graphics. Although these displays cannot deliver, for example, high-definition radiological images, they can produce hard-copy printouts of display screens, graphs and tables, and signals such as those needed for an electrocardiogram (ECG). A complete on-line CPR reduces the necessity for printing multipart copies of these printouts.

A short response time has proven to be an important factor in successful CPR systems (Bleich and Slack, 1989). This requirement refers primarily to retrieving data, but it is equally important for inputting data. In future CPR systems, the displays and reporting formats of CPR data are likely to be configured or modified by users. Thus, the same data may be presented differently, or in different combinations, to different health care professionals, each of whom has differing "views" or "windows" into the same CPR (see Chapter 2). Customizing data in this way is a difficult capability to implement but will produce a system that is much more attractive to end users.

Text Processing

To establish a diagnosis, physicians and other health care professionals use patient information in a textual form—for example, the patient history and the results of the physical examination. With a CPR system, professionals search for and retrieve such text from database systems using query languages, which in the past were often idiosyncratic to a particular system. In recent years, gradual progress has been made in standardizing such languages.

Natural language understanding, or the ability of the computer system to selectively extract meaning from textual data, has been slower to evolve because of its inherently greater complexity (Obermeier, 1989). Compounding this complexity is the slow development of efforts to encourage a more uniform vocabulary in health care. Automated speech-recognition systems may help to add uniformity and consistency to vocabularies for the CPR by encouraging the speaker to use clinically relevant, yet consistent, terminology.

In the 1990s, text-processing systems for translating the narrative found in discharge summaries and other parts of the CPR are likely to be used to generate codes for billing. As text-processing systems improve in accuracy and performance, they may be used to extract significant phrases or attributes from the CPR that could assist the user in searching related databases. For example, attributes derived from the CPR might be matched against the terms and concepts in the National Library of Medicine's (NLM) Unified Medical Language System (UMLS; Humphreys and Lindberg, 1989). Improved text-processing systems would make it possible to use data from the CPR, in conjunction with the UMLS, to lead practitioners

The National Academies of Sciences, Engineering, and Medicine
500 Fifth St. N.W. | Washington, D.C. 20001

Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement