Under the global background of the accelerated reconstruction of the smart healthcare ecosystem, artificial intelligence technology is deeply driving the transformation of the healthcare paradigm from experience-driven to data-knowledge dual-wheel driven. As a treasure of Chinese civilization, the core value of traditional Chinese medicine lies in the individualized diagnosis and treatment system based on "syndrome differentiation and treatment". The integration of multimodal diagnosis and treatment data and the construction of intelligent decision-making models will become the key path to break through the bottleneck of the modernization of traditional Chinese medicine. This research is based on the strategic orientation of "Healthy China 2030" and relies on the national science and technology major project of the team. It explores the establishment of a "three-stage four-dimensional" model of "data layer - knowledge layer - decision-making layer" and "feature extraction - relationship reasoning - dynamic correction - clinical verification" through a closed-loop verification mechanism of "human-machine collaboration - knowledge iteration", to promote the digital and intelligent transformation of traditional Chinese medicine.
Informed-evidence decision-making (IEDM) has emerged as the predominant principle of providing guidance for policy-making and practice, however, the best available evidences requisite of performing successfully IEDM. Different forms of evidence and different kinds of review questions call for the development of new approaches that are designed to more effectively and rigorously identify and synthesize the evidence. Fourteen methods of reviewing literature have been recently used to identify and synthesize evidence, of which scoping reviews is increasing popular. This article introduces aspects such as background, purpose and methodological frame work and explains the process of it with an example so that facilitating the dissemination and utilization of scoping review in China.
This article explores the application and research progress of shared decision-making (SDM) tools in ultra-early vascular recanalization therapy for ischemic stroke, focusing on analyzing the functional characteristics and advantages and disadvantages of various tools. Based on functional goals, SDM tools can be divided into four categories: brief decision aids, risk communication tools, patient information tools, and prognosis assessment tools. These tools can assist patients and doctors in making informed treatment decisions quickly in time-sensitive situations, providing a reference for optimizing stroke revascularization treatment. Additionally, SDM tools can facilitate communication between doctors and patients, enabling patients to better understand the risks and benefits of treatment options, leading to choices more aligned with personal preferences and values. Through an in-depth study of these SDM tools, it is expected to improve the diagnostic and treatment efficiency for stroke patients, reduce decision conflicts, promote collaboration between doctors and patients, and provide new ideas and methods for stroke treatment and management.
ObjectiveTo systematically review the influencing factors of breast cancer patients in treatment decision-making. MethodsWeb of Science, PubMed, EMbase, The Cochrane Library, JBI Evidence Synthesis, CINAHL, CNKI, WanFang Data, VIP and CBM databases were electronically searched to collect qualitative studies about the influencing factors of breast cancer patients in treatment decision-making from inception to October 2021. Two reviewers independently screened literature, extracted data and assessed the risk of bias of the included studies. Then, the results were combined using integrating methods. ResultsA total of 13 studies were included. Sixty-seven results were extracted, with 22 results retained after incorporation and removal of duplications. The 22 results were grouped according to their similarities to form 6 categories. These categories resulted in two synthesized findings: influencing factors of patient participation in treatment decision-making and integration results and influencing factors of patients' treatment options. ConclusionBreast cancer patient participation in treatment decisions is influenced not only by internal factors, but also by family and external situational factors. When patients choose treatment, they consider not only their personal values and preferences, but also the impact of treatment on their families and their expectations. The choice is also influenced by social and cultural aspects.
There is an increase in published studies relevant to the development of patient version of guidelines (PVGs). We reviewed, summarised and analysed the current developments in this field, with the aim of informing domestic scholars of the factors to consider when developing quality PVGs. We proposed three recommendations: (1) the concept of patient guidelines needs to be better defined; (2) a platform for the dissemination of PVGs should be established to support their implementation; and (3) a standardised PVGs development methodology should be developed to ensure the quality of the PVGs.
Artificial intelligence (AI) is reshaping evidence-based clinical decision-making. From the perspective of clinical decision-making, this paper explores the collaborative value of AI in life-cycle health management. While AI can enhance early disease screening efficiency (e.g., medical image analysis) and assist clinical decision-making through personalized health recommendations, its reliance on non-specialized data necessitates the development of dedicated AI systems grounded in high-quality, specialty-specific evidence. AI should serve as an auxiliary tool to evidence-based clinical decision-making, with physicians’ comprehensive judgment and humanistic care remaining central to medical decision-making. Clinicians must improve the reliability of decision making through refining prompt design and cross-validating AI outputs, while actively participate in AI tool optimization and ethical standard development. Future efforts should focus on creating specialty-specific AI tools based on high-quality evidence, establishing dynamic guideline update systems, and formulating medical ethical standards to position AI as a collaborative partner for physicians in implementing life-cycle health management.
Objective To explore the factors which affect shared decision-making and develop strategies to get patients actively involved in clinical decision-making. Methods We conducted a survey on 566 patients of a Class A Hospital in Sichuan with group random sampling method. The data were collected by the use of anonymous selfadministered questionnaires. We used SPSS 10.0 to analyse the data. Results A total of 600 questionnaires were distributed at random, of which 565 were completed. There were 68% patients who had some knowledge of the disease, and 93% who were willing to participate in clinical decision-making. The patients’ biggest concerns were: treatment effect, cost and doctors’ skills. The biggest difficulties that patients worried about were: long-time waiting in out-patient departments and limited time to communicate with doctors. Conclusion As more and more patients would like to involve in shared decision-making, doctors need to provide patients with more choices and help them make a right decision in their treatment.