Date
10-16-2025
Department
School of Nursing
Degree
Doctor of Nursing Practice (DNP)
Chair
Shade Odedina
Keywords
Artificial Intelligence (AI), Workflow Efficiency, Electronic Health Record (EHR), Clinician Burnout, Documentation Time, Patient Outcomes (PHQ-9, GAD-7), Evidence-Based Practice.
Disciplines
Educational Assessment, Evaluation, and Research
Recommended Citation
Hagjoo, Nazanin Azimi, "Advanced Artificial Intelligence Software in Mental Health Field Integrating into Practice" (2025). Doctoral Dissertations and Projects. 7529.
https://digitalcommons.liberty.edu/doctoral/7529
Abstract
The integration of artificial intelligence software in health care has delivered revolutionary changes by enhancing efficiency, increasing accuracy, saving time, and improving clinician life balance. In very recent years, the mental health field also has started to be transformed by AI software; the adoption of AI technologies has helped to meet the demand for mental health services. AI tools have great potential to improve the efficiency and efficacy of clinicians, as they can provide diagnostic tools, smart treatment plans, improved medication refill processes, and monitoring of patient outcomes and well-being, as well as prevent medication errors. Such exciting innovations help to meet the critical need for mental health services in a time of a global increase in mental health disorders. The practice change involved the integration of an AI-enabled electronic health record (EHR) documentation assistant to streamline clinical notetaking and support evidence-based care planning. Clinicians were trained on the use of the tool, and data were collected on average documentation time per patient, number of patients seen daily, clinician-reported workflow satisfaction, and patient-reported outcomes using standardized measures including the PHQ-9 and GAD-7. Key results demonstrated that AI integration reduced average documentation time, increased the number of patients seen per day, and improved clinician-reported workflow efficiency. Patient outcome measures indicated modest but positive improvements in depressive and anxiety symptoms over the project period. Implications for practice include the potential for AI-assisted documentation to decrease clinician burnout, optimize clinical productivity, and enhance the quality of patient care. Wider adoption of AI technologies in mental.
