A Safe and Practical Cycle for Team-Based Development and Implementation of In-House Clinical Software.

PURPOSE: Due to a gap in published guidance, we describe our robust cycle of in-house clinical software development and implementation, which has been used for years to facilitate the safe treatment of all patients in our clinics. METHODS AND MATERIALS: Our software development and implementation cycle requires clarity in communication, clearly defined roles, thorough commissioning, and regular feedback. Cycle phases include design requirements and use cases, development, physics evaluation testing, clinical evaluation testing, and full clinical release. Software requirements, release notes, test suites, and a commissioning report are created and independently reviewed before clinical use. Software deemed to be high-risk, such as those that are writable to a database, incorporate the use of a formal, team-based hazard analysis. Incident learning is used to both guide initial development and improvements as well as to monitor the safe use of the software. RESULTS: Our standard process builds in transparency and establishes high expectations in the development and use of custom software to support patient care. Since moving to a commercial planning system platform in 2013, we have applied our team-based software release process to 16 programs related to scripting in the treatment planning system for the clinic. CONCLUSIONS: The principles and methodology described here can be implemented in a range of practice settings regardless of whether or not dedicated resources are available for software development. In addition to teamwork with defined roles, documentation, and use of incident learning, we strongly recommend having a written policy on the process, using phased testing, and incorporating independent oversight and approval before use for patient care. This rigorous process ensures continuous monitoring for and mitigatation of any high risk hazards.

The Fusion of Incident Learning and Failure Mode and Effects Analysis for Data-Driven Patient Safety Improvements.

PURPOSE: Incident learning is a critical part of the quality improvement process for all radiation therapy clinics. Failure mode and effects analysis has also been adopted as a hazard analysis method within the field of radiation oncology based on the recommendations of American Association of Physicists in Medicine Task Group 100. In this work, we demonstrate a fusion of these techniques that is efficient and transferrable to all types of clinics and that allows data-driven targeting of the highest risk error types. METHODS AND MATERIALS: Four clinical physicists recorded safety events detected during physics treatment plan quality assurance over a 27-month period. Events were sorted into the broad categories of either a documentation or plan construction error. Events were further stratified into subcategories until sufficiently discriminated against for analysis. Event risks were quantified using reduced-resolution TG-100 severity scores combined with observed occurrence rates. The highest risk categories were examined for intervention strategies. RESULTS: A total of 871 events were identified over the study period. Of these, 652 (74.9%) were classified as low severity, 178 (20.4%) as medium severity, and 41 (4.7%) as high severity. Four of the top 5 ranked categories could be targeted by a preplanning chart rounds. Several of the categories could be targeted by additional automation in the planning and QA processes. CONCLUSIONS: The retrospective classification and risk analysis of safety events allows clinics to design targeted workflow and quality assurance changes aimed at reducing the occurrence of high-risk events. The method presented here leverages incident learning efforts that many clinics are already performing, allows the severity of events to be efficiently assigned, and generates actionable results without requiring a complete failure mode and effects analysis.

Improving patient safety and workflow efficiency with standardized pretreatment radiation therapist chart reviews.

PURPOSE: Radiation therapists play a critical role in ensuring patient safety; however, they are sometimes given insufficient time to perform quality assurance (QA) of a patient's treatment chart and documentation before the start of treatment. In this work, we show the benefits of introducing a formal therapist prestart QA checklist, completed in a quiet space well in advance of treatment, into our workflow. METHODS AND MATERIALS: A therapist prestart QA checklist was created by analyzing in-house variance reports and treatment unit delays over 6 months. Therapists were then given dedicated time and workspace to perform their checks within the dosimetry office of our department. The effectiveness of the checklist was quantified by recording the percentage of charts that underwent QA before treatment, the percentage of charts with errors needing intervention, and treatment unit delays during a nearly 2-year period. The frequency and types of errors found by the prestart QA were also recorded. RESULTS: Through the use of therapist prestart QA, instances of treatment unit delays were reduced by up to a factor of 9 during the first year of the program. At the outset of this new initiative, nearly 40% of charts had errors requiring intervention, with the majority being scheduling related. With upstream workflow changes and automation, this was reduced over the period of a year to about 10%. CONCLUSIONS: The number of treatment unit delays was dramatically reduced by using a formal therapist prestart QA checklist completed well in advance of treatment. The data collected via the checklist continue to be used for further quality improvement efforts.