-non-artificial-mind\/","og_site_name":"Technology and Operations Management","og_image":[{"width":1920,"height":1096,"url":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/ThinkstockPhotos-466268301-1.jpg","type":"image\/jpeg"}],"twitter_card":"summary_large_image","twitter_misc":{"Est. reading time":"5 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/can-artificial-intelligence-help-address-the-complexities-of-non-artificial-mind\/","url":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/can-artificial-intelligence-help-address-the-complexities-of-non-artificial-mind\/","name":"Can artificial intelligence help address the complexities of non-artificial mind? - Technology and Operations Management","isPartOf":{"@id":"https:\/\/d3.harvard.edu\/platform-rctom\/#website"},"primaryImageOfPage":{"@id":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/can-artificial-intelligence-help-address-the-complexities-of-non-artificial-mind\/#primaryimage"},"image":{"@id":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/can-artificial-intelligence-help-address-the-complexities-of-non-artificial-mind\/#primaryimage"},"thumbnailUrl":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/ThinkstockPhotos-466268301-1.jpg","datePublished":"2018-11-13T22:53:59+00:00","breadcrumb":{"@id":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/can-artificial-intelligence-help-address-the-complexities-of-non-artificial-mind\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/d3.harvard.edu\/platform-rctom\/submission\/can-artificial-intelligence-help-address-the-complexities-of-non-artificial-mind\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/can-artificial-intelligence-help-address-the-complexities-of-non-artificial-mind\/#primaryimage","url":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/ThinkstockPhotos-466268301-1.jpg","contentUrl":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/ThinkstockPhotos-466268301-1.jpg","width":1920,"height":1096},{"@type":"BreadcrumbList","@id":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/can-artificial-intelligence-help-address-the-complexities-of-non-artificial-mind\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/d3.harvard.edu\/platform-rctom\/"},{"@type":"ListItem","position":2,"name":"Submissions","item":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/"},{"@type":"ListItem","position":3,"name":"Can artificial intelligence help address the complexities of non-artificial mind?"}]},{"@type":"WebSite","@id":"https:\/\/d3.harvard.edu\/platform-rctom\/#website","url":"https:\/\/d3.harvard.edu\/platform-rctom\/","name":"Technology and Operations Management","description":"MBA Student Perspectives","potentialAction":[{"@type":"性视界Action","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/d3.harvard.edu\/platform-rctom\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"}]}},"_links":{"self":[{"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/hck-submission\/34256","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/hck-submission"}],"about":[{"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/types\/hck-submission"}],"author":[{"embeddable":true,"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/users\/11577"}],"replies":[{"embeddable":true,"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/comments?post=34256"}],"version-history":[{"count":0,"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/hck-submission\/34256\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/media\/34340"}],"wp:attachment":[{"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/media?parent=34256"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/categories?post=34256"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}