期刊名称：International Journal of Population Data Science
电子版ISSN：2399-4908
出版年度：2018
卷号：3
期号：4
页码：1-1
DOI：10.23889/ijpds.v3i4.832
出版社：Swansea University
摘要：5 on physician chart audit. The sensitivity of the Machine Learning was disappointing: 28% (95% CI: 21% to 36%).specificity was: 94% (95% CI: 93% to 96%), positive predictive value: 53% (95% CI: 42% to 64%), negative predictive value: 86% (95% CI: 83% to 88%). ResultsThere was little overlap between the EMR and administrative data definitions using the same population. Of the 29,382 eligible administrative data community dwelling patients over 65 years old, with a linkable EMR record, 2398 (8.15%) were identified as frail using the administrative data definition, but only 16.1% of these were frail according to the EMR definition. Of the 2396 who were identified as frail in EMR data, only 375 (15.7%) were identified as frail using the administrative data definition. Conclusion/ImplicationsWe are not yet able to develop a reliable administrative data definition of frailty to identify community living individuals to support health service planning. The lack of agreement between the results obtained from EMR and administrative data definitions suggests that further refinement is necessary. Identification of frailty remains complex.
