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深化家庭医生签约服务是深化医药卫生体制改革、强化基层医疗卫生服务、实现"健康中国"战略目标的重要选择,也是当前更好维护人民群众健康的重要途径。为有效推进签约服务工作,国家陆续推出各项政策,全国各地也在积极进行实践探索,成效明显。但是,签约服务仍面临诸多问题,其中"执行难"是签约服务深度推进的一大困境。通过史密斯政策执行过程模型,结合签约服务政策执行过程,发现签约服务仍存在法治性不足、政策执行人员水平不高、激励不足、政策环境影响等诸多制约因素。因此,需要从法律和制度方面进行顶层设计、提升执行人员素质和职业认同、建立医患互信、优化政策执行环境等角度进行政策创新,探索家庭医生签约服务可持续发展的路径。 相似文献
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《Diagnostic and interventional imaging》2020,101(9):555-564
PurposeThe purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsEighty-nine patients with AIP (65 men, 24 women; mean age, 59.7 ± 13.9 [SD] years; range: 21–83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1 ± 12.3 [SD] years; range: 36–86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5 mm thickness/increment) were compared with thick-slices images (3 or 5 mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.ResultsThe pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%), 83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33; 95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).ConclusionsRadiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%. 相似文献
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目的本研究立足于项目组前期研究的成果上,积极探索吉林辽宁两省目标设置水平的差异,并进一步探究受目标设置影响下的工作落实结果情况,探讨产生差异的原因。
方法以系统穷尽的方式收集吉林辽宁两省2000至2017年有关目标与工作落实情况的指标,利用Spearman相关和线性回归分析吉林辽宁两省目标设置对于突发应急工作落实情况的影响。
结果吉林辽宁两省突发应急领域的目标设置水平与工作落实情况总体均呈现上升趋势,截至2017年,吉林目标设置水平与工作落实情况分别为46%与60%,辽宁为60%与53.3%,且目标设置水平与工作落实呈正相关。
结论有公众需要为依据且定量可考的目标设置对于工作落实、推进、完善具有积极的正反馈作用,建立科学量化的突发应急目标设置评价体系是适宜可行的。 相似文献
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Julia H. Vermylen Gordon J. Wood Elaine R. Cohen Jeffrey H. Barsuk William C. McGaghie Diane B. Wayne 《Journal of pain and symptom management》2019,57(3):682-687
Introduction
Physician communication impacts patient outcomes. However, communication skills, especially around difficult conversations, remain suboptimal, and there is no clear way to determine the validity of entrustment decisions. The aims of this study were to 1) describe the development of a simulation-based mastery learning (SBML) curriculum for breaking bad news (BBN) conversation skills and 2) set a defensible minimum passing standard (MPS) to ensure uniform skill acquisition among learners.Innovation
An SBML BBN curriculum was developed for fourth-year medical students. An assessment tool was created to evaluate the acquisition of skills involved in a BBN conversation. Pilot testing was completed to confirm improvement in skill acquisition and set the MPS.Outcomes
A BBN assessment tool containing a 15-item checklist and six scaled items was developed. Students' checklist performance improved significantly at post-test compared to baseline (mean 65.33%, SD = 12.09% vs mean 88.67%, SD = 9.45%, P < 0.001). Students were also significantly more likely to have at least a score of 4 (on a five-point scale) for the six scaled questions at post-test. The MPS was set at 80%, requiring a score of 12 items on the checklist and at least 4 of 5 for each scaled item. Using the MPS, 30% of students would require additional training after post-testing.Comments
We developed a SBML curriculum with a comprehensive assessment of BBN skills and a defensible competency standard. Future efforts will expand the mastery model to larger cohorts and assess the impact of rigorous education on patient care outcomes. 相似文献9.
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