Leveraging National Claims and Hospital Big Data: Cohort Study on a Statin-Drug Interaction Use Case. Bannay, A., Bories, M., Le Corre, P., Riou, C., Lemordant, P., Van Hille, P., Chazard, E., Dode, X., Cuggia, M., & Bouzillé, G. JMIR medical informatics, 9(12):e29286, December, 2021. doi abstract bibtex BACKGROUND: Linking different sources of medical data is a promising approach to analyze care trajectories. The aim of the INSHARE (Integrating and Sharing Health Big Data for Research) project was to provide the blueprint for a technological platform that facilitates integration, sharing, and reuse of data from 2 sources: the clinical data warehouse (CDW) of the Rennes academic hospital, called eHOP (entrepôt Hôpital), and a data set extracted from the French national claim data warehouse (Système National des Données de Santé [SNDS]). OBJECTIVE: This study aims to demonstrate how the INSHARE platform can support big data analytic tasks in the health field using a pharmacovigilance use case based on statin consumption and statin-drug interactions. METHODS: A Spark distributed cluster-computing framework was used for the record linkage procedure and all analyses. A semideterministic record linkage method based on the common variables between the chosen data sources was developed to identify all patients discharged after at least one hospital stay at the Rennes academic hospital between 2015 and 2017. The use-case study focused on a cohort of patients treated with statins prescribed by their general practitioner or during their hospital stay. RESULTS: The whole process (record linkage procedure and use-case analyses) required 88 minutes. Of the 161,532 and 164,316 patients from the SNDS and eHOP CDW data sets, respectively, 159,495 patients were successfully linked (98.74% and 97.07% of patients from SNDS and eHOP CDW, respectively). Of the 16,806 patients with at least one statin delivery, 8293 patients started the consumption before and continued during the hospital stay, 6382 patients stopped statin consumption at hospital admission, and 2131 patients initiated statins in hospital. Statin-drug interactions occurred more frequently during hospitalization than in the community (3800/10,424, 36.45% and 3253/14,675, 22.17%, respectively; P\textless.001). Only 121 patients had the most severe level of statin-drug interaction. Hospital stay burden (length of stay and in-hospital mortality) was more severe in patients with statin-drug interactions during hospitalization. CONCLUSIONS: This study demonstrates the added value of combining and reusing clinical and claim data to provide large-scale measures of drug-drug interaction prevalence and care pathways outside hospitals. It builds a path to move the current health care system toward a Learning Health System using knowledge generated from research on real-world health data.
@article{bannay_leveraging_2021,
title = {Leveraging {National} {Claims} and {Hospital} {Big} {Data}: {Cohort} {Study} on a {Statin}-{Drug} {Interaction} {Use} {Case}},
volume = {9},
issn = {2291-9694},
shorttitle = {Leveraging {National} {Claims} and {Hospital} {Big} {Data}},
doi = {10.2196/29286},
abstract = {BACKGROUND: Linking different sources of medical data is a promising approach to analyze care trajectories. The aim of the INSHARE (Integrating and Sharing Health Big Data for Research) project was to provide the blueprint for a technological platform that facilitates integration, sharing, and reuse of data from 2 sources: the clinical data warehouse (CDW) of the Rennes academic hospital, called eHOP (entrepôt Hôpital), and a data set extracted from the French national claim data warehouse (Système National des Données de Santé [SNDS]).
OBJECTIVE: This study aims to demonstrate how the INSHARE platform can support big data analytic tasks in the health field using a pharmacovigilance use case based on statin consumption and statin-drug interactions.
METHODS: A Spark distributed cluster-computing framework was used for the record linkage procedure and all analyses. A semideterministic record linkage method based on the common variables between the chosen data sources was developed to identify all patients discharged after at least one hospital stay at the Rennes academic hospital between 2015 and 2017. The use-case study focused on a cohort of patients treated with statins prescribed by their general practitioner or during their hospital stay.
RESULTS: The whole process (record linkage procedure and use-case analyses) required 88 minutes. Of the 161,532 and 164,316 patients from the SNDS and eHOP CDW data sets, respectively, 159,495 patients were successfully linked (98.74\% and 97.07\% of patients from SNDS and eHOP CDW, respectively). Of the 16,806 patients with at least one statin delivery, 8293 patients started the consumption before and continued during the hospital stay, 6382 patients stopped statin consumption at hospital admission, and 2131 patients initiated statins in hospital. Statin-drug interactions occurred more frequently during hospitalization than in the community (3800/10,424, 36.45\% and 3253/14,675, 22.17\%, respectively; P{\textless}.001). Only 121 patients had the most severe level of statin-drug interaction. Hospital stay burden (length of stay and in-hospital mortality) was more severe in patients with statin-drug interactions during hospitalization.
CONCLUSIONS: This study demonstrates the added value of combining and reusing clinical and claim data to provide large-scale measures of drug-drug interaction prevalence and care pathways outside hospitals. It builds a path to move the current health care system toward a Learning Health System using knowledge generated from research on real-world health data.},
language = {eng},
number = {12},
journal = {JMIR medical informatics},
author = {Bannay, Aurélie and Bories, Mathilde and Le Corre, Pascal and Riou, Christine and Lemordant, Pierre and Van Hille, Pascal and Chazard, Emmanuel and Dode, Xavier and Cuggia, Marc and Bouzillé, Guillaume},
month = dec,
year = {2021},
pmid = {34898457},
keywords = {administrative claims, big data, data linking, data warehousing, drug interactions, health care, statins},
pages = {e29286},
}
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{"_id":"uoGvvPHGzAfueX8cs","bibbaseid":"bannay-bories-lecorre-riou-lemordant-vanhille-chazard-dode-etal-leveragingnationalclaimsandhospitalbigdatacohortstudyonastatindruginteractionusecase-2021","author_short":["Bannay, A.","Bories, M.","Le Corre, P.","Riou, C.","Lemordant, P.","Van Hille, P.","Chazard, E.","Dode, X.","Cuggia, M.","Bouzillé, G."],"bibdata":{"bibtype":"article","type":"article","title":"Leveraging National Claims and Hospital Big Data: Cohort Study on a Statin-Drug Interaction Use Case","volume":"9","issn":"2291-9694","shorttitle":"Leveraging National Claims and Hospital Big Data","doi":"10.2196/29286","abstract":"BACKGROUND: Linking different sources of medical data is a promising approach to analyze care trajectories. The aim of the INSHARE (Integrating and Sharing Health Big Data for Research) project was to provide the blueprint for a technological platform that facilitates integration, sharing, and reuse of data from 2 sources: the clinical data warehouse (CDW) of the Rennes academic hospital, called eHOP (entrepôt Hôpital), and a data set extracted from the French national claim data warehouse (Système National des Données de Santé [SNDS]). OBJECTIVE: This study aims to demonstrate how the INSHARE platform can support big data analytic tasks in the health field using a pharmacovigilance use case based on statin consumption and statin-drug interactions. METHODS: A Spark distributed cluster-computing framework was used for the record linkage procedure and all analyses. A semideterministic record linkage method based on the common variables between the chosen data sources was developed to identify all patients discharged after at least one hospital stay at the Rennes academic hospital between 2015 and 2017. The use-case study focused on a cohort of patients treated with statins prescribed by their general practitioner or during their hospital stay. RESULTS: The whole process (record linkage procedure and use-case analyses) required 88 minutes. Of the 161,532 and 164,316 patients from the SNDS and eHOP CDW data sets, respectively, 159,495 patients were successfully linked (98.74% and 97.07% of patients from SNDS and eHOP CDW, respectively). Of the 16,806 patients with at least one statin delivery, 8293 patients started the consumption before and continued during the hospital stay, 6382 patients stopped statin consumption at hospital admission, and 2131 patients initiated statins in hospital. Statin-drug interactions occurred more frequently during hospitalization than in the community (3800/10,424, 36.45% and 3253/14,675, 22.17%, respectively; P\\textless.001). Only 121 patients had the most severe level of statin-drug interaction. Hospital stay burden (length of stay and in-hospital mortality) was more severe in patients with statin-drug interactions during hospitalization. CONCLUSIONS: This study demonstrates the added value of combining and reusing clinical and claim data to provide large-scale measures of drug-drug interaction prevalence and care pathways outside hospitals. It builds a path to move the current health care system toward a Learning Health System using knowledge generated from research on real-world health data.","language":"eng","number":"12","journal":"JMIR medical informatics","author":[{"propositions":[],"lastnames":["Bannay"],"firstnames":["Aurélie"],"suffixes":[]},{"propositions":[],"lastnames":["Bories"],"firstnames":["Mathilde"],"suffixes":[]},{"propositions":[],"lastnames":["Le","Corre"],"firstnames":["Pascal"],"suffixes":[]},{"propositions":[],"lastnames":["Riou"],"firstnames":["Christine"],"suffixes":[]},{"propositions":[],"lastnames":["Lemordant"],"firstnames":["Pierre"],"suffixes":[]},{"propositions":[],"lastnames":["Van","Hille"],"firstnames":["Pascal"],"suffixes":[]},{"propositions":[],"lastnames":["Chazard"],"firstnames":["Emmanuel"],"suffixes":[]},{"propositions":[],"lastnames":["Dode"],"firstnames":["Xavier"],"suffixes":[]},{"propositions":[],"lastnames":["Cuggia"],"firstnames":["Marc"],"suffixes":[]},{"propositions":[],"lastnames":["Bouzillé"],"firstnames":["Guillaume"],"suffixes":[]}],"month":"December","year":"2021","pmid":"34898457","keywords":"administrative claims, big data, data linking, data warehousing, drug interactions, health care, statins","pages":"e29286","bibtex":"@article{bannay_leveraging_2021,\n\ttitle = {Leveraging {National} {Claims} and {Hospital} {Big} {Data}: {Cohort} {Study} on a {Statin}-{Drug} {Interaction} {Use} {Case}},\n\tvolume = {9},\n\tissn = {2291-9694},\n\tshorttitle = {Leveraging {National} {Claims} and {Hospital} {Big} {Data}},\n\tdoi = {10.2196/29286},\n\tabstract = {BACKGROUND: Linking different sources of medical data is a promising approach to analyze care trajectories. The aim of the INSHARE (Integrating and Sharing Health Big Data for Research) project was to provide the blueprint for a technological platform that facilitates integration, sharing, and reuse of data from 2 sources: the clinical data warehouse (CDW) of the Rennes academic hospital, called eHOP (entrepôt Hôpital), and a data set extracted from the French national claim data warehouse (Système National des Données de Santé [SNDS]).\nOBJECTIVE: This study aims to demonstrate how the INSHARE platform can support big data analytic tasks in the health field using a pharmacovigilance use case based on statin consumption and statin-drug interactions.\nMETHODS: A Spark distributed cluster-computing framework was used for the record linkage procedure and all analyses. A semideterministic record linkage method based on the common variables between the chosen data sources was developed to identify all patients discharged after at least one hospital stay at the Rennes academic hospital between 2015 and 2017. The use-case study focused on a cohort of patients treated with statins prescribed by their general practitioner or during their hospital stay.\nRESULTS: The whole process (record linkage procedure and use-case analyses) required 88 minutes. Of the 161,532 and 164,316 patients from the SNDS and eHOP CDW data sets, respectively, 159,495 patients were successfully linked (98.74\\% and 97.07\\% of patients from SNDS and eHOP CDW, respectively). Of the 16,806 patients with at least one statin delivery, 8293 patients started the consumption before and continued during the hospital stay, 6382 patients stopped statin consumption at hospital admission, and 2131 patients initiated statins in hospital. Statin-drug interactions occurred more frequently during hospitalization than in the community (3800/10,424, 36.45\\% and 3253/14,675, 22.17\\%, respectively; P{\\textless}.001). Only 121 patients had the most severe level of statin-drug interaction. Hospital stay burden (length of stay and in-hospital mortality) was more severe in patients with statin-drug interactions during hospitalization.\nCONCLUSIONS: This study demonstrates the added value of combining and reusing clinical and claim data to provide large-scale measures of drug-drug interaction prevalence and care pathways outside hospitals. 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