Fries Brant E, Shugarman Lisa R, Morris John N, Simon Samuel E, James Mary
Institute of Gerontology and School of Public Health, University of Michigan, Ann Arbor 48109-2007, USA.
Gerontologist. 2002 Aug;42(4):462-74. doi: 10.1093/geront/42.4.462.
To develop a screening system for Michigan's MI Choice publicly funded home- and community-based services programs, to aid in identifying both individuals eligible for services and their most appropriate level of care (LOC).
Identify assessment items from the Minimum Data Set for Home Care (MDS-HC) assessment instrument that are predictive of five LOCs determined by expert care managers: nursing home, home care, intermittent personal care, homemaker, and information and referral (without services).
The algorithm based on approximately 30 client characteristics agrees with expert opinions substantially better (kappa =.62) than systems based on activities of daily living and instrumental activities of daily living only (kappa <.40).
The screening algorithm can be used both over the telephone to identify clients who will not be fully assessed (as they are unlikely to receive services) and in person to recommend the appropriate LOC.
开发一种用于密歇根州“我的选择”公共资助的居家和社区服务项目的筛查系统，以帮助确定符合服务条件的个人及其最合适的护理级别（LOC）。
从居家护理最低数据集（MDS-HC）评估工具中识别评估项目，这些项目可预测由专业护理经理确定的五个护理级别：养老院、居家护理、间歇性个人护理、家务助理以及信息与转介（无服务）。
基于约30个客户特征的算法与专家意见的一致性（kappa = 0.62）明显优于仅基于日常生活活动和工具性日常生活活动的系统（kappa < 0.40）。
该筛查算法既可以通过电话用于识别那些不会接受全面评估的客户（因为他们不太可能接受服务），也可以亲自用于推荐合适的护理级别。
