According to one expert, only 20 percent of the knowledge physicians use to make diagnosis and treatment decisions today is evidence based. The result? One in five diagnoses are incorrect or incomplete and nearly 1.5 million medication errors are made in the US every year. Given the growing complexity of medical decision making, how can health care providers address these problems?
The information medical professionals need to support improved decision making is available. Medical journals publish new treatments and discoveries every day. Patient histories give clues. Vast amounts of electronic medical record data provide deep wells of knowledge. Some would argue that in this information is the insight needed to avoid every improper diagnosis or erroneous treatment.
In fact, the amount of medical information available is doubling every five years and much of this data is unstructured - often in natural language. And physicians simply don't have time to read every journal that can help them keep up to date with the latest advances - 81 percent report that they spend five hours per month or less reading journals. Computers should be able to help, but the limitations of current systems have prevented real advances. Natural language is complex. It is often implicit: the exact meaning is not completely and exactly stated. In human language, meaning is highly dependent on what has been said before, the topic itself, and how it is being discussed: factually, figuratively or fictionally - or a combination.
How Watson can address healthcare challenges
Watson uses natural language capabilities, hypothesis generation, and evidence-based learning to support medical professionals as they make decisions. For example, a physician can use Watson to assist in diagnosing and treating patients. First the physician might pose a query to the system, describing symptoms and other related factors. Watson begins by parsing the input to identify the key pieces of information. The system supports medical terminology by design, extending Watson's natural language processing capabilities.
Watson then mines the patient data to find relevant facts about family history, current medications and other existing conditions. It combines this information with current findings from tests and instruments and then examines all available data sources to form hypotheses and test them. Watson can incorporate treatment guidelines, electronic medical record data, doctor's and nurse's notes, research, clinical studies, journal articles, and patient information into the data available for analysis.
Watson will then provide a list of potential diagnoses along with a score that indicates the level of confidence for each hypothesis.
The ability to take context into account during the hypothesis generation and scoring phases of the processing pipeline allows Watson to address these complex problems, helping the doctor and patient make more informed and accurate decisions.
Learn more about how Watson can help you address your most challenging problems - now and into the future.