Dataset Information

Increasing radiology capacity within the lung cancer pathway: centralised work-based support for trainee chest X-ray reporting radiographers.

ABSTRACT: 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.

SUBMITTER: Woznitza N

PROVIDER: S-EPMC6119729 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Increasing radiology capacity within the lung cancer pathway: centralised work-based support for trainee chest X-ray reporting radiographers.

Woznitza Nick N Steele Rebecca R Piper Keith K Burke Stephen S Rowe Susan S Bhowmik Angshu A Maughn Sue S Springett Kate K

Journal of medical radiation sciences 20180527 3

<h4>Introduction</h4>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.<h4>Methods</h4>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 ...[more]

PMID: 29806102

Similar Datasets

Project description:ObjectivesTo explore radiographers' actions toward inappropriate referrals and hindrances to assessing referrals.MethodsAn online survey was distributed to radiographers via the International Society of Radiographers and Radiological Technologists (ISRRT) networks. The questionnaire consisted of 5-point Likert scale questions on radiographers' actions to supplement referral information, actions for unjustified referrals and hindrances to referral assessment. The questionnaire was validated using a test-retest reliability analysis. Kappa values ≥ 0.6 were accepted. SPSS software was used for data analysis and chi-square tests to compare subgroups.ResultsTotal responses received were 279. The most reported actions to supplement missing referral information were to ask the patient or relative, examine the body region of concern and check medical records (73%, 70%, 67%, responded often/always, respectively). The actions when confronted with unjustified referrals were reported equally to consult the radiologist, referring clinician and radiographer (69-68% often/always responses). The hindering factors ranked high (agreed/strongly agreed responses) pertained to inadequate information in referral forms (83%), ineffective communication among healthcare professionals (79%), lack of training (70%) and allocated time (61%). Statistically significant associations were observed for a few actions and hindrances with education level, modality of practice and responsibility to screen imaging referrals.ConclusionRadiographers consult colleagues about suspected unjustified referrals. Effective communication pathways, training and time allocation to improve radiographers' skills to assess referrals may enhance appropriate imaging and delivery of quality patient care.Key points• Radiographers' actions of supplementing missing information in radiology referrals facilitate provision of high-quality health services. • Radiographers' strategy when confronted with inappropriate referrals is to consult radiologists and referring clinicians. • Better inter-professional communication and organisation of tasks can facilitate radiographers' participation in referral assessment to ensure appropriate imaging.

Project description:BackgroundStructured reporting and standardized criteria are increasingly recognized as means of improving both radiological and clinical practice by allowing for better content and clarity. Our aim was to examine oncologists' opinions and expectations concerning the radiologist's report to identify general needs in daily practice and ways to improve interdisciplinary communication.MethodsA 19-question survey was sent to 230 oncologists from three different countries (France, Romania, Switzerland) identified on the online web pages of different hospitals and private clinics. The survey was sent by electronic mail with an online survey program (Google Forms®). All recipients were informed of the purpose of the study. The data were collected by the online survey program and analysed through filtering the results and cross-tabulation.ResultsA total of 52 responses were received (response rate of 22.6%). The majority of the respondents (46/52, 88%) preferred the structured report, which follows a predefined template. Most of the respondents (40/52, 77%) used RECIST 1.1 or iRECIST in tumour assessment. Nearly half of the oncologists (21/52, 40%) measured 1-3 cases per week. On a 10-point Likert scale, 34/52 (65%) oncologists rated their overall level of satisfaction with radiologists' service between 7 and 10. In contrast, 12/52 (19%) oncologists rated the radiologists' service between 1 and 4. Moreover, 42/52 (80%) oncologists acknowledged that reports created by a radiologist with a subspecialty in oncologic imaging were superior to those created by a general radiologist.ConclusionStructured reports in oncologic patients and the use of RECIST criteria are preferred by oncologists in their daily clinical practice, which signals the need for radiologists also to implement such reports to facilitate communication. Furthermore, most of the oncologists we interviewed recognized the added value provided by radiologists specializing in oncologic imaging. Because this subspecialty is present in only a few countries, generally in large clinics, further training might become a challenge; nevertheless, intensive efforts should be made to enhance expertise in cancer imaging.

Project description:Radiology reports contain a radiologist's interpretations of images, and these images frequently describe spatial relations. Important radiographic findings are mostly described in reference to an anatomical location through spatial prepositions. Such spatial relationships are also linked to various differential diagnoses and often described through uncertainty phrases. Structured representation of this clinically significant spatial information has the potential to be used in a variety of downstream clinical informatics applications. Our focus is to extract these spatial representations from the reports. For this, we first define a representation framework based on the Spatial Role Labeling (SpRL) scheme, which we refer to as Rad-SpRL. In Rad-SpRL, common radiological entities tied to spatial relations are encoded through four spatial roles: Trajector, Landmark, Diagnosis, and Hedge, all identified in relation to a spatial preposition (or Spatial Indicator). We annotated a total of 2,000 chest X-ray reports following Rad-SpRL. We then propose a deep learning-based natural language processing (NLP) method involving word and character-level encodings to first extract the Spatial Indicators followed by identifying the corresponding spatial roles. Specifically, we use a bidirectional long short-term memory (Bi-LSTM) conditional random field (CRF) neural network as the baseline model. Additionally, we incorporate contextualized word representations from pre-trained language models (BERT and XLNet) for extracting the spatial information. We evaluate both gold and predicted Spatial Indicators to extract the four types of spatial roles. The results are promising, with the highest average F1 measure for Spatial Indicator extraction being 91.29 (XLNet); the highest average overall F1 measure considering all the four spatial roles being 92.9 using gold Indicators (XLNet); and 85.6 using predicted Indicators (BERT pre-trained on MIMIC notes). The corpus is available in Mendeley at http://dx.doi.org/10.17632/yhb26hfz8n.1 and https://github.com/krobertslab/datasets/blob/master/Rad-SpRL.xml.

Dataset Information

Increasing radiology capacity within the lung cancer pathway: centralised work-based support for trainee chest X-ray reporting radiographers.

Publications

Increasing radiology capacity within the lung cancer pathway: centralised work-based support for trainee chest X-ray reporting radiographers.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets