The push for clinical decision support technology in medicine is a logical consequence of our experiences as consumers and the need for intelligent support at the bedside. However, the reality is that clinical decision support technology has yet to demonstrate sufficient practical value in clinical practice.
Decision support system applications have a long history in the field of oncology. Almost from the birth of the so-called “expert systems,” there have been programs dedicated to helping practitioners diagnose, treat, or monitor cancers. Among the first to explore this area
was ONCOCIN in the mid-’80s. Like many other systems, ONCOCIN was based on a set of rules, encoding actions to trigger in specific situations. More generally, a clinical decision support system such as ONCOCIN is a computer program that takes, as input, the description of a medical situation and provides, as a result, information supporting the practitioner in making the appropriate decisions concerning this situation
. Such a program intends to capture—to encode—medical knowledge and to apply problem-solving methods to automatically “reason” based upon this knowledge.
Although there has been a lot of research, resulting in several prototypes and experiments in applying decision support systems in medicine, none have ever been able to support the daily practice of oncology. Expert systems
, after generating so much enthusiasm, have been disappointing and acquired a bad reputation. They were blamed for not being adaptive enough to particular situations, not being helpful enough—as they were not able to explain their results—and also not being dynamic enough, as they were hard to modify to keep pace with the advances in medicine.
However, the underlying technologies for decision support systems have evolved immensely since the early expert systems. Research in knowledge management has produced adequate methods for capturing and maintaining computerized expertise, and provided the appropriate level of interaction between the system and the practitioner. Therefore, this might be the time to consider once again the question of decision support systems in oncology: Is their bad reputation still justified, and what are the obstacles to widespread adoption?The Kasimir project
The beginnings of an answer to these questions may come from our experience in building a modern decision support system in oncology: the Kasimir project
Born in 1997, the Kasimir project gathers specialists in oncology, as well as researchers in psycho-ergonomics and in computer science, with the goal of supporting practitioners in managing and using decision knowledge in the Lorraine region of France. This decision knowledge can be found in decision protocols: documents summarizing the action to be applied in common medical situations according to the state of the art in medicine and following the principle of evidence-based medicine. For example, the breast cancer treatment protocol provides the necessary information to help a physician select, depending on the patient and on her cancer, the standard treatment to apply. Other decision protocols considered in this project concern the treatment of prostate cancers, the surveillance of breast cancers, and the inclusion of patients in clinical trials.
The Kasmir Project focuses on two levels of decision support: protocol application and protocol adaptation. The study of protocol application has led to computer programs within the Kasimir system that are stable and are based on standard methods and tools used in knowledge-based systems. Such a program has a user-friendly interface
for describing a patient and her cancer, and for displaying the associated standard decision. Some studies carried on by physicians have shown a statistically significant improvement in decision protocol observance when the physicians use the Kasimir system, compared
to their observance when they use paper-based versions of the same protocol.