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.
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 systematically review the issues and countermeasures in supervising medical insurance funds under the DRG/DIP payment model. Methods The CNKI, WanFang Data, CBM, VIP, PubMed, Web of Science, and Embase databases were electronically searched to collect studies related to objectives from inception to March 15, 2024. Two reviewers independently screened the literature, extracted data, and assessed the quality of the included studies using the Critical Appraisal Skills Programme (CASP). Excel 2019 was used for data extraction and organization, and a thematic synthesis approach was employed for analysis. Results Nineteen qualitative studies were included. Nine studies identified key issues in fund supervision under the DRG/DIP model: inadequate regulatory mechanisms, weak regulatory capacity, low informatization, and traditional regulatory concepts. Fourteen studies proposed optimization strategies, including establishing a regulatory system, customizing regulatory indicators, creating a performance evaluation mechanism, developing talent, promoting multi-party regulation, enhancing intelligent supervision systems, and improving stakeholder collaboration and communication. Conclusion The DRG/DIP payment model faces challenges in medical insurance fund supervision, including weak mechanisms and capacity. Improving regulatory efficiency and ensuring medical service quality requires strengthening the regulatory system, customizing indicator systems, and enhancing talent development.
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.
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.
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.
The shortage of health workforce in rural and remote areas has been commonly concerned by every country around the word. It is one of world health issues, challenging the aspirations of achieving equity. In this regard, WHO developed the Global Policy Recommendations to improve the accessibility of the health workforce in rural and remote areas through improved retention. This article focuses on the key steps of the policy guideline developed from evidence-based medicine methodology and from angle of guideline development, mainly about background, issues, evidence retrieval and selection, quality grading of evidence, and the forming of recommendation plan, in order to further explore how to correctly understand, obtain, evaluate and apply currently available research evidence, and how to use the GRADE system to make scientific and feasible recommendations in the decision-making process, emphasizing the importance of evidence and the GRADE system in the evidence-based health decision-making.