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Objectives: Health system responsiveness is a complicated issue that guides researchers in determining an efficient methodology to enhance the understanding of health care systems' perspectives. This study examined the relationship between patients' experience profiles and their satisfaction with medical outcomes. Design: The authors used 11 items from the Taiwanese Annual National Health Insurance survey and used latent class analysis (LCA) to develop experience profiles for 2012 Traditional Chinese Medicine (TCM) patients. A logistic regression model was developed to explore differences in patients' satisfaction with their treatment outcome across four groups identified using the LCA, with adjustment for demographic factors. Subjects: Patients' experiences with medical services in TCM in 2015. Outcome measures: Patients' satisfaction with their treatment outcome. Results: Participants (N = 2012) were divided into four groups. Regardless of gender and age, Group 2 (nall) held the lowest satisfaction with treatment outcome relative to those reported by the other three groups (odds ratios with p-value: Group 1 [pall]: 7.40, Group 3 [nComm]: 1.37, Group 4 [Skip]: 2.20, p < 0.001). Respondents with a more favorable patient experience reported higher satisfaction with treatment outcome. Conclusions: An increased favorable treatment experience in TCM (such as positive accessibility and better interpersonal relationships with providers) is associated with higher satisfaction with treatment outcome. The World Health Organization emphasizes that traditional medicine integrated with Western medicine can play an important role in achieving the goal of "Health for All." Health authorities should develop strategies aimed at enhancing patients' experiences in TCM to contribute to a positive care process. [ABSTRACT FROM AUTHOR]

• In this work, a new calibration model transfer strategy which combined direct orthogonal signal correction with slope and bias correction (DOSC-SBC) was proposed and applied to maintain the predictive abilities of the original calibration model for new source batches. • The original calibration model can be used for predicting the transformed test samples without updating the model's coefficients though DOSC-SBC. • It will get good results in the multivariate calibration model transfer and be applicable to online or inline monitoring the preparation process of TCM. Extraction process plays an important role in the pretreatment of Flos Lonicerae Japonicae for Qingkailing oral liquid and is also one of the most popular preparation processes of traditional Chinese medicine (TCM). In order to monitor the content of API in the extraction solution, a PLS model was developed based on NIR spectroscopy. However, due to the raw materials' natural variability introduced into the extraction process of TCM or variations in spectral measurement conditions, it was unable to foresee the changes in the spectral response for new source batches, which could render a calibration model invalid. The development of a reliable multivariate calibration model for each new source batch is usually time-consuming and costly. In this work, a new calibration model transfer strategy which combined direct orthogonal signal correction with slope and bias correction (DOSC-SBC) was proposed and applied to maintain the predictive abilities of the original calibration model for new source batches. Performance of this model transfer strategy has been validated by two new individual batches which raw materials were from different sources, and the predictive relative errors of the samples from the above new batches decreased from 15.67% and 19.82% to 8.35% and 6.80% respectively after using DOSC-SBC strategy, demonstrating the good performance of the proposed model transfer strategy. [ABSTRACT FROM AUTHOR]