Verbal De-escalation of the Agitated Patient: Consensus Statement of the American Association for Emergency Psychiatry Project BETA De-escalation Workgroup

Agitation is an acute behavioral emergency requiring immediate intervention. Traditional methods of treating agitated patients, ie, routine restraints and involuntary medication, have been replaced with a much greater emphasis on a noncoercive approach. Experienced practitioners have found that if such interventions are undertaken with genuine commitment, successful outcomes can occur far more often than previously thought possible. In the new paradigm, a 3-step approach is used. First, the patient is verbally engaged; then a collaborative relationship is established; and, finally, the patient is verbally de-escalated out of the agitated state. Verbal de-escalation is usually the key to engaging the patient and helping him become an active partner in his evaluation and treatment; although, we also recognize that in some cases nonverbal approaches, such as voluntary medication and environment planning, are also important. When working with an agitated patient, there are 4 main objectives: (1) ensure the safety of the patient, staff, and others in the area; (2) help the patient manage his emotions and distress and maintain or regain control of his behavior; (3) avoid the use of restraint when at all possible; and (4) avoid coercive interventions that escalate agitation. The authors detail the proper foundations for appropriate training for de-escalation and provide intervention guidelines, using the “10 domains of de-escalation.

New Streaming Algorithms for Fast Detection of Superspreaders

High-speed monitoring of Internet traffic is an important and challenging problem, with applications to realtime attack detection and mitigation, traffic engineering, etc. However, packet-level monitoring requires fast streaming algorithms that use very little memory and little communication among collaborating network monitoring points. In this paper, we consider the problem of detecting superspreaders, which are sources that connect to a large number of distinct destinations. We propose new streaming algorithms for detecting superspreaders and prove guarantees on their accuracy and memory requirements. We also show experimental results on real network traces. Our algorithms are substantially more efficient (both theoretically and experimentally) than previous approaches. We also extend our algorithms to identify superspreaders in a distributed setting, with sliding windows, and when deletions are allowed in the stream (which lets us identify sources that make a large number of failed connections to distinct destinations). More generally, our algorithms are applicable to any problem that can be formulated as follows: given a stream of (x, y) pairs, find all the x’s that are paired with a large number of distinct y’s. We call this the heavy distinct-hitters problem. There are many network security applications of this general problem. This paper discusses these applications and, for concreteness, focuses on the superspreader problem.

New Streaming Algorithms for Fast Detection of Superspreaders

High-speed monitoring of Internet traffic is an important and challenging problem, with applications to realtime attack detection and mitigation, traffic engineering, etc. However, packet-level monitoring requires fast streaming algorithms that use very little memory and little communication among collaborating network monitoring points. In this paper

New Streaming Algorithms for Fast Detection of

conclusions contained here are those of the authors and should not be interpreted as necessarily representing th

New Streaming Algorithms for Fast Detection of Superspreaders

High-speed monitoring of Internet traffic is an important and challenging problem, with applications to realtime attack detection and mitigation, traffic engineering, etc. However, packet-level monitoring requires fast streaming algorithms that use very little memory and little communication among collaborating network monitoring points. In this paper, we consider the problem of detecting superspreaders, which are sources that connect to a large number of distinct destinations. We propose new streaming algorithms for detecting superspreaders and prove guarantees on their accuracy and memory requirements. We also show experimental results on real network traces. Our algorithms are substantially more efficient (both theoretically and experimentally) than previous approaches. We also extend our algorithms to identify superspreaders in a distributed setting, with sliding windows, and when deletions are allowed in the stream (which lets us identify sources that make a large number of failed connections to distinct destinations). More generally, our algorithms are applicable to any problem that can be formulated as follows: given a stream of (x; y) pairs, find all the x’s that are paired with a large number of distinct y’s. We call this the heavy distinct-hitters problem. There are many network security applications of this general problem. This paper discusses these applications and, for concreteness, focuses on the superspreader problem

References

Scutella. Dynamic shortest paths minimizing travel times and costs. Networks