IHP 435 Module 5 Six Sigma Short Paper Example

When the Average Hides the Problem: Six Sigma for Stat Troponin Turnaround

[Student Name]

Southern New Hampshire University

IHP 435: Performance Improvement Measurement and Methodologies

Module Five Short Paper

[Instructor Name]

[Date]

When the Average Hides the Problem: Six Sigma for Stat Troponin Turnaround

When a patient arrives with chest pain, the emergency physician needs a troponin result quickly to decide whether a heart attack is under way. Cedar Point's laboratory reports stat troponin turnaround monthly as a median: 41 minutes, comfortably inside the 60-minute target. Emergency physicians, however, complain that results are sometimes very late. A pull of 1,200 stat troponin tests from the past quarter showed that 216, or 18%, took longer than 60 minutes, and 40 took longer than two hours. This paper argues that the problem is variation, explains why Six Sigma suits it and walks through how the method would be applied.

Variation as the Problem

A process can have an acceptable average and still fail patients regularly. For troponin, every late result is a potential delay in treatment or disposition, and a patient waiting two hours does not benefit from the fact that most others waited forty minutes. The goal is not simply a lower average but a narrower spread, so that nearly every result arrives on time. Methods focused on flow and waste, such as Lean, might shorten the typical time, but a problem defined by unpredictable failures calls for a method designed to find and remove the causes of variation.

What Six Sigma Aims For

Chassin (1998) introduced many health care leaders to Six Sigma, a quality approach developed in manufacturing whose name refers to a level of performance equivalent to about 3.4 defects per million opportunities. He compared health care processes with this standard and found wide differences: some, such as deaths caused by anesthesia, approached very high reliability, while others, such as the use of proven medications after heart attacks, fell far short. His point was that health care routinely tolerates defect rates that other industries would not, and that the tools of Six Sigma could help close the gap if organizations committed to them.

Calculating the Troponin Process Sigma Level

Treating each test as one opportunity and a result over 60 minutes as a defect, the troponin process produced 216 defects in 1,200 opportunities, or 180,000 defects per million opportunities. Using standard conversion tables, that corresponds to a process sigma level of roughly 2.4, far below the Six Sigma standard. The number is useful less as a target than as a baseline: it tells the team how much variation must be removed and gives a common language for progress.

DMAIC Applied

Six Sigma projects follow five phases: define, measure, analyze, improve and control. In Define, the team states the problem and scope: stat troponin tests from the emergency department, from order to result, with a target of 95% within 60 minutes. In Measure, it confirms data accuracy and maps the steps: order, collection, labeling, transport, receipt, centrifugation, analysis and result verification, with time stamps at each. In Analyze, it looks for where and when delays occur. In Improve, it tests changes targeting the root causes. In Control, it builds monitoring to hold the gains.

What the Analysis Found

Breaking the 216 late results down by step showed that most delays occurred before the sample reached the laboratory. Late results clustered during shift changes at 7 a.m. and 7 p.m., when samples sat unsent, and on evenings when the pneumatic tube system was down for maintenance, requiring hand delivery. A smaller group involved mislabeled samples that had to be redrawn. Analysis time inside the laboratory was consistent. The root causes were therefore handoffs at shift change, tube system downtime without a backup plan and labeling errors.

Improvements

Changes target each cause. Stat troponin draws are timed-stamped at collection, and a visual alert on the emergency department tracking board flags any sample not received by the laboratory within 15 minutes. A runner is assigned during tube system downtime, with downtime scheduled only during low-volume hours. Bedside label printing with barcode scanning reduces labeling errors. Each change is tested on a small scale before spreading.

People and Roles

Six Sigma projects are often led by trained specialists, but this one depends on the people who touch the sample. The team includes an emergency department nurse, a phlebotomist, a laboratory technologist, a transport aide, the laboratory manager and a quality analyst trained in Six Sigma methods, with the emergency department medical director as sponsor. Frontline members supplied the insight that shift change was the weak point, something the data alone suggested but could not explain. Meetings are held weekly for thirty minutes during the analyze and improve phases, and results are posted in both the laboratory and the emergency department so each group sees how its part of the process affects the other. Giving the transport aide and phlebotomist a formal voice also builds the ownership needed for the controls to last after the project team disbands.

Control with Statistical Process Control

Benneyan (2003) explains that statistical process control uses control charts to tell the ordinary scatter of a steady process apart from unusual shifts that point to a new cause worth chasing. Charts plot data over time with limits calculated from the process's own variation. For the troponin process, a p chart of the weekly percentage of late results and an individuals chart of daily median turnaround will be maintained by the laboratory. A point beyond a limit, such as a spike in late results, triggers review within a day, and the charts will show whether the improvements have narrowed variation.

Expected Results

If the three root causes are addressed, the team expects the share of late results to fall from 18% to under 5% within four months, which would raise the process to roughly a 3.2 sigma level. That is still far from six sigma, and the team will say so openly; the aim is steady, measured progress rather than a slogan.

Evidence and Caution

DelliFraine et al. (2010) reviewed the health care literature on both methods and found that most were case reports with weak designs, so the evidence that these methods reliably improve outcomes is limited. That caution supports the careful measurement built into DMAIC and the control phase: Cedar Point will judge the project by its own data over time rather than by assumption.

Conclusion

A median of 41 minutes concealed a process with a sigma level of about 2.4. Six Sigma fits because the problem is variation with identifiable causes, and DMAIC provides the structure to find them, test fixes and hold the gains with control charts.

References

Benneyan, J. C. (2003). Statistical process control as a tool for research and healthcare improvement. Quality and Safety in Health Care, 12(6), 458-464. https://doi.org/10.1136/qhc.12.6.458

Chassin, M. R. (1998). Is health care ready for Six Sigma quality? The Milbank Quarterly, 76(4), 565-591. https://doi.org/10.1111/1468-0009.00106

DelliFraine, J. L., Langabeer, J. R., & Nembhard, I. M. (2010). Assessing the evidence of Six Sigma and Lean in the health care industry. Quality Management in Health Care, 19(3), 211-225. https://doi.org/10.1097/QMH.0b013e3181eb140e