Georgina Mualuko

workflow optimization project

Transcript

1. Workflow Optimization Project Subtitle: Patient Flow & Process Mapping Georgina Mualuko – MSHA Practicum Student Lead Analyst & Proposal Developer Travis Walters - Practice Director Texas Oncology-Tyler
2. Site/Organization Overview (TxO- Tyler) • Multidisciplinary Care o Radiation Oncology o Medical Oncology o Gynecologic Oncology o Infusion o Labs o Research o Imaging • Regional Hub o Functions as a regional referral hub through partnership with CHRISTUS Health, streamlining access to oncology care for surrounding communities. o Integrated Services o Coordinated cancer care across specialties o Advanced treatment & diagnostic technologies • Research & Innovation o Active clinical trials o Access to leading-edge therapies
3. Project Overview Goal: Reduce patient total time in department by addressing workflow inefficiencies Needs & Assessments: • High wait times observed • Morning clustering → bottlenecks • Occasional lab → EMR posting delays Objectives: • Identify delays across lab, office visit, and infusion • Compare patient cohorts by check-in time • Visualize bottlenecks • Recommend actionable improvements
4. Key Findings (Summary Chart) • Early cohort patients (7:20– 8:30) spend 3–6 hrs • Later cohorts (9:30 onward) average 1–3 hrs • Morning clustering creates cascading delays • Lab result and office visit gaps amplify infusion delays 194.75 129.14 177 84.86 113.57 100.33 0 50 100 150 200 250 7:20-8:00 8:00-8:30 8:30-9:00 9:00-9:30 9:30-10:00 10:00-10:30 Time in Mins Average Total Duration (min)
5. Patient Flow Visualization
6. Average wait times 10.1875 22.13043478 56.6 14 11.57142857 Average Lab Wait (min) Average Wait Lab → Office Visit (min) Average Wait Lab → Infusion (min) Average Wait Before Infusion (No Labs) Average Wait Before Office Visit (Office Only) Average (Minutes)
7. Average Duration for appointments (min) 3.90625 50.86667 180.4 Lab Office Visit Infusion Lab Time • Average: 6–7 minutes once blood draw begins • Quick process, but bottlenecked by result delays → EMR posting Office Visit • Average: ~45 minutes per visit • Range varies depending on complexity of case Infusion • Average: ~120 minutes (2 hours) • Largest portion of patient stay; variation by regimen
8. Identified Bottlenecks in Workflow • Morning Appointment Clustering (7:30–8:30 AM): High patient concentration overloads labs and infusion, creating cascading delays. • Lab Result Delays (Lab → EMR): Posting lag extends idle time before office visits and infusion, showing a critical workflow dependency. • Prolonged Infusion Waits: Patients often wait 30–60+ minutes post-lab due to infusion room congestion. • Limited Scheduling Flexibility: Fixed early slots limit distribution of patient flow, driving avoidable bottlenecks. • Lab Staffing Alignment: Current patterns don’t fully match peak demand, contributing to uneven processing times. • Patient Lab Timing: Same-day labs increase workflow pressure; day-prior labs may smooth patient flow.
9. Implementation Approach Data Collection – Extracted patient logs from labs, office visits, and infusion and from direct observation of the workflow Cohort Analysis – Grouped patients by appointment/check-in times Wait Time Metrics – Calculated average durations across workflows Charts – Broke down how much of total patient time is spent in each stage to highlight bottlenecks
10. Barriers • Scheduling Routines: Established appointment patterns made it difficult to test alternative flows during mapping. • Technology Limitations: EMR posting delays highlighted IT challenges that were outside the project scope to resolve. • Workforce Availability: Staffing shortages limited flexibility in exploring process adjustments. • Resource Constraints: Budgetary and equipment limitations restricted the ability to test higher-capacity scenarios. • Data Gaps: Some workflow steps (e.g., manual result posting, informal staff hand- offs) were harder to capture consistently.
11. Outcomes observed/ Anticipated Observed Findings • Significant wait times before and between appointments (labs, office visits, infusion) • Staff workload uneven, especially during morning clustering • Patient satisfaction impacted by delays and bottlenecks Anticipated with Proposed Solutions • Reduced Wait Times across all stages of care. • More balanced staff workload throughout the day • Improved patient satisfaction through smoother, more predictable flow • Baseline data established to support long-term scheduling and workflow improvements
12. Sustainability & Hand-Off • Baseline data and workflow maps can continue to be referenced for scheduling and staffing reviews. • Findings integrated into ongoing huddles and quality improvement meetings. • Hand-off: Data and visuals shared with Travis Walters (Practice Director) to guide future initiatives. • Even if not continuously updated, the one- time analysis creates a reference point for future improvements.
13. Stakeholders' reception • leadership (Practice Director): Provided guidance on approach, regular check-ins, and direction on aligning with organizational priorities. • Nursing/Infusion Staff: Shared firsthand accounts of daily challenges (clustering, delays), which validated areas to explore in data analysis. • Lab Staff: Contributed insight into result posting and turnaround times, highlighting how these impact downstream processes. • Overall: Stakeholders actively supported the process of gathering and mapping data as a foundation for future decisions, even though results have not yet been formally presented.
14. Lessons Learned • Establishing baseline data is critical for understanding processes before recommending changes. • Process inefficiencies are interconnected—delays in one step often create downstream bottlenecks. • Stakeholder perspectives added depth to the analysis, ensuring findings reflected real-world workflow. • I learned to avoid “jumping to solutions” and instead focus on root-cause identification and objective analysis. • Leadership takeaway: Effective change relies on listening, mapping, and clarifying problems before moving to action.