Case Study: Using np Charts To Address On-Time Medication Delivery

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ichael J. Cleary, Ph.D.

Hy Sedrate, director of quality for St. Recover in the Longrun Hospital, has been worried about his organization's future, and more specifically about his own future. St. Recover has been acquired, through a series of complex arrangements, by Santa Cura Hospitals, a large regional organization made up of some two dozen hospitals and clinics. Sedrate has learned to manage quality systems at St. Recover, but only through the prowess of his SPC team, which works nonstop to analyze data in the hospital.

Sedrate knows that he will not be promoted to the larger organization without a dramatic demonstration of quality improvement in his organization. And the alternative is grim, since there are apparently plenty of other quality directors in the same position within the Santa Cura system.

After walking around the hospital and musing on the possibilities, Hy Sedrate determines that a highly visible project that he is capable of supervising lies in the number of medications that are either incomplete or inaccurate when they are delivered from the pharmacy. "Prescription drugs are always in the news," he figures, "so everyone will notice our improvement."

With that confidence, he arranges for the pharmacists to gather data relating to missed medications. Hy Sedrate knows that he will have plenty of data in this area, since the pharmacy has been collecting data for the Joint Commission on Healthcare Accreditation. He decides that the data should be considered as attributes data, since he will be measuring nonconforming items, defined as those medications that fail to be delivered on time. Further, he believes that an np-chart is the appropriate way to analyze the data, since in effect he will be examining the number of deliveries with mistakes.

He develops the following chart, based on the data provided by the pharmacists:

When he sees the chart, Hy Sedrate is somewhat disappointed, since the system seems to be in control. "I was hoping to see lots of out-of-control points so we could eliminate special causes," he mutters to himself, deciding that this np-chart gives him nothing dramatic with which to enhance his portfolio in the hospital. He's so worried about what he can do that he finds himself losing sleep at night. Is his anxiety merited?

Hy Sedrate's high blood pressure is pointless, since the pharmacy process gives him plenty of opportunities to improve quality. The point is not just to identify out-of-control conditions, but to improve the process. The np-chart reflects that the process is now generating an average of 10.2 non-conformities per week.

With the use of brainstorming, cause-and-effect analysis, Pareto diagrams, and other problem solving and data analysis tools, Hy Sedrate's improvement team can identify ways to reduce the number of missed prescriptions even further.

Quality improvement is not simply data gathering, but analyzing data and organizing it in order to determine ways to improve the process. The Plan-Do-Study-Act cycle focuses on continuously examining processes in order to bring them to even greater levels of predictability.

About The Author
Michael J. Cleary, Ph.D., founder and president of PQ Systems, Inc., in Dayton, OH, is a noted authority in the field of quality management and a professor of management science (emeritus) at Wright State University in Dayton.

Dr. Cleary is a charter member and regional director of the Education Division of the American Society for Quality Control and played a principal role in developing the Transformation of American Industry® national training project. He served on the planning committee for the U.S.-Japanese Business Conference in Tokyo, and has presented papers on statistical process control and the applications of quality management principles to a variety of audiences in Korea, China, France, Great Britain, Australia, Singapore, and Japan. He is author of Data Analysis Handbook Using SPSS, used in university classrooms throughout the nation.

A 24-year professorship in management science has enabled Dr. Cleary to conduct extensive research and garner valuable experience in expanding quality management methods. He has been a leader in bringing quality management into the curriculum of the College of Business, and has published many articles and papers on issues related to quality management, statistical applications, and decision sciences. He has published articles on quality management and statistical process control in a variety of academic and professional journals and is a frequent speaker on issues related to the management philosophy and statistical methods of W. Edwards Deming.

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