New quality trend puts spotlight on success

Using appreciative inquiry to boost performance

When you do a root-cause analysis or educate staff about improving patient safety, do you sometimes feel your message is all "doom and gloom?" If so, take note of a growing trend, which spotlights events that went well, as opposed to what went wrong.

Appreciative inquiry (AI) has been used for some time in various industries as a novel way to give feedback, conduct performance appraisals, and hire employees. "Organizations are looking for a positive way to communicate with and to direct their human capital," explains Joseph Roebuck, a Succasunna, NJ-based consultant specializing in AI. Now, cutting-edge quality managers are using this approach to boost performance improvement efforts and morale at the same time.

"AI can be used in conjunction with quality improvement efforts by flipping over the initial question of the planning phase of an improvement effort," he says. In other words, instead of asking, "What is being done wrong or poorly?" ask, "What is it that we do best?"

As a quality manager, you can have a dramatic impact on staff morale by using the AI method, adds Tina Maund, MS, RN, CPHQ, director of performance improvement for Overlook Hospital in Summit, NJ, where AI has been used successfully for several projects. "When you focus on what went well, people feel really good about that. They are much more energized, instead of viewing it as another uphill battle." Staff are quick to offer suggestions, she adds, such as, "Here’s something that always works well for me; can we try that?"

Still, it can be a difficult transition to switch your focus from the traditional critical thinking and problem analysis methods of quality improvement, Roebuck says.

He suggests using AI principles in combination with these time-tested methods. Here are some ways to use AI for performance improvement:

• Perform a root-cause analysis.

Usually, this is done to find out the causes of the process failure, but you also can determine the root cause of an exceptionally effective process, Maund says.

Recently, a root-cause analysis was done for a case that triggered a review because of delays that exceeded time goals. The goal is 90 minutes or less from "door to balloon," meaning the time a patient arrives in the emergency department (ED) until the balloon is inflated for angioplasty if the patient is eligible for treatment, she explains.

A detailed analysis was done to track every step in the process, beginning with the time of the patient’s call to emergency medical services (EMS). "We were able to identify several factors that had slowed the process down," Maund says.

In addition to the outlying case that caused concern, the team examined the facility’s best-time case, which was 48 minutes.

The two cases were compared head to head, using process flowcharts and cause-and-effect diagrams. "The differences really jump off the page at you," she says. "We were looking for glitches that needed to be addressed, but also drivers of best-time performance." For the best-time case, the electrocardiogram (EKG) was done prior to the patient’s arrival, the results had been transmitted, and the room was set up, so the team was 100% ready to go when the patient arrived, she explains.

The team learned a great deal from this comparison process, Maund says, such as reinforcing the need for obtaining the EKG results before the patient arrives. Another key area that came to light is the importance of educating patients to call EMS immediately after the onset of heart attack symptoms, to shorten the time from onset of symptoms to arrival, and to expedite patient evaluation and early treatment through EMS protocols, she explains.

"EMS will assess the patient and do a field EKG and transmit it so the ED physician can read it, be aware of the assessment findings, and when indicated, anticipate that we will be initiating the angioplasty protocol, so we are ready to go when the patient arrives," she explains. "Whereas, if the patient drives himself to the hospital, we don’t even know he is coming."

To use AI principles to improve this area, Maund says she would identify a population that has learned key symptoms and generally responds with quick follow-up. "In obstetrics and pediatrics, generally people are very responsive to learning about symptoms of problems and following up quickly on them."

This is in sharp contrast to the delays in the general adult population, even though people are aware of symptoms of stroke and heart attack, she says. The goal would be to identify factors that account for the difference in behavior and see if the drivers of the quick-response behavior in pregnancy and children can be adapted to achieve quick calls to EMS when heart attack and stroke symptoms appear, Maund explains.

In the best-time case, when the patient arrived, the ED staff already knew his story and could match that with the clinical picture they were seeing, which expedited care, Maund says. "It’s important for the EMS people to get enough detail to flesh out the picture and communicate that with the staff here."

In the longer case, communication of the patient’s story from EMS was limited. "This involved system issues," she says. "It was 3 a.m. when EMS arrived; the ED was very busy with a code going on and also had another critically ill patient." However, during the case review, the team emphasized the importance of following up with EMS. "They emphasized that we are going to be making decisions based on the patient’s story and EKG, and those are major ingredients in determining whether the patient is a candidate for angioplasty," Maund says.

This triggers some advance preparation, which saves time once the patient arrives, she says. The team is exploring ways to enhance critical communication even when resources are stretched, Maund adds.

• Reduce mislabeled blood specimens.

At Overlook, efforts were being made to decrease the number of mislabeled blood specimens, Maund says. "This doesn’t happen frequently. In fact, it’s something that happens very seldom; and thankfully, we have never had a critical impact as a result. But obviously, we want them to be labeled 100% correctly because the potential for patient harm is there."

After an extensive root-cause analysis and a major process redesign, the problem still existed, Maund says.

Instead of focusing only on the problem units and their processes, she met with staff from four areas that always had correctly labeled specimens, including pediatrics and the preadmission testing area. The goal was to discover what they were doing right, she explains. "We got a group together in a room and spent a couple of hours going through the process."

It was discovered that there was a major difference in the processes used between the two groups, she says. On the units that had 100% correctly labeled specimens, they focused on one patient at a time. The pediatrics staff explained that they obtain the labels for a child who needs blood drawn, take the child to a separate area to draw the blood, and then go back to the regular patient room, always taking a single patient through the process.

Labels and identification band are checked before the procedure, and the tubes of blood are immediately labeled and placed in one area to be sent to the lab before the staff member turns his or her attention to the next patient. A similar process is used in preadmission testing, with focus on only one patient at a time.

As a result of this discovery, the process was revised for all units to include these steps:

  • Obtain a single set of patient labels, and get the necessary equipment.
  • Do a visual check of the labels and ID band before you draw blood.
  • Label the tube, and before you leave the room, check the labeled tube with the ID band.

Interruptions must be limited, especially for routine morning blood draws, when nurses and patient care technicians likely are to field requests to turn patients or accompany them to the bathroom. "The goal is to just focus on this particular task until it is completed," Maund says.