13 research outputs found

    Clinical reasoning in the emergency medical services: an integrative review

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    Abstract: Clinical reasoning is the process of gathering and understanding information conducted by clinicians in the emergency medical services (EMS) so as to make informed decisions. Research on clinical reasoning spans several disciplines, but a comprehensive view of the process is lacking. To our knowledge, no review of clinical reasoning in the EMS has been conducted. Aim: The aim was to investigate the nature, deployment, and factors influencing EMS clinicians’ clinical reasoning by means of a review. Method: Data was collected through searches in electronic databases, networking among research teams ,colleagues and friends, “grey literature,” and through ancestry searches. A total of 38 articles were deemed eligible for inclusion and were analyzed using descriptive thematic analysis. The analysis resulted in an overarching finding -namely, the importance for EMS clinicians to adjust for perceived control in unpredictable situations. Within this finding, 3 themes emerged in terms of EMS clinicians’ clinical reasoning: (1) maintaining a holistic view of the patient; (2) keeping an open mind; and (3) improving through criticism. Seven subthemes subsequently emerged from these three themes. Results: This review showed that EMS clinicians’ clinical reasoning begins with the information that they are given about a patient. Based on this information, clinicians calculate the best route to the patient and which equipment to use, and they also assess potential risks. They need to be constantly aware of what is happening on the scene and with the patient and strive to control the situation. This striving also enables EMS clinicians to work safely and effectively in relation to the patient, their relatives, other clinicians, associated organizations, and the wider community. A lack of contextually appropriate guidelines results in the need for creativity and forces EMS clinicians to use “workarounds” to solve issues beyond the scope of the guidelines available. In addition, they often lack organizational support and fear repercussions such as litigation, unemployment, or blame by their EMS or healthcare organization or by patients and relatives. Conclusion: Clinical reasoning is influenced by several factors. Further research is needed to determine which influencing factors can be addressed through interventions to minimize their impact on patient outcomes

    Clinical Reasoning among Registered Nurses in Emergency Medical Services: A Case Study

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    In emergency medical services (EMS), the clinical reasoning (CR) of registered nurses (RNs) working in ambulance care plays an important role in providing care and treatment that is timely, accurate, appropriate and safe. However, limited existing knowledge about how CR is formed and influenced by the EMS mission hinders the development of service provision and decision support tools for RNs that would further enhance patient safety. To explore the nature of CR and influencing factors in this context, an inductive case study examined 34 observed patient–RN encounters in an EMS setting focusing on ambulance care. The results reveal a fragmented CR approach involving several parallel decision-making processes grounded in and led by patients’ narratives. The findings indicate that RNs are not always aware of their own CR and associated influences until they actively reflect on the process, and additional research is needed to clarify this complex phenomenon.Bedömning och beslutsfattande i ambulanssjukvĂ„r

    Organizational factors influencing clinical reasoning in a Swedish emergency medical service organization: An explorative qualitative case study

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    Introduction Clinical reasoning (CR) among healthcare professionals working in emergency medical services (EMS) who focus on ambulance care is a vital part of ensuring timely and safe patient care. The EMS environment continually fluctuates, so clinicians constantly need to adapt to new situations. Organizational support is described as important for CR, but overall, research on organizational influences for CR in an EMS context is lacking. An increased understanding of these influencing factors can assist in the development of EMS by strengthening CR among clinicians. Therefore, the purpose of this study was to investigate the organizational factors influencing EMS clinicians’ CR. Methods Using a qualitative single case study design, an EMS organization in southwestern Sweden was explored. Data were collected from participant observations of patient encounters, individual and group interviews with clinicians and organizational representatives, and organizational document audits. Data were analyzed using qualitative content analysis and triangulation of data sources. Results The results revealed several organizational influencing factors. Collaboration and information sharing internally and externally were emphasized as essential components influencing CR. Additionally, the structure for the clinicians’ ‘room for action’ appeared confused and created uncertainties for CR related to decision mandates. Conclusion The conclusion is that organizational factors do play an important role in clinicians’ CR. Moreover, the EMS community needs to develop suitable forums for discussing and developing these influencing factors across organizational hierarchies. Finally, clarification is needed on clinicians’ ‘room for action’ within their own organization but also with possible collaborators.

    Overview of the primers and probe in the DENV RT-PCR assay.

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    <p>The reverse primers DENV_R1–3 (A) and DENV_R4 (B) were specifically designed to target DENV serotypes 1–3 and serotype 4, respectively. Vertical bars and percentages show the fraction of sequences with nucleotides deviating from the consensus of DENV serotypes 1–3 (A) and serotype 4 (B). Percentages below 1 are not shown. Numbers indicate genomic positions.</p

    Results of the DENV RT-PCR assay performed on serum samples obtained 1 to 9 days after onset of symptoms.

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    <p>(A) Archived serum samples that had been collected from 60 patients on days 1–3 (n = 5), 4 (n = 10), 5 (n = 7), 6 (n = 11), 7 (n = 12), 8 (n = 8), and 9 (n = 7) after disease onset were tested by the DENV RT-PCR assay, an NS1 antigen detection test, IgM capture ELISA, and IFA detecting DENV-specific IgG antibodies. The criteria for a positive result in each of these analyses are explained in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003416#s2" target="_blank">Methods</a>. The curves show the percent of samples testing positive in the individual assays for the specified days after disease onset. (B) Viral load in samples collected on days 1–9 after onset of symptoms. Each dot represents the mean of results for duplicate samples from a single patient. GCE = genome copy equivalents.</p

    Flow-chart showing the laboratory test results for samples from returning travelers with dengue-compatible symptomatology.

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    <p>Serum samples collected during January and February 2014 were tested consecutively by the newly developed DENV RT-PCR method, an NS1 antigen detection test, IgM capture ELISA, and/or an in-house IFA detecting DENV-specific IgG antibodies. The criteria for a positive result in each individual assay are explained in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003416#s2" target="_blank">Methods</a>. The results of the laboratory analysis of the first sample arriving at the Public Health Agency of Sweden are shown. w/o = without.</p

    Dynamic range and limit of detection of the DENV RT-PCR assay.

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    <p>(A) The linear dynamic range of the DENV RT-PCR assay was determined by testing triplicates of 10-fold serially diluted in vitro transcribed RNA. The sequences of the transcript RNA (RNA[DENV_R1–3] and RNA[DENV_R4]) were matched with the two reverse primers DENV_R1–3 and DENV_R4, respectively. Each dot represents the mean Cq-value from three replicates, the error bars indicate the 95% confidence interval, and the lines illustrate the result of the lin-log regression analysis. (B and C) Limit of detection was determined by assaying eight replicates of twofold serially diluted RNA transcripts in three separate experiments, and the results of testing are shown for RNA[DENV_R1–3] (B) and RNA[DENV_R4] (C). Horizontal lines indicate mean values, boxes denote the 25th to 75th percentiles and whiskers the 5–95% percentiles, and dots represent outliers. The number of positives per total number of replicates tested is given above each box. Limit of detection was defined as the last dilution in which transcript RNA was detected in all 24 replicates. GCE = genome copy equivalents.</p
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