Post Traumatic Stress Syndrome patients helped through the use of artificial intelligence, machine learning and reporting analytics.
PTSD (posttraumatic stress disorder) is a mental health problem that some people develop after experiencing or witnessing a life-threatening event, like combat, a natural disaster, a car accident, or sexual assault.
During this kind of event, you may not have any control over what's happening, and you may feel very afraid.
from trauma, military-related, COVID related, and other life event-related.
Post-traumatic stress syndrome symptoms include:
1. Intrusive Thoughts
3. Avoiding Reminders of the Event
4. Memory Loss
5. Negative Thoughts About Self and the World
6. Self-Isolation; Feeling Distant
7. Anger and Irritability
8. Reduced Interest in Favorite Activities
10. Difficulty Concentrating
12. Vivid Flashbacks
13. Avoiding People, Places, and Things Related to the Event
14. Casting Blame
15. Difficulty Feeling Positive Emotions
16. Exaggerated Startle Response
17. Risky Behaviors
PTSD prevalence in the USA is 5.7% affecting 8.6MM patients and costing ~$19,000 per patient.
PTSD for the military affects ~138,000 since '00 with an 11-20% prevalence costing ~$21,000 per patient.
Extending PTSD due to COVID, the CDC reports COVID mental health, substance use, and suicidal ideation increases during the COVID-19 pandemic.
40% of U.S. adults reported struggling with mental health or substance use during the COVID pandemic.
Younger adults, racial/ethnic minorities, essential workers, and unpaid adult caregivers reported having experienced disproportionately worse mental health outcomes, increased substance use and elevated suicidal ideation.
Teen suicide and violence in schools have continued to increase. Suicide is now the #2 cause of death of teens which has increased 56% over the past 10 years. PTSD is certainly a concern.
Analytics for Living’s Machine Learning & Artificial Intelligence algorithms and syndicated tools can identify at-risk populations.
Syndicated tools identify subnational geographic areas of concern among priority population segments; utilization of RX, therapy, patient types (young adults, racial/ethnic minorities, essential workers), and military.
Integrating syndicated Analytics-for-Livings ML/AI tools with public, community, educational, employer, health system(s) and corporation data enables prioritization within subsets of the populations and persons at risk.