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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.
Objective To investigate the decision-making situation of doctors in the township hospitals in Gaolan, Gansu province, and to discuss its scientificity and rationality. Methods Self-designed questionnaire was adopted to investigate the clinical decision-making situation of 108 doctors from 7 township hospitals in Gaolan county. The investigation contained three parts as follows: basic information of respondents, general information of clinical decision-making evidence, and comparison between respondents’ decision-making situation and current best clinical evidence. Results Among the total 108 questionnaires distributed, 89 valid were retrieved. The feedback showed that 79% of the doctors diagnosed and treated patients in accordance with medical textbooks; 53% took curative effect into consideration in the first place; 33% failed to consider patients’ willingness properly when making clinical decisions; and 52% made clinical therapy regimen for common diseases based on the evidence which was different from that in BMJ published Clinical Evidence. Conclusion While making clinical decisions, doctors in the township hospitals do not adequately refer to the best clinical evidence as their decision-making basis, and fail to take patients’ value and willingness into consideration properly. It is necessary to promote the concept of evidence-based medicine and spread the best evidence in the township health departments.
After the completion of a clinical trial, its conclusion generally depends on the results of statistical analysis of the main outcome, that is, whether the P-value in the hypothesis test is less than the α level of the hypothesis test, usually α=0.05. The size of the P-value indicates the sufficient degree of reason for making the hypothesis judgment, and can be interpreted as to determine whether a conclusion is statistically significant but does not involve the difference in the degree of drug effects or other effects. Fragility index, which is, the minimum number of patients required to change the occurrence of a target outcome event to a non-target outcome event from a statistically significant outcome to a non-significant outcome, can be used to assist in understanding of clinical trial statistical inference results and assisting in clinical decision making This paper discusses the concept, calculation method and clinical application of the fragility index, and recommends that the fragility index be routinely reported in all future randomized controlled trials to help patient clinicians and policymakers make appropriate and optimal decisions.
Objective To apply the method of evidence-based medicine to identify the best therapy option for an emergency patient with upper gastrointestinal hemorrhage. Methods According to time and logical sequence of clinical events, a complete decision tree was built after the following steps to find the best treatment: clear decision-making, drawing decision tree graphics, listing the outcome probability, giving appropriate values to the final outcome, calculating and determining the best strategies. Results The performance of endoscopic therapy for the patient with upper gastrointestinal hemorrhage within the first six hours had little effect on the prognosis. Interventional therapy after the failure of endoscopic therapy had less mortality than direct surgical exploration. Conclusion Making clinical decision analyses via drawing the decision tree can help doctors clarify their ideas, get comprehensive views of clinical problems, and ultimately choose the best treatment strategy for patients.
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.
The analysis of big data in medical field cannot be isolated from the high quality clinical database, and the construction of first aid database in our country is still in the early stage of exploration. This paper introduces the idea and key technology of the construction of multi-parameter first aid database. By combining emergency business flow with information flow, an emergency data integration model was designed with reference to the architecture of the Medical Information Mart for Intensive Care III (MIMIC-III), created by Computational Physiology Laboratory of Massachusetts Institute of Technology (MIT), and a high-quality first-aid database was built. The database currently covers 22 941 medical records for 19 814 different patients from May 2015 to October 2017, including relatively complete information on physiology, biochemistry, treatment, examination, nursing, etc. And based on the database, the first First-Aid Big Data Datathon event, which 13 teams from all over the country participated in, was launched. The First-Aid database provides a reference for the construction and application of clinical database in China. And it could provide powerful data support for scientific research, clinical decision making and the improvement of medical quality, which will further promote secondary analysis of clinical data in our country.
1背景早在1987年英国爱丁堡皇家医院就开始着手研究快速的治疗流程分类系统给心肌梗塞的患者所带来的时间经济效益，就此对快速流程的研究正式拉开了序幕。到了20世纪90年代初，欧洲部分医院的急诊科首先从科室角度开始迅速推广快速流程； 同时涉及麻醉方面的流程效率改革和创新逐步兴起。20世纪90年代末麻醉专业从门诊麻醉模式、手术及麻醉前干预上，开始逐步提升快速流程的综合管理能力。正是在20世纪90年代末，快速流程的理念被正式提出，在当时它还有一个名称叫做多模式康复流程。这种理念随之在欧美国家流行起来，大量的临床实践不断在进行。1994年，美国Engelman等就提出了冠状动脉旁路“fast-track recovery”的概念，并建立了一套相应的快速康复程序，通过实践发现其的确能够加快患者的术后康复、缩短住院时间。至此快速流程作为一项高效的临床运作模式被正式纳入临床具体病种的应用中。从2001年至今，心脏外科及结直肠外科的快速流程已趋于成熟，并已成功地渗透到外科领域的多个环节……