Teaching sterile skills in anesthesia:Is providing context helpful for robust skill acquisition?

Epidural anesthesia is an invasive medical procedure for pain relief. However, current teaching methods are not sufficient for acquiring proper aseptic technique in this procedure (Friedman et al., 2008). In the present study we examined whether a context-providing method, previously successfully used for training Boeing pilots (Taatgen, Huss & Anderson, 2008), might be superior to the current practice of teaching the steps in the procedure as a list of actions.We taught 37 undergraduate medical students the preparation and execution of the first part of the epidural anesthesia procedure with either List instructions or Context instructions. In the List condition, the order of the actions had to be remembered and executed in that particular order. In the Context condition, participants were given instructions with photographs that showed the pre-conditions of a set of actions (“before”) and the post-condition (“after”), together with a description of the actions to be performed within the set. Thus, the List approach relies heavily on memory, whereas in the Context method at least part of action control should be delegated to the environment to prime the appropriate actions. However, contrary to expectations, participants in the Context condition performed worse than participants receiving the more traditional List instruction: they made significantly more sterility errors. We conclude that a better instruction method to learn a procedure does not necessarily lead to better aseptic technique and suggest that the concept of sterility be taught separately as well

Prehospital risk stratification in patients with chest pain

OBJECTIVES: The History, ECG, Age, Risk Factors and Troponin (HEART) Score is a decision support tool applied by physicians in the emergency department developed to risk stratify low-risk patients presenting with chest pain. We assessed the potential value of this tool in prehospital setting, when applied by emergency medical services (EMS), and derived and validated a tool adapted to the prehospital setting in order to determine if it could assist with decisions regarding conveyance to a hospital. METHODS: In 2017, EMS personnel prospectively determined the HEART Score, including point-of-care (POC) troponin measurements, in patients presenting with chest pain, in the north of the Netherlands. The primary endpoint was a major adverse cardiac event (MACE), consisting of acute myocardial infarction or death, within 3 days. The components of the HEART Score were evaluated for their discriminatory value, cut-offs were calibrated for the prehospital setting and sex was substituted for cardiac risk factors to develop a prehospital HEART (preHEART) Score. This score was validated in an independent prospective cohort of 435 patients in 2018. RESULTS: Among 1208 patients prospectively recruited in the first cohort, 123 patients (10.2%) developed a MACE. The HEART Score had a negative predictive value (NPV) of 98.4% (96.4-99.3), a positive predictive value (PPV) of 35.5% (31.8-39.3) and an area under the receiver operating characteristic curve (AUC) of 0.81 (0.78-0.85). The preHEART Score had an NPV of 99.3% (98.1-99.8), a PPV of 49.4% (42.0-56.9) and an AUC of 0.85 (0.82-0.88), outperforming the HEART Score or POC troponin measurements on their own. Similar results were found in a validation cohort. CONCLUSIONS: The HEART Score can be used in the prehospital setting to assist with conveyance decisions and choice of hospitals; however, the preHEART Score outperforms both the HEART Score and single POC troponin measurements when applied by EMS personnel in the prehospital setting

Smartwatch based automatic detection of out-of-hospital cardiac arrest: Study rationale and protocol of the HEART-SAFE project

Out-of-hospital cardiac arrest (OHCA) is a leading cause of mortality. Immediate detection and treatment are of paramount importance for survival and good quality of life. The first link in the ‘chain of survival’ after OHCA – the early recognition and alerting of emergency medical services – is at the same time the weakest link as it entirely depends on witnesses. About one half of OHCA cases are unwitnessed, and victims of unwitnessed OHCA have virtually no chance of survival with good neurologic outcome. Also in case of a witnessed cardiac arrest, alerting of emergency medical services is often delayed for several minutes. Therefore, a technological solution to automatically detect cardiac arrests and to instantly trigger an emergency response has the potential to save thousands of lives per year and to greatly improve neurologic recovery and quality of life in survivors. The HEART-SAFE consortium, consisting of two academic centres and three companies in the Netherlands, collaborates to develop and implement a technical solution to reliably detect OHCA based on sensor signals derived from commercially available smartwatches using artificial intelligence. In this manuscript, we describe the rationale, the envisioned solution, as well as a protocol outline of the work packages involved in the development of the technology

Smartwatch based automatic detection of out-of-hospital cardiac arrest: Study rationale and protocol of the HEART-SAFE project

Out-of-hospital cardiac arrest (OHCA) is a leading cause of mortality. Immediate detection and treatment are of paramount importance for survival and good quality of life. The first link in the ‘chain of survival’ after OHCA – the early recognition and alerting of emergency medical services – is at the same time the weakest link as it entirely depends on witnesses. About one half of OHCA cases are unwitnessed, and victims of unwitnessed OHCA have virtually no chance of survival with good neurologic outcome. Also in case of a witnessed cardiac arrest, alerting of emergency medical services is often delayed for several minutes. Therefore, a technological solution to automatically detect cardiac arrests and to instantly trigger an emergency response has the potential to save thousands of lives per year and to greatly improve neurologic recovery and quality of life in survivors. The HEART-SAFE consortium, consisting of two academic centres and three companies in the Netherlands, collaborates to develop and implement a technical solution to reliably detect OHCA based on sensor signals derived from commercially available smartwatches using artificial intelligence. In this manuscript, we describe the rationale, the envisioned solution, as well as a protocol outline of the work packages involved in the development of the technology

Smartwatch based automatic detection of out-of-hospital cardiac arrest: Study rationale and protocol of the HEART-SAFE project

Out-of-hospital cardiac arrest (OHCA) is a leading cause of mortality. Immediate detection and treatment are of paramount importance for survival and good quality of life. The first link in the ‘chain of survival’ after OHCA – the early recognition and alerting of emergency medical services – is at the same time the weakest link as it entirely depends on witnesses. About one half of OHCA cases are unwitnessed, and victims of unwitnessed OHCA have virtually no chance of survival with good neurologic outcome. Also in case of a witnessed cardiac arrest, alerting of emergency medical services is often delayed for several minutes. Therefore, a technological solution to automatically detect cardiac arrests and to instantly trigger an emergency response has the potential to save thousands of lives per year and to greatly improve neurologic recovery and quality of life in survivors. The HEART-SAFE consortium, consisting of two academic centres and three companies in the Netherlands, collaborates to develop and implement a technical solution to reliably detect OHCA based on sensor signals derived from commercially available smartwatches using artificial intelligence. In this manuscript, we describe the rationale, the envisioned solution, as well as a protocol outline of the work packages involved in the development of the technology