OPSCB/574 Competency 2 Assessment and Rubric

Title: Competency 1 - Assessment

Course: OPSCB/574: Creating Value Through Operations

University of Phoenix

Date: November 22, 2025

The current check in process at the outpatient clinic was evaluated to determine how variation affects patient flow and service delivery. The process involves patient arrival, insurance verification, demographic confirmation and entry into the electronic medical record system. The evaluation showed that most delays occur when insurance cannot be verified and when patients arrive without complete paperwork. These factors increase cycle time and create bottlenecks during peak appointment hours. The existing workflow relies heavily on manual data entry which increases variability in task completion time. According to Montgomery (2020) unmanaged process variation leads to reduced predictability and lower performance outcomes in healthcare settings. The results showed that the process functions but does not consistently meet expected performance.

The chosen task for analysis is check-in delay, which is a measurable element that directly affects patient flow and satisfaction. Check-in delays, which measure the time from a patient’s scheduled appointment to completed registration, provide insight into the effectiveness of pre registration procedures and the consistency of staff performance. This metrics was selected because it is quantifiable, actionable and reflective of the overall process performance.

To evaluate this process using Six Sigma, the DMAIC framework was applied. The Define phase identified critical-to-quality (CTQ) elements, including scheduling accuracy, pre registration completion and minimal check in delays. Defects were explicitly defined as any scheduling error, missing insurance verification or excessive delay exceeding five minutes. Establishing clear CTQs ensures that subsequent measurement and analysis focus on metrics that truly reflect process performance (Antony, 2014).

In the Measure phase, historical data from a representative sample of patient appointments were collected. Metrics included appointments, scheduling error, insurance verified, check in delays, check in time, cycle time and no show. Data analysis revealed significant variability across staff shifts, appointment types, and peak hours, demonstrating both common cause and special cause variation within the process (Montgomery, 2019). This variability highlights opportunities for standardization and targeted interventions.

Finally, the Control phase incorporated mechanisms to sustain improvements, including monthly performance dashboards, periodic process audits, and updated standard operating procedures (SOPs). By implementing these control mechanisms, the process can maintain reduced variability, improve accuracy, and enhance overall efficiency, aligning with Six Sigma principles of minimizing defects and optimizing performance (Pande et al., 2000).

1. Define

Goal: Reduce defects and variance in the patient scheduling and registration process to improve patient experience, decrease no shows and scheduling errors and reduce rework for patient access staff. CTQs (Critical to Quality):

· Accurate appointment scheduling (date/time, provider, visit type)

· Timely confirmation/notification to patient (within 48 hours)

· Correct insurance verification prior to visit

· On-time patient arrival vs. scheduled appointment

· Scheduling errors (wrong date, wrong provider, wrong visit type)

· Missed pre-registration insurance verifications

· No shows or late cancellations not handled per policy

· Check-in delays > 15 minutes beyond scheduled appointment time

2. Measure

Key process metrics to collect:

· Number of scheduling transactions per week

· Scheduling error rate = (# scheduling errors) / (total scheduling transactions)

· Pre-registration completion rate = (# completed pre-registrations within target window) / (appointments)

· No-show rate = (# no-shows) / (scheduled appointments)

· Average check-in delay (minutes)

· Cycle time for scheduling transaction (minutes)

Data collection plan:

· Sample frame: 90 consecutive business days or 50 scheduling transactions (whichever is reached first)

· Data fields: Appointment ID, Scheduling Error, Insurance Verified, Check In Delay Min, Cycle Time Min, No Show, Mean check In Delays, Upper Control Limit check In Delays and Lower Control Limit check In Delays

· Measurement system check: interval times

· Scheduling error rate < 1%

· Pre-registration completion ≥ 95% within 48 hours of appointment

· No-show rate ≤ 5%

· Average check-in delay ≤ 5 minutes

3. Analyze

Recommended analytical steps:

· Build a SIPOC to document suppliers, inputs, process steps, outputs and customers

· Map the process (detailed swim lane) to identify handoffs and non value add steps

· Use Pareto analysis to identify the top error categories (e.g., wrong visit type, incorrect insurance)

· Conduct root cause analysis (5 Whys / Fishbone) for top defects (e.g., ambiguous appointment types, lack of standardized scripts, EHR usability issues)

· Use basic hypothesis testing and correlation analysis to test suspected drivers (e.g., does appointment volume by time of day correlate with higher error rates?)

· Stratify metrics by staff, shift, channel (phone vs. online), and provider to find patterns.

Preliminary findings (likely scenarios):

· A small number of error types (e.g., incorrect visit type, missing insurance info) account for most defects

· Peak call periods and high staff workload correlate with higher error rate

· Lack of standardized appointment templates across providers contributes to booking mistakes

4. Improve

Targeted improvement actions:

· Standardize appointment types and templates in the scheduling system (reduce ambiguity)

· Implement scripting and job aids for schedulers (standard questions and verification checklist)

· Introduce a mandatory insurance verification step flagged in the system with hard stops for incomplete verifications

· Automate confirmation messages (SMS/email) triggered at booking and 48 hours prior to appointment

· Pilot a scheduling coach/peer review for new hires and during peak periods

Pilot plan:

· Run a 30 day pilot with the improved templates and script on one provider group

· Monitor scheduling error rate, pre registration completion and check in delay weekly

· Use the pilot results to estimate effect size and update process capability calculations

Expected Six Sigma benefits:

· Fewer defects and rework, leading to reduced cycle time and staffing strain

· Improved patient experience (lower no shows and shorter wait times)

· Potential measurable shift in sigma level (for example, moving from ~3σ to ~4σ) depending on baseline.

5. Control

Control mechanisms to sustain improvements:

· Control plan with key metrics, owners, frequency and acceptable limits.

· Real time dashboards for scheduling error rate, pre registration rate and no show rate

· Monthly process audits and monthly review meetings with Patient Access leadership

· Training refreshers and hard stops in EHR for insurance verification

· Documented standard operating procedures (SOPs) and change management sign off

Step 2: Evaluation of Control Chart and Process Metrics

The control chart shows that the check in process is stable with no points outside the upper or lower control limits. The mean check in delay is 7.06 minutes with an upper control limit of 15.56 minutes and a lower control limit of 2.83 minutes which shows that variation is the result of common causes. Although the process is in control the process capability index shows that it is not capable of meeting expected performance because the Cp value is 0.88 and the Cpk value is 0.83. A capable process requires a value greater than one which means the spread is narrower than the specification limits (Montgomery 2020). The results show that patients may still experience delays even though the process is predictable. The process requires improvement to decrease variation and center performance within the limits.

The evaluation of the outpatient clinic check in process showed that the process is stable but not capable of consistently meeting performance expectations for timely patient intake. The process includes several administrative steps that contribute to longer cycle time including manual verification and inconsistent prearrival preparation. The process was evaluated using Statistical Process Control methods to determine whether the observed variation was predictable and whether the process required corrective action or systematic redesign. The results showed that all data points fell within the control limits which indicates that the process is under control and that no special cause variation is present. Although the process is stable the capability indices show that the process cannot consistently meet expected standards which indicates the need for improvement.

Process Evaluation Using Six Sigma

The evaluation showed that Lean is the most appropriate improvement method for this process because Lean emphasizes the removal of non value added steps and the creation of continuous flow. Lean tools such as workflow mapping standard work and 5S can reduce delays and improve task consistency. According to Kim et al. (2019) Lean reduces waste through the redesign of process steps to support predictable flow and timely service. The results suggest that insurance verification should be standardized and completed before arrival to reduce waiting at the front desk. The use of electronic document upload may eliminate the need for manual data entry and reduce cycle time. After standardization and stabilization are achieved Six Sigma may be applied to reduce variation and further improve performance. Six Sigma requires a stable process before application because the method focuses on reducing variation rather than correcting foundational design issues (Montgomery 2020).

Evaluation of Control Chart and Process Metrics Using SPC

The evaluation of the control chart and process metrics confirmed that the process mean was 7.06 minutes and remained within the upper and lower control limits of 15.56 and 2.83 minutes. The absence of data points outside the limits shows that no instability was present during the observation period. However the process capability index showed that the values of Cp and Cpk were below one which indicates that the process cannot meet required specifications. According to Yang and El-Haik (2009) a Cp value below one shows that the spread of the process is wider than acceptable and a Cpk value below one shows that the process is not centered. Although the control limits show that the process is predictable it does not meet the performance level needed to ensure that patients experience a consistent check in time. The results show that improvement is required to align process performance with the desired outcome.

Standard SPC rules, such as the Western Electric rules, were applied to identify points outside control limits, consecutive runs, and trends (Durivage, 2021). These rules helped differentiate assignable causes from natural process variation and guided targeted corrective actions.

Evaluation of Lean, Six Sigma, and Other Tools

The results indicate that the patient scheduling process would benefit from a Lean Six Sigma approach. Six Sigma tools, such as DMAIC, root cause analysis, and control charts, are ideal for reducing defects and improving process capability. Lean tools, including standard work, error-proofing (poka-yoke), visual job aids, and workflow simplification, would complement Six Sigma by eliminating non-value-added steps such as redundant data entry, handoffs, and rework (George et al., 2005).

Other tools, such as predictive analytics for no-show reduction and automated patient communication platforms, could further enhance efficiency and patient satisfaction. Combining Lean and Six Sigma provides a comprehensive approach that addresses both process variation and operational waste.

Description of the SPC Project and Recommendations for Improvement

The SPC project focused on developing a robust monitoring system for patient scheduling and registration. The objectives were to quantify process variation, distinguish between common- and special-cause variation, and measure capability against performance targets. Data were collected, control charts developed, signals interpreted, and variation sources identified.

Recommendations:

The recommendations for improvement include implementing standard work for insurance verification updating training procedures and using automation to reduce manual tasks. Lean workflow mapping should be used to identify waste and redesign the process to support a continuous flow. The clinic should implement prearrival verification to ensure that insurance and documentation are completed before the appointment. The use of electronic check in may reduce waiting and improve patient satisfaction. These improvements are expected to reduce variation and center the process mean within the required specification limits. After these changes are implemented Six Sigma may be applied to further reduce variation and improve performance capability. The evaluation shows that improvement is required even though the process is stable because capability deficiencies affect patient experience and operational efficiency.

Implementing these recommendations is expected to improve process stability, reduce defect rates, enhance patient satisfaction and increase process capability, moving the workflow closer to Six Sigma quality levels.

References

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Durivage, M. (2021). 7 rules for properly interpreting control charts. Quality Systems Compliance LLC.

George, M. L., Rowlands, D., Price, M., & Maxey, J. (2005). The Lean Six Sigma pocket toolbook: A quick reference guide to nearly 100 tools for improving quality and speed. McGraw-Hill.

Montgomery, D. C. (2019). Introduction to statistical quality control (8th ed.). Wiley.

Pande, P. S., Neuman, R. P., & Cavanagh, R. R. (2000). The Six Sigma way: How GE, Motorola, and other top companies are honing their performance. McGraw-Hill.

Pyzdek, T., & Keller, P. (2018). The Six Sigma handbook (5th ed.). McGraw-Hill.

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Montgomery DC. (2020). Introduction to Statistical Quality Control. Wiley.

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