Artificial intelligence and end user tools to develop a nurse duty roster scheduling system.

Objectives: A nurse duty roster is usually prepared monthly in a hospital ward. It is common for nurses to make duty shift requests prior to scheduling. A ward manager normally spends more than a working day to manually prepare and subsequently to optimally adjust the schedule upon staff requests and hospital policies. This study aimed to develop an automatic nurse roster scheduling system with the use of open-source operational research tools by taking into account the hospital standards and the constraints from nurses. Methods: Artificial intelligence and end user tools operational research tools were used to develop the code for the nurse duty roster scheduling system. To compare with previous research on various heuristics in employee scheduling, the current system was developed on open architecture and adopted with real shift duty requirements in a hospital ward. Results: The schedule can be generated within 1 min under both hard and soft constraint optimization. All hard constraints are fulfilled and most nurse soft constraints could be met. Compared with those schedules prepared manually, the computer-generated schedules were more optimally adjusted as real time interaction among nurses and management personnel. The generated schedules were flexible to cope with daily and hourly duty changes by redeploying ward staff in order to maintain safe staffing levels. Conclusions: An economical but yet highly efficient and user friendly solution to nurse roster scheduling system has been developed and adopted using open-source operational research methodology. The open-source platform is found to perform satisfactorily in scheduling application. The system can be implemented to different wards in hospitals and be regularly updated with new hospital polices and nurse manpower by hospital information personnel with training in artificial intelligence.

Patient-Derived Nasopharyngeal Cancer Organoids for Disease Modeling and Radiation Dose Optimization.

Effective radiation treatment (RT) for recurrent nasopharyngeal cancers (NPC), featuring an intrinsic hypoxic sub-volume, remains a clinical challenge. Lack of disease-specific in-vitro models of NPC, together with difficulties in establishing patient derived xenograft (PDX) models, have further hindered development of personalized therapeutic options. Herein, we established two NPC organoid lines from recurrent NPC PDX models and further characterized and compared these models with original patient tumors using RNA sequencing analysis. Organoids were cultured in hypoxic conditions to examine the effects of hypoxia and radioresistance. These models were then utilized to determine the radiobiological parameters, such as α/ß ratio and oxygen enhancement ratio (OER), characteristic to radiosensitive normoxic and radioresistant hypoxic NPC, using simple dose-survival data analytic tools. The results were further validated in-vitro and in-vivo, to determine the optimal boost dose and fractionation regimen required to achieve effective NPC tumor regression. Despite the differences in tumor microenvironment due to the lack of human stroma, RNA sequencing analysis revealed good correlation of NPC PDX and organoid models with patient tumors. Additionally, the established models also mimicked inter-tumoral heterogeneity. Hypoxic NPC organoids were highly radioresistant and had high α/ß ratio compared to its normoxic counterparts. In-vitro and in-vivo fractionation studies showed that hypoxic NPC was less sensitive to RT fractionation scheme and required a large bolus dose or 1.4 times of the fractionated dose that was effective against normoxic cells in order to compensate for oxygen deficiency. This study is the first direct experimental evidence to predict optimal RT boost dose required to cause sufficient damage to recurrent hypoxic NPC tumor cells, which can be further used to develop dose-painting algorithms in clinical practice.

Simplified sigmoidal curve fitting for a 6 MV FFF photon beam of the Halcyon to determine the field size for beam commissioning and quality assurance.

BACKGROUND: An O-ring gantry-type linear accelerator (LINAC) with a 6-MV flattening filter-free (FFF) photon beam, Halcyon, includes a reference beam that contains representative information such as the percent depth dose, profile and output factor for commissioning and quality assurance. However, because it does not provide information about the field size, we proposed a method to determine all field sizes according to all depths for radiation therapy using simplified sigmoidal curve fitting (SCF). METHODS: After mathematical definition of the SCF using four coefficients, the defined curves were fitted to both the reference data (RD) and the measured data (MD). For good agreement between the fitting curve and the profiles in each data set, the field sizes were determined by identifying the maximum point along the third derivative of the fitting curve. The curve fitting included the field sizes for beam profiles of 2 × 2, 4 × 4, 6 × 6, 8 × 8, 10 × 10, 20 × 20 and 28 × 28 cm2 as a function of depth (at 1.3, 5, 10 and 20 cm). The field size results from the RD were compared with the results from the MD using the same condition. RESULTS: All fitting curves show goodness of fit, R2, values that are greater than 0.99. The differences in field size between the RD and the MD were within the range of 0 to 0.2 cm. The smallest difference in the field sizes at a depth of 10 cm, which is a surface-to-axis distance, was reported. CONCLUSION: Application of the SCF method has been proven to accurately capture the field size of the preconfigured RD and the measured FFF photon beam data for the Halcyon system. The current work can be useful for beam commissioning as a countercheck methodology to determine the field size from RD in the treatment planning system of a newly installed Halcyon system and for routine quality assurance to ascertain the correctness of field sizes for clinical use of the Halcyon system.