The health decision support system in Canada is embodied in Electronic Health Record Solutions, which includes Public Health Surveillance System, Drug Information Systems, Laboratory Information Systems, etc. By virtue of business intelligence and other advanced information techology, the system can realize the function of statistical analysis, data mining, prediction, and transform information into predicting and guiding knowledge, thus support health decision-making in terms of health surveillance, resources allocation and quality control. The success lie in b support from the federal government, efficient responsible organization Infoway as well as comprehensive and strategic design, which can be served as good enlightenment for HDSS construction of China.
This article outlined the background and the concept of health decision support system (HDSS), discussed its application status in medical service, hospital management, public health management and other fields in the following four developed countries: United States, UK, Canada and Australia. It also introduced the main functions of MYCIN, CPOE, DHCP, Panorama etc., and summarized the above four countries’ successful experiences, including, actively promoting the application of decision support technology in health field through closely combining with the practical needs, valuing evidenced-based resources development and repository research, making scientific health policy to guide the orderly development of decision support systems, and strengthening the construction of E-health infrastructure and core systems, in hopes of providing reference for the development of health decision support system in China.
ObjectiveTo verify the reliability of Anticlot Assistant, a patient self-management system for warfarin therapy assisted by artificial intelligence.MethodsIt was a single-center, prospective cohort study. The eligible 34 participants were recruited consecutively between November 29, 2017 to September 27, 2018 and managed by warfarin therapy via Anticlot Assistant. The recommendations of Anticlot Assistant were examined and verified by the doctors to ensure the security. Medical records were exported from the the background management system. An univariate analysis compared the outcomes between accepted and overridden records and a logistic regression model was built to determine independent predictors of the outcomes. The research team analyzed 153 medical records, which were from 18 participants and were input by 19 doctors. There were 97 records with doctor accepting the suggestion and 56 records with doctor rejecting the suggestion .ResultsWhen the doctors accepted the recommendations, the percentage of the next-test international normalized ratio (INR) in the therapeutic range was higher (64.95% vs. 44.64%, RR=2.298, 95%CI 1.173 to 4.499, P=0.014). The logistic regression analysis revealed that accepting the recommendations was an independent predictor for the next-test INR being in the therapeutic range after controlling potentially confounding factors (OR=2.446, 95%CI 1.103 to 5.423, P=0.028).ConclusionThe algorithm of Anticlot Assistant is reasonable and reliable.
In order to understand the latest progress of health decision support system (HDSS) construction, grasp the law of HDSS development and adopt the international advanced experience, this paper took Australia for example, presented a comparative analysis on the construction practices, including the contents, features and system functions of national construction guidelines for HDSS in different periods, and showed the integral development level of Australia HDSS was still in the exploratory stage, and its construction goal, function orientation and construction mechanism got improved gradually with the deep development of public health information. Additionally, to assure the accuracy and safety of HDSS function, Australia has been laying stress on the standard specification construction and system function authentication.
Objective To explore the use of ChatGPT (Chat Generative Pre-trained Transformer) in pediatric diagnosis, treatment and doctor-patient communication, evaluate the professionalism and accuracy of the medical advice provided, and assess its ability to provide psychological support. Methods The knowledge databases of ChatGPT 3.5 and 4.0 versions as of April 2023 were selected. A total of 30 diagnosis and treatment questions and 10 doctor-patient communication questions regarding the pediatric urinary system were submitted to ChatGPT versions 3.5 and 4.0, and the answers to ChatGPT were evaluated. Results The answers to the 40 questions answered by ChatGPT versions 3.5 and 4.0 all reached the qualified level. The answers to 30 diagnostic and treatment questions in ChatGPT 4.0 version were superior to those in ChatGPT 3.5 version (P=0.024). There was no statistically significant difference in the answers to the 10 doctor-patient communication questions answered by ChatGPT 3.5 and 4.0 versions (P=0.727). For prevention, single symptom, and disease diagnosis and treatment questions, ChatGPT’s answer scores were relatively high. For questions related to the diagnosis and treatment of complex medical conditions, ChatGPT’s answer scores were relatively low. Conclusion ChatGPT has certain value in assisting pediatric diagnosis, treatment and doctor-patient communication, but the medical advice provided by ChatGPT cannot completely replace the professional judgment and personal care of doctors.
In combination with the national health informatization construction in UK during the past ten years, this article introduced the resource construction of decision making knowledge library like British Electronic Medicine Library Clinical Pathway Database and NHS Evidence, as well as the function and application of clinical decision support system (CDSS) like PRODIGY, medical knowledge map and so on, discussed the development characteristics and construction experiences of British health decision support system (HDSS). And aiming directly at Chinese specific circumstances, this article offered some suggestions on promoting China HDSS development, for instance, dynamically integrating CDSS with patients’ diagnosis and treatment procedure through the electronic medical record system, strengthening the resources construction of knowledge library, establishing localized clinical pathway, and so on.
Generative artificial intelligence (AI) technology plays a significant role in enhancing data application capabilities, improving disease diagnosis and treatment plans, and advancing health management, drug development, genetic analysis, and precision medicine. However, due to the diagnostic complexity, treatment diversity, and high technical demands of orthopedic diseases, the application of generative AI in orthopedics is still in its early exploration stage. This paper, based on the experience of applying generative AI, summarizes the concept, working principles, progress of application in orthopedics, as well as the existing shortcomings and optimization strategies, aiming to provide valuable insights for the application of generative AI in orthopedics clinical practice.
ObjectivesTo provide an overview of whether the clinical decision support system (CDSS) was effective in reducing medication error and improving medication safety and to assess the quality of available scientific evidence.MethodsPubMed, EMbase, The Cochrane Library, CBM, WanFang Data, VIP and CNKI databases were electronically searched to collect systematic reviews (SRs) on application of clinical decision support system in the medication error and safety from January, 1996 to November, 2018. Two reviewers independently screened literature, extracted data and then evaluated methodological quality of included SRs by using AMSTAR tool.g AMSTAR tool.ResultsA total of 20 SRs including 256 980 healthcare practitioners and 1 683 675 patients were included. Specifically, 16 studies demonstrated moderate quality and 4 demonstrated high quality. 19 SRs evaluated multiple process of care outcome: 9 were sufficient evidence, 6 were limited evidence, and 7 were insufficient evidence which proved that CDSS had a positive effect on process outcome. 13 SRs evaluated reported patient outcomes: 1 with sufficient evidence, 3 with limited evidence, and 9 without sufficient evidence.ConclusionsCDSS reduces medication error by inconsistently improving process of care measures and seldom improving patient outcomes. Larger samples and longer-term studies are required to ensure a larger and more reliable evidence base on the effects of CDSS intervention on patient outcomes.
Guideline implementation with decision support checklist (GUIDES) aims to assist the self-reflection of evidence-based clinical decision support system (CDSS) related professionals to enhance the process monitor and continuous improvement of evidence-based CDSS. This paper interpreted the development process, target user, and assessment method of GUIDES, analyzed the practical value of GUIDES through a typical example, and then reflected on the GUIDES and current studies on evidence-based CDSS in China. It is expected to provide references for future studies.
Lung cancer is a leading cause of cancer-related morbidity and mortality worldwide. Coupled with the substantial workload, the clinical management of lung cancer is challenged by the critical need to efficiently and accurately process increasingly complex medical information. In recent years, large language models (LLMs) technology has undergone explosive development, demonstrating unique advantages in handling complex medical data by leveraging its powerful natural language processing capabilities, and its application value in the field of lung cancer diagnosis and treatment is continuously increasing. The paper systematically analyzes that the exceptional potential of LLMs in lung cancer auxiliary diagnosis, tumor feature extraction, automatic staging, progression/outcome analysis, treatment recommendations, medical documentation generation, and patient education. However, they face critical technical and ethical challenges including inconsistent performance in complex integrated decision-making (e.g., TNM staging, personalized treatment suggestions) and "black box" opacity issues, along with dilemmas such as training data biases, model hallucinations, data privacy concerns, and cross-lingual adaptation challenges ("data colonization"). Future directions should prioritize constructing high-quality multimodal corpora specific to lung cancer, developing interpretable and compliant specialized models, and achieving seamless integration with existing clinical workflows. Through dual drivers of technological innovation and ethical standardization, LLMs should be prudently advanced for holistic lung cancer management processes, ultimately promoting efficient, standardized, and personalized diagnosis and treatment practices.