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The Root of All Data: One organization's important new approach to measuring performance

data-collection

My policy of our board and by long-standing tradition, the EMS division of the Montgomery County Hospital„

District (MCHD) is committed to providing the best prehospital care possible. As„a part of that, our system has an aggressive, non-disciplinary quality improvement (QI) program that allows us to provide our community with a high level of service.„

Over the years, we_ve followed accepted EMS industry approaches to data analysis. When we found opportunities for improvement on operational and quality issues, we made changes and rolled them out to our staff via quarterly CE sessions.„In general, these approaches were successful at maintaining and improving the overall quality of our service.„

But there was one concern: The level of improvement was inconsistent with the level of effort involved. Despite our ardent attempts, we weren_t completely satisfied with the level of progress in certain areas. Something was wrong, and we weren_t sure what. We knew it wasn_t a data problem; MCHD is data-centric. We_ve utilized a host of data-collection tools, including computerized medical records, fully integrated computer-aided dispatch, satellite tracking of our deployed resources, electronic billing and a host of other data points. So we got to thinking.

An Epiphany

Several years ago, a theory emerged. Perhaps our approach to quality had a systemic problem. Perhaps the processes we, and most others in our industry, were using didn_t allow us to see and react to„the "story behind the story."

Frankly, our data and analytical processes allowed us to identify issues, but„they didn_t give us the level of understanding necessary to uncover the root causes. We also began to realize our methods of communicating issues and remedies to staff weren_t up to the task at hand. There had to be a better way.

We determined the answer lay in revising our approach, adopting different methods for analyzing data and communicating the results. We had significant amounts of data on almost every element of operations and patient care, but we needed a better framework„for analyzing that data, as well as a better„set of principles for passing the information along to those who needed it.

We investigated quality management„in other industries and found that underlying the success of most QI programs in the manufacturing and business world was a consistent and well-defined analytical framework. Leading organizations worldwide were using these frameworks to reduce time, costs, errors and redundancy. These programs include Total Quality Management, Lean, Six Sigma and many others. After reviewing our options, we chose Six Sigma as our new foundation for managing quality.

Although it_s not our intention to go into detail about the Six Sigma methodology, it_s helpful to understand what it is and how it works. Six Sigma was originally developed by Motorola as a strategy for quality management. At its heart are data-driven quantitative techniques to integrate various quality and performance tools and approaches into a well-organized package.

The foundation for the Six Sigma methodology is referred to as "DMAIC":„Define the process you wish to improve,„measure it by developing appropriate data sets,„analyze your performance by employing appropriate quantitative and statistical tools,„improve that process by taking the appropriate steps, and, once improvement is noted,„control it to ensure it_s sustained (see circle graphic, January 2009„JEMS,„ p. 44).

Although Six Sigma_s goal (to not exceed a failure rate of 3.4 defects per million in produced output) is clearly aimed at the manufacturing world, using the tools and techniques offered by this methodology and driving failure rates as low as possible is a worthy goal for EMS.

The Transition

To bring the new quality management approach to our organization, we needed a rallying point to engender ownership among our stakeholders. Thus was born an initiative we call "MCHD Measures."

With the quantitative strategies of Six Sigma, adapted for use in the EMS environment, we set about to close the loop by improving our communication with the information end users. We adopted an„age-old model regarding the learning process. That model holds that "analyzed data yields information, which when appropriately disseminated, results in wisdom" (

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