Optimizing Trauma Systems: A Geospatial Analysis of the Victorian State Trauma System.

OBJECTIVE: The aim of this study was to develop a data-driven approach to assessing the influence of trauma system parameters and optimizing the configuration of the Victorian State Trauma System (VSTS). SUMMARY BACKGROUND DATA: Regionalized trauma systems have been shown to reduce the risk of mortality and improve patient function and health-related quality of life. However, major trauma case numbers are rapidly increasing and there is a need to evolve the configuration of trauma systems. METHODS: A retrospective review of major trauma patients from 2016 to 2018 in Victoria, Australia. Drive times and flight times were calculated for transport to each of 138 trauma receiving hospitals. Changes to the configuration of the VSTS were modeled using a Mixed Integer Linear Programming algorithm across 156 simulations. RESULTS: There were 8327 patients included in the study, of which 58% were transported directly to a major trauma service (MTS). For adult patients, the proportion of patients transported directly to an MTS increased with higher transport time limit, greater probability of helicopter emergency medical service utilization, and lower hospital patient threshold numbers. The proportion of adult patients transported directly to an MTS varied from 66% to 90% across simulations. Across all simulations for pediatric patients, only 1 pediatric MTS was assigned. CONCLUSIONS: We have developed a robust and data-driven approach to optimizing trauma systems. Through the use of geospatial and mathematical models, we have modeled how potential future changes to trauma system characteristics may impact on the optimal configuration of the system, which will enable policy makers to make informed decisions about health service planning into the future.

Supporting the Problem-Solving Loop: Designing Highly Interactive Optimisation Systems.

Efficient optimisation algorithms have become important tools for finding high-quality solutions to hard, real-world problems such as production scheduling, timetabling, or vehicle routing. These algorithms are typically "black boxes" that work on mathematical models of the problem to solve. However, many problems are difficult to fully specify, and require a "human in the loop" who collaborates with the algorithm by refining the model and guiding the search to produce acceptable solutions. Recently, the Problem-Solving Loop was introduced as a high-level model of such interactive optimisation. Here, we present and evaluate nine recommendations for the design of interactive visualisation tools supporting the Problem-Solving Loop. They range from the choice of visual representation for solutions and constraints to the use of a solution gallery to support exploration of alternate solutions. We first examined the applicability of the recommendations by investigating how well they had been supported in previous interactive optimisation tools. We then evaluated the recommendations in the context of the vehicle routing problem with time windows (VRPTW). To do so we built a sophisticated interactive visual system for solving VRPTW that was informed by the recommendations. Ten participants then used this system to solve a variety of routing problems. We report on participant comments and interaction patterns with the tool. These showed the tool was regarded as highly usable and the results generally supported the usefulness of the underlying recommendations.

What do Constraint Programming Users Want to See? Exploring the Role of Visualisation in Profiling of Models and Search.

Constraint programming allows difficult combinatorial problems to be modelled declaratively and solved automatically. Advances in solver technologies over recent years have allowed the successful use of constraint programming in many application areas. However, when a particular solver's search for a solution takes too long, the complexity of the constraint program execution hinders the programmer's ability to profile that search and understand how it relates to their model. Therefore, effective tools to support such profiling and allow users of constraint programming technologies to refine their model or experiment with different search parameters are essential. This paper details the first user-centred design process for visual profiling tools in this domain. We report on: our insights and opportunities identified through an on-line questionnaire and a creativity workshop with domain experts carried out to elicit requirements for analytical and visual profiling techniques; our designs and functional prototypes realising such techniques; and case studies demonstrating how these techniques shed light on the behaviour of the solvers in practice.

A radiobiology-based inverse treatment planning method for optimisation of permanent l-125 prostate implants in focal brachytherapy.

Treatment plans for ten patients, initially treated with a conventional approach to low dose-rate brachytherapy (LDR, 145 Gy to entire prostate), were compared with plans for the same patients created with an inverse-optimisation planning process utilising a biologically-based objective. The 'biological optimisation' considered a non-uniform distribution of tumour cell density through the prostate based on known and expected locations of the tumour. Using dose planning-objectives derived from our previous biological-model validation study, the volume of the urethra receiving 125% of the conventional prescription (145 Gy) was reduced from a median value of 64% to less than 8% whilst maintaining high values of TCP. On average, the number of planned seeds was reduced from 85 to less than 75. The robustness of plans to random seed displacements needs to be carefully considered when using contemporary seed placement techniques. We conclude that an inverse planning approach to LDR treatments, based on a biological objective, has the potential to maintain high rates of tumour control whilst minimising dose to healthy tissue. In future, the radiobiological model will be informed using multi-parametric MRI to provide a personalised medicine approach.