Research Problems Categorized by Quadrant for Automation
that intermixes equipment, administrative procedures, and real people. Accordingly, research on automation for medicine will require a multidisciplinary team approach, including technical, medical, and social science expertise. Good design cannot be added on afterward, and intensive cooperative efforts involving people from all disciplines affected by any IT-based system are necessary from the start.
Box 5.4 describes some of the technical research challenges for automation organized by quadrant.
The data relevant to health care are highly heterogeneous, and the types and quantity of data evolve rapidly. In addition to patient-record information that exists in multiple forms, health care requires data about drugs and diagnoses, including data from signals captured by biomedical devices, voice recordings, and data captured as codes. Data are typically stored in multiple locations on multiple systems. Sometimes such data are stored in structured databases, and in other cases relevant data are found in legacy systems, structured files, and databases and text files behind Web forms. Data are increasingly multimedia and high-dimensional, including voice, imaging, and continuous biomedical signals. Data of various types have different degrees of reliability, ranging from test