DBA 8000 Module 4 Final Project: A Lean Six Sigma Decision Study of Stat Turnaround Time Example

From 74 to 90 Percent in an Hour: A DMAIC Decision Study of Emergency Laboratory Turnaround

Student Name

Doctor of Business Administration, Thomas Edison State University

DBA-8000: Critical Thought and Informed Action: Models of Decision Making and Organization Achievement

Mentor Name

Month Day, Year

From 74 to 90 Percent in an Hour: A DMAIC Decision Study of Emergency Laboratory Turnaround

Why This Decision

Emergency physicians need potassium results quickly; a delayed result can delay treatment for patients with heart rhythm problems or kidney failure. The network's goal is for nine in ten emergency potassium results to reach clinicians within an hour of the order. Last quarter we achieved 74 percent. The question is not only how to improve, but which of several possible fixes to fund, and whether the answer affects the core laboratory decision analyzed in Modules 1 to 3.

Define

George et al. (2005) describe DMAIC, define, measure, analyze, improve and control, as Six Sigma's structured method for improving existing processes, with tools matched to each phase.

Measure

We extracted timestamps for 1,200 emergency potassium orders over six weeks: order, collection, receipt in the laboratory, start of testing and result verification. The median order-to-result time was 48 minutes, but the 90th percentile was 82 minutes: most results were fast, and a long tail of delays caused the misses. We confirmed the data's accuracy by observing 60 specimens directly and comparing the observed times with the system's timestamps; 57 matched within two minutes.

Analyze

A Pareto analysis of the 312 results reported after 60 minutes, sorted by the stage where most of the delay occurred, showed that a few causes dominated.

Transport and hemolysis together account for nearly two thirds of late results. A fishbone diagram built by the team traced each to root causes. Transport delays came from methods, specimens sent in batches when the tube station was busy, and machines, a pneumatic tube system without priority routing. Hemolysis came from methods and people: specimens drawn through intravenous catheters, and new nurses untrained in collection technique. The five whys applied to hemolysis led to an onboarding program that covers the electronic record but not specimen collection.

Improve: Choosing Among Solutions

The team generated seven possible solutions. Rather than adopt all of them, we compared them with a weighted decision matrix, applying the decision discipline of Module 1. Scores are 1 to 5.

We chose the top four: collection training, priority tube routing, a dedicated centrifuge and autoverification. Point-of-care testing scored low because of cost and the quality control burden it would place on emergency staff. Ranking the options this way follows the decision matrix logic Peterson (2017) sets out, with outcomes judged across the states that might occur.

Guarding Against Bias in the Choice

The bias safeguards from Module 2 applied here. To avoid anchoring on the tube vendor's promised transit time, we timed test runs ourselves: priority routing cut median transit from 9 to 4 minutes, close to but not quite the vendor's 3. To counter confirmation, the emergency nurse educator, who initially doubted the hemolysis data, was asked to review the specimens herself; she found the catheter-draw pattern independently, which strengthened her support for the training.

Results of a Pilot

We piloted the four solutions in one emergency department for eight weeks. The share of potassium results within 60 minutes rose from 73 to 89 percent, the hemolysis rate fell from 7.8 to 3.9 percent, and the 90th percentile fell from 84 to 61 minutes.

Control

Linderman et al. (2003) argued that Six Sigma works partly through goal setting: specific, challenging goals combined with structured methods. The control plan keeps the goal visible.

Culture and Complexity

In Cynefin terms, turnaround is a complicated problem: cause and effect can be found through analysis, which is why DMAIC worked. The culture of site autonomy helped here: each emergency department and laboratory owned its improvements, and the pilot site's success persuaded the second. De Koning et al. (2006) reported that Lean Six Sigma in hospitals succeeds when it is led by staff who own the processes and supported by data, a pattern our project followed.

What the Study Means for the Core Lab

The study changes the core lab decision. Stat potassium and other emergency tests must be reported within an hour; specimens cannot travel to a central laboratory miles away. Any core lab must therefore leave a rapid-response laboratory at each hospital for stat work. That lowers the core lab's savings, since each hospital keeps some staff and analyzers, and it strengthens the case, already favored in Modules 2 and 3, for a phased consolidation of routine, non-urgent testing only. Decision theory framed the investment; DMAIC revealed an operational constraint the matrix had not captured.

Conclusion

DMAIC turned a vague complaint about slow results into a defined problem, measured causes and chosen solutions that raised timely results from 74 to 89 percent in a pilot. The project also showed how the course's tools fit together: decision matrices to choose among solutions, bias safeguards to protect the choice, attention to culture and complexity to make the change stick, and an operational study that improved a strategic decision.

References

de Koning, H., Verver, J. P. S., van den Heuvel, J., Bisgaard, S., & Does, R. J. M. M. (2006). Lean Six Sigma in healthcare. Journal for Healthcare Quality, 28(2), 4-11. https://doi.org/10.1111/j.1945-1474.2006.tb00596.x

George, M. L., Rowlands, D., Price, M., & Maxey, J. (2005). The Lean Six Sigma pocket toolbook. McGraw-Hill.

Linderman, K., Schroeder, R. G., Zaheer, S., & Choo, A. S. (2003). Six Sigma: A goal-theoretic perspective. Journal of Operations Management, 21(2), 193-203. https://doi.org/10.1016/S0272-6963(02)00087-6

Peterson, M. (2017). An introduction to decision theory (2nd ed.). Cambridge University Press.