In terms of using technologies to make human error less likely, health care is way behind most other industries. Starbucks has more built-in checks for errors in making a cup of coffee than the health care system does for treating a patient. It is hard to grasp that health care is based mostly on intuition and a common knowledge base rather than on a built-in system.

Having said this, it is important to say also that there are many, many opportunities to bring disciplines together and improve the situation. Improvements in care delivery through the wise application of innovations are within our reach—if we bring in the right disciplines and support the right research. Statisticians and others who are poised to make real breakthroughs in terms of improving health care delivery should be working alongside health care researchers.

This will require that specific changes be put in place. How can these people be brought together? How should current training programs be changed? What about career paths? Are we willing to provide academic rewards (in addition to peer-reviewed publications) to people whose contributions make “smart” applications of knowledge and technologies to improve care? A senior researcher recently challenged us with the following questions, “Would you advise a young person to get into the improvement arena? Is it a viable career path?” As the National Institutes of Health turn attention to reengineering clinical research, they will also confront the issues of academic incentives.

Another major challenge facing us is how to accelerate knowledge transfer. In the old model, a researcher conceives a study, carries out the study, and publishes the results. Then the results trickle down, and people eventually change their practices. The old model works much too slowly.

There are many opportunities for improving the safety and quality of health care. We must rigorously evaluate the potential of new technologies so that the improvements that are possible are realized and so that we avoid costly investments in applications that yield little gain, and may actually impede progress.



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