It is essential to improve the practice of community healthcare service for the resolution of the problem of inadequate and overly expensive medical services, to promote the harmonization of doctor-patient relationship. From the aspects of the introduction of community healthcare service and the necessity of its standard management, the civil legal relation of community healthcare and its major problems, as well as the rights and duties of community doctors, the authors discussed the importance and necessity of scientific management, right protection by law as well as sound and orderly development of community healthcare service.
World Federation of Acupuncture-Moxibustion Societies (WFAS) standard Norms for Formulation and Evaluation of the Clinical Practice Guidelines of Acupuncture and Moxibustion (Hereinafter referred to as Norms) is the first methodological specification for the development of guidelines of acupuncture and moxibustion (Acup-Mox) issued by an international academic organization. The Norms stipulates the principles, procedures, review process and requirements of the development of WFAS guidelines of Acup-Mox. It also proposes the development method, evaluation method, and reporting standards of WFAS guidelines of Acup-Mox. This article introduces the development process of the Norms and provides an interpretation of the methodological supplementary requirements for key links such as "formulation of clinical questions", "evidence retrieval, evaluation and synthesis", and "consensus decision-making", as well as the "framework and contents of recommendation" to provide relevant references for users in learning and using the Guidelines.
ObjectiveTo evaluate the quality differences in recommendations generated by large language models (LLMs) and clinical practitioners for sarcopenia-related questions. MethodsA sarcopenia knowledge base was constructed based on the latest domestic and international research and consensus guidelines. Using the Python environment, a locally deployed and sarcopenia-focused hybrid vertical LLM (referred to as LC) was implemented via LangChain-LLM. Eight fixed questions covering etiology, diagnosis, and prevention were selected, along with eight virtual patient cases. The evaluation team assessed the quality of answers generated by LC and written by clinical practitioners. Quantitative analysis was performed on the precision, recall, and F1 scores (harmonic mean of precision and recall) of treatment recommendations. ResultsThe responses were generally perceived as "possibly written by humans or AI", with a stronger inclination toward being AI-generated, although the accuracy of such judgments was low. Regarding answer quality attributes, LC's responses were superior to those of clinical practitioners in guideline consistency (P<0.01), exhibited similar acceptability (P>0.05), showed better practicality (P<0.05), and had a lower proportion of "1–2 errors" (P<0.05). Quantitative analysis of treatment recommendations indicated that LC and GPT-4.0 outperformed clinical practitioners in recall and F1 scores (P<0.05), with minimal differences between LC and GPT-4.0. ConclusionThe locally deployed sarcopenia-focused hybrid vertical LLM demonstrates high accuracy and applicability in addressing sarcopenia-related issues, outperforming clinical practitioners and exhibiting strong clinical decision-support capabilities.