When treating patients, doctors use their own experience, the combined knowledge of their colleagues, and the latest advances in medicine and technology to identify the best course of action. But that remedy alone can’t save a patient unless the doctor also has access to the tools and equipment needed to deliver the recommended treatment.
When essential medical equipment is unavailable due to repairs, a doctor’s ability to save lives is diminished. But a recent healthcare quality project shows how an important tool that’s still new in many facilities—data analysis—can help hospitals ensure that doctors have the equipment they need to serve their patients.
A Lean Six Sigma team in Amman, Jordan, examined the factors affecting equipment downtime, and identified elements of the maintenance process that could improve equipment availability. Using the knowledge they acquired by collecting data and analyzing it with Minitab Statistical Software, the team has implemented a new equipment maintenance procedure that’s making a difference in ensuring critical equipment is working and ready when it’s needed.
A project team used Minitab Statistical Software to analyze data that would identify medical equipment with the highest percentage of downtimes, evaluate the causes of failure, and improve equipment availability in the hospital.
According to a study done by the World Health Organization (WHO), nearly 50% of medical devices in developed countries do not function or are not maintained properly due to the lack of an effective management policy. With an increasing number of patients being affected by the absence of medical equipment, the project team sought to minimize this obstacle. The team gathered data from seven hospitals during a seven-year period and evaluated their processes for maintaining almost 700 different types of medical equipment, each of which was categorized according to the cause of downtime.
Working within the Six Sigma project framework called DMAIC (for Define, Measure, Analyze, Improve, and Control), the project team’s challenges included identifying reasons for variation in equipment downtime so they could focus their efforts where they would make the biggest difference. Ultimately, the team wanted to improve repair procedures to make equipment more readily available and ensure that patients promptly received life-saving treatments.
How Minitab Helped
After collecting downtime data based on medical equipment type and cause of equipment failure for each hospital, the project team used Minitab to identify the equipment types with the highest percentage of downtimes and the most common causes for those downtimes.
Displaying the data using Pareto charts showed the team that the majority of downtimes were caused by electrical issues, and the percentage of faults was highest among diagnostic and life support machines. They also assigned priorities to different equipment types, ensuring that maintenance was provided for medical equipment classified as “Priority 1,” a category that included critical, specialized, and scarce equipment such as ventilators, anesthesia machines, blood gas analyzers, MRI machines, and CT scanners.
The Pareto chart above highlights the most common causes of medical equipment failures, and identifies where to focus improvement efforts.
The team also used process capability analysis to determine whether the correction maintenance processes in place for each hospital met downtime specifications, and to provide insight into how these processes might be refined. The results of this analysis confirmed that the hospitals’ equipment repair processes had significant room for improvement.
The capability sixpack above displays generic data similar to that of the project team. It combines tools into a single display for easy process evaluation.
Having gained insight into the most frequent types and causes of medical equipment failing, the team used Minitab’s regression analysis to determine the main factors affecting equipment downtime. Their analysis showed it was not the actual maintenance time that ultimately led to the lack of available equipment, but rather the time required to diagnose the faulty equipment, to decide how to proceed with repairs, and to return the equipment after repairs were completed. Additional factors that hampered the repair process included delays in detecting device failures, registering requests for service, and closing work orders.
From the regression analysis, the team identified factors where improvements could be made so downtimes could be reduced. They devised an improved repair process, which uses control charts created in Minitab to continually monitor staff performance, downtime of medical devices, time to process purchasing orders, decision time for how to proceed with repair, and delivery time of repaired device.
The control chart above plots process data in a time-ordered sequence to identify variation. Similar to the one used by the project team, it allows for easy comparison between data collected before and after a process improvement.
They then tested the new system by implementing it first in a single facility. That facility reduced equipment downtime by 35% and increased the sigma level from 2.881 to 3.708 without incurring any additional costs.
Quality tools such as the Pareto chart, capability sixpack, and control chart contributed to a new process that ensures reasonable medical equipment downtimes and enables the proper treatment of patients. As more healthcare facilities begin using data analysis and the Lean Six Sigma methodology, projects like this one provide great examples of how even improving the efficiency of activities that aren’t directly patient-related—like equipment management procedures—can have a huge impact on a facility’s ability to provide quality care for every patient.
This story was adapted from a case study presented at the 2012 International Conference on Industrial Engineering and Operations Management in Istanbul, Turkey.