Objective To construct an evaluation index system of the competitiveness of private hospitals, and to provide references for guiding, supervising, and managing the high-quality development of private hospitals. Methods An index pool was constructed by the literature analysis method. Index screening was completed using the modified Delphi method. The analytic hierarchy process, entropy weight method, and combination weight method were used to determine the index weight. Results The competitiveness evaluation index system of private hospitals was constructed, which included 5 primary indexes and 36 secondary indexes. The combination weight methods were resource allocation (0.366 8), service capacity (0.470 8), service efficiency (0.033 7), quality and safety (0.121 3), and financial management (0.007 3). Conclusion The constructed evaluation index system of competitiveness of private hospitals is scientific, targeted, and operable.
In the realm of data mining based on modern acupuncture clinical research, the impact of literature features such as literature quality, evidence level, sample size, and clinical efficacy on the quality of data mining outcomes remains uncertain. These issues are significant factors restricting the translational application of data mining research results. We suggest employing both entropy weight and linear weighting techniques to assess the specified indicators. This assessment results in a comprehensive weighted score for acupuncture prescriptions, serving as the foundation for our ensuing data mining endeavors. In this study, migraine research serves as an example to contrast the efficacy of weighted algorithms against that of classical algorithms. The findings demonstrate that the algorithm introduced in this research significantly contributes to studies focusing on the dispersed selection of acupuncture points. Its superiority lies in cluster analysis, where it adeptly discerns potential patterns in the amalgamation of acupoints. This algorithm amalgamates evidence-based acupuncture with data mining processes, providing innovative perspectives that augment the caliber of research in acupuncture data mining. Nonetheless, additional research is essential to corroborate these results.