INDUSTRIAL ENGINEERING

Assistant Professor Ben-Tzion Karsh (center), Family Medicine Clinical Professor John Beasley (right) and graduate student Kamisha Hamilton (left) are working to develop a feedback system for family medicine physicians and clinicians, and their patients. (42K JPG)

Learning from medical mistakes

An accidental needle stick. Incorrectly entered data. The wrong prescription. Lost test results. Although medical errors don't always result in physical harm, they are events physicians and clinicians hope to avoid.

Assistant Professor Ben-Tzion Karsh (center), Family Medicine Clinical Professor John Beasley (right) and graduate student Kamisha Hamilton (left) want to help health care professionals learn from their mistakes. Eventually, the group hopes to design a user-centered error-reporting system that supplies family medicine physicians and clinicians with real-time feedback and a searchable database of lessons learned.

To develop criteria for such a system, the trio held a series of focus groups to solicit input from doctors from the Wisconsin Academy of Family Physicians as well as nurses and medical assistants in family practice. Their comments will help the researchers decide everything from what information to report and who will administer the data to what features will motivate people to use the system. The project is funded by University-Industry Relations at the University of Wisconsin-Madison.

Manufacturing the right fit

Each year's new car models sport fancier features and consumer price tags ranging from $10,000 to more than $100,000. For automakers, a redesigned model might cost billions of dollars — mostly for prototypes and new tooling. Assistant Professor Darek Ceglarek's method can help manufacturers reduce the number of costly prototypes and shorten new-product launch. Called a stream-of-variation-analysis (SOVA) model, it can reduce expensive dimensional errors during the design and production ramp-up phases.

Already, GM and DaimlerChrysler have implemented his methodology. Eventually, the SOVA models will become comprehensive software packages through which manufacturers in a variety of industries can simulate, predict and then eliminate errors both in a product's design and in its anticipated production process. Ceglarek's research will lay the theoretical foundation for reconfigurable and reusable assembly systems.

In addition, Ceglarek is also collaborating with researchers at Texas A&M University and Motorola to develop methods to analyze and optimize a distributed sensor system in automotive and electronics assembly processes. The methods will help manufacturers install sensors in production lines to track and measure errors as they occur.

Restraining cross-training

Cross-training employees can increase workers' level of interest in their jobs and help cover during turnover and absenteeism, but what seems like a win-win situation may not always be the case. When workers cross-train at too many positions, they excel at none, says Assistant Professor David Nembhard, who studies workplace learning and forgetting behaviors.

To increase worker productivity and product quality, Nembhard is currently studying how to assign workers to jobs that regularly develop new technologies and manufacture new products. When he collected worker data at an inspection station for car radios, a product that changes annually, he discovered that slow learners reach a higher end-performance level, whereas faster learners often peak at a lower level. And experienced employees will learn a complex job more rapidly than inexperienced workers.

From that data, Nembhard created optimization models to help managers decide how much cross-training is best in different scenarios. Eventually he will develop rules of thumb that many companies with similar job-scheduling situations can apply.

Indicators ensure quality care

More than two million residents in 17,000 nursing homes worldwide benefit from quality indicators developed under Professor David Zimmerman at the Center for Health Systems Research and Analysis (CHSRA).

The 24 indicators assess nursing home care quality and essentially are mechanisms that state and federal inspectors, accreditation agencies and nursing homes themselves use to target 12 areas of nursing home care that need review for improvement. The data they generate are based on facilities' mandatory quarterly assessments of each resident, called the minimum data set (MDS).

Nursing homes submit MDS information to their state and now can access their quality reports via software CHSRA researchers developed. The software enables nursing homes not only to monitor their progress, but also compare reports with those of other facilities in their state. Soon the reports will be available online.

Now the U.S. Centers for Medicare and Medicade Services is using the indicators as the basis for a second-generation set of quality mechanisms that not only would apply to nursing homes, but also to other settings, such as post-acute care. Zimmerman is consulting for the project.