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The National Optimal Lung Cancer Pathway recommends rapid progression from abnormal chest X-rays (CXRs) to CT. The impact of the more rapid reporting on the whole pathway is unknown. The aim of this study was to determine the impact of immediate reporting of CXRs requested by primary care by radiographers on the time to diagnosis of lung cancer. METHOD: People referred for CXR from primary care to a single acute district general hospital in London attended sessions that were prerandomised to either immediate radiographer (IR) reporting or standard radiographer (SR) reporting within 24 hours. CXRs were subsequently reported by radiologists blind to the radiographer reports to test the reliability of the radiographer report. Radiographer and local radiologist discordant cases were reviewed by thoracic radiologists, blinded to reporter. RESULTS: 8682 CXRs were performed between 21 June 2017 and 4 August 2018, 4096 (47.2%) for IR and 4586 (52.8%) for SR. Lung cancer was diagnosed in 49, with 27 (55.1%) for IR. The median time from CXR to diagnosis of lung cancer for IR was 32 days (IQR 19, 70) compared with 63 days (IQR 29, 78) for SR (p=0.03).8258 CXRs (95.1%) were reported by both radiographers and local radiologists. In the 1361 (16.5%) with discordance, the reviewing thoracic radiologists were equally likely to agree with local radiologist and radiographer reports. CONCLUSIONS: Immediate reporting of CXRs from primary care reduces time to diagnosis of lung cancer by half, likely due to rapid progress to CT. Radiographer reports are comparable to local radiologist reports for accuracy. TRIAL REGISTRATION: International Standard Randomised Controlled Trial Number ISRCTN21818068. Registered on 20 June 2017.
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Medicina Geral , Neoplasias Pulmonares , Humanos , Raios X , Reprodutibilidade dos Testes , Radiografia , Neoplasias Pulmonares/diagnóstico por imagemRESUMO
Therapeutic radiographers (TRs) are well-placed to deliver advice to cancer patients; however, limited research exists on their practices in providing advice on healthy lifestyle behaviours. Through an online survey, this study aimed to explore TRs' current practices, barriers, and facilitators around delivering healthy behaviour advice to cancer patients. An online survey was sent to 72 radiotherapy departments in the UK and 583 TRs responded to the survey. Findings showed that levels of enquiry and provision of advice on healthy behaviours were low, with less than 25% advising patients on physical activity, healthy eating, weight management, smoking cessation, and reducing alcohol intake as standard practice. Lack of knowledge, resources, and training were identified as barriers, in addition to perceived lack of patient interest and lack of time. TRs reported a strong desire to undergo training to enable them to deliver health behaviour advice to patients, with an identified preference for online training. Cancer patients look to healthcare professionals for advice on health behaviours, and TRs are well-placed to deliver this advice. The findings of this study provide insight into the areas that need addressing to enable TRs to support positive health behaviours among cancer patients.
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Neoplasias , Abandono do Hábito de Fumar , Aconselhamento , Comportamentos Relacionados com a Saúde , Humanos , Neoplasias/prevenção & controle , Inquéritos e QuestionáriosRESUMO
INTRODUCTION: Pneumothorax and pneumomediastinum have both been noted to complicate cases of coronavirus disease 2019 (COVID-19) requiring hospital admission. We report the largest case series yet described of patients with both these pathologies (including nonventilated patients). METHODS: Cases were collected retrospectively from UK hospitals with inclusion criteria limited to a diagnosis of COVID-19 and the presence of either pneumothorax or pneumomediastinum. Patients included in the study presented between March and June 2020. Details obtained from the medical record included demographics, radiology, laboratory investigations, clinical management and survival. RESULTS: 71 patients from 16 centres were included in the study, of whom 60 had pneumothoraces (six with pneumomediastinum in addition) and 11 had pneumomediastinum alone. Two of these patients had two distinct episodes of pneumothorax, occurring bilaterally in sequential fashion, bringing the total number of pneumothoraces included to 62. Clinical scenarios included patients who had presented to hospital with pneumothorax, patients who had developed pneumothorax or pneumomediastinum during their inpatient admission with COVID-19 and patients who developed their complication while intubated and ventilated, either with or without concurrent extracorporeal membrane oxygenation. Survival at 28â days was not significantly different following pneumothorax (63.1±6.5%) or isolated pneumomediastinum (53.0±18.7%; p=0.854). The incidence of pneumothorax was higher in males. 28-day survival was not different between the sexes (males 62.5±7.7% versus females 68.4±10.7%; p=0.619). Patients aged ≥70 years had a significantly lower 28-day survival than younger individuals (≥70â years 41.7±13.5% survival versus <70â years 70.9±6.8% survival; p=0.018 log-rank). CONCLUSION: These cases suggest that pneumothorax is a complication of COVID-19. Pneumothorax does not seem to be an independent marker of poor prognosis and we encourage continuation of active treatment where clinically possible.
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COVID-19/complicações , Enfisema Mediastínico/epidemiologia , Enfisema Mediastínico/virologia , Pneumotórax/epidemiologia , Pneumotórax/virologia , SARS-CoV-2 , Adulto , Fatores Etários , Idoso , Idoso de 80 Anos ou mais , COVID-19/mortalidade , COVID-19/terapia , Oxigenação por Membrana Extracorpórea , Feminino , Hospitalização , Humanos , Incidência , Masculino , Enfisema Mediastínico/terapia , Pessoa de Meia-Idade , Pneumotórax/terapia , Prognóstico , Respiração Artificial , Estudos Retrospectivos , Fatores Sexuais , Taxa de Sobrevida , Reino Unido , Adulto JovemAssuntos
Inteligência Artificial/estatística & dados numéricos , Doenças Transmissíveis Emergentes/epidemiologia , Infecções por Coronavirus/diagnóstico por imagem , Pandemias/estatística & dados numéricos , Pneumonia Viral/diagnóstico por imagem , Tomografia Computadorizada por Raios X/estatística & dados numéricos , COVID-19 , Doenças Transmissíveis Emergentes/diagnóstico por imagem , Infecções por Coronavirus/epidemiologia , Bases de Dados Factuais/estatística & dados numéricos , Diagnóstico por Imagem/métodos , Diagnóstico por Imagem/estatística & dados numéricos , Feminino , Humanos , Masculino , Pneumonia Viral/epidemiologia , Desenvolvimento de Programas , Radiografia Torácica/métodos , Radiografia Torácica/estatística & dados numéricos , Tomografia Computadorizada por Raios X/métodos , Reino UnidoRESUMO
INTRODUCTION: Missed fractures are the most frequent diagnostic error attributed to clinicians in UK emergency departments and a significant cause of patient morbidity. Recently, advances in computer vision have led to artificial intelligence (AI)-enhanced model developments, which can support clinicians in the detection of fractures. Previous research has shown these models to have promising effects on diagnostic performance, but their impact on the diagnostic accuracy of clinicians in the National Health Service (NHS) setting has not yet been fully evaluated. METHODS AND ANALYSIS: A dataset of 500 plain radiographs derived from Oxford University Hospitals (OUH) NHS Foundation Trust will be collated to include all bones except the skull, facial bones and cervical spine. The dataset will be split evenly between radiographs showing one or more fractures and those without. The reference ground truth for each image will be established through independent review by two senior musculoskeletal radiologists. A third senior radiologist will resolve disagreements between two primary radiologists. The dataset will be analysed by a commercially available AI tool, BoneView (Gleamer, Paris, France), and its accuracy for detecting fractures will be determined with reference to the ground truth diagnosis. We will undertake a multiple case multiple reader study in which clinicians interpret all images without AI support, then repeat the process with access to AI algorithm output following a 4-week washout. 18 clinicians will be recruited as readers from four hospitals in England, from six distinct clinical groups, each with three levels of seniority (early-stage, mid-stage and later-stage career). Changes in the accuracy, confidence and speed of reporting will be compared with and without AI support. Readers will use a secure web-based DICOM (Digital Imaging and Communications in Medicine) viewer (www.raiqc.com), allowing radiograph viewing and abnormality identification. Pooled analyses will be reported for overall reader performance as well as for subgroups including clinical role, level of seniority, pathological finding and difficulty of image. ETHICS AND DISSEMINATION: The study has been approved by the UK Healthcare Research Authority (IRAS 310995, approved on 13 December 2022). The use of anonymised retrospective radiographs has been authorised by OUH NHS Foundation Trust. The results will be presented at relevant conferences and published in a peer-reviewed journal. TRIAL REGISTRATION NUMBERS: This study is registered with ISRCTN (ISRCTN19562541) and ClinicalTrials.gov (NCT06130397). The paper reports the results of a substudy of STEDI2 (Simulation Training for Emergency Department Imaging Phase 2).
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Inteligência Artificial , Fraturas Ósseas , Humanos , Estudos Prospectivos , Fraturas Ósseas/diagnóstico por imagem , Radiografia/métodos , Reino Unido , Projetos de Pesquisa , Erros de DiagnósticoRESUMO
BACKGROUND: Artificial intelligence (AI)-enabled applications are increasingly being used in providing healthcare services, such as medical imaging support. Sufficient and appropriate education for medical imaging professionals is required for successful AI adoption. Although, currently, there are AI training programmes for radiologists, formal AI education for radiographers is lacking. Therefore, this study aimed to evaluate and discuss a postgraduate-level module on AI developed in the UK for radiographers. METHODOLOGY: A participatory action research methodology was applied, with participants recruited from the first cohort of students enrolled in this module and faculty members. Data were collected using online, semi-structured, individual interviews and focus group discussions. Textual data were processed using data-driven thematic analysis. RESULTS: Seven students and six faculty members participated in this evaluation. Results can be summarised in the following four themes: a. participants' professional and educational backgrounds influenced their experiences, b. participants found the learning experience meaningful concerning module design, organisation, and pedagogical approaches, c. some module design and delivery aspects were identified as barriers to learning, and d. participants suggested how the ideal AI course could look like based on their experiences. CONCLUSIONS: The findings of our work show that an AI module can assist educators/academics in developing similar AI education provisions for radiographers and other medical imaging and radiation sciences professionals. A blended learning delivery format, combined with customisable and contextualised content, using an interprofessional faculty approach is recommended for future similar courses.
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INTRODUCTION: Twitter journal clubs are a relatively new adaptation of an established continuing professional development (CPD) activity within healthcare. The medical radiation science (MRS) journal club 'MedRadJClub' (MRJC) was founded in March 2015 by a group of academics, researchers and clinicians as an international forum for the discussion of peer-reviewed papers. To investigate the reach and impact of MRJC, a five-year analysis was conducted. METHODS: Tweetchat data (number of participants, tweets and impressions) for the first five years of MRJC were extracted and chat topics organised into themes. Fifth anniversary MRJC chat tweets were analysed and examples of academic and professional outputs were collated. RESULTS: A total of 59 chats have been held over five years with a mean of 41 participants and 483,000 impressions per hour-long synchronous chat. Ten different tweetchat themes were identified, with student engagement/preceptorship the most popular. Eight posters or oral presentations at conferences, one social media workshop and four papers have been produced. Qualitative analysis revealed five core themes relating to the perceived benefits of participation in MRJC: (1) CPD and research impact, (2) professional growth and influencing practice, (3) interdisciplinary learning and inclusion, (4) networking and social support and (5) globalisation. CONCLUSION: MRJC is a unique, multi-professional, global community with consistent engagement. It is beneficial for both CPD, research engagement, dissemination and socialisation within the MRS community.
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Mídias Sociais , Atenção à Saúde , Humanos , Internacionalidade , OrganizaçõesRESUMO
INTRODUCTION: As a profession, radiographers have always been keen on adapting and integrating new technologies. The increasing integration of artificial intelligence (AI) into clinical practice in the last five years has been met with scepticism by some, who predict the demise of the profession, whilst others suggest a bright future with AI, full of opportunities and synergies. Post COVID-19 pandemic need for economic recovery and a backlog of medical imaging and reporting may accelerate the adoption of AI. It is therefore timely to appreciate practitioners' perceptions of AI used in clinical practice and their perception of the short-term impact on the profession. AIM: This study aims to explore the perceptions of AI in the UK radiography workforce and to investigate its current AI applications and future technological expectations of radiographers. METHODS: An online survey (Qualtricsâ) was created by a team of radiography AI experts. The survey was disseminated via social media and professional networks in the UK. Demographic information and perceptions of the impact of AI on several aspects of the radiography profession were gathered, including the current use of AI in practice, future expectations and the perceived impact of AI on the profession. RESULTS: 411 responses were collected (80% diagnostic radiographers (DR); 20% therapeutic radiographers (TR)). Awareness of AI used in clinical practice is low, with DR respondents suggesting AI will have the most value/potential in cross sectional imaging and image reporting. TR responses linked AI as having most value in treatment planning, contouring, and image acquisition/matching. Respondents felt that AI will impact radiographers' daily work (DR, 79.6%; TR, 88.9%) by standardising some aspects of patient care and technical factors of radiography practice. A mixed response about impact on careers was reported. CONCLUSIONS: Respondents were unsure about the ways in which AI is currently used in practice and how AI will impact on careers in the future. It was felt that AI integration will lead to increased job opportunities to contribute to decision making as an end user. Job security was not identified as a cause for concern.
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Inteligência Artificial , COVID-19 , Estudos Transversais , Humanos , Pandemias , Reino UnidoRESUMO
OBJECTIVES: To survey the diagnostic radiography workforce in the United Kingdom (UK) at an organisational level to ascertain the scope of advanced practice and compliance with Health Education England standards for multiprofessional advanced clinical practice (ACP). METHODS: 174 diagnostic imaging departments were invited to participate in a cross-sectional electronic survey focused upon advanced level practice and their educational and accreditation expectations (October-December 2019). Breast imaging, computed tomography, fluoroscopy, interventional radiology, lithotripsy, magnetic resonance imaging and projectional radiography were included. RESULTS: A total of 97 responses were received, of which 79 were eligible for inclusion (45%). Respondents reported advanced-level practice roles across all imaging modalities, which included clinical reporting, procedural-based and combined roles. Radiograph and mammogram reporting were most prevalent (95 and 67% of Trusts), with fluoroscopy the most frequent procedure-only role (25%). Only 39% of trusts required adherence to the four pillars of ACP within job descriptions, and only 12% requiring a full Masters qualification. CONCLUSIONS: Diagnostic radiographer reporting and procedure-based roles in the NHS are varied and widespread. However, inconsistencies in fulfilment against the expected standards for advanced practice exist. Realignment of advanced-level roles to delineate enhanced and advanced clinical practice may ensure consistency between roles and professions. A requirement for accreditation as an advanced (clinical) practitioner with adherence to advanced practice requirements could therefore provide value to accreditation for both individual practitioners and Trusts. ADVANCES IN KNOWLEDGE: Within the UK, diagnostic radiographer roles previously self-identified as advanced-level practice may be termed enhanced practice when not adhering to expected ACP standards.
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Introduction: The use of artificial intelligence (AI) in medical imaging and radiotherapy has been met with both scepticism and excitement. However, clinical integration of AI is already well-underway. Many authors have recently reported on the AI knowledge and perceptions of radiologists/medical staff and students however there is a paucity of information regarding radiographers. Published literature agrees that AI is likely to have significant impact on radiology practice. As radiographers are at the forefront of radiology service delivery, an awareness of the current level of their perceived knowledge, skills, and confidence in AI is essential to identify any educational needs necessary for successful adoption into practice. Aim: The aim of this survey was to determine the perceived knowledge, skills, and confidence in AI amongst UK radiographers and highlight priorities for educational provisions to support a digital healthcare ecosystem. Methods: A survey was created on Qualtrics® and promoted via social media (Twitter®/LinkedIn®). This survey was open to all UK radiographers, including students and retired radiographers. Participants were recruited by convenience, snowball sampling. Demographic information was gathered as well as data on the perceived, self-reported, knowledge, skills, and confidence in AI of respondents. Insight into what the participants understand by the term "AI" was gained by means of a free text response. Quantitative analysis was performed using SPSS® and qualitative thematic analysis was performed on NVivo®. Results: Four hundred and eleven responses were collected (80% from diagnostic radiography and 20% from a radiotherapy background), broadly representative of the workforce distribution in the UK. Although many respondents stated that they understood the concept of AI in general (78.7% for diagnostic and 52.1% for therapeutic radiography respondents, respectively) there was a notable lack of sufficient knowledge of AI principles, understanding of AI terminology, skills, and confidence in the use of AI technology. Many participants, 57% of diagnostic and 49% radiotherapy respondents, do not feel adequately trained to implement AI in the clinical setting. Furthermore 52% and 64%, respectively, said they have not developed any skill in AI whilst 62% and 55%, respectively, stated that there is not enough AI training for radiographers. The majority of the respondents indicate that there is an urgent need for further education (77.4% of diagnostic and 73.9% of therapeutic radiographers feeling they have not had adequate training in AI), with many respondents stating that they had to educate themselves to gain some basic AI skills. Notable correlations between confidence in working with AI and gender, age, and highest qualification were reported. Conclusion: Knowledge of AI terminology, principles, and applications by healthcare practitioners is necessary for adoption and integration of AI applications. The results of this survey highlight the perceived lack of knowledge, skills, and confidence for radiographers in applying AI solutions but also underline the need for formalised education on AI to prepare the current and prospective workforce for the upcoming clinical integration of AI in healthcare, to safely and efficiently navigate a digital future. Focus should be given on different needs of learners depending on age, gender, and highest qualification to ensure optimal integration.
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OBJECTIVES: Therapeutic radiographers (TRs) are well placed to deliver health behaviour change advice to those living with and beyond cancer (LWBC). However, there is limited research on the opinions of TRs around delivering such advice to those LWBC. This study aimed to explore TRs' practices and facilitators in delivering advice on physical activity, healthy eating, alcohol intake, smoking and weight management. SETTING AND PARTICIPANTS: Fifteen UK-based TRs took part in a telephone interview using a semi-structured interview guide. Data was analysed using the framework analysis method. RESULTS: Emergent themes highlighted that TRs are mainly aware of the benefits of healthy behaviours in managing radiotherapy treatment related side effects, with advice provision lowest for healthy eating and physical activity. Participants identified themselves as well placed to deliver advice on improving behaviours to those LWBC, however reported a lack of knowledge as a limiting factor to doing so. The TRs reported training and knowledge as key facilitators to the delivery of advice, with a preference for online training. CONCLUSIONS: There is a need for education resources, clear referral pathways and in particular training for TRs on delivering physical activity and healthy eating advice to those LWBC.
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Exercício Físico , Neoplasias , Terapia Comportamental , Comportamentos Relacionados com a Saúde , Humanos , Pesquisa QualitativaRESUMO
OBJECTIVE: To investigate the profiles of advanced clinical practitioners (ACPs) in the allied health professions (AHPs) and their skills, attributes, experiences and involvement in new models of care. METHODS: A 2 phase, cross sectional, mixed method survey of AHP ACPs across London was conducted in 2018-2019. Online questionnaires were completed by 127 AHP ACPs and then semi-structured interviews were conducted with 15 AHP ACPs. RESULTS: The survey results gave a comprehensive overview of the attributes of AHPs in ACP roles across London. There was considerable variability between role titles, types and levels of qualification, and evolution of the roles. The respondents predominately worked in clinical practice, and less frequently in other ACP domains (research, leadership and management, education). The interview findings provided in-depth insights into the AHP ACP roles within four themes: being advanced, career pathways, outcomes of the advanced practitioner role and influencing and transforming. The "Being advanced" theme highlighted that expert practice comprised confident and autonomous practice, leadership, and applying specialist and expert decision-making skills. "Career pathways" highlighted the diversity within the participants' roles, titles, career opportunities and development. In the "Outcomes of the advanced clinical practitioner role" theme, the ACPs described their services as prompter, more accessible and providing an improved patient journey. The "Influencing and transforming" theme highlighted networking and dissemination and ideas for innovation, influencing and transforming services. CONCLUSION: This is the first comprehensive profile of ACP roles across AHPs and indicates that these roles are already having a positive impact on healthcare services and supporting new models of care. However, establishing the necessary infrastructure, standardization and governance for ACP roles across sectors, along with the career pathways, funding, sustainability and education, could increase impact in the future.
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Radiographer preliminary image evaluation, within strong governance and audit systems, can help reduce diagnostic errors in the emergency setting. Radiographers, clinicians and radiologists should work together as a team to improve patient care and outcomes.
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Interpretação de Imagem Radiográfica Assistida por Computador/normas , Radiologistas/normas , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Radiologistas/educaçãoRESUMO
RATIONALE AND OBJECTIVES: Chest X-rays (CXR) are one of the most frequently requested imaging examinations and are fundamental to many patient pathways. The aim of this study was to investigate the diagnostic accuracy of CXR interpretation by reporting radiographers (technologists). METHODS: A cohort of consultant radiologists (n = 10) and reporting radiographers (technologists; n = 11) interpreted a bank (n = 106) of adult CXRs that contained a range of pathologies. Jack-knife alternate free-response receiver operating characteristic (JAFROC) methodology was used to determine the performance of the observers (JAFROC v4.2). A noninferiority approach was used, with a predefined margin of clinical insignificance of 10% of average consultant radiologist diagnostic accuracy. RESULTS: The diagnostic accuracy of the reporting radiographers (figure of merit = 0.828, 95% confidence interval 0.808-0.847) was noninferior to the consultant radiologists (figure of merit = 0.788, 95% confidence interval 0.766-0.811), P < .0001. CONCLUSIONS: With appropriate postgraduate education, reporting radiographers are able to interpret CXRs at a level comparable to consultant radiologists.
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Pessoal Técnico de Saúde/normas , Cardiopatias/diagnóstico por imagem , Pneumopatias/diagnóstico por imagem , Radiologistas/normas , Erros de Diagnóstico , Humanos , Variações Dependentes do Observador , Estudos Prospectivos , Curva ROC , Radiografia TorácicaRESUMO
INTRODUCTION: Diagnostic capacity and time to diagnosis are frequently identified as a barrier to improving cancer patient outcomes. Maximising the contribution of the medical imaging workforce, including reporting radiographers, is one way to improve service delivery. METHODS: An efficient and effective centralised model of workplace training support was designed for a cohort of trainee chest X-ray (CXR) reporting radiographers. A comprehensive schedule of tutorials was planned and aligned with the curriculum of a post-graduate certificate in CXR reporting. Trainees were supported via a hub and spoke model (centralised training model), with the majority of education provided by a core group of experienced CXR reporting radiographers. Trainee and departmental feedback on the model was obtained using an online survey. RESULTS: Fourteen trainees were recruited from eight National Health Service Trusts across London. Significant efficiencies of scale were possible with centralised support (48 h) compared to traditional workplace support (348 h). Trainee and manager feedback overall was positive. Trainees and managers both reported good trainee support, translation of learning to practice and increased confidence. Logistics, including trainee travel and release, were identified as areas for improvement. CONCLUSION: Centralised workplace training support is an effective and efficient method to create sustainable diagnostic capacity and support improvements in the lung cancer pathway.
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Capacitação em Serviço/métodos , Neoplasias Pulmonares/diagnóstico por imagem , Radiografia Torácica/normas , Radiologistas/educação , Adulto , Inglaterra , Feminino , Humanos , Capacitação em Serviço/normas , Masculino , Radiologistas/normas , Materiais de EnsinoRESUMO
BACKGROUND: Diagnostic capacity and suboptimal logistics are consistently identified as barriers to timely diagnosis of cancer, especially lung cancer. Immediate chest X-ray (CXR) reporting for patients referred from general practice is advocated in the National Optimal Lung Cancer Pathway to improve time to diagnosis of lung cancer and to reduce inappropriate urgent respiratory medicine referral for suspected cancer (2WW) referrals. The aim of radioX is to examine the impact of immediate reporting by radiographers of CXRs requested by general practice (GP) on lung cancer patient pathways. METHODS: A two-way comparative study that will compare the time to diagnosis of lung cancer for patients. Internal comparison will be made between those who receive an immediate radiographer report of a GP CXR compared to standard radiographer GP CXR reporting over a 12-month period. External comparison will be made with a similar, neighbouring hospital trust that does not have radiographer CXR reporting. Primary outcome is the effect on the speed of the lung cancer pathway (diagnosis of cancer or discharge). Secondary outcomes include the effect of the pathway on efficiency including the number of repeat CXRs performed in a timely fashion for suspected infection and the effect of immediate reporting of GP CXRs on patient satisfaction. DISCUSSION: The radioX trial will examine the hypothesis that immediate reporting of CXRs referred from GP reduces the time to diagnosis of lung cancer or discharge from the lung cancer pathway. TRIAL REGISTRATION: International Standard Randomised Controlled Trial Number ISRCTN21818068 . Registered on 20 June 2017.
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Procedimentos Clínicos , Detecção Precoce de Câncer/métodos , Medicina Geral , Neoplasias Pulmonares/diagnóstico por imagem , Radiografia Torácica , Radiologistas , Protocolos Clínicos , Inglaterra , Humanos , Comunicação Interdisciplinar , Neoplasias Pulmonares/terapia , Equipe de Assistência ao Paciente , Satisfação do Paciente , Valor Preditivo dos Testes , Prognóstico , Encaminhamento e Consulta , Projetos de Pesquisa , Tempo para o Tratamento , Fluxo de TrabalhoRESUMO
INTRODUCTION: Social media has emerged as a powerful platform for engagement and learning. There is a growing trend toward the use of social media among health care professionals and professional groups to disseminate and discuss knowledge. Twitter is one tool that may enhance continuing professional development (CPD) for the medical radiation technologist. To evaluate the potential benefits of Twitter to CPD among medical radiation technologists, this study explored the integration of Bloom's taxonomy with Twitter-based professional activities. APPROACH: In 2015, the Medical Radiation Journal Club (https://medradjclub.wordpress.com/) commenced a monthly Twitter-based journal club for medical radiation professionals. This study investigates the application of Bloom's taxonomy of the Twitter-based journal club for CPD purposes. OUTCOME: The Twitter-based journal club provides a valuable platform for CPD. The combination of journal articles, supplementary reading, online blog, and the one-hour Twitter discussion engages all levels of Bloom's taxonomy; remember, understand, apply, analyze, evaluate, and create. A deeper insight revealed that the Twitter journal club provides an authentic learning environment suitable for CPD in which participants consume, collaborate, and produce. CONCLUSIONS: This evaluation demonstrated that the Twitter journal club can provide an authentic learning environment with all the cognitive dimensions afforded in a formal classroom or face-to-face journal club. Indeed, in some ways, these cognitive dimensions are enhanced in the Twittersphere.
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Professions grapple with defining advanced practice and the characteristics of advanced practitioners. In nursing and allied health, advanced practice has been defined as 'a state of professional maturity in which the individual demonstrates a level of integrated knowledge, skill and competence that challenges the accepted boundaries of practice and pioneers new developments in health care'. Evolution of advanced practice in Australia has been slower than in the United Kingdom, mainly due to differences in demography, the health system and industrial relations. This article describes a conceptual model of advanced practitioner characteristics in the medical radiation professions, taking into account experiences in other countries and professions. Using the CanMEDS framework, the model includes foundation characteristics of communication, collaboration and professionalism, which are fundamental to advanced clinical practice. Gateway characteristics are: clinical expertise, with high level competency in a particular area of clinical practice; scholarship and teaching, including a masters qualification and knowledge dissemination through educating others; and evidence-based practice, with judgements made on the basis of research findings, including research by the advanced practitioner. The pinnacle of advanced practice is clinical leadership, where the practitioner has a central role in the health care team, with the capacity to influence decision making and advocate for others, including patients. The proposed conceptual model is robust yet adaptable in defining generic characteristics of advanced practitioners, no matter their clinical specialty. The advanced practice roles that evolve to meet future health service demand must focus on the needs of patients, local populations and communities.
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This letter refutes the suggestion made the authors of other letters to the Editor that the proposed model of the characteristics of advanced practitioners excludes research.