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Background: Emergency medical services (EMS) are often patients' first point of contact for urgent and emergency care needs. Patients are triaged over the phone and may receive an ambulance response, with potential conveyance to the hospital. A recent scoping review suggested disparities in EMS patient care in the United States. However, it is unknown how health inequalities impact EMS care in other developed countries and how inequalities are being addressed. Objectives: This rapid evidence map of published literature aims to map known health inequalities in EMS patients and describe interventions reducing health inequalities in EMS patient care. Methods: The search strategy consisted of EMS synonyms and health inequality synonyms. The MEDLINE/PubMed database was searched from 1 January 2010 to 26 July 2022. Studies were included if they described empirical research exploring health inequalities within ambulance service patient care. Studies were mapped on to the EMS care interventions framework and Core20PLUS5 framework. Studies evaluating interventions were synthesised using the United Kingdom Allied Health Professions Public Health Strategic Framework. Results: The search strategy yielded 771 articles, excluding duplicates, with two more studies added from hand searches. One hundred studies met the inclusion criteria after full-text review. Inequalities in EMS patient care were predominantly situated in assessment, treatment and conveyance, although triage and response performance were also represented. Studies mostly explored EMS health inequalities within ethnic minority populations, populations with protected characteristics and the core issue of social deprivation. Studies evaluating interventions reducing health inequalities (n = 5) were from outside the United Kingdom and focused on older patients, ethnic minorities and those with limited English proficiency. Interventions included community paramedics, awareness campaigns, dedicated language lines and changes to EMS protocols. Conclusions: Further UK-based research exploring health inequalities of EMS patients would support ambulance service policy and intervention development to reduce health inequality in urgent and emergency care delivery.
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Care must be taken to mitigate the effect of cognitive bias in times of frequent common presentations. The etiology of bicytopenias and pancytopenias must always be carefully investigated. Blast cells in low count B ALL may not be seen on a peripheral smear and diagnosis often requires confirmational bone marrow aspirate with flow cytometry and molecular typing.
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Background: The COVID-19 pandemic placed significant demand on the NHS, including ambulance services, but it is unclear how this affected ambulance service staff and paramedics in other clinical settings (e.g. urgent and primary care, armed services, prisons). This study aimed to measure the self-perceived preparedness and impact of the first wave of the pandemic on paramedics' psychological stress and perceived ability to deliver care. Methods: Ambulance clinicians and paramedics working in other healthcare settings were invited to participate in a three-phase sequential online survey during the acceleration (April 2020), peak (May 2020) and deceleration (September/October 2020) phases of the first wave of COVID-19 in the United Kingdom. Recruitment used social media, Trust internal bulletins and the College of Paramedics' communication channels, employing a convenience sampling strategy. Data were collected using purposively developed open- and closed-ended questions and the validated general health questionnaire-12 (GHQ-12). Data were analysed using multi-level linear and logistic regression models. Results: Phase 1 recruited 3717 participants, reducing to 2709 (73%) by phase 2 and 2159 (58%) by phase 3. Participants were mostly male (58%, n = 2148) and registered paramedics (n = 1992, 54%). Mean (standard deviation) GHQ-12 scores were 16.5 (5.2) during phase 1, reducing to 15.2 (6.7) by phase 3. A total of 84% of participants (n = 3112) had a GHQ-12 score ≥ 12 during the first phase, indicating psychological distress. Participants that had higher GHQ-12 scores were feeling unprepared for the pandemic, and reported a lack of confidence in using personal protective equipment and managing cardiac arrests in confirmed or suspected COVID-19 patients. Conclusions: Most participants reported psychological distress, the reasons for which are multi-factorial. Ambulance managers need to be aware of the risks to staff mental health and take action to mitigate these, to support staff in the delivery of unscheduled, emergency and urgent care under these additional pressures.
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Aims: Ambulance pre-alerts are used to inform receiving emergency departments (EDs) of the arrival of critically unwell or rapidly deteriorating patients who need time-critical assessment or treatment immediately upon arrival. Inappropriate use of pre-alerts can lead to EDs diverting resources from other critically ill patients. However, there is limited guidance about how pre-alerts should be undertaken, delivered or communicated. We aimed to map existing pre-alert guidance from UK NHS ambulance services to explore consistency and accessibility of existing guidance. Methods: We contacted all UK ambulance services to request documentation containing guidance about pre-alerts. We reviewed and mapped all guidance to understand which conditions were recommended for a pre-alert and alignment with Association of Ambulance Chief Executives (AACE) and Royal College of Emergency Medicine (RCEM) pre-alert guidance. We reviewed the language and accessibility of guidance using the AGREE II tool. Results: We received responses from 15/19 UK ambulance services and 10 stated that they had specific pre-alert guidance. We identified noticeable variations in conditions declared suitable for pre-alerts in each service, with a lack of consistency within each ambulance service's own guidance, and a lack of alignment with the AACE/RCEM pre-alert guidance. Services listed between four and 45 different conditions suitable for pre-alert. There were differences in physiological thresholds and terminology, even for conditions with established care pathways (e.g. hyperacute stroke, ST segment elevation myocardial infarction). Pre-alert criteria were typically listed in several short sections in lengthy handover procedure policy documents. Documents appraised were of poor quality with low scores below 35% for applicability and overall. Implications: There is a clear need for ambulance services to have both policies and tools that complement each other and incorporate the same list of pre-alertable conditions. Clinicians need a single, easily accessible document to refer to in a time-critical situation to reduce the risk of making an incorrect pre-alert decision.
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PURPOSE: Suspected seizures present challenges for ambulance services, with paramedics reporting uncertainty over whether or not to convey individuals to emergency departments. The Risk of ADverse Outcomes after a Suspected Seizure (RADOSS) project aims to address this by developing a risk assessment tool utilizing structured patient care record and dispatch data. It proposes a tool that would provide estimates of an individual's likelihood of death and/or recontact with emergency care within 3 days if conveyed compared to not conveyed, and the likelihood of an 'avoidable attendance' occurring if conveyed. Knowledge Exchange workshops engaged stakeholders to resolve key design uncertainties before model derivation. METHOD: Six workshops involved 26 service users and their significant others (epilepsy or nonepileptic attack disorder), and 25 urgent and emergency care clinicians from different English ambulance regions. Utilizing Nominal Group Techniques, participants shared views of the proposed tool, benefits and concerns, suggested predictors, critiqued outcome measures, and expressed functionality preferences. Data were analysed using Hamilton's Rapid Analysis. RESULTS: Stakeholders supported tool development, proposing 10 structured variables for predictive testing. Emphasis was placed on the tool supporting, not dictating, care decisions. Participants highlighted some reasons why RADOSS might struggle to derive a predictive model based on structured data alone and suggested some non-structured variables for future testing. Feedback on prediction timeframes for service recontact was received, along with advice on amending the 'avoidable attendance' definition to prevent the tool's predictions being undermined by potential overuse of certain investigations in hospital. CONCLUSION: Collaborative stakeholder engagement provided crucial insights that can guide RADOSS to develop a user-aligned, optimized tool.