Care coordination for older adults: Process, outcomes, cost

描述（由申请人提供）：最近的研究提供证据表明，护理协调可以改善住院、再住院、急诊室使用等结局，并降低成本。密苏里州大学辛克莱护理学院的一个项目是“就地老龄化”（AIP），该项目利用护士护理协调作为干预措施，以改善社区居住的老年人的护理。AIP为加强护士护理协调干预提供的服务提供者是高级护理，这是一家家庭保健机构，也是一家以家庭和社区为基础的服务提供者。在AIP研究结束后，高级护理继续作为家庭保健（HHC）机构存在。利用AIP研究和HHC机构的独特数据，我们建议进行二次分析，以（1）确定AIP护士护理协调员使用增强护士协调的护理协调干预措施、干预活动和干预剂量，以及提供传统HHC的HHC护士，（2）描述AIP护士护理协调员和HHC护士使用的干预措施的任何差异，最后（3）确定AIP组和HHC组中老年人的住院、再住院、急诊室使用和费用结果是否不同。最初的AIP分析没有将具体的护理协调干预与结果联系起来，没有确定与干预相关的具体活动，没有探索干预剂量，也没有将结果与传统的HHC进行比较。数据将比较两组接受服务的老年人，2000-2002年AIP组中加强护士护理协调的老年人和2003-2005年接受传统HHC但没有加强护士护理协调的老年人。自然语言处理（NLP）方法将用于确定护理协调员在协调老年人护理时使用的干预措施和活动。NLP将人类生成的文本转换为机器可读（结构化）信息。我们建议使用一种独特的迭代方法来进行NLP，其中可用的活动是从现有的电子医疗记录中提取的，而新的活动则是从第一次发现的信息不足的记录中手工识别的。每次通过后，专家团队将检查提取的活动，以确保它们与护理协调相关。将通过确定每种干预类别和目标最常发生的活动类型以及最能代表该类别和目标的活动类型，制定代表AIP和HHC的干预概况。将制定干预措施的剂量分布，最后，将比较AIP组和HHC组的住院、再住院、急诊室使用和费用的结局。 公共卫生相关性：本研究通过确定实际的护理协调干预活动和剂量，并制定干预方案供未来研究和实践使用，为研究和临床实践提供信息。自然语言处理方法将增加我们如何成功使用电子病历文本数据的知识。

DESCRIPTION (provided by applicant): Recent research provides evidence that care coordination improves outcomes such as hospitalization, rehospitalization, emergency room use, and reduces cost. One program using nurse care coordination as an intervention to improve care for community dwelling older adults' was Aging in Place (AIP) at the University of Missouri's Sinclair School of Nursing. AIP's service provider for the enhanced nurse care coordination intervention was Senior Care, a home health agency and Home and Community Based service provider. After the AIP study ended Senior Care continued to exist as a home health care (HHC) agency. Taking advantage of the unique data from the AIP study, and the HHC agency we propose to perform a secondary analysis to (1) identify care coordination interventions, intervention activities, and intervention doses used by AIP nurse care coordinators using enhanced nurse coordination, and HHC nurses providing traditional HHC , (2) describe any differences in interventions used by AIP nurse care coordinators, and HHC nurses, and finally (3) to identify whether outcomes of hospitalization, rehospitalization, emergency room use, and cost differ for older adults in the AIP and HHC groups. The original AIP analysis did not link specific nurse care coordination interventions with outcomes, identify the specific activities associated with interventions, explore intervention dose, or compare outcomes to traditional HHC. Data will compare two groups of older people who received services, those with enhanced nurse care coordination in the AIP group from the years 2000-2002 and those who received traditional HHC without enhanced nurse care coordination from the years 2003-2005. Natural Language Processing (NLP) methods will be used to identify interventions and activities nurse care coordinators used when coordinating care for older adults. NLP transforms human-generated text into machine-readable (structured) information. We propose to use a unique iterative approach to NLP in which the available activities are extracted from existing electronic medical records from Senior Care, while new activities are identified by hand from records in which not enough information was found in the first pass. After each pass, a team of experts will check the extracted activities to assure that they are relevant to care coordination. Intervention profiles that are representative for AIP and HHC will be developed by determining what types of activities occur most frequently for each intervention category and target, and which ones are most representative of the category and target. Dose profiles of interventions will be developed, and finally, outcomes of hospitalization, rehospitalization, emergency room use, and cost will be compared between the AIP and HHC groups. PUBLIC HEALTH RELEVANCE: This study informs research and clinical practice by identifying actual care coordination intervention activities and dose, and developing intervention profiles for future use in research and practice. The Natural Language Processing method will add to our knowledge of how to successful use electronic medical record text data.