Onehealth, an evidence-based decision-making software, is based on the United Nations' epidemiological reference modules to predict the effect of health services. Onehealth is a large database. The software is using activitybased costing, simulating investment costs of health system and changes of mortality in different coverage levels. By the cost of inputs/avoid deaths, it could quantify the cost of health services effectiveness and provide an intuitive basis for the rational allocation of health resources. This study introduces the relevant concepts, model structures and applications of Onehealth. We took the study of child nutrition interventions in Sudan for example and to present Onehealth tool's operating. As a new auxiliary and evidence-based decision-making software with scientific and rigorous theoretical approach, Onehealth has practical significance on the national or regional macro decision-making.
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 evaluate the effectiveness of the shared decision-making scheme in postoperative out-of-hospital extended care for patients with total hip/knee arthroplasty (THA/TKA). Methods Patients who underwent THA/TKA in the Department of Orthopedic Surgery at West China Hospital of Sichuan University between October 2023 and April 2024 were included using convenience sampling. Patients were divided into the control group (odd-numbered dates) and the intervention group (even-numbered dates) based on the surgical dates. The intervention group was received care guided by a shared decision-making protocol, whereas the control group was followed the standard post-arthroplasty follow-up procedures. Differences between the two groups were compared in terms of decision-making capacity, decision satisfaction, and hip/knee function at the following time points: on the day of discharge, 3 weeks after discharge, 2 months after discharge, and 3 months after discharge. Results A total of 118 patients were included, with 59 cases in each group. There were no significant differences in demographic and clinical characteristics between the two groups (P>0.05). In terms of decision-making, compared with the control group, the experimental group had stronger decision-making ability, lower decision-making conflict, and more satisfaction with the decision-making process (P<0.05). In terms of joint function, the experimental group showed better joint function than the control group at 3 weeks, 2 months, and 3 months after surgery (P<0.05). There was no significant difference in the pain dimension of the Western Ontario and McMaster Universities Osteoarthritis index between the two groups (P=0.199). Conclusions Compared with the traditional follow-up protocol, the shared decision-making protocol can enhance patient engagement in medical decision-making, reduce decisional conflict, improve satisfaction with the decision-making process, and simultaneously promote joint functional recovery and expedite the rehabilitation process.
Patients with severe traumatic brain injury (TBI) have a higher mortality rate, often dying within a few hours after injury. The management of trauma site, transportation, and early hospital stay is closely related to the outcome of TBI patients. The final success rate of TBI patients varies after different prehospital treatments, and the quality of prehospital treatment for TBI needs to be further improved. Therefore, the TBI prehospital management guideline emerged, and the third version of the guideline was released in April 2023. In order to provide better advice and guidance on the treatment of prehospital TBI, this article interprets the key points of updating the third edition of the prehospital TBI management guideline.
Evidence-based medicine advocates to support clinical decision-making with the best evidence, which is useful to objectively evaluate the clinical efficacy of traditional Chinese medicine and optimize clinical diagnosis and treatment. However, significant individualized characteristics identified from syndrome differentiation and treatment are incompatible with evidence-based clinical decision-making, which highlights population-level evidence, to some extent. In recent years, a number of new methods and technologies have been introduced into individualized clinical efficacy evaluation research of traditional Chinese medicine to assist managing and processing complex and multivariate information. These methods and technologies share similarities with evidence-based medicine, and are expected to link the clinical practice of traditional Chinese medicine with evidence-based clinical decision-making. They will guide the development of evidence-based clinical decision-making in traditional Chinese medicine.
Objective To establish a cooperative decision-making model of county-level public hospitals, so as to freely select the best partner in different decision-making units and promote the optimal allocation of medical resources. Methods The input and output data of 10 adjacent county-level public hospitals in Henan province from 2017 to 2019 was selected. Based on the traditional data envelopment analysis (DEA) model, a generalized fuzzy DEA cooperative decision-making model with better applicability to fuzzy indicators and optional decision-making units was constructed. By inputting index information such as total number of employees, number of beds, annual outpatient and emergency volume, number of discharged patients, total income and hospital grade evaluation, the cooperation efficiency intervals of different hospitals were calculated to scientifically select the best partner in different decision-making units.Results After substituting the data of 10 county-level public hospitals in H1-H10 into the model, taking H2 hospital as an example to make cooperative decision, among the four hospitals in H1, H2, H7 and H10 of the same scale, under optimistic circumstances, the best partner of H2 hospital was H7 hospital, and the cooperation efficiency value was 1.97; in a pessimistic situation, the best partner of H2 hospital was H10 hospital, and the cooperation efficiency value was 0.98. The model had good applicability in the cooperative decision-making of county-level public hospitals. Conclusion The generalized fuzzy DEA model can better evaluate the cooperative decision-making analysis between county-level public hospitals.
In recent years, the concept of population medicine has emerged as a research field that has important implications for healthcare practice and policy decision-making. It specifically aims to improve overall health of patient populations and safety, quality and efficiency of healthcare system. This paper descried the background, definition and characteristics of population medicine, discussed relationship between population medicine and population health and evidence-based medicine. It also introduced Department of Population Medicine at Harvard Medical School as a world-class model in the field of population medicine, discussed the needs and potential strategies for developing population medicine research in China, and briefly outlined the current development of population medicine in China.