In fact, the authors write, "An organization with less cumulative experience than its rivals can still achieve a performance advantage if it more thoroughly exploits its opportunities for learning."
The study, "Organizational Differences in Rates of Learning: Evidence from the Adoption of Minimally Invasive Cardiac Surgery," by Gary P. Pisano, Richard M.J. Bohmer, and Amy C. Edmondson, Harvard Business School, appears in the current issue of Management Science, an INFORMS publication.
Best Case, Worst Case
The authors offer a case study of one of the speediest learners in the study, a respected community hospital performing 1,200 cardiac operations a year. At this hospital, the surgeon who adopted the procedure for the hospital handpicked the team based on their prior experience working together and their demonstrated ability to work as a group. Team members began a high degree of cross-departmental communication and cooperation even before the first operation.
To build team feeling, the adopting surgeon made sure that the individuals who went to the training program performed the first 15 cases together before any new team members were changed.
Explained one doctor on the team, "The surgeon needs to be willing to allow himself to become a partner [with the rest of the team] so he can accept input."
In contrast, one of the worst performing hospitals is a large, nationally renowned academic medical center. This hospital picked team members largely on the basis of availability. Only three of the four core-team members who attended training were present for the first case. There were no meetings to discuss cases before operations.
The staff involved with the procedure didn't bond. Said one nurse, "We don't have any real teams here. It's just who gets assigned on any given day."
Not only was team membership unstable during early operations, but the surgeon compounded learning difficulties by shunning the kind of repetition that helps medical professionals master a difficult procedure, explaining "I try to do something new on every case."
Different Rates of Organizational Learning
The study maintains that organizational learning processes allow some organizations to benefit more than others from equivalent levels of experience. The authors argue that learning curves can vary across organizations engaged in the same learning task due to organizational learning.
To investigate, they examined cardiac surgery departments implementing a new technology for minimally invasive cardiac surgery. In contrast to conventional surgery, in which the roles of each team member are well established, the new procedure expanded the roles of non-surgeons assisting the operation.
The study focused on a single dimension of learning: reduction in the time required to perform the new procedure.
Procedure time is the single most important factor driving the total costs of treating and caring for a cardiac surgery patient, the authors write. Procedure time is also a good indicator of the way an organization, rather than individuals, learns. And reducing procedure time is desirable for the patient's well being.
To examine learning rates, the authors analyzed data on operative procedure times from a sample of 660 patients who underwent the new operation at 16 different institutions. All the teams participating were chosen because they were regarded as excellent in their field. All adopting hospitals received the same three-day training session before performing the operation.
The authors constructed four mathematical models to evaluate the data.
The results confirmed that cumulative experience is a significant predictor of learning, and further reveal that the slope of the learning curve varies significantly across organizations. In their results, they found that initial procedure times varied across hospitals in the sample. They also found an overall tendency for procedure time to fall with cumulative experience. At the same time, they found statistically significant differences in the learning curves across the sample of 16 sites.
The superior surgical team mentioned above had slower than average predicted procedure time for its first seven cases. The models show, however, that by case 50, this team was able to undertake a comparable procedure significantly faster than the typical team in the sample.
The estimated net adjusted procedure time for this hospital at case 50 was approximately 132 minutes, versus 220 minutes for the sample average.
Looking at the data from case 40, the superior hospital's estimated 143-minute time was the shortest net adjusted procedure time in the sample. The largest estimate, 305 minutes, was more than 2.5 hours longer.
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Journal
Management Science