{"id":31377,"date":"2018-11-13T16:41:09","date_gmt":"2018-11-13T21:41:09","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/the-future-of-preventative-behavioral-healthcare-powered-by-mindstrong-health\/"},"modified":"2018-11-13T23:02:59","modified_gmt":"2018-11-14T04:02:59","slug":"the-future-of-preventative-behavioral-healthcare-powered-by-machine-learning","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/the-future-of-preventative-behavioral-healthcare-powered-by-machine-learning\/","title":{"rendered":"The Future of Preventative Behavioral Healthcare Powered by Machine Learning"},"content":{"rendered":"

(702 Words)<\/p>\n

The Challenges of Addressing Mental Health Disorders<\/b><\/em><\/p>\n

Mental health disorders are on track to be the leading cause of disability by 2020, with an annual global economic impact of $1 trillion.[1]<\/a> Machine learning technology utilized at Mindstrong Health aims to dramatically impact healthcare providers ability to diagnose and treat these disorders. The current process of diagnosing and treating is ineffective because it requires the patient to break from their normal routine, display symptoms at the time of the assessment, and is difficult to scale due to the requirement of a staffed facility.[2]<\/a> Mindstrong is addressing mental health needs by using machine learning algorithms to collect and analyze data from patients smartphones to rapidly assess and provide updates to mental health providers.\u00a0[3]<\/a>\u00a0Through early detection and intervention, Mindstrong’s solution reduces healthcare usage, while providing better patient care.<\/p>\n

 <\/p>\n

Mindstrong Health\u2019s Ground-Breaking Solution <\/em><\/strong><\/p>\n

Mindstrong Health focuses on bringing measurement science to mental health services. Its smartphone application passively acquires data by monitoring how a user interacts with their smartphone through swipes, taps, and keystrokes, rather than the content of what they type. This data is encrypted and analyzed remotely using machine learning and\u00a0compared to Mindstrong’s database to assess a patients mental health. The resulting cognition and emotion bench marking information, referred to as digital biomarkers, are shared with the patient\u2019s medical provider to allow the provider to better serve the patient.\u00a0 Clinical\u00a0studies with leading research universities confirmed that Mindstrong\u2019s application was very effective in monitoring patients mental health, specifically for identifying when a patient was depressed.[2]<\/a><\/p>\n

https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Mindshare-Health.mp4<\/a><\/video><\/div>\n

 <\/p>\n

Challenges Faced by Mindstrong Health\u00a0<\/em><\/strong><\/p>\n

A few critical questions management is focused on addressing over the next few years are: [5]<\/a><\/p>\n

    \n
  1. How and when to market its smartphone application to the general market<\/li>\n
  2. How to maintain security of sensitive patient data<\/li>\n
  3. How to expand the product beyond initial successes in depression<\/li>\n<\/ol>\n

    Mindstrong’s product currently serves high risk depression patients but it is planning to release its application to the public over the next few years. [5]<\/a>\u00a0<\/sup>Expanding the population sample will be critical to improving the accuracy of its data set, but questions remain over whether the general population would adopt a passive software solution that constantly evaluates and provides updates to providers of the users mental health conditions. Critical to building broad consumer appeal for its application will be maintaining security of patient data and using the data only for the purposes described to patients. [6]<\/a>\u00a0While Mindstrong has been explicit on how it collects, uses and shares patient data, management is evaluating the appropriate long-term strategy.[7]<\/a> Building upon its initial successes with depression, Mindstrong is also focused on expanding its addressable market by identifying digital biomarkers associated with other mental health disorders such as, psychotic disorders, schizophrenia, and PTSD by conducting clinical studies with universities such as Stanford, the University of Michigan, and Kings College. [4]<\/a><\/p>\n

     <\/p>\n

    How Mindstrong Health Can Address these Challenges<\/em><\/strong><\/p>\n

    To address the identified issues in the short and medium terms, I would suggest the company focus on transparency around product efficacy, accelerate product development, and simplify its message on data security.<\/p>\n

      \n
    1. Mindstrong can accelerate the general perception of product effectiveness by providing additional transparency on how its patented machine learning algorithm identifies digital biomarkers by sharing software code and data findings with the public.<\/li>\n
    2. Important clinical studies in high commercial potential areas such PTSD are not expected to be completed until 2022. Mindstrong could accelerate the trials and protect its market leading position by allocating additional resources to the studies.<\/li>\n
    3. If Mindstrong hopes to bring its product to the general market it must simplify its message on how it collects, analyzes, stores, secures and shares patient data. Its website provides this information in a form that is too dense for the general consumer to understand.<\/li>\n<\/ol>\n

       <\/p>\n

      Open Questions:<\/em><\/strong><\/p>\n

        \n
      1. What are the implication of our smartphones having the ability to assess our mental health and provide insights and alerts to healthcare providers?<\/li>\n
      2. What is the best channel strategy to bring Mindstrong\u2019s consumer facing product to the market to optimize commercial potential? (ie DTC, through health networks, through providers or just to patients at risk of mental illness or healthy patients?)<\/li>\n<\/ol>\n

         <\/p>\n

         <\/p>\n

        [1]<\/a> Brunier, Alison. “Investing in Treatment for Depression and Anxiety Leads to Fourfold Return.” Investing in Treatment for Depression and Anxiety Leads to Fourfold Return. November 13, 2016. Accessed November 13, 2018. http:\/\/www.who.int\/en\/news-room\/detail\/13-04-2016-investing-in-treatment-for-depression-and-anxiety-leads-to-fourfold-return.<\/p>\n

        [2]<\/a> Dagum, Paul. “Digital Biomarkers of Cognitive Function.”\u00a0Npj Digital Medicine\u00a01, no. 1 (2018). doi:10.1038\/s41746-018-0018-4.<\/p>\n

        [3]<\/a> “Science.” Mindstrong Health. March 28, 2018. Accessed November 12, 2018. https:\/\/mindstronghealth.com\/science\/.<\/p>\n

        [4]<\/a> “Clinical Programs.” Mindstrong Health. March 28, 2018. Accessed November 12, 2018. https:\/\/mindstronghealth.com\/clinical-programs\/<\/p>\n

        [5]<\/a> Metz, Rachel. “The Smartphone App That Can Tell You’re Depressed before You Know It Yourself.” MIT Technology Review. October 30, 2018. Accessed November 13, 2018. https:\/\/www.technologyreview.com\/s\/612266\/the-smartphone-app-that-can-tell-youre-depressed-before-you-know-it-yourself\/.<\/p>\n

        [6]<\/a> Wyman, Oliver. “Cybersecurity: The Hidden Dangers of Healthcare IoT.” August 22, 2016. Accessed November 13, 2018. https:\/\/health.oliverwyman.com\/2016\/08\/cybersecurity_theh.html.<\/p>\n

        [7]<\/a> “Privacy.” Mindstrong Health. October 24, 2018. Accessed November 12, 2018. https:\/\/mindstronghealth.com\/privacy\/<\/p>\n","protected":false},"excerpt":{"rendered":"

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