The Heart of the Matter: Hospital's Improved Diagnostic Process Saves Lives and Money

You expect to find many lifesaving techniques in hospitals-expensive medical research, groundbreaking procedures-but when it comes to treating patients with cardiovascular disease, the approach one Taiwanese hospital used might surprise you: data analysis.

Heart disease is the leading cause of death in Taiwan, so it's no wonder the country's doctors and engineers are looking for ways to improve treatment options.  

That's why a Lean Six Sigma project team at a hospital in Taipei City examined the process for treating patients suffering from acute ST-elevation myocardial infarction (STEMI), a heart attack caused by coronary heart disease. Reducing the wait time between diagnosis and treatment could save many lives.

Patients with STEMI are diagnosed through electrocardiogram findings and cardiac markers, and the recommended course of treatment for these patients is angioplasty completed within 90 minutes of arrival.

Medical professionals refer to this period as the door-to-balloon (D2B) time, because angioplasty involves inserting a small balloon inside the blocked blood vessel with a catheter. When inflated at the site of the blockage, the balloon enables blood flow to resume.

The project team analyzed D2B time-which includes an electrocardiogram, the wait time before the operation, and the time for balloon inflation-using Minitab Statistical Software.

However, you can only trust the results of an analysis if you trust the data you're analyzing. To ensure the data were trustworthy, the project team used Minitab to conduct a Gage R&R Study of their system for collecting data. The study confirmed their measurement system was reliable. Once they verified the precision of their measurements, the team analyzed D2B data from 40 STEMI cases that occurred over a nine-month period.

Analysis of the data was performed using a normality test and the results demonstrated the data were not normally distributed. Once a Box-Cox transformation normalized the data, the team used the transformed data to plot the I-MR control chart shown below to look for unusual sources of variation in the data.

In addition to using an I-MR control chart, the project team also used Minitab to conduct a process capability analysis to determine whether their process met performance specifications. In this case, the upper specification limit for D2B time was 90 minutes. The results of the capability analysis confirmed that the hospital's handling of STEMI cases had significant room for process improvement.

The team examined each step in handling a STEMI patient and identified several areas in which efficiency could be significantly enhanced, including confirming the diagnosis, medicating the patient, preparing for the operation, transferring the patient to the catheterization laboratory, and inflating the balloon.

After assessing the STEMI process, the team implemented improvements such as sending patients who arrive with chest pain directly to an electrocardiogram test, printing treatment sheets automatically as opposed to writing them, making a STEMI medication pack available in the emergency department, contacting the catheterization staff upon diagnosis confirmation, setting up all STEMI operation equipment in one box, and not providing an explanation of the procedure to medical students during the operation.  

The team then collected additional data and reevaluated the process. Using Minitab to analyze the new data, the team demonstrated that the average D2B time dropped from 139.2 to 57.9 minutes-a 58.4% improvement. Furthermore, capability analysis showed this new process could meet specifications.

A more efficient process means patients receive angioplasty more quickly, which will save lives. Moreover, the average hospital stay for STEMI patients has decreased by three days since the new process was implemented, and the hospital has saved $4.4 million in medical resources.

Applying data analysis and Lean Six Sigma methods to the health care system doesn't grab headlines like an experimental surgery might. But as more hospitals use data analysis to make procedures better, faster, and safer, its benefits will be seen every day in the faces of patients whose lives are saved.

This story was adapted from an article published in the December 2011 issue of the African Journal of Business Management.

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