Bm week 7 pdsa
Mentors
NU725Unit5WorkflowGapAnalysis.pptxWorkflow Gap Analysis: Improving Psychiatric Follow-Up and Documentation Efficiency Through AI-Assisted Technology
Brittany S. Martin
Herzing University
NU725 Technology and Nursing Informatics in Advanced Practice
Dr. Anne Hill
June 7, 2026
Goal for the use of technology
Enhance psychiatric information recording efficiency
Minimize provider administrative burden (Karri et al., 2024)
Improve follow up appointment attendance
Utilize accurate and consistent documentation
Improve patient satisfaction and continuity of care
Help facilitate timely clinical decision making
The main goal of AI documentation technology is to boost efficiency and optimize patient care. Providers are already spending a lot of time on charting and paperwork, which leads to burnout and delayed charting (Karri et al., 2024). AI can help complete parts of documentation processes in a way that ensures accuracy and compliance. Automated reminders and tracking help boost adherence to follow-up visits. The technology also helps to improve continuity of care by enabling proper documentation of timely and complete information in the EHR.
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Current State
The presence of delayed psychiatric follow-up appointments.
A high proportion of missed telehealth visits
A medical record is created for patients after a patient visit.
Lack of consistency in the quality of documentation across providers (Cascalla, 2026)
Greater provider time to complete charts
Inefficiencies in the workflow decrease access for patients
At present, there are inefficiencies in psychiatric practice with regard to patient documentation and follow-up. Lack of reminder systems leads to the loss of many patient appointments. Technology is frequently underutilized and inaccurate at the end of the day, forcing providers to work extra hours charting (Cascalla, 2026). Documentation varies from one to the next, impacting consistency and quality. These are factors that impact provider burnout, patient satisfaction and delayed care coordination. This existing workflow is problematic to service efficiently and optimally for patients.
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Desired Future State.
The envisioned future state is an efficient workflow with the help of AI-based documentation technology. Providers will be able to finish documentation faster when seeing patients. Coupled with automated reminders, patients will be more likely to adhere to treatment plans and fewer appointments will be missed. Standardized templates will help to ensure regulatory compliance and consistency in documentation. The future workflow will reduce administrative requirements, enhance the satisfaction of the patients and enable the providers to be more involved in actual patient care activities.
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AI-powered real-time documentation support
All patient appointments are automatically scheduled.
Standardized documentation templates
Better follow-up management for telehealth services
Time spent completing charts has been reduced.
Improved patient engagement and satisfaction
Gap Analysis Table
Gap analysis is the process of comparing the current process performance with the desired outcome (Fadhel et al., 2024). Documentation inefficiencies, missed appointments and provider burnout are significant workflow gaps. Proposed interventions include AI-assisted documentation software, appointment reminders, standardized documentation templates, and patient tracking tools. These options overcome operational obstacles and promote sustainable workflow enhancements. Continuous evaluation and staff involvement will be important to ensure success.
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Proposed Remedy/Solution
AI-powered documentation within EHR.
Automated appointment reminder system
The standardized psychiatric assessment templates have been used.
A follow-up tracking system for patients is automated.
Telehealth workflow optimization
Data for improving quality
The suggested solution involves technology integration in order to rectify workflow shortcomings. This time-saving, accuracy-focused AI-powered documentation solution is for any organization. Automated reminders help maintain patient attendance. The use of standardized templates helps to ensure consistency between providers. Tracking tools are provided for follow up to track patient engagement and treatment compliance. Data analytics are an incredibly useful source of performance feedback for ongoing initiatives to improve quality.
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Implementation Strategy
Conduct workflow assessment
Provide training for train providers and train staff
Configure EHR integration
Test the technology in a small-scale manner
Evaluate performance outcomes
Expand implementation organization-wide
A structured process will be used for implementation. The organization will first carry out the workflow evaluation to determine the needs for the operation. Training will be provided to providers and staff on the new technology. Information technology specialists will complete the process of integrating EHRs. The effectiveness will be judged during a pilot phase prior to full deployment. The quality indicators will be used to assess the performance outcomes, and successful practices will be disseminated across the organization.
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Summary of Gap Analysis
Actually, they have snags in their current workflow, delays and inefficiencies.
Burnout will come and go with documentation burden.
Adherence in the follow-up needs to be improved.
AI technology solves workflow problems.
Standardization enhances quality and uniformity
Improved outcomes are good for patients and providers.
To conclude, the current workflow has several blind spots that have a negative impact on patient care, provider satisfaction and organizational efficiency. Poor documentation and failure to keep appointments lead to poor outcomes and higher workload. With AI-powered technology, there is an evidence-based answer to documentation efficiency, patient follow-up, and workflow standardization. Effective implementation will enhance the quality of care, provider satisfaction and organizational performance.
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References
Cascalla, G. (2026). Strategies to Reduce the Physician Documentation Burden in Adult Primary Care. https://search.proquest.com/openview/464aa22af3f93697ff04c207993c9481/1.pdf?pq-origsite=gscholar&cbl=18750&diss=y
Fadhel, M. A., Duhaim, A. M., Albahri, A. S., Al-Qaysi, Z. T., Aktham, M. A., Chyad, M. A., ... & Gu, Y. (2024). Navigating the metaverse: unraveling the impact of artificial intelligence—a comprehensive review and gap analysis. The Artificial Intelligence Review, 57(9), 264. https://www.researchgate.net/profile/Laith-Alzubaidi/publication/383240535_Navigating_the_metaverse_unraveling_the_impact_of_artificial_intelligence-a_comprehensive_review_and_gap_analysis/links/66c475975f116e7c53077ba2/Navigating-the-metaverse-unraveling-the-impact-of-artificial-intelligence-a-comprehensive-review-and-gap-analysis.pdf
Karri, H. K., Begum, A., & George, L. (2024). Optimizing Healthcare Efficiency: The Role of Artificial Intelligence in Medical Records Management. International Journal of Engineering and Management Research, 14(6), 55-67. https://www.researchgate.net/profile/Lina-George/publication/395810956_Optimizing_Healthcare_Efficiency_The_Role_of_Artificial_Intelligence_in_Medical_Records_Management/links/68d4f2a1f3032e2b4be3153c/Optimizing-Healthcare-Efficiency-The-Role-of-Artificial-Intelligence-in-Medical-Records-Management.pdf
