Name
Abstract | ||
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Alarmed physiological monitors have become a standard part of the ICU. While the alarms generated by these monitors can be important indicators of an altered physiological condition, most are unhelpful to medical staff due to a high incidence of false and clinically insignificant alarms. High numbers of false/insignificant alarms can lead to several adverse consequences such as increased patient anxiety, distraction of clinicians, and decreased efficiency in delivery of care. Furthermore, repeated false/insignificant alarms may increase the chance that healthcare providers ignore clinically significant alarms. In this paper we review the current state of intelligent alarm processing and describe an integrated systems methodology to extract clinically relevant information from physiological data. Such a method would aid significantly in the reduction of false alarms and provide nursing staff with a more reliable indicator of patient condition. I. INTRODUCTION Modern intensive care units (ICU) are equipped with a large array of alarmed monitors and devices which are used in an attempt to detect clinical changes at the earliest possible moment, so as to prevent any further deterioration in a patients condition. The effectiveness of these systems depends on the sensitiv- ity and specificity of the alarms, as well as on the responses of the ICU staff to the alarms. However, when large numbers of alarms are either technically false, or true, but clinically irrelevant, response efficiency can be decreased, reducing the quality of patient care and increased patient (and family) anxiety. Previous studies indicate that, in some cases, over 90 percent of the alarms generated are either false or clinically insignificant alarms(1). Moreover, as the number of monitoring equipment in- creases, the number of false/insignificant alarms increases (2). With this increase in the number of alarms it is no surprise that nurses and physicians are frustrated by the flood of noise (3) and in some cases implement their own filtering techniques (4). ICUs, therefore, are in great need of tools to help clinicians analyze the huge amount of data recorded and to support them in decision making tasks. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/IEMBS.2006.260913 | EMBC |
Keywords | DocType | Volume |
that is,most alarm systems are generally based on threshold crossings,ii. intelligent alarm processing currently,they trigger when the current reading exceeds a preset boundary. this method however,data mining,integrable system,filtering,patient monitoring,intelligent systems | Conference | Suppl |
ISSN | Citations | PageRank |
1557-170X | 4 | 0.66 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Craig B. Laramee | 1 | 4 | 0.66 |
| Leann Lesperance | 2 | 4 | 0.66 |
| Don Gause | 3 | 4 | 0.66 |
| Ken McLeod | 4 | 4 | 0.66 |