@inproceedings{cb46e483292d4cd99b533f825c47870e,
title = "Formalization and Computation of Diabetes Quality Indicators with Patient Data from a Chinese Hospital",
abstract = "Clinical quality indicators are tools to measure the quality of healthcare and can be classified into structure-related, process-related and outcome-related indicators. The objective of this study is to investigate whether Electronic Medical Record (EMR) data from a Chinese diabetes specialty hospital can be used for the automated computation of a set of 38 diabetes quality indicators, especially process-related indicators. The clinical quality indicator formalization (CLIF) method and tool and SNOMED CT were adopted to formalize diabetes indicators into executable queries. The formalized indicators were run on the patient data to test the feasibility of their automated computation. In this study, we successfully formalized and computed 32 of 38 quality indicators based on the EMR data. The results indicate that the data from our Chinese EMR can be used for the formalization and computation of most diabetes indicators, but that it can be improved to support the computation of more indicators.",
keywords = "Clinical quality, Diabetes mellitus, Electronic medical record, Formalization, Quality indicators, Secondary use of patient data",
author = "Haitong Liu and {ten Teije}, Annette and Kathrin Dentler and Jingdong Ma and Shijing Zhang",
year = "2017",
doi = "10.1007/978-3-319-55014-5_2",
language = "English",
isbn = "9783319550138",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer/Verlag",
pages = "23--35",
editor = "David Ria{\~n}o and Richard Lenz and Manfred Reichert",
booktitle = "Knowledge Representation for Health Care",
note = "HEC International Joint Workshop on Knowledge Representation for Health Care, KR4HC/ProHealth 2016 ; Conference date: 02-09-2016 Through 02-09-2016",
}
