临床决策支持系统对新冠肺炎等新型突发传染性疾病的监测预警功能设计

目的 临床决策支持系统(CDSS)基于循证医学和医院真实病历库建设了系统全面的知识库,同时可以实时处理医院内电子病历数据,文章提出了基于CDSS对新型突发传染性疾病的监测预警功能方案设计,利用知识库引擎鉴别诊断类似“新冠肺炎”等新型病例,通过合理对接医院疾病预防与感染控制信息系统,及时上传系统筛查的病历数据和分析结果,可以为决策部门及时预警此类新型突发传染性疾病提供全面客观的数据,提高对该类疾病的灵敏感知、及时决策、提前预警能力。     

临床决策支持系统

正临床决策支持系统(Clinical?Decision?Support?System,CDSS)是运用专家系统的设计原理和方法,模拟医学专家诊断、治疗疾病的思维过程编制的计算机程序。它可以帮助医生和护士解决复杂的临床问题,作为医护人员诊断、治疗、护理和     

临床决策支持系统在基层抗菌药物合理使用中的作用

目的探讨临床决策支持系统（CDSS）对规范基层门诊医师抗菌药物使用的可行性及效果。 方法选取成都地区5家使用CDSS的诊所,收集2017年7月至2018年9月的门诊病历及处方,根据诊断筛选入组处方5128张,对比CDSS使用前、后1年抗菌药物的使用情况,并评估CDSS对规范基层门诊医师抗菌药物使用的影响。 结果在使用CDSS后1年,门诊抗菌药物使用更规范;对单纯上呼吸道感染的治疗中,使用CDSS后1年的门诊抗菌药物的合理用药情况得到明显改善（P<0.05）。 结论CDSS对规范基层医师的门诊抗菌药物使用有较好的干预作用。     

基于临床决策支持系统的新生儿早期诊疗护理预警模型的构建与应用

目的构建新生儿早期诊疗护理预警模型的架构体系,保障新生儿早期诊疗护理质量安全。方法梳理出以护理程序为基础的新生儿早期诊疗护理的工作流程,构建基于临床决策支持系统(CDSS)的新生儿早期诊疗护理预警模型。于2018年6-9月构建知识库,并于2018年10月开始进行临床应用。评价基于CDSS系统的新生儿早期诊疗护理预警模型的应用效果(新生儿风险评估及时率、预防措施落实与风险评估合格率)、护士对系统满意度及早产儿家属满意度。结果护士应用新生儿早期诊疗护理预警的临床决策支持系统后,新生儿风险评估及时率(96.94%)高于系统应用前(85.18%),差异有统计学意义(P0.001)。预防措施落实与风险评估合格率(98.13%)高于系统应用前(90.35%),差异有统计学意义(P0.001)。护士对系统的可用性评价为(18.63±1.13)分,数据可靠性为(14.22±1.42)分,系统支持力度为(13.51±2.23)分,使用意向为(8.51±1.52)分,总体满意度为(4.42±0.51)分,净收益为(4.19±0.63)分。早产儿家属满意度处于较高水平。结论基于临床决策系统的新生儿早期诊疗护理预警模型的构建可以为临床护士在照护新生儿时对疾病早期识别和护理干预提供正确高效的决策支持,提升护士对临床常规执行的依从性和评判性思维能力,提高护士护理质量管理能力,提高医护患多角色满意度。     

基于临床决策支持系统电子护理文书质量控制录入系统的设计与应用

通过建立和应用基于临床决策支持系统(Clinical Decision Support System,CDSS)的电子护理文书质量控制录入系统,将护理记录信息项结构化、建立录入信息决策知识库、嵌入提醒与自动生成护理任务功能,保障了电子护理文书录入的规范性、完整性和连续性,提高了临床电子护理文书质量,同时也可有效地指导护士的临床决策,从而减少护理差错产生。     

基于指南的血管可视化技术循证实践在儿童外周静脉留置针穿刺中的效果研究

目的观察基于指南的儿童外周静脉留置针穿刺血管可视化技术循证实践效果。方法将静脉输液治疗标准指南中得到的相关最佳证据应用到儿科病房外周静脉留置针穿刺血管可视化技术的护理实践中,以渥太华证据转化模式为指导框架,通过研究场所的现场评估、团队讨论构建循证策略和实践方案、循证方案临床实践以及对实践效果的系统审核和过程反思4个阶段的临床实践,比较循证护理实践前后护士对循证实践方案中各临床审查指标的依从性,护士进行留置针静脉穿刺一次成功率和穿刺时间以及患儿家长对血管可视化技术使用的满意程度。结果循证实践后护士对各审查指标的依从性显著提高(P<0.05);护士静脉留置一次穿刺成功率由82.98%提高到91.91%(P<0.05),留置针穿刺时间明显缩短(P<0.01);患儿家长对留置针穿刺的满意度提高。结论基于指南的儿童外周静脉留置针穿刺血管可视化技术循证实践可提高儿童静脉留置一次性穿刺成功率,缩短穿刺时间,提高家长满意度。     

333例初诊急性白血病的CDSS实验指标分析

目的:分析初诊急性白血病(AL)患者的CDSS 5项实验指标,为临床及早诊治AL继发的DIC提供实验室依据。方法:将333例AL患者分为AML-M1等(AML-M1-M5,其它AML和ALL)7个组,并以100例健康体检者作为对照组。观察各组的实验指标变化、CDSS评分值并进行两两比较,同时分析所有病例中的早期死亡等临床事件。结果:5项实验指标中阳性率最高的是血小板(59.76%),其次是D-D(30.93%),而APTT最低(2.70%);与对照组比较,除APTT(3组)和FIB(其他AML组)外,其余实验指标结果的差异均有非常显著的统计学意义(PAML-M0.01);所有AL的CDSS之实验室指标评分值为1.50±1.51分,而达到现症DIC诊断标准者(≥4分)47例,阳性率为14.11%;CDSS评分值最高为AML-M3组,为3.34±1.71分。AL DIC患者早期死亡和脑(肺)出血事件发生率均明显高于AL非DIC患者(P0.05,P0.01)。结论:CDSS量化式积分法标准客观易行,对急性白血病继发DIC的早诊断、早治疗具有重要的临床意义。     

PARIHS模式核心元素衍变与发展的文献范畴分析

目的对健康服务领域研究成果应用的行动促进(promoting action on research implementation in health severice,PARIHS)循证概念框架模式核心元素的衍变与发展进行文献范畴分析(scoping review),为临床护理人员更好地理解和应用PARIHS模式提供参考依据。方法采用文献范畴分析法,系统检索涉及PARIHS模式的文献,双人进行文献筛选和数据提取后,对PARIHS模式核心元素概念发展的文献进行重点分析。结果最终纳入10篇核心文献,通过对其分析和整合,呈现了PARIHS模式的三大核心元素,即证据、组织环境、促进措施的发展及衍变过程。结论 PARIHS模式有较好的灵活性、直观的吸引力,详细阐释了循证实践过程中多要素的动态关系,体现了证据应用的复杂性,可指导循证实践的实施;但其相关元素及亚元素定义尚不够清晰且缺乏有效的评估工具,仍需进一步研究各元素之间动态关系。     

护理决策支持系统的应用进展

综述了决策支持系统的发展、护理决策支持系统的应用和构建 3 方面。 其中,护理决策支持系统的应用分别从循证护理、标准化护理语言和其他类型的护理决策支持系统的应用进行介绍,护理决策支持系统的构建分别从知识库、方法库、逻辑推理规则的构建进行阐述。     

黑龙江省三甲综合医院临床护士循证护理能力现状及其影响因素研究

目的了解黑龙江省三甲综合医院临床护士循证护理能力水平,分析其影响因素。方法采用方便抽样法,于2020年5—7月对黑龙江省5所三甲综合医院的810名临床护士进行问卷调查,问卷包括一般资料调查表,循证实践知识、态度、行为问卷和循证护理能力评定量表。本研究回收有效电子问卷799份,有效回收率为98.6%。采用SPSS 21.0统计学软件进行数据分析。结果799名临床护士的循证护理能力总分为(35.69±25.95)分。多重线性回归分析显示,教育水平、职务及循证实践知识技能是影响黑龙江省三甲综合医院临床护士循证护理能力的影响因素(P<0.05)。结论黑龙江省三甲综合医院临床护士的循证护理能力水平较低,亟须提高。建议黑龙江省三甲综合医院应加大对临床护士循证护理能力的重视,同时医院主管护理科研及教育的管理者应提高自身的循证护理能力,推动本医院的护理继续教育工作,开展线上、线下循证护理相关学习课程或讲座,鼓励临床护士将所学循证知识应用于临床护理工作中。     

临床决策辅助系统减少药物差错的系统评价再评价

目的对临床决策辅助系统应用于药物差错、药物安全领域的系统评价(SR)进行再评价。方法计算机检索PubMed、EMbase、The Cochrane Library、CBM、WanFang Data、VIP和CNKI数据库,搜集临床决策辅助系统应用于药物差错、药物安全领域的SR,检索时限均从1996年1月至2018年11月。由2名研究者独立筛选文献、提取资料后,采用AMSTAR标准评价纳入SR的方法学质量。结果共纳入20个SR,包括256 980名医务工作者和1 683 675名患者。纳入的SR中,高质量4个、中等质量16个。19个SR报告了多个过程结局指标:9个有充分证据证明临床决策辅助系统对过程结局有积极影响,6个证据有限,7个尚无充分证据。13个SR报告了患者结局:仅1个有充分证据证明临床决策辅助系统对患者结局有积极影响,3个证据有限,9个尚无充分证据。结论现有临床决策辅助系统减少药物差错的SR方法学质量为中到高等。临床决策辅助系统可显著改善与药物相关的过程结局;但对患者结局的影响目前仅有1个SR充分证据支持,结论尚不能确定,仍需大样本,长期随访的研究来证实。     

Cost consequences of implementing an electronic decision support system for ordering laboratory tests in primary care: evidence from a controlled prospective study in the Netherlands

BACKGROUND: The economic consequences of interventions to promote rational, evidence-based use of laboratory tests by physicians are not yet fully understood. We evaluated the cost consequences of a computer-based, guideline-driven decision-support system (CDSS) for ordering blood tests in primary care. METHODS: We installed the CDSS in 118 practices [159 general practitioners (GPs)] throughout The Netherlands and calculated the costs of the intervention in this group. During a period of 6 months before and 6 months after installation of the CDSS, the test-ordering behavior of 87 (109 GPs) of these 118 study practices was studied and the results were compared with those of a nonhistorical control group that did not receive the CDSS. In addition the costs of laboratory requests were calculated for both groups. RESULTS: Total intervention costs, comprising development costs and installation costs, amounted to 79,000 euro (670 euro per practice). Whereas the introduction of the CDSS did not affect the number of order forms submitted to the laboratories, it did reduce the number of blood tests per order form. As a result, the CDSS yielded mean savings on the costs of laboratory requests of 847 euro per practice per 6 months. CONCLUSIONS: This study demonstrates that providing electronic decision support for ordering blood tests in primary care represents an economically promising concept. Savings on laboratory costs are achievable and not offset by disproportionally high intervention costs.     

Assessing the impact of clinical information-retrieval technology in a family practice residency

RATIONALE AND OBJECTIVE: Evidence-based sources of information do not integrate self-assessment tools to assess the impact of a users' search for clinical information. We present a method to evaluate evidence-based sources of information, by systematically assessing the impact of searches for clinical information in everyday practice. METHODS: We integrated an information management tool (InfoRetriever 2003) with an educational intervention in a cohort of 26 family medicine residents. An electronic impact assessment scale was used by these doctors to report the perceived impact of each item of information (each hit) retrieved on hand-held computer. We compared the types of impact associated with hits in two distinct categories: clinical decision support systems (CDSS) vs. clinical information-retrieval technology (CIRT). Information hits in CDSS were defined as any hit in the following InfoRetriever databases: Clinical Prediction Rules, History and Physical Exam diagnostic calculator and Diagnostic Test calculator. CIRT information hits were defined as any hit in: Abstracts of Cochrane Reviews, InfoPOEMs, evidence-based practice guideline summaries and the Griffith's 5 Minute Clinical Consult. RESULTS: The impact assessment questionnaire was linked to 5160 information hits. 4946 impact assessment questionnaires were answered (95.9%), and 2495 contained reports of impact (48.4%). Reports of positive impact on doctors were most frequently in the areas of learning and practice improvement. In comparison to CDSS, CIRT hits were more frequently associated with learning and recall. CDSS hits were more frequently associated with reports of practice improvement. CONCLUSIONS: Our new method permits systematic and comparative assessment of impact associated with distinct categories of information.     

A national survey of computerized decision support systems available to nurses in England

Aim  To examine the characteristics of computerized decision support systems (CDSS) currently available to nurses working in the National Health Service (NHS) in England.
Method  A questionnaire survey sent to a stratified random sample of 50% of all NHS care providers (Trusts) in England, asking respondents to provide information on CDSS currently used by nurses.
Results  Responses were received from 108 of the 277 Trusts included in the sample. Electronic patient record systems were the most common type of CDSS reported by Trusts ( n  = 61) but they were least likely to have features that have been associated with improved clinical outcomes.
Conclusions  The availability of CDSS with features that have been associated with improved patient outcomes for nurses in the NHS in England is limited. There is some evidence that the nature of the Trust affects whether or not nurses have access to CDSS to assist their decision making.
Implications for nursing management  The implementation of CDSS is increasing throughout the NHS. Many CDSS are introduced without adequate evidence to support its introduction and there is little evaluation of the benefits once they are implemented. Policy makers and nursing management should consider whether the introduction of CDSS aids nurse decision making and benefits patient outcomes.     

Initial implementation and evaluation of a Hepatitis C treatment clinical decision support system (CDSS): A nurse practitioner-driven quality improvement initiative

Background

Clinician compliance with clinical guidelines in the treatment of patients with Hepatitis C (HCV) has been reported to be as low as 18.5%. Treatment is complex and patient compliance is often inconsistent thus, active clinician surveillance and support is essential to successful outcomes. A clinical decision support system (CDSS) embedded within an electronic health record can provide reminders, summarize key data, and facilitate coordination of care. To date, the literature is bereft of information describing the implementation and evaluation of a CDSS to support HCV treatment.

Objective

The purpose of this case report is to describe the design, implementation, and initial evaluation of an HCV-specific CDSS while piloting data collection metrics and methods to be used in a larger study across multiple practices.

Methods

The case report describes the design and implementation processes with preliminary reporting on impact of the CDSS on quality indicator completion by comparing the pre-CDSS group to the post-CDSS group.

Results

The CDSS was successfully designed and implemented using an iterative, collaborative process. Pilot testing of the clinical outcomes of the CDSS revealed high rates of quality indicator completion in both the pre- and post-CDSS; although the post-CDSS group received a higher frequency of reminders (4.25 per patient) than the pre-CDSS group (.25 per patient).

Conclusions

This case report documents the processes used to successfully design and implement an HCV CDSS. While the small sample size precludes generalizability of findings, results did positively demonstrate the feasibility of comparing quality indicator completion rates pre-CDSS and post-CDSS. It is recommended that future studies include a larger sample size across multiple providers with expanded outcomes measures related to patient outcomes, staff satisfaction with the CDSS, and time studies to evaluate efficiency and cost effectiveness of the CDSS.     

Cluster-randomized,controlled trial of computer-based decision support for selecting long-term anti-thrombotic therapy after acute ischaemic stroke

BACKGROUND: Identifying the appropriate long-term anti-thrombotic therapy following acute ischaemic stroke is a challenging area in which computer-based decision support may provide assistance. Aim: To evaluate the influence on prescribing practice of a computer-based decision support system (CDSS) that provided patient-specific estimates of the expected ischaemic and haemorrhagic vascular event rates under each potential anti-thrombotic therapy. DESIGN: Cluster-randomized controlled trial. METHODS: We recruited patients who presented for a first investigation of ischaemic stroke or TIA symptoms, excluding those with a poor prognosis or major contraindication to anticoagulation. After observation of routine prescribing practice (6 months) in each hospital, centres were randomized for 6 months to either control (routine practice observed) or intervention (practice observed while the CDSS provided patient-specific information). We compared, between control and intervention centres, the risk reduction (estimated by the CDSS) in ischaemic and haemorrhagic vascular events achieved by long-term anti-thrombotic therapy, and the proportions of subjects prescribed the optimal therapy identified by the CDSS. RESULTS: Sixteen hospitals recruited 1952 subjects. When the CDSS provided information, the mean relative risk reduction attained by prescribing increased by 2.7 percentage units (95%CI -0.3 to 5.7) and the odds ratio for the optimal therapy being prescribed was 1.32 (0.83 to 1.80). Some 55% (5/9) of clinicians believed the CDSS had influenced their prescribing. CONCLUSIONS: Cluster-randomized trials provide excellent frameworks for evaluating novel clinical management methods. Our CDSS was feasible to implement and acceptable to clinicians, but did not substantially influence prescribing practice for anti-thrombotic drugs after acute ischaemic stroke.     

Decision Support Technology in Knowledge Translation

Information technologies, and specifically clinical decision support systems (CDSSs), are tools that can support the process of knowledge translation in the delivery of emergency department (ED) care. It is essential that during the implementation process, careful consideration be given to the workflow and culture of the ED environment where the system is to be utilized. Despite significant literature addressing factors contributing to successful deployment of these systems, the process is frequently problematic. Careful research and analysis are essential to evaluate the impact of the CDSS on the delivery of ED care, its influence on the health care providers, and the impact of the CDSS on clinical decision-making processes and information behaviors. The logistical and educational implications of CDSSs in the ED must also be considered. The specialty of emergency medicine must actively collaborate with other stakeholders in the design, implementation, and evaluation of CDSSs that will be utilized during the delivery of care to our patients.     

Precision diagnosis: a view of the clinical decision support systems (CDSS) landscape through the lens of critical care

Improving diagnosis and treatment depends on clinical monitoring and computing. Clinical decision support systems (CDSS) have been in existence for over 50 years. While the literature points to positive impacts on quality and patient safety, outcomes, and the avoidance of medical errors, technical and regulatory challenges continue to retard their rate of integration into clinical care processes and thus delay the refinement of diagnoses towards personalized care. We conducted a systematic review of pertinent articles in the MEDLINE, US Department of Health and Human Services, Agency for Health Research and Quality, and US Food and Drug Administration databases, using a Boolean approach to combine terms germane to the discussion (clinical decision support, tools, systems, critical care, trauma, outcome, cost savings, NSQIP, APACHE, SOFA, ICU, and diagnostics). References were selected on the basis of both temporal and thematic relevance, and subsequently aggregated around four distinct themes: the uses of CDSS in the critical and surgical care settings, clinical insertion challenges, utilization leading to cost-savings, and regulatory concerns. Precision diagnosis is the accurate and timely explanation of each patient’s health problem and further requires communication of that explanation to patients and surrogate decision-makers. Both accuracy and timeliness are essential to critical care, yet computed decision support systems (CDSS) are scarce. The limitation arises from the technical complexity associated with integrating and filtering large data sets from diverse sources. Provider mistrust and resistance coupled with the absence of clear guidance from regulatory bodies further retard acceptance of CDSS. While challenges to develop and deploy CDSS are substantial, the clinical, quality, and economic impacts warrant the effort, especially in disciplines requiring complex decision-making, such as critical and surgical care. Improving diagnosis in health care requires accumulation, validation and transformation of data into actionable information. The aggregate of those processes—CDSS—is currently primitive. Despite technical and regulatory challenges, the apparent clinical and economic utilities of CDSS must lead to greater engagement. These tools play the key role in realizing the vision of a more ‘personalized medicine’, one characterized by individualized precision diagnosis rather than population-based risk-stratification.     

Clinical decision support in critical care nursing

A clinical decision support system (CDSS) is a computerized application that helps clinicians detect and prevent untoward clinical events such as drug interactions, errors of omission, and trends in symptomatology. A CDSS in healthcare usually is built around an alerting system based on rules of logic. The alerting system of a CDSS can notify clinicians immediately on clinical data entry, or it can generate alerts over time after relating data from multiple sources. A CDSS for nurses and patients offers immediate benefits for nurses and patients by detecting potential drug-laboratory and drug-drug combinations and impending pharmacologic complications, monitoring microbiology results, and helping nurses relate symptoms to pharmacology and medication side effects. Other benefits include savings in time and money and reductions in morbidity and mortality. A CDSS presents an opportunity for nursing informatics and critical care nursing to collaborate for the benefit of the patient and the profession.