Purpose:
Historically many radiation medicine programs have maintained their Quality Control (QC) test results in paper records or Microsoft Excel worksheets. Both these approaches represent significant logistical challenges, and are not predisposed to data review and approval. It has been our group's aim to develop and implement web based software designed not just to record and store QC data in a centralized database, but to provide scheduling and data review tools to help manage a radiation therapy clinics Equipment Quality control program.
Methods:
The software was written in the Python programming language using the Django web framework. In order to promote collaboration and validation from other centres the code was made open source and is freely available to the public via an online source code repository. The code was written to provide a common user interface for data entry, formalize the review and approval process, and offer automated data trending and process control analysis of test results.
Results:
As of February 2014, our installation of QAtrack+ has 180 tests defined in its database and has collected ∼22 000 test results, all of which have been reviewed and approved by a physicist via QATrack+'s review tools. These results include records for quality control of Elekta accelerators, CT simulators, our brachytherapy programme, TomoTherapy and Cyberknife units. Currently at least 5 other centres are known to be running QAtrack+ clinically, forming the start of an international user community.
Conclusion:
QAtrack+ has proven to be an effective tool for collecting radiation therapy QC data, allowing for rapid review and trending of data for a wide variety of treatment units. As free and open source software, all source code, documentation and a bug tracker are available to the public at https://bitbucket.org/tohccmedphys/qatrackplus/.
The evaluation of treatment plans for compliance with prescribed treatment objectives is often a manual process that is susceptible to errors or omissions arising from non‐standardized procedures, time constraints or human factors. In an effort to improve the consistency and accuracy of this process we have developed software that automates the comparison between patient dose‐volume histograms (DVH) and pre‐defined dosimetric constraints. Treatment objectives and constraints (i.e. careplans) are stored in a database, categorized by disease site. Constraints have optional soft and hard limits which can be either modified or overridden on a per‐patient basis at the discretion of the clinician. Configurable plugins automate the import of patient DVH data from the treatment planning system (currently supporting Monaco, XiO, and TomoTherapy). An optional report summarizing the results of a comparison can be generated for inclusion in the patient treatment record. Results from each comparison are stored in a database enabling the analysis of program wide care plan compliance, average or mean doses to critical organs, or other values of interest. The software is run on a centralized server and accessed by users through any modern web browser. It will be released as free, open source software in the near future.
Given the present National Health Service reorganization, appraisal of the work of a dietetic department is of current importance. This study provides baseline information and outlines the work of a dietetics service within a district acute‐unit. The majority of dietetics referrals were from medical consultants and general practitioners whereas fewer referrals were received from surgical consultants and other medical specialities. Time allocation for the dietitians was predominantly either patient support (35.0%) or management, administrative and clerical duties (33.0%). Catering related duties accounted for 11.0% of the dietitians time.
The type of dietary treatments reflects the general‐hospital's function, with most being qualitative in nature, and few specialist treatments. Patients failure‐to‐attend rate was considerable (15.0%).
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