A Computational Study on the Role of Parameters for Identification of Thyroid Nodules by Infrared Images (and Comparison with Real Data)

Model-Optimizing Radiofrequency Parameters of 3D Finite Element Analysis for Ablation of Benign Thyroid Nodules

Radiofrequency (RF) ablation represents an efficient strategy to reduce the volume of thyroid nodules. In this study, a finite element model was developed with the aim of optimizing RF parameters, e.g., input power and treatment duration, in order to achieve the target volume reduction rate (VRR) for a thyroid nodule. RF ablation is modelled as a coupled electro-thermal problem wherein the electric field is applied to induce tissue heating. The electric problem is solved with the Laplace equation, the temperature distribution is estimated with the Pennes bioheat equation, and the thermal damage is evaluated using the Arrhenius equation. The optimization model is applied to RF electrode with different active tip lengths in the interval from 5 mm to 40 mm at the 5 mm step. For each case, we also explored the influence of tumour blood perfusion rate on RF ablation outcomes. The model highlights that longer active tips are more efficient as they require lesser power and shorter treatment time to reach the target VRR. Moreover, this condition is characterized by a reduced transversal ablation zone. In addition, a higher blood perfusion increases the heat dispersion, requiring a different combination of RF power and time treatment to achieve the target VRR. The model may contribute to an improvement in patient-specific RF ablation treatment.

Using information technology to optimize the identification process for outpatients having blood drawn and improve patient satisfaction

BACKGROUND

This study explored the application effect of information technology in optimizing the patient identification process.

METHODS

The method for optimizing the identification process involved in drawing blood among outpatients using information technology was executed from July 2020. In this paper, 959 patients who had blood drawn from January to June 2020 were included as the pre-optimization group, and 1011 patients who had blood drawn from July to December 2019 were included as the post-optimization group. The correct rate of patient identification, waiting time, and patient satisfaction before and after the optimization were statistically analyzed. The changes in these three indexes before and after the optimization implementation, as well as the application effects, were compared.

RESULTS

The correct rate of patient identification after optimization (99.80%) was higher than before optimization (98.02%) (X² = 13.120; P < 0.001), and the waiting time for having blood drawn was also significantly shortened (t = 8.046; P < 0.001). The satisfaction of patients was also significantly improved (X² = 20.973; P < 0.001).

CONCLUSIONS

By combining information technology with the characteristics of blood collection in our hospital, using the call system to obtain patient information, then scan the QR code of the guide sheet for automatic verification, and finally manually reconfirm patient information, which can significantly reduce the occurrence of identification errors, improve work efficiency and improve patients' satisfaction.