Discussion One- Initial Post: Project Idea AI Driven Brainstorming
Electronic Health Records (EHRs) were designed to improve care quality, optimize workflows, and support seamless documentation. However, nursing documentation and workflows in family practice facilities often remain fragmented and inefficient. This discussion examines how artificial intelligence (AI) can help generate strategies to streamline clinical documentation. These strategies must prioritize nurses’ input, as their firsthand experience is essential to effective change (Johnson et al., 2025). Standardized processes and organizational tools, such as visual flowcharts, can help nurse leaders identify failures early, enhance risk mitigation, and prevent adverse patient outcomes (Roussel et al., 2023).
Current Practice
As a nurse leader in a family practice facility, implementing workflow improvement projects, forming EHR optimization committees, and reducing nurse burnout are crucial strategies for addressing persistent gaps in healthcare. Gaps are evident in several areas, including documentation processes that are often time-consuming and repetitive, and a focus on completing mandatory fields rather than capturing clinically meaningful information. By targeting these areas through focused initiatives, healthcare organizations can address gaps and inefficiencies in documentation (Demsash et al., 2023).
Mission and Values
To guide these efforts, the following PICO question can be used: Among registered nurses providing direct patient care in a family practice clinic, how does implementing streamlined, standardized, workflow-aligned electronic documentation practices, compared with current documentation processes, affect documentation time, perceived documentation burden, and documentation quality? Prioritizing quality through documentation ensures this family practice facility delivers safe, evidence-based care, underscoring the importance of its mission and values. Efficient documentation is directly linked to these values by improving accuracy and timeliness, supporting compliance, and helping staff track care outcomes.
Aim and Organizational Improvement
The project’s purpose is to improve nursing documentation by making it more efficient, clear, and clinically valuable by reducing unnecessary and repetitive charting tasks. The specific aim is to reduce documentation time by 20% and reduce duplicate or unnecessary charting fields by 30% within six months, while maintaining or improving documentation quality and compliance. Streamlining these processes is intended to enhance patient-centered care and reduce documentation burden. Key strategies for this process include removing or merging duplicate documentation, using smart phrases and auto-populated fields, and adopting evidence-based templates for consistency. Making critical information, such as allergies and fall risk, highly visible supports safety. Role-specific EHR training and assigned superusers can improve proficiency and satisfaction. Engaging frontline staff in redesigning documentation and piloting changes ensures practical solutions. Finally, ongoing metric tracking and consistent feedback support continuous quality improvement (Johnson et al., 2025).
Reflection
Utilizing artificial intelligence during the brainstorming phase of this quality improvement (QI) project helped transform broad concerns into more focused, actionable ideas by creating potential problem areas, contributing factors, and possible interventions. The AI-generated PICO question provided a structured framework for evaluating the impact of documentation improvement initiatives and aligns with the goal of reducing inefficiencies and enhancing the value of clinical records. Additionally, AI has enabled an innovative range of solutions; however, it does not fully understand the unique culture of a family practice facility. Therefore, it is important to evaluate and refine the ideas presented to include clinical judgment.
References
Demsash, A. W., Kassie, S. Y., Dubale, A. T., Chereka, A. A., Ngusie, H. S., Hunde, M. K., Emanu, M. D., Shibabaw, A. A., & Walle, A. D. (2023). Health professionals routine practice documentation and its associated factors in a resource-limited setting: a cross-sectional study. BMJ Health & Care Informatics, 30(1), 17. https://doi.org/10.1136/bmjhci-2022-100699
Johnson, L. G., Macieira, T. G. R., Madandola, O. O., Priola, K. J. B., & Keenan, G. M. (2025). Charting the path forward: Nursing perspectives on documentation and change. Nursing Outlook, 73(4), 102463. https://doi.org/10.1016/j.outlook.2025.102463
Roussel, L., Harris, J. L., & Thomas, P. L. (2023). Management and leadership for nurse administrators (9th ed.). Jones & Bartlett Learning.
CrossPost #2
Initial Post Quality Improvement Project
Topic Ideation and Final Project Selection
To help narrow my focus, I used ChatGPT to brainstorm a few potential quality improvement ideas relevant to USA Children’s and Women’s surgical services department. The main options included improving first-case on-time starts, strengthening surgical site infection prevention practices, and addressing inconsistent operating room (OR) block time utilization and scheduling. All were relevant, but block utilization stood out as the most practical and highest impact because it affects staffing, overtime, delays, and overall workflow every day. For that reason, I chose to focus this project on improving OR block time use and creating more consistent weekly caseloads.
Practice Concern
In surgical services, the OR volume varies significantly from day to day and week to week. Some days rooms go unused and staff are sent home, while other days feel overloaded with delays and overtime. This inconsistency creates stress for staff, drives up costs, and can affect patient flow and safety. As one of the most resource-intensive areas of the hospital, the OR benefits significantly from better block time use, which supports USA Healths mission to help people lead longer, better lives by reducing delays, improving patient flow, and promoting safe, reliable care.
Right now, block time is largely based on historical patterns and provider preference rather than actual utilization data. Unused time is often released too late or not advertised enough to fill, and non-emergent add-on cases tend to pile onto already busy days. Research supports that more intentional block planning helps balance workloads and keeps downstream areas running more smoothly (Heider et al., 2022). Other healthcare scheduling studies show similar improvements in efficiency and patient flow when decisions are guided by data (Abdalkareem et al., 2021). Together, this suggests that inconsistent block use is something we can improve with better processes, not just something we have to accept.
Proposed Changes and Evaluation
This project will focus on reviewing historical utilization patterns, creating clearer expectations for early block release and sharing available openings, exploring a shared flex block process, and using simple dashboards to make utilization more visible to service lines. The goal is to create a steadier daily schedule and better match staffing to demand. If successful, we would expect to see fewer delays, less overtime, and more consistent room use. These ideas are supported by evidence showing that structured scheduling practices improve OR efficiency and utilization (Soh et al., 2022). Similar quality improvement approaches that standardize processes and address common causes of delays have reduced operational waste in surgical settings without adding cost (Robson Chase et al., 2025).
AI Reflection
ChatGPT was helpful for organizing my thinking and narrowing the focus of the project. It helped me compare several options, introduced a few approaches I had not initially considered, and structured the idea using a simple PICO framework that made the literature search more focused and useful for identifying evidence-based scheduling improvement strategies. Overall, the tool made the planning process much more efficient while still allowing me to tailor the project to specific departmental needs.
References
Abdalkareem, Z. A., Amir, A., Al-Betar, M. A., Ekhan, P., & Hammouri, A. I. (2021). Healthcare scheduling in Optimization Context: A Review. Health and Technology, 11(3), 445469. https://doi.org/10.1007/s12553-021-00547-5
Heider, S., Schoenfelder, J., Koperna, T., & Brunner, J. O. (2022). Balancing control and autonomy in master surgery scheduling: Benefits of ICU quotas for Recovery Units. Health Care Management Science, 25(2), 311332. https://doi.org/10.1007/s10729-021-09588-8
OpenAI. (2025). ChatGPT [Large language model]. https://chat.openai.com/
Robson Chase, M. E., Anderson, M. J., Stephens, W. A., Levy, B. E., Lantz, S., Goforth, J., Newcomb, M. R., & Harris, A. M. (2025). Utilizing quality improvement methodology to decrease surgical delays. The Joint Commission Journal on Quality and Patient Safety, 51(78), 474485. https://doi.org/10.1016/j.jcjq.2025.04.004
Soh, K. W., Walker, C., OSullivan, M., & Wallace, J. (2022). Innovative operating room scheduling metric for creating surgical lists with desirable room utilization rates. Operations Management Research, 17(2), 544567. https://doi.org/10.1007/s12063-022-00313-4