Engineering Management Tool for Minimizing Errors and Costs in Diagnosing PTSD in Veterans

As of 2020, the Department of Defense and Department of Veterans Affairs reported that 11 to 20% of Operation Iraqi Freedom/Operation Enduring Freedom, 12% of Gulf War, and 15% of Vietnam veterans have been diagnosed with PostTraumatic Stress Disorder. However, misdiagnosis rates have also been of concern due to the complexities of comorbidities (such as Traumatic Brain Injury and depression) with Post-Traumatic Stress Disorder, the subjective nature of clinician assessments and patients’ self-reporting of symptoms, and the numerous other limitations of traditional diagnostic methods. Patient self-diagnosis rates of the mental disorder have been found to be 20-fold higher than diagnoses using clinician-administered instruments and methods. Furthermore, many health organizations continue to struggle with diagnostic safety due to the lack of available methods to reliably detect, define, measure, and mitigate diagnostic errors. Reducing defective diagnoses is an example of Lean Engineering management of “waste” (e.g., systems or processes that are defective, costly, and/or time-consuming). This praxis demonstrated how using a Lean automation tool, such as an AI classifier, for clinical decision support can generate efficiencies and mitigate Post-Traumatic Stress Disorder diagnosis errors and expenses, thereby reducing waste.

Author

• Le, Jacqueline

Language

• en

Keyword

• Artificial Intelligence
• PTSD
• Machine Learning
• Health Engineering
• Lean Engineering
• Post-Traumatic Stress Disorder

Date created

• 2023

Type of Work

• Dissertation

License

• All rights reserved

Rights statement

• In Copyright

GW Unit

• Engineering Management

Degree

• D.Engr.

Advisor

• Kamp, John C
• Etemadi, Amir

Committee Member(s)

• Jones, Michael
• Etemadi, Amir
• Kamp, John C

Persistent URL

•  https://scholarspace.library.gwu.edu/etd/d504rm15f