Process assessment by automated computation of healthcare quality indicators in hospital electronic health records: a systematic review of indicators. Chazard, E., Babaousmail, D., Schaffar, A., Ficheur, G., & Beuscart, R. Studies in Health Technology and Informatics, 210:867–871, 2015.
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Paper abstract bibtex
Paper abstract bibtex The objective of the work is to extract healthcare process quality indicators from the literature, and to evaluate which of them could be automatically computed using routinely collected data from electronic health records (EHRs). A minimal set of data commonly available in EHRs is first defined. The initial bibliographic query enables to identify 8,744 papers, among which 126 papers describe 440 process indicators. 22.3% of indicators can be automatically computed. The computation of the indicators mostly require diagnoses (99%), drug prescriptions (59%), medical procedures (48%), administrative data (30%), laboratory results (20%), free-text reports with basic keyword research (19%), linkage with the patient's previous stays (11%) and dependence assessment (3%). 77.7% of indicators cannot be automatically computed, mostly because they require a linkage with outpatient data (61%), structured data that are usually not available (43%), unstructured data (26%) or the trace of an information that was given to the patient (8%).
@article{chazard_process_2015,
title = {Process assessment by automated computation of healthcare quality indicators in hospital electronic health records: a systematic review of indicators},
volume = {210},
copyright = {All rights reserved},
issn = {0926-9630},
shorttitle = {Process assessment by automated computation of healthcare quality indicators in hospital electronic health records},
url = {http://www.chazard.org/emmanuel/pdf_articles/paper_2015_mie_automatedprocessindicatorcomputation.pdf},
abstract = {The objective of the work is to extract healthcare process quality indicators from the literature, and to evaluate which of them could be automatically computed using routinely collected data from electronic health records (EHRs). A minimal set of data commonly available in EHRs is first defined. The initial bibliographic query enables to identify 8,744 papers, among which 126 papers describe 440 process indicators. 22.3\% of indicators can be automatically computed. The computation of the indicators mostly require diagnoses (99\%), drug prescriptions (59\%), medical procedures (48\%), administrative data (30\%), laboratory results (20\%), free-text reports with basic keyword research (19\%), linkage with the patient's previous stays (11\%) and dependence assessment (3\%). 77.7\% of indicators cannot be automatically computed, mostly because they require a linkage with outpatient data (61\%), structured data that are usually not available (43\%), unstructured data (26\%) or the trace of an information that was given to the patient (8\%).},
language = {eng},
journal = {Studies in Health Technology and Informatics},
author = {Chazard, Emmanuel and Babaousmail, Djaber and Schaffar, Aurélien and Ficheur, Grégoire and Beuscart, Régis},
year = {2015},
pmid = {25991279},
pages = {867--871},
}Downloads: 0
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