Managing the Prevention of In-Hospital Resuscitation by Early Detection and Treatment of High-Risk Patients

In hospitalized patients, cardiorespiratory collapse mostly occurs after a distinct period of deterioration. This deterioration can be discovered by a systematic quantification of a set of clinical parameters. The combination of such a detection system—to identify patients at risk in an early stage —and a rapid response team—which can intervene immediately—can be implemented to prevent life-threatening situations and reduce the incidence of in-hospital cardiac arrests outside the intensive care setting. The effectiveness of both of these systems is influenced by the used trigger criteria, the number of rapid response team (RRT) activations, the in- or exclusion of patients with a DNR code >3, proactive rounding, the team composition, and its response time. Each of those elements should be optimized for maximal efficacy, and both systems need to work in tandem with little delay between patient deterioration, accurate detection, and swift intervention. Dependable diagnostics and scoring protocols must be implemented, as well as the organization of a 24/7 vigilant and functional experienced RRT. This implies a significant financial investment to provide an only sporadically required fast intervention and sustained alertness of the people involved

Medical emergencies related to ethanol and illicit drugs at an annual, nocturnal, indoor, electronic dance music event

Introduction: Medical problems are frequently encountered during electronic dance music (EDM) events. Problem: There are uncertainties about the frequencies and severity of intoxications with different types of recreational drugs: ethanol, "classical" illicit party drugs, and new psychoactive substances (NPS). Methods: Statistical data on the medical problems encountered during two editions of an indoor electronic dance event with around 30,000 attendants were retrieved from the Belgian Red Cross (Mechelen, Belgium) database. Data on drug use were prospectively collected from the patient (or a bystander), the clinical presentation, and/or toxicological screening. Results: In the on-site medical station, 487 patients were treated (265 in 2013 and 222 in 2014). The most frequent reasons were trauma (n = 171), headache (n = 36), gastro-intestinal problems (n = 44), and intoxication (n = 160). Sixty-nine patients were transferred to a hospital, including 53 with severe drug-related symptoms. Analysis of blood samples from 106 intoxicated patients detected ethanol in 91.5%, 3,4-methylenedioxymethamphetamine (MDMA) in 34.0%, cannabis in 30.2%, cocaine in 7.5%, amphetamine in 2.8%, and gamma-hydroxybutyric acid (GHB) in 0.9% of patients (alone or in combination). In only six of the MDMA-positive cases, MDMA was the sole substance found. In 2014, the neuroleptic drug clozapine was found in three cases and ketamine in one. Additional analyses for NPS were performed in 20 cases. Only in one agitated patient, the psychedelic phenethylamines 25B-NBOMe and 25C-NBOMe were found. Conclusions: At this particular event, recreational drug abuse necessitated on-site medical treatment in one out of 350 attendants and a hospital transfer in one out of 1,000. Ethanol remains the most frequently abused (legal) drug, yet classical illicit recreational drugs are also frequently (co-) ingested. The most worrying observation was high-risk poly-drug use, especially among MDMA users. Regarding NPS, the number of cases was low and the clinical presentations were rather mild. It should be stressed that these observations only apply to this particular event and cannot be generalized to other EDM events

Integrated declarative process and decision discovery of the emergency care process

Deviations and variations are the norm rather than the exception in medical diagnosis and treatment processes. Physicians must leverage their knowledge and experience to choose an appropriate variation for each patient. However, this knowledge and experience is often tacit. Process modeling offers a way to convert tacit to explicit knowledge. Many process mining techniques have been developed due to the difficulty of doing this manually, yet, they often neglect the decisions themselves, and these proposed techniques are just one piece of a comprehensive process discovery method. In this paper, we use the Action Design Research methodology to develop a method for process and decision discovery of medical diagnosis and treatment processes. The method was iteratively improved and validated by applying it to a practical setting, which was the emergency medicine department of a hospital. An analysis of the resulting model shows that previously tacit knowledge was successfully made explicit