{"id":18457,"date":"2016-11-18T15:20:34","date_gmt":"2016-11-18T20:20:34","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/ginger-io-striking-a-balance-between-humans-and-technology-in-mental-health\/"},"modified":"2016-11-18T15:20:34","modified_gmt":"2016-11-18T20:20:34","slug":"ginger-io-striking-a-balance-between-humans-and-technology-in-mental-health","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/ginger-io-striking-a-balance-between-humans-and-technology-in-mental-health\/","title":{"rendered":"Ginger.io: striking a balance between humans and technology in mental health"},"content":{"rendered":"

Challenges in mental health<\/strong><\/em><\/p>\n

In the U.S., one in five\u00a0adults experiences mental illness in a given year\u00a0[1]. At nearly $200 billion in lost earnings per year [2], the associated economic impact is undeniably significant, but\u00a0almost\u00a060% of those experiencing mental illness today are\u00a0left\u00a0untreated\u00a0[3]. The field of mental healthcare\u00a0desperately needs to\u00a0improve outcomes, and\u00a0digitization\u00a0has immense potential to transform it by connecting patients, providers,\u00a0services and data in innovative ways.<\/p>\n

Historically, patient-reported outcomes (PROs) on mental, physical and social health status were the primary inputs used to assess a patient\u2019s risk for mental health issues and facilitate clinical decisions [4]. The incomplete and biased nature of PROs often led to costly repercussions when diagnoses were missed or delayed. However, obtaining continuous data on patient health-related behavior was difficult and costly, involving proprietary hardware or labor-intensive monitoring [4].<\/p>\n

Harnessing big data and machine learning to disrupt mental health delivery<\/strong><\/em><\/p>\n

Ginger.io’s business model is built on obtaining and analyzing data that is timely, objective, and much less costly to collect. The company monitors patient behavior through smartphones and\u00a0provides timely, objective data to providers. Providers are empowered to intervene effectively, boosting preventative measures and lowering overall costs<\/strong> (e.g., before patients fill up ERs or run up medical bills). Therefore, the business model targeted hospitals and provider groups as customers, since they bore the \u201cfinancial risk\u201d of poor outcomes<\/strong> [5].<\/p>\n

\"Example<\/a>
Example “passive” data patterns that signify early depression. Source: ginger.io<\/figcaption><\/figure>\n

By amassing and analyzing large sets of \u201cpassive\u201d mobile data<\/strong>, Ginger.io can detect if a patient with mental illness is acting symptomatic [6]. The company uses machine learning to detect complex patterns of behavior across multiple dimensions and predict when a user may need help<\/strong>. For example, a sudden decrease in social interactions (e.g., reduced volume of calls or texts) or physical activity (e.g., less movement captured by motion sensors) could signify that a user with depression is experiencing problems. When the app detects unusual patterns, it promptly notifies both patient and provider to take action as needed [6].<\/p>\n

Other key components of the operating model included:<\/p>\n