The National Institute of Mental health (NIMH, 2017) identify the primary care as the setting for more than half of all mental health care visits annually in the United States.  Of the 75% of adults with depression who access primary care only half are accurately diagnosed (NIMH, 2017).  Further, it is important to note, less than half of all mental health referrals from primary care ever follow through with scheduling the appointment (NIMH, 2017).  Primary care providers are encouraged to screen and treat patients with mental health problems and refer more challenging cases to specialists.  The American Academy of Family Practitioners (AAFP, 2015) mention this can be especially difficult because of the shortage of mental health providers particularly in rural areas. The problem is further complicated by payers who won’t pay for mental health codes submitted by primary care providers (AAFP, 2015).  These and other challenges in treating mental health in the primary care setting create an environment for problem solving. In the age of informatics, electronics, internet access, and social media, potential solutions have evolved. Robots or artificial intelligence, (AI) applications are available to treat and screen for mental health conditions (Bakker, Kazantzis, Rickwood, & Rickard, 2016; Strand, Gammon, & Ruland, 2017).  The cost for utilizing online therapists is much cheaper than face to face treatment, and if a robot or AI is used the cost is even less.  Facebook messenger recently launched Woebot created by Alison Darcy PhD from Stanford.  Woebot is available 24 hours a day seven days a week to give insight, monitor moods, teach and help users feel better (Fitzpatrick, Darcy, & Vierhile, 2016). Woebot is designed for young adults who suffer from depression or anxiety to feel better (Fitzpatrick, Darcy, & Vierhile, 2016).

            

 

10/10/2017

Current Innovation

Opinion

            The need for innovation for mental health is high. The familiarity of many especially young people, with technology continues to increase. The marriage of artificial intelligence and therapy even as a tool for screening has promise. The capability of designing a chatbot to offer meaningful therapeutic value is not hard for me to imagine. More research is needed as individuals seek out therapy of this kind. For provider’s we will need to have strong evidence before we refer a client to a chatbot. I look forward to a time where evidence supports the use of artificial intelligence use in treating mental health issues. Access to therapy would be much more convenient and may offer relief to someone in a time of need. 

* Easy access to care

* Low cost

* Comfortable setting

* Diminishes stigma as an    obstacle.

* Lack of regulation

* Research is limited on effects of internet-based treatment.

Bakker, D., Kazantzis, N., Rickwood, D., & Rickard, N. (2016). Mental Health Smartphone Apps: Review and Evidence-Based Recommendations for Future Developments. JMIR Mental Health, 3(1), e7. http://doi.org/10.2196/mental.4984

Fitzpatrick, K. K., Darcy, A., Vierhile, M. (2016). Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): A randomized control trial. Journal of Medical Internet Research Mental Health (2017); 4(2) e19.doi:102196/mental.7785

National Institute of Mental Health (February, 2017). Integrated care. Retrieved from https://www.nimh.nih.gov/health/topics/integrated-care/index.shtml

Mental health America (2017). 2016 State of mental health in America. Retrieved from http://www.mentalhealthamerica.net/issues/2016-state-mental-health-america-access-care-data

Strand, M., Gammon, D., & Ruland, C. M. (2017). Transitions from biomedical to recovery-oriented practices in mental health: a scoping review to explore the role of Internet-based interventions. BMC Health Services Research, 171-14. doi:10.1186/s12913-017-2176-5