@article{ficheur_elderly_2016,
	title = {Elderly {Surgical} {Patients}: {Automated} {Computation} of {Healthcare} {Quality} {Indicators} by {Data} {Reuse} of {EHR}},
	volume = {221},
	issn = {0926-9630},
	shorttitle = {Elderly {Surgical} {Patients}},
	abstract = {The objective of the work is to implement and evaluate the automated computation of 9 healthcare quality indicators, by data reuse of electronic health records, in the field of elderly surgical patients.
METHODS: Data are extracted from EHR, including administrative data, ICD10 diagnoses, laboratory results, procedures, administered drugs, and free-text letters. The indicators are implemented by a medical data reuse specialist. The conformity rate is automatically computed (3.5 minutes for 15,000 inpatient stays and 9 indicators). A medical expert reviews 45 stays per indicator. The precision is the proportion of non-conform inpatient stays among the cases detected as non-conform by the algorithms.
RESULTS: the paper describes the implemented algorithms, the conformity rates and the precisions. Two indicators have a precision of 0\%, 3 indicators have a precision of 40 to 60\%, and four indicators have a precision from 80 to 100\% (for 2 of them, the conformity rate is lower than 2.5\%!). This demonstrates that automated quality screening is possible and enables to detect threatening situations. The implementation of the indicators requires special skills in medicine, medical information sciences, and algorithmics. Failures of precision are mainly due to defaults of information quality (missing codes), and could benefit from text analysis.},
	language = {ENG},
	journal = {Studies in Health Technology and Informatics},
	author = {Ficheur, Grégoire and Schaffar, Aurélien and Caron, Alexandre and Balcaen, Thibaut and Beuscart, Jean-Baptiste and Chazard, Emmanuel},
	year = {2016},
	pmid = {27071884},
	pages = {92--96},
}

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METHODS: Data are extracted from EHR, including administrative data, ICD10 diagnoses, laboratory results, procedures, administered drugs, and free-text letters. The indicators are implemented by a medical data reuse specialist. The conformity rate is automatically computed (3.5 minutes for 15,000 inpatient stays and 9 indicators). A medical expert reviews 45 stays per indicator. The precision is the proportion of non-conform inpatient stays among the cases detected as non-conform by the algorithms. RESULTS: the paper describes the implemented algorithms, the conformity rates and the precisions. Two indicators have a precision of 0%, 3 indicators have a precision of 40 to 60%, and four indicators have a precision from 80 to 100% (for 2 of them, the conformity rate is lower than 2.5%!). This demonstrates that automated quality screening is possible and enables to detect threatening situations. The implementation of the indicators requires special skills in medicine, medical information sciences, and algorithmics. Failures of precision are mainly due to defaults of information quality (missing codes), and could benefit from text analysis.","language":"ENG","journal":"Studies in Health Technology and Informatics","author":[{"propositions":[],"lastnames":["Ficheur"],"firstnames":["Grégoire"],"suffixes":[]},{"propositions":[],"lastnames":["Schaffar"],"firstnames":["Aurélien"],"suffixes":[]},{"propositions":[],"lastnames":["Caron"],"firstnames":["Alexandre"],"suffixes":[]},{"propositions":[],"lastnames":["Balcaen"],"firstnames":["Thibaut"],"suffixes":[]},{"propositions":[],"lastnames":["Beuscart"],"firstnames":["Jean-Baptiste"],"suffixes":[]},{"propositions":[],"lastnames":["Chazard"],"firstnames":["Emmanuel"],"suffixes":[]}],"year":"2016","pmid":"27071884","pages":"92–96","bibtex":"@article{ficheur_elderly_2016,\n\ttitle = {Elderly {Surgical} {Patients}: {Automated} {Computation} of {Healthcare} {Quality} {Indicators} by {Data} {Reuse} of {EHR}},\n\tvolume = {221},\n\tissn = {0926-9630},\n\tshorttitle = {Elderly {Surgical} {Patients}},\n\tabstract = {The objective of the work is to implement and evaluate the automated computation of 9 healthcare quality indicators, by data reuse of electronic health records, in the field of elderly surgical patients.\nMETHODS: Data are extracted from EHR, including administrative data, ICD10 diagnoses, laboratory results, procedures, administered drugs, and free-text letters. The indicators are implemented by a medical data reuse specialist. The conformity rate is automatically computed (3.5 minutes for 15,000 inpatient stays and 9 indicators). A medical expert reviews 45 stays per indicator. The precision is the proportion of non-conform inpatient stays among the cases detected as non-conform by the algorithms.\nRESULTS: the paper describes the implemented algorithms, the conformity rates and the precisions. Two indicators have a precision of 0\\%, 3 indicators have a precision of 40 to 60\\%, and four indicators have a precision from 80 to 100\\% (for 2 of them, the conformity rate is lower than 2.5\\%!). This demonstrates that automated quality screening is possible and enables to detect threatening situations. The implementation of the indicators requires special skills in medicine, medical information sciences, and algorithmics. 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