Abstract TP192: A3 in Action: Enhancing Stroke Program Management Through Lean Process Improvement

Background: The A3 methodology is a key component of Lean performance improvement and is designed to uncover the gaps that exist between current state and future state. The structure of A3 thinking aligns with the scientific method and nursing process by focusing on addressing root causes to resolve problems. At this Comprehensive Stroke Center, the stroke program applied A3 methodology to enhance program efficiency in the setting of growing priorities while creating focus, engagement, and driving process improvement. Purpose: The purpose of this study was to utilize Lean A3 methodology to improve stroke program efficiency by focusing on reducing waste and enhancing interdisciplinary collaboration. Methods: The stroke team identified that 50% of their time was spent on manual data abstraction, which reduced meeting collaboration as the coordinator lacked time to organize and facilitate meaningful dialogue. The team defined the program’s current state by time spent on manual abstraction, number of automated reports utilized, percent of one-way dialogue in meetings, and number of programmatic evaluations done annually. Next, attainable target state metrics were set. Artificial intelligence (AI) was used to assist in brainstorming ideas into actionable gaps. The team then used the 5-whys method to identify the root causes of each gap. Finally, solution approaches were implemented using plan-do-study-act (PDSA) cycles. Results: Solution approaches included leveraging AI to optimize workflows, maximizing electronic health record (EHR) data reporting, and implementing organizational planning tools. The stroke coordinator also developed a template for leaders to report their unit data and quality initiatives at meetings. A detailed completion plan that listed ownership, due dates, and status of each action item tracked the team’s progress. Conclusions: Applying the A3 methodology helped organize stroke program operations, establish concrete deadlines, and initiate a shift towards automation. On-going monitoring is scheduled to evaluate effectiveness and adjust as additional priorities present themselves. In conclusion, the stroke team reported improved efficiency and collaboration across departments, highlighting how the A3 methodology enhanced internal reporting and reduced waste, ultimately supporting accurate Get With The Guidelines abstraction and quality stroke care.

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Publication Details

Journal
Stroke
Published
2026-01-29
DOI
https://doi.org/10.1161/str.57.suppl_1.tp192
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
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article

Abstract TP192: A3 in Action: Enhancing Stroke Program Management Through Lean Process Improvement

Erin I. Lewis, Ceria Sullivan, Nicholas Padilla
Stroke
Artificial Intelligence in Healthcare and Education
article

Abstract TP192: A3 in Action: Enhancing Stroke Program Management Through Lean Process Improvement

Erin I. Lewis, Ceria Sullivan, Nicholas Padilla
article en

Abstract

Background: The A3 methodology is a key component of Lean performance improvement and is designed to uncover the gaps that exist between current state and future state. The structure of A3 thinking aligns with the scientific method and nursing process by focusing on addressing root causes to resolve problems. At this Comprehensive Stroke Center, the stroke program applied A3 methodology to enhance program efficiency in the setting of growing priorities while creating focus, engagement, and driving process improvement. Purpose: The purpose of this study was to utilize Lean A3 methodology to improve stroke program efficiency by focusing on reducing waste and enhancing interdisciplinary collaboration. Methods: The stroke team identified that 50% of their time was spent on manual data abstraction, which reduced meeting collaboration as the coordinator lacked time to organize and facilitate meaningful dialogue. The team defined the program’s current state by time spent on manual abstraction, number of automated reports utilized, percent of one-way dialogue in meetings, and number of programmatic evaluations done annually. Next, attainable target state metrics were set. Artificial intelligence (AI) was used to assist in brainstorming ideas into actionable gaps. The team then used the 5-whys method to identify the root causes of each gap. Finally, solution approaches were implemented using plan-do-study-act (PDSA) cycles. Results: Solution approaches included leveraging AI to optimize workflows, maximizing electronic health record (EHR) data reporting, and implementing organizational planning tools. The stroke coordinator also developed a template for leaders to report their unit data and quality initiatives at meetings. A detailed completion plan that listed ownership, due dates, and status of each action item tracked the team’s progress. Conclusions: Applying the A3 methodology helped organize stroke program operations, establish concrete deadlines, and initiate a shift towards automation. On-going monitoring is scheduled to evaluate effectiveness and adjust as additional priorities present themselves. In conclusion, the stroke team reported improved efficiency and collaboration across departments, highlighting how the A3 methodology enhanced internal reporting and reduced waste, ultimately supporting accurate Get With The Guidelines abstraction and quality stroke care.

StrokeVol. 57(Suppl_1)
Rex Hospital (US)
Openalex Percentile: Top 76%
Artificial Intelligence in Healthcare and Education
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