Using a whiteboard web application for tracking treatment workflow metrics for dosimetrists and physicians

Treatment time of image-guided radiotherapy with a Halcyon 2.0 system

INTRODUCTION

The treatment fraction time is a key indicator of the external beam radiotherapy process. The Halcyon system was designed to improve the clinical workflow, according to the manufacturer (Varian Medical Systems). Few works studied the actual delivery efficiency of the Halcyon system. This work analyzed the treatment time on a Halcyon 2.0 unit for a variety of sites along a period of 9 months.

MATERIALS AND METHODS

Treatment time included patient setup, image acquisition, image-guided online couch correction, and radiation delivery time. Data were extracted from the ARIA oncology information system and were studied as a function of the treatment site, the delivery modality, and the time from the first day of treatments with the Halcyon 2.0 system in our institution.

RESULTS

A total of 8599 fractions were delivered during the analyzed period (69.5% from VMAT plans, and 30.5% from IMRT plans). The number of fractions by site ranged from 30 for anal canal to 1933 for prostate. Five sites (prostate, lung, pelvis with prostate, breast, and gynecological sites) accounted for the 84% of the fractions. After a 2-week adaptation period of the staff, the daily mean treatment time was reduced to less than 12 min. The mean treatment time of all the fractions amounted to 10.5 ± 3.8 min.

CONCLUSIONS

The Halcyon 2.0 allowed delivering online image-guided radiation therapy in all fractions with total treatment time consistently below the 12-min standard time slot, for most of the analyzed treatment sites.

A national perspective about the current work situation at modern radiotherapy departments

Background

The radiotherapy (RT) community faces great challenges to meet the growing cancer incidence, especially regarding workload and recruitment of personnel. Workflow-related issues affect involved professions differently since they have specific expertise and various roles in the workflow. To obtain an objective understanding of the current working situation and identify workflow bottle necks in RT, we conducted a national survey on this topic in 2018.

Materials and Methods

All 17 (photon-based) RT departments in Sweden were invited to participate in the study, which targeted both managers and employees in RT. Descriptive statistics were calculated for each profession and for small, medium and large departments (2/3-4/≥5 linacs).

Results

Altogether, 364 filled-in questionnaires were returned (32/332 managers/employees; 94% response rate). Managers reported a general need for more staff (all professions). Small departments reported no problems with waiting times (0/3); whereas 2/3 of medium and large departments did (medium: 5/8, large: 2/3). All professions had a positive attitude towards working in RT (mean = 86%, 0/100%=negative/positive attitude). Organizational issues were ranked highest among reoccurring events that were most frustrating/had most negative effect on the work environment. The most severe workflow-related problems were reported to originate at contouring.

Conclusion

Future efforts to improve the modern RT workflow need to focus on how to make positive mechanisms at small departments useful in larger settings. Our data also reveal that strong leadership and improved routines at contouring are warranted by all RT professions to reduce frustration related to organizational issues and to increase work effectivity.

Harnessing data science to advance radiation oncology

Radiation oncology, a major treatment modality in the care of patients with malignant disease, is a technology‐ and computer‐intensive medical specialty. As such, it should lend itself ideally to data science methods, where computer science, statistics, and clinical knowledge are combined to advance state‐of‐the‐art care. Nevertheless, data science methods in radiation oncology research are still in their infancy and successful applications leading to improved patient care remain scarce. Here, we discuss data interoperability issues within and across organizational boundaries that hamper the introduction of big data and data science techniques in radiation oncology. At the semantic level, creating common underlying models and codification of the data, including the use of data elements with standardized definitions, an ontology, remains a work in progress. Methodological issues in data science and in the use of large population‐based health data registries are identified. We show that data science methods and big data cannot replace randomized clinical trials in comparative effectiveness research by reviewing a series of instances where the outcomes of big data analyses and randomized trials are at odds. We also discuss the modern wave of machine learning and artificial intelligence as represented by deep learning and convolutional neural networks. Finally, we identify promising research avenues and remain optimistic that the data sources in radiation oncology can be linked to yield important insights in the near future. We argue that data science will be a valuable complement to, but not a replacement of, the traditional hypothesis‐driven translational research chain and the randomized clinical trials that form the backbone of evidence‐based medicine.