Process assessment by automated computation ...
Type de document :
Article dans une revue scientifique: Article de synthèse/Review paper
PMID :
URL permanente :
Titre :
Process assessment by automated computation of healthcare quality indicators in hospital electronic health records: a systematic review of indicators
Auteur(s) :
Chazard, Emmanuel [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Babaousmail, Djaber [Auteur]
Schaffar, Aurelien [Auteur]
Ficheur, Gregoire [Auteur]
Beuscart, Regis [Auteur]

Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Babaousmail, Djaber [Auteur]
Schaffar, Aurelien [Auteur]
Ficheur, Gregoire [Auteur]

Beuscart, Regis [Auteur]
Titre de la revue :
Studies in health technology and informatics
Nom court de la revue :
Stud Health Technol Inform
Numéro :
210
Pagination :
867-71
Date de publication :
2015-01-01
ISSN :
0926-9630
Mot(s)-clé(s) en anglais :
Process Assessment
Quality Indicators
Electronic Health Records
Data reuse
Guideline Adherence
Quality Indicators
Electronic Health Records
Data reuse
Guideline Adherence
Discipline(s) HAL :
Sciences du Vivant [q-bio]
Résumé en anglais : [en]
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 ...
Lire la suite >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%).Lire moins >
Lire la suite >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%).Lire moins >
Langue :
Anglais
Audience :
Internationale
Vulgarisation :
Non
Établissement(s) :
CHU Lille
Université de Lille
Université de Lille
Date de dépôt :
2019-12-09T18:20:08Z