Reducing radiotherapy waiting times for palliative patients: The role of the Advanced Practice Radiation Therapist

Job, Mary; Holt, Tanya; Bernard, Anne

doi:10.1002/jmrs.243

Cited by 33 publications

(65 citation statements)

References 13 publications

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“…The inter‐observer variability between the APRT and the RO in defining palliative radiation therapy fields is similar to that documented in the literature between clinicians. These results, combined with the reduction in wait times for patients to receive palliative radiotherapy from referral and reduced hospital visits from consult to first radiotherapy treatment, justify the establishment of the APRT role.…”

Section: Resultsmentioning

confidence: 99%

“…The APRT palliative role evaluated in this study was established to expand the scope of the existing Rapid Response Palliative Radiotherapy clinic. Development of this role has shown to be beneficial to patients by improving wait times from referral to treatment, decreasing hospital visits and improving continuity of care . The role of the APRT was expanded to include field definition on DRR with the premise that this would reduce planning times and allow ROs additional time to address their expanding workload.…”

Section: Discussionmentioning

confidence: 99%

“…For field definition, patients were allocated into two different pathways. Those allocated to the first pathway (APRT managed) consisted of patients who were referred either directly to the APRTor to one of four nominated ROs. This group of patients had all aspects of their radiation therapy, from referral to treatment, managed by the APRT in consultation with the treating RO.…”

Section: Methodsmentioning

confidence: 99%

“…In 2014 a palliative advanced practice radiation therapy (APRT) role was established at Radiation Oncology Princess Alexandra Raymond Terrace (ROPART), Australia to fill a gap in our palliative service …”

Section: Introductionmentioning

confidence: 99%

“…This would justify the APRT role in the department by demonstrating that the palliative patient's radiotherapy treatment from referral had been streamlined. The evaluation of the impact of the APRT role and referral pathway on patient waiting times has been previously published …”

Section: Introductionmentioning

confidence: 99%

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An evaluation of an advanced practice role in palliative radiation therapy

Job

Holt

Bernard

2019

J of Medical Radiation Sci

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Introduction The purpose of the study was to evaluate the palliative advanced practice radiation therapy (APRT) role with respect to the impact on waiting times for patients from referral to radiation treatment delivery, the ability of the APRT to define palliative radiation therapy fields and patient satisfaction. The evaluation of the impact of the APRT role and referral pathway on patient waiting times has been previously published. Methods Patients were allocated to two different pathways; APRT and standard. Patients in the APRT pathway had their radiotherapy treatment managed by the APRT including defining their palliative fields blinded to the radiation oncologist (RO). Results Of the 150 palliative patients, 94 had their radiation therapy managed by the APRT and 56 were managed through the standard pathway. 82/92 APRT defined fields were accepted by the RO. Conclusions Inter‐observer variability between the APRT and the RO in defining palliative radiation therapy fields is similar to that reported in the literature between clinicians. With previously published reduced wait times from referral to treatment for palliative patients, the establishment of the APRT role is justified.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An evaluation of an advanced practice role in palliative radiation therapy

Job

Holt

Bernard

2019

J of Medical Radiation Sci

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Overcoming uncertainty: A framework to guide the implementation of Australian radiation therapy advanced practitioners

Matthews

Duchesne

2023

J of Medical Radiation Sci

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IntroductionThe implementation of radiation therapy advanced practice in Australia has not yet been broadly realised. With anticipated growing demands on cancer services, it is imperative to understand why this is the case, and to strategise a way forward. As a result, we explored the factors influencing the implementation of advanced practitioner radiation therapists (APRT) in Australia. The research outcome was a complex process of Navigating Uncertainty, which described the contextual, social and personal factors surrounding implementation successes and challenges. Further synthesis of the findings was undertaken to highlight the fundamental features influencing this process, with the intention to provide a useful understanding for practitioners seeking APRT implementation.MethodsData were collected through national online focus groups and case studies with 53 participants. Analysis identified a constructivist grounded theory process of Navigating Uncertainty. Further analysis of the categories and properties of the process was undertaken to synthesise findings at a higher level of abstraction.ResultsFour overarching and intertwined factors were influencing the implementation of APRT. Uncertainty occurred when practitioners attempted to conceptualise and assimilate the new role into the workplace. Power was apparent in the advocacy and legitimisation of the APRT by centre leaders. Value was vital to achieving purposeful outcomes. Identity was evident in the personal transition of the APRT, and in the boundary work with others.ConclusionRecognising and negotiating uncertainty, power, value and identity is essential for APRT implementation strategies to succeed. A framework to guide practitioners towards the implementation of APRT has been described that embodies these factors.

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Evaluation of a multiview architecture for automatic vertebral labeling of palliative radiotherapy simulation CT images

et al. 2020

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The purpose of this work was to evaluate the performance of X-Net, a multiview deep learning architecture, to automatically label vertebral levels (S2-C1) in palliative radiotherapy simulation CT scans. Methods: For each patient CT scan, our automated approach 1) segmented spinal canal using a convolutional-neural network (CNN), 2) formed sagittal and coronal intensity projection pairs, 3) labeled vertebral levels with X-Net, and 4) detected irregular intervertebral spacing using an analytic methodology. The spinal canal CNN was trained via fivefold cross validation using 1,966 simulation CT scans and evaluated on 330 CT scans. After labeling vertebral levels (S2-C1) in 897 palliative radiotherapy simulation CT scans, a volume of interest surrounding the spinal canal in each patient's CT scan was converted into sagittal and coronal intensity projection image pairs. Then, intensity projection image pairs were augmented and used to train X-Net to automatically label vertebral levels using fivefold cross validation (n = 803). Prior to testing upon the final test set (n = 94), CT scans of patients with anatomical abnormalities, surgical implants, or other atypical features from the final test set were placed in an outlier group (n = 20), whereas those without these features were placed in a normative group (n = 74). The performance of X-Net, X-Net Ensemble, and another leading vertebral labeling architecture (Btrfly Net) was evaluated on both groups using identification rate, localization error, and other metrics. The performance of our approach was also evaluated on the MICCAI 2014 test dataset (n = 60). Finally, a method to detect irregular intervertebral spacing was created based on the rate of change in spacing between predicted vertebral body locations and was also evaluated using the final test set. Receiver operating characteristic analysis was used to investigate the performance of the method to detect irregular intervertebral spacing. Results: The spinal canal architecture yielded centroid coordinates spanning S2-C1 with submillimeter accuracy (mean AE standard deviation, 0.399 AE 0.299 mm; n = 330 patients) and was robust in the localization of spinal canal centroid to surgical implants and widespread metastases. Cross-validation testing of X-Net for vertebral labeling revealed that the deep learning model performance (F 1 score, precision, and sensitivity) improved with CT scan length. The X-Net, X-Net Ensemble, and Btrfly Net mean identification rates and localization errors were 92.4% and 2.3 mm, 94.2% and 2.2 mm, and 90.5% and 3.4 mm, respectively, in the final test set and 96.7% and 2.2 mm, 96.9% and 2.0 mm, and 94.8% and 3.3 mm, respectively, within the normative group of the final test set. The X-Net Ensemble yielded the highest percentage of patients (94%) having all vertebral bodies identified correctly in the final test set when the three most inferior and superior vertebral bodies were excluded from the CT scan. The method used to detect labeling failures had 67% sensitivity and 95% specificity w...

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Reducing radiotherapy waiting times for palliative patients: The role of the Advanced Practice Radiation Therapist

Cited by 33 publications

References 13 publications

An evaluation of an advanced practice role in palliative radiation therapy

An evaluation of an advanced practice role in palliative radiation therapy

Overcoming uncertainty: A framework to guide the implementation of Australian radiation therapy advanced practitioners

Evaluation of a multiview architecture for automatic vertebral labeling of palliative radiotherapy simulation CT images

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