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Susan King and Vicki Bier

Professor Vicki Bier (right) with Susan King, an attorney who consulted on the project. (Large image)

Industrial and Systems Engineering

Preparing for a pandemic

Unlike many scientists, Professor Vicki Bier hopes local, state and federal government agencies won’t need to apply the results of her research. With funding from the Wisconsin Department of Health and Family Services, Bier is studying how society might function during a pandemic, a new disease that could spread to and incapacitate millions of people.

“At the peak of a pandemic, we might have 30- to 40-percent absenteeism—people who are sick or taking care of sick family members,” says Bier. “This could have a severe impact.”

Bier and her colleagues are examining three areas that lie beyond the scope of traditional public health or emergency management, but that would be important in a pandemic. Among those issues are managing large-scale school closings and determining which businesses are critical to keep open, both in terms of basic needs—for example, grocery stores, pharmacies, utilities and the like—and economic need. The group also is developing criteria for canceling entertainment events.

In addition, the group is examining the needs of people with low-wage jobs in areas likely to be hard-hit by a pandemic. The researchers are examining options ranging from emergency unemployment compensation to a moratorium on utility cutoffs.

Though Bier and colleagues are preparing for a worst-case scenario, they are hoping for the best. “This could have a severe enough effect that we ought to be planning for it ahead of time, rather than scrambling to figure out how we’re going to manage after the fact,” says Bier.

Critical care:
Team optimizes medication-error ID processes

Mistakes are part of life— but in healthcare, mistakes also can be a part of death.

Nurses often are a safety net for finding errors in patients’ medication; however, they can’t catch everything. “We want to create a way that we can help systematically redesign the medication administration processes to make mistakes easier to identify and recover from,” says Associate Professor Ben-Tzion Karsh.

With principal investigator Professor Pascale Carayon and Associate Professor of Nursing Mary Ellen Murray, Karsh is conducting a multi-method study of how nurses contribute to medication management, including finding and fixing errors that have slipped through the system.

In two Wisconsin hospitals, the team is observing nurses in both pediatric and adult intensive care units, where even small medication mistakes could have adverse effects. These are places where nurses have developed their own strategies for error recovery and management. Using their expertise in human factors engineering, the researchers can identify holes in the system, especially in areas often overlooked in the nurses’ everyday routine.

The researchers also are soliciting input from the nurses. In focus groups and interviews, as well as during on-the-job shadowing, the nurses explain their thought processes and systems, helping the researchers understand the reasoning behind established routines. “This isn’t about training the nurses to do something better, but about redesigning the entire process to make it easier for the nurses,” says Karsh.

One area the team is focusing on is how medical technology helps or hinders medication administration. For example, both hospitals use medical information technology such as medication barcoding and computerized ordering. The team hopes to identify situations when technology can help nurses avoid errors, but also situations in which the technology could introduce error. “We want make error recovery a standard part of thinking in the field of patient safety,” says Karsh.

Predicting—and preventing—medical equipment failures

System failures in medical devices are costly to both patient care on the part of the hospital and maintenance service on the part of the manufacturer. Predicting, and therefore preventing, these failures is difficult at best, thanks to the challenge of determining causality. Associate Professor Shiyu Zhou and graduate student Zhiguo Li teamed up with GE Healthcare engineers Suresh Choubey and Crispian Sievenpiper to develop an easy way to use the GE Healthcare automated information system to identify and predict system errors.

For example, an MRI machine in a hospital is constantly recording data, such as temperature, patient position and operation action, in a system event log. The GE Healthcare network receives the logs, so that if a machine fails, engineers there can try to discover the cause. However, the overabundance of data makes it difficult to determine the combination of events that actually contributes to machine failure. Engineers must study the entire event sequence and figure out what went wrong—a time-consuming and unreliable practice.

In a simpler solution, Zhou and his team applied a model typically used in medicine to determine which treatment options have the best patient-survival rates. By treating the machines like patients, with system failure equaling death, the team described the relationship between machine activities and failure events. The model looks at factors or events in the system logs that tend to occur in combination preceding an equipment failure. It identifies these factors, tagged “frequent failure signatures,” and uses them to predict failures, thus allowing GE Healthcare the opportunity to fix a medical device before its system crashes.

The researchers’ next step is to increase the prediction window and optimize the model to minimize failures without increasing unnecessary maintenance.

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