Data Science in Mental Health

Data Science in Mental Health

Emily Halford 11/11/2020 7
Data Science in Mental Health

Data Science in Healthcare

Data science has developed an increasing presence in healthcare as new data sources and analytic techniques have become available. Although there is a somewhat blurry line between the academic research and clinical trials that have been an integral part of healthcare for decades and work that falls under the new umbrella of “data science,” it is undeniable that the use of new technologies and data sources in healthcare has boomed.

  1. Developments towards precision medicine using genomics data (such as the 1000 Genomes Project from the National Institutes of Health), which tend to focus on chronic diseases like heart disease and diabetes
  2. Hospital operations analyses, which often look like standard business analyses (e.g., predicting when the ER will need more staff)

Burden of Mental Health Problems

The burden of mental illness in the United States is extraordinary. Approximately one in five Americans lives with a mental illness, and this rate is even higher among young people, women, LGBTQIA+ individuals, and certain minority groups. Among 10- to 34-year-olds, suicide is the second leading cause of death.


Image from:

Unfortunately, most people with a mental health problem are not receiving appropriate treatment. In 2018, only 43.3% of adults with a mental illness received treatment for it. Among those who do eventually receive treatment, the average delay between symptom onset and treatment initiation is a staggering 11 years. While there are several contributing factors to this treatment gap, inaccessibility of mental health providers is one of the most important. About 40% of Americans live in a designated mental healthcare shortage area, and many mental health professionals are inaccessible to lower-income Americans as they do not participate in insurance plans.


Image with permission from RHIhub ( using data from the Health Resources and Services Administration (HRSA).

How Data Science Can Help

Clearly, there is enormous unmet need within the United States regarding mental health. There are two primary ways in which data science can help bridge this gap.


There are several noteworthy challenges standing in the way of advancing data science in mental health, although they are far from insurmountable.


Although data science in mental health is less developed than data science in other areas of healthcare, crucially important and incredibly exciting work is already happening in this area. Below, I will describe 3 promising case studies that demonstrate the enormous potential for data science to revolutionize the state of mental health care in the United States.

Case Study 1: Crisis Text Line

The Crisis Text Line provides free, 24/7 crisis counseling by trained volunteers via text. These volunteer counselors have handled over 5 million text conversations since the line’s inception in 2013. A tech company at heart, Crisis Text Line embraces the importance of using their data to improve the care which they provide and to enhance our broader understanding of mental health problems.

 Screen capture of

The Crisis Text Line has also taken steps to ensure that the sensitive data shared with them by texters remains safe. Texters can request that their data be scrubbed at any point, and a built-in algorithm removes personally-identifiable information such as names, phone numbers, and places.

Case Study 2: Ellie the 3-D Chat Bot


Ellie in action:

Case Study 3: Quartet Health

Quartet Health is a NYC-based mental health tech company that is committed to improving the quality and accessibility of mental health care through their four-pronged, wraparound approach.

Using Google Search Data to Understand COVID-19's Impact on Suicidality 

My final example comes from a study that I personally contributed to. As COVID-19 spread rapidly throughout the United States in March and April, my team and I became curious about the impact that this virus and the lockdowns used to contain it might have on suicidality and suicide risk factors.

  • Help-seeking queries: crisis text line, national suicide prevention lifeline + national suicide hotline, disaster distress helpline + disaster distress hotline
  • General mental health queries: depression, panic attack, anxiety
  • Financial difficulty queries: unemployment, I lost my job, laid off, furlough, loan
  • Uncategorized queries: loneliness



Percent difference with 95% CIs for all included queries. Image by author.


Percent difference with 95% CIs, limited to queries with smaller percent differences for visualization purposes. Image by author.


Although the mental health field stands to benefit enormously from an increased use of data science, developments in this area lag behind those made in other areas of healthcare. That being said, exciting advancements have been made by organizations such as the Crisis Text Line, University of Southern California’s Institute for Creative Technologies, and Quartet Health. The success of these projects underscores the importance and potential benefit of data science applications in mental health.

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  • John Marshall

    Mental heath is a serious issue. Thanks for this wonderful analysis !

  • Kieran Paget

    We still have a lot of work to do in changing the stigmas attached to mental health!

  • Andrew Ogier

    Ii have mental health problems.... even doctors couldn't help me....

  • Nicholas Moss

    Thanks so much for this important info

  • Kevin Harris

    Let's spread positivity and break the stigma

  • Simon Carr

    People who suffer from mental health problems do not receive adequate treatment

  • Lucas Walker

    Mental health will soon be a thing of the past thanks to data science.

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Emily Halford

Data Science & Mental Health Expert

Emily is a data analyst working in psychiatric epidemiology in New York City. She is a suicide-prevention professional who is enthusiastic about taking a data-driven approach to the mental health field. Emily holds a Master of Public Health from Columbia University.

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