![]() The problem of information overload has been identified as a key factor in medical errors. Noise, especially from alarms, has been implicated as the one of the five most common reasons for poor sleep quality in the ICU. Alarms contribute significantly to this increase. Sound pressure levels in the ICU are frequently in excess of the permissible noise exposure recommended by the US Environmental Protection Agency (45 dBA per 24 h with peaks up to 96 dBAper 24 h above the threshold of hearing). ![]() Noise levels in hospitals are escalating. Sound pollution is another important disadvantage of false alarms. All of these responses can lead to serious adverse effects. High numbers of false-positive alarms push clinicians to change thresholds to inappropriate levels, or to simply ignore them. With high alarm frequency, care providers risk becoming “desensitized” and/or confused. įrequent and particularly false alerts, come with a cost. also found that most of the alarms generated by bedside monitors were clinically insignificant and only distracted bedside providers. In a landmark paper, Lawless investigated alarms in a pediatric ICU and demonstrated that 94 % of the generated alarms were not clinically important. Furthermore, one of the earliest attempt to classify the rate of false ICU alarms demonstrated that only eight out of 1,455 alerts indicated a life-treating situation. However, in study by Tsien and Fackler, only 8 % of all alarms were determined to be true alarms with associated clinical significance. As designed, device-embedded alarms are intended to monitor vital cardiopulmonary functions and to increase patient safety and quality of care by allowing early detection of significant abnormalities. Nearly half (45 %) of such adverse events in the Critical Care Safety Study were deemed preventable. Notably, adverse events in ICUs occur at a rate of 81 per 1,000 patient-days and serious errors occur at a rate of 150 per 1,000 patient-days. In the OR 32 % of negative outcomes cases were potentially preventable by additional monitoring. The need for monitoring in acute care environment such as ICU and the operation room (OR) was clearly established by malpractice claims analysis. The complexity of the technology used in the ICU setting greatly contributes to the occurrence of errors and adverse events. The Joint Commission on Accreditation of Healthcare Organizations defines a clinical alarm as any alarm that is intended to protect the individual receiving care or alert the staff that the individual is at increased risk and needs immediate assistance. Unfortunately these alerts rarely result in meaningful clinical interventions and often contribute to alert fatigue in the ICU. These alerts represent examples of DSS built on algorithms within medical devices required to have high sensitivity for critical events. An estimated 40 alarms can be activated by such patients at any given instance. As an example, ventilated patients in the intensive care unit (ICU) are attached to multiple diagnostic, monitoring and therapeutic devices, all generating data and alerts. ![]() On that background, DSS has been proposed as a potential solution. However, with the advent of electronic medical records, the volume of new data being generated continues to grow and threatens overwhelming physicians with “information overload”. The fundamental goal of clinical medicine is to make decisions that match the patient with a correct diagnosis and treatment plan. In the medical arena, where decision-making is predominantly driven by patient data and clinical knowledge, DSS finds a natural niche. ![]() These systems were initially researched and used in production scheduling, financial analysis and clinical medicine. Computerized decision support systems (DSS) became feasible with the development of microcomputers in the mid-1960s. “Decision Support System” is a generic term for any computer application that enhances an individual’s or group’s ability to make decisions.
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