Andre Elisseeff leads a research team at IBM’s Zurich Research Lab that in September demonstrated a prototype system that will allow doctors to view their patients’ electronic health record (eHR) using three-dimensional images of the human body. Called the Anatomic and Symbolic Mapper Engine, the system maps the information in a patient’s eHR to a 3-D image of the human body. A doctor first clicks the computer mouse on a particular part of the image, which triggers a search of the patient’s eHR to retrieve the relevant information. The patient’s information corresponding to that part of the image is then displayed, including text entries, lab results, and medical images, such as magnetic resource imaging. The doctor can zoom in on the image to retrieve selective information or narrow the search parameters by time or other factors.
“The 3-D coordinates in the model are mapped to anatomical concepts, which serve as an index onto the electronic health record. This means that you can retrieve the information by just clicking on the relevant anatomical part. It’s both 3-D navigation and a 3-D indexed map,” explains Elisseeff.
Elisseeff makes clear that the mapper engine is not just a 3-D imaging system. In addition to connecting to a patient’s eHR, the images displayed are linked to the 300 000 medical terms defined by the SNOMED (Systematized Nomenclature of Medicine) international standard, a copy of which the mapper engine accesses from a local database. “It is impossible for doctors to remember all these terms, and they will need some assistance in the near future. Medical standards are at least as complicated to doctors as normal medical terms are to patients,” Elisseeff notes.
Furthermore, Elisseeff says, “The SNOMED terminology is also a knowledge repository. It is how we include the medical knowledge into the mapper engine, how we tell the computer that a finger is a part of the hand or that flu and fever can be related. The glue between graphical objects and the electronic health record is fundamentally based on this computerized medical knowledge. This is the core of our work. The visible part of the application is the 3-D model. But the most challenging part is building the links such that they are clinically relevant.”
“You can think of it as being like Google Earth for the body,” is how Elisseeff frames the mapper engine. “We see this as a way to manage the increasing complexity that will come in using computers in medicine.”
