Systematic review: the barriers and facilitators for minority ethnic groups in accessing urgent and prehospital care

Introduction Research addressing inequalities has focused predominantly on primary and community care; few initiatives relate to the prehospital environment. We aimed to identify in the literature barriers or facilitators experienced by patients from black and minority ethnic (BME) communities in accessing prehospital care and to explore the causes and consequences of any differences in delivery. Methods We conducted a systematic literature review and narrative synthesis. Electronic and journal hand searches from 2003 through 2013 identified relevant evaluative studies (systematic reviews, randomised controlled trials, quasi-experimental, case and observational studies). A researcher extracted data to determine characteristics, results and quality, each checked by a second reviewer. The main outcome measures were delays in patient calls, mortality rates and 30-days survival post discharge. Results Eighteen studies met criteria for the review: two concerned services in England and Wales and 15 were United States based. Reported barriers to accessing care were generic (and well-known) given the heterogeneity of BME groups: difficulties in communication where English was the patient’s second language; new migrants’ lack of knowledge of the health care system leading to inappropriate emergency calls; and cultural assumptions among clinical staff resulting in inappropriate diagnoses and treatment. There were limited reported facilitators to improvement, such as the need for translation services and staff education, but the latter were poorly described or developed. Where outcomes were discussed, there was evidence for race-related disparity in mortality and survival rates. This could reflect differences in condition severity, delays between onset and initiation of calls, or the scope of response and assistance. Conclusion The paucity of literature and difficulties of transferring findings from US to UK context identified an important research gap. Further studies should be undertaken to investigate UK differences in prehospital care and outcomes for BME groups, followed by qualitative approaches to understand barriers and enablers to equitable access

Optimal allocation of defibrillator drones in mountainous regions

Responding to emergencies in Alpine terrain is quite challenging as air ambulances and mountain rescue services are often confronted with logistics challenges and adverse weather conditions that extend the response times required to provide life-saving support. Among other medical emergencies, sudden cardiac arrest (SCA) is the most time-sensitive event that requires the quick provision of medical treatment including cardiopulmonary resuscitation and electric shocks by automated external defibrillators (AED). An emerging technology called unmanned aerial vehicles (or drones) is regarded to support mountain rescuers in overcoming the time criticality of these emergencies by reducing the time span between SCA and early defibrillation. A drone that is equipped with a portable AED can fly from a base station to the patient's site where a bystander receives it and starts treatment. This paper considers such a response system and proposes an integer linear program to determine the optimal allocation of drone base stations in a given geographical region. In detail, the developed model follows the objectives to minimize the number of used drones and to minimize the average travel times of defibrillator drones responding to SCA patients. In an example of application, under consideration of historical helicopter response times, the authors test the developed model and demonstrate the capability of drones to speed up the delivery of AEDs to SCA patients. Results indicate that time spans between SCA and early defibrillation can be reduced by the optimal allocation of drone base stations in a given geographical region, thus increasing the survival rate of SCA patients

Cardiopulmonary resuscitation quality: Widespread variation in data intervals used for analysis

AIM: There is a growing body of evidence for the relationship between CPR quality and survival in cardiac arrest patients. We sought to describe the characteristics of the analysis intervals used across studies. METHODS: Relevant papers were selected as described in our recent systematic review. From these papers we collected information about (1) the time interval used for analysis; (2) the event that marked the beginning of the analysis interval; and (3) the minimum amount of CPR quality data required for a case to be included in the analysed cohort. We then compared this data across papers. RESULTS: Twenty-one studies reported on the association between CPR quality and cardiac arrest patient survival. In two thirds of studies data from the start of the resuscitation episode was analysed, in particular the first 5minutes. Commencement of the analysis interval was marked by various events including ECG pad placement and first chest compression. Nine studies specified a minimum amount of data that had to have been collected for the individual case to be included in the analysis; most commonly one minute of data. The use of shorter intervals allowed for inclusion of more cases as it included cases that did not have a complete dataset. CONCLUSION: To facilitate comparisons across studies, a standardized definition of the data analysis interval should be developed; one that maximises the amount of cases available without compromising the data's representability of the resuscitation effort

A Wireless ECG Monitoring System for Healthcare

With aging of population, there has been a significant increase in the number of patients suffering from cardiovascular diseases. This results in an increased cost of healthcare associated with hospitalization, treatment and monitoring. In this paper, an architectural framework of a system that utilizes mobile technologies to enable continuous, wireless, electrocardiogram (ECG) monitoring of patients anytime anywhere is presented. The intelligent agents residing in the system detect any anomalous ECG readings and trigger an alarm that would be sent to the healthcare center in case of an emergency. The proposed system would not only provide a better quality of life to the patients by giving them the independence to move around freely in addition to continuous monitoring of heart but will also save healthcare costs associated with prolonged hospitalization of cardiac patients

A New Paradigm for Human Resuscitation Research Using Intelligent Devices

Objectives: To develop new methods for studying correlations between the performance and outcome of resuscitation efforts in real-world clinical settings using data recorded by automatic devices such as automatic external defibrillators (AEDs), and to explore effects of shock timing and chest compression depth in the field. Methods: In 695 records of AED use in the pre-hospital setting, continuous compression data were recorded using AEDs capable of measuring sternal motion during compressions, together with timing of delivered shocks and the electrocardiogram. In patients who received at least one shock, putative return of spontaneous circulation (P-ROSC) was defined as a regular, narrow complex electrical rhythm \u3e 40 beats/min with no evidence of chest compressions at the end of the recorded data stream. Transient return of spontaneous circulation (t-ROSC) was defined as the presence of a post-shock organized rhythm \u3e 40 beats/min within 60 seconds, and sustained 30 seconds. 2x2 contingency tables were constructed to examine the association between these outcomes and dichotomized time of shock delivery or chest compression depth, using the Mood median test for statistical significance. Results: The probability of P-ROSC for first shocks delivered \u3c 50 seconds (the median time) after the start of resuscitation was 23%, versus 11% for first shocks \u3e 50 seconds (p=0.028, one tailed). Similarly, the probability of t-ROSC for shorter times to shock was 29%, compared to the 15% for delayed first shocks (p=0.016). For shocks occurring \u3e3 minutes after initiation of rescue attempts, the probability of t-ROSC with pre-shock average compression depth \u3e 5 cm was more than double that with compression depth \u3c 5 cm (17.7% vs. 8.3%, p=0.028). For shocks \u3e5 minutes the effect of deeper compressions increased (23.4% vs. 8.2%, p=0.008). Conclusions: Much can be learned from analysis of performance data automatically recorded by modern resuscitation devices. Use of the Mood median test of association proved to be sensitive, valid, distribution independent, noise-resistant, and also resistant to biases introduced by the inclusion of hopeless cases. Efforts to shorten the time to delivery of the first shock and to encourage deeper chest compressions after the first shock are likely to improve resuscitation success. Such refinements can be effective even after an unknown period of preceding downtime

Artificial intelligence for clinical decision support for monitoring patients in cardiovascular ICUs: a systematic review

Background: Artificial intelligence (AI) and machine learning (ML) models continue to evolve the clinical decision support systems (CDSS). However, challenges arise when it comes to the integration of AI/ML into clinical scenarios. In this systematic review, we followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA), the population, intervention, comparator, outcome, and study design (PICOS), and the medical AI life cycle guidelines to investigate studies and tools which address AI/ML-based approaches towards clinical decision support (CDS) for monitoring cardiovascular patients in intensive care units (ICUs). We further discuss recent advances, pitfalls, and future perspectives towards effective integration of AI into routine practices as were identified and elaborated over an extensive selection process for state-of-the-art manuscripts. Methods: Studies with available English full text from PubMed and Google Scholar in the period from January 2018 to August 2022 were considered. The manuscripts were fetched through a combination of the search keywords including AI, ML, reinforcement learning (RL), deep learning, clinical decision support, and cardiovascular critical care and patients monitoring. The manuscripts were analyzed and filtered based on qualitative and quantitative criteria such as target population, proper study design, cross-validation, and risk of bias. Results: More than 100 queries over two medical search engines and subjective literature research were developed which identified 89 studies. After extensive assessments of the studies both technically and medically, 21 studies were selected for the final qualitative assessment. Discussion: Clinical time series and electronic health records (EHR) data were the most common input modalities, while methods such as gradient boosting, recurrent neural networks (RNNs) and RL were mostly used for the analysis. Seventy-five percent of the selected papers lacked validation against external datasets highlighting the generalizability issue. Also, interpretability of the AI decisions was identified as a central issue towards effective integration of AI in healthcare