The impact of the TelEmergency program on rural emergency care: An implementation study.

Introduction Timely, appropriate intervention is key to improving outcomes in many emergent conditions. In rural areas, it is particularly challenging to assure quality, timely emergency care. The TelEmergency (TE) program, which utilizes a dual nurse practitioner and emergency medicine-trained, board-certified physician model, has the potential to improve access to quality emergency care in rural areas. The objective of this study was to examine how the implementation of the TE program impacts rural hospital Emergency Department (ED) operations. Methods Methods included a before and after study of the effect of the TE program on participating rural hospitals between January 2007 and December 2008. Data on ED and hospital operations were collected one year prior to and one year following the implementation of TE. Data from participating hospitals were combined and compared for the two time periods. Results Nine hospitals met criteria for inclusion and participated in the study. Total ED volumes did not significantly change with TE implementation, but ED admissions to the same rural hospital significantly increased following TE implementation (6.7% to 8.1%, p-value = 0.02). Likewise, discharge rates from the ED declined post-initiation (87.1% to 80.0%, p-value = 0.003). ED deaths and transfer rates showed no significant change, while the rate of patient discharge against medical advice significantly increased with TE use. Discussion In this analysis, we found a significant increase in the rate of ED admissions to rural hospitals with TE use. These findings may have important implications for the quality of emergency care in rural areas and the sustainability of rural hospitals' EDs.

Prevalence of intubation rescue by air medical personnel during transfers from rural emergency departments.

OBJECTIVE: Non-emergency-trained providers in rural emergency departments (ED) often lack the skills required for emergency resuscitations and rely on air medical transport teams to provide the initial airway stabilization of these patients. In this study, we determined the prevalence with which endotracheal intubations are required of air medical personnel upon arrival to rural EDs including intubations that were first attempted by the local provider. METHODS: A retrospective database review was conducted of all air medical transfers from rural hospitals for a 28-month period. Those patients requiring an airway were categorized according to which provider initiated the intubation procedure. The prevalence of intubations performed by air medical and local providers was recorded as the percent of the total number of intubations. RESULTS: There were a total of 217 patients from 11 rural EDs requiring airway support. Air medical personnel were responsible for 85% of the intubations. Alternative airway support was necessary in 5% of the patients after unsuccessful intubation attempts. The failed intubations tended to be slightly older and female. CONCLUSION: Our study suggests that the vast majority of the intubations for patients requiring a helicopter evacuation from these rural settings are performed by the air medical personnel.

Functionality of empirical model-based predictive analytics for the early detection of hemodynamic instabilty.

UNLABELLED: Background. Monitoring cardiovascular hemodynamics in the modern clinical setting is a major challenge. Increasing amounts of physiologic data must be analyzed and interpreted in the context of the individual patient’s pathology and inherent biologic variability. Certain data-driven analytical methods are currently being explored for smart monitoring of data streams from patients as a first tier automated detection system for clinical deterioration. As a prelude to human clinical trials, an empirical multivariate machine learning method called Similarity-Based Modeling (“SBM”), was tested in an In Silico experiment using data generated with the aid of a detailed computer simulator of human physiology (Quantitative Circulatory Physiology or “QCP”) which contains complex control systems with realistic integrated feedback loops. Methods. SBM is a kernel-based, multivariate machine learning method that that uses monitored clinical information to generate an empirical model of a patient’s physiologic state. This platform allows for the use of predictive analytic techniques to identify early changes in a patient’s condition that are indicative of a state of deterioration or instability. The integrity of the technique was tested through an In Silico experiment using QCP in which the output of computer simulations of a slowly evolving cardiac tamponade resulted in progressive state of cardiovascular decompensation. Simulator outputs for the variables under consideration were generated at a 2-min data rate (0.083Hz) with the tamponade introduced at a point 420 minutes into the simulation sequence. The functionality of the SBM predictive analytics methodology to identify clinical deterioration was compared to the thresholds used by conventional monitoring methods. Results. The SBM modeling method was found to closely track the normal physiologic variation as simulated by QCP. With the slow development of the tamponade, the SBM model are seen to disagree while the simulated biosignals in the early stages of physiologic deterioration and while the variables are still within normal ranges. Thus, the SBM system was found to identify pathophysiologic conditions in a timeframe that would not have been detected in a usual clinical monitoring scenario. Conclusion. In this study the functionality of a multivariate machine learning predictive methodology that that incorporates commonly monitored clinical information was tested using a computer model of human physiology. SBM and predictive analytics were able to differentiate a state of decompensation while the monitored variables were still within normal clinical ranges. This finding suggests that the SBM could provide for early identification of a clinical deterioration using predictive analytic techniques. KEYWORDS: predictive analytics, hemodynamic, monitoring.