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Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions
Research Problems Categorized byQuadrant for Automation
Quadrant 1 (General—applied efforts). Application of automation systems that exist; more use of business process integration technology as it exists in information technology; application of simple rules that can make a big difference
Quadrant 2 (Health care—applied efforts). Codification of low-hanging fruit; use of open-source and other community techniques to pool necessary information to produce better automation rules; application of simple things first, like electronic messaging, automated scheduling of various resources, and so on, and an emphasis on avoiding paralysis by analysis
Quadrant 3 (General—advanced efforts). Explanation, self-testing of efficacy, advanced learning, and management of false-negative and false-positive conditions
Quadrant 4 (Health care—advanced efforts). Extension of underlying data uses and modeling to improve model precision (e.g., more data feeding into drug interactions systems could be used to reduce false alarms); efforts to ensure that outcomes are known to the system so that it can self-report and learn
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.
5.2.3 Data Sharing and Collaboration
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