自2000年以来,有特殊医疗保健需求的儿童(CSHCN)筛选器(CS)已在全国范围内广泛使用,国家,并在当地作为一种标准化和简短的基于调查的方法来识别经历慢性身体的儿童群体,心理,行为,或其他条件,并且还需要儿童常规使用的健康和相关服务的类型和数量。关于CS的常见问题包括与其开发和使用相关的问题;其概念框架和潜在的识别不足或过度识别;其根据服务需求和日常生活影响的复杂性对CSHCN进行分层的能力;以及其在临床环境中的潜在应用以及与其他识别方法的比较。这篇评论回顾了发展,设计,以及使用CS的结果,并综合了过去13年进行的研究的结果以及CS的最新结果,以通过自2001年以来就CS提供的技术援助,简要解决有关该工具的12个最常见问题。在一系列分析中,CS始终如一地识别出需要或使用超过常规类型或数量的医疗和健康相关服务并且对医疗保健有共同需求的慢性病儿童的子集。包括护理协调,获得专业和基于社区的服务,加强家庭参与。存在评分算法以根据需求的复杂性和更高的护理成本对CSHCN进行分层。将CS数据与临床诊断代码算法组合可以增强进一步识别有意义的子组的能力。临床应用最适合识别和表征患者人群,并评估患有多种慢性疾病的儿童的质量和系统改进影响。其他临床应用需要进一步的实施研究。在临床环境中使用CS是有限的,因为在大多数环境中或在电子健康记录中,整合标准化的患者报告的健康信息还不是常见的做法。CS继续证明作为非条件特定的有效性,基于人群的工具,可以解决许多条件或诊断清单的限制,包括许多个体疾病的患病率相对较低,诊断内的差异和医疗服务需求的跨诊断相似性,功能,和护理质量。
Since 2000, the Children with Special Health Care Needs (CSHCN) Screener (CS) has been widely used nationally, by states, and locally as a standardized and brief survey-based method to identify populations of children who experience chronic physical, mental, behavioral, or other conditions and who also require types and amounts of health and related services beyond those routinely used by children. Common questions about the CS include those related to its development and uses; its conceptual framework and potential for under- or overidentification; its ability to stratify CSHCN by complexity of service needs and daily life impacts; and its potential application in clinical settings and comparisons with other identification approaches. This
review recaps the development, design, and findings from the use of the CS and synthesizes findings from studies conducted over the past 13 years as well as updated findings on the CS to briefly address the 12 most common questions asked about this tool through technical assistance provided regarding the CS since 2001. Across a range of analyses, the CS consistently identifies a subset of children with chronic conditions who need or use more than a routine type or amount of medical- and health-related services and who share common needs for health care, including care coordination, access to specialized and community-based services, and enhanced family engagement. Scoring algorithms exist to stratify CSHCN by complexity of needs and higher costs of care. Combining CS data with clinical diagnostic code algorithms may enhance capacity to further identify meaningful subgroups. Clinical application is most suited for identifying and characterizing populations of patients and assessing quality and system improvement impacts for children with a broad range of chronic conditions. Other clinical applications require further implementation research. Use of the CS in clinical settings is limited because integration of standardized patient-reported health information is not yet common practice in most settings or in electronic health records. The CS continues to demonstrate validity as a non-condition-specific, population-based tool that addresses many of the limits of condition or diagnosis checklists, including the relatively low prevalence of many individual conditions and substantial within-diagnosis variations and across-diagnoses similarities in health service needs, functioning, and quality of care.