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Automated comparison of last hospital main diagnosis and underlying cause of death ICD10 codes, France, 2008-2009
Swedish National Board of Health and Welfare, Center for Epidemiology, Stockholm, Sweden .
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2014 (English)In: BMC Medical Informatics and Decision Making, ISSN 1472-6947, Vol. 14, 44- p.Article in journal (Refereed) Published
Abstract [en]

BACKGROUND: In the age of big data in healthcare, automated comparison of medical diagnoses in large scale databases is a key issue. Our objectives were: 1) to formally define and identify cases of independence between last hospitalization main diagnosis (MD) and death registry underlying cause of death (UCD) for deceased subjects hospitalized in their last year of life; 2) to study their distribution according to socio-demographic and medico-administrative variables; 3) to discuss the interest of this method in the specific context of hospital quality of care assessment.

METHODS: 1) Elaboration of an algorithm comparing MD and UCD, relying on Iris, a coding system based on international standards. 2) Application to 421,460 beneficiaries of the general health insurance regime (which covers 70% of French population) hospitalized and deceased in 2008-2009.

RESULTS: 1) Independence, was defined as MD and UCD belonging to different trains of events leading to death 2) Among the deaths analyzed automatically (91.7%), 8.5% of in-hospital deaths and 19.5% of out-of-hospital deaths were classified as independent. Independence was more frequent in elder patients, as well as when the discharge-death time interval grew (14.3% when death occurred within 30 days after discharge and 27.7% within 6 to 12 months) and for UCDs other than neoplasms.

CONCLUSION: Our algorithm can identify cases where death can be considered independent from the pathology treated in hospital. Excluding these deaths from the ones allocated to the hospitalization process could contribute to improve post-hospital mortality indicators. More generally, this method has the potential of being developed and used for other diagnoses comparisons across time periods or databases.

Place, publisher, year, edition, pages
2014. Vol. 14, 44- p.
National Category
Public Health, Global Health, Social Medicine and Epidemiology
URN: urn:nbn:se:uu:diva-240275DOI: 10.1186/1472-6947-14-44PubMedID: 24898538OAI: oai:DiVA.org:uu-240275DiVA: diva2:776100
Available from: 2015-01-06 Created: 2015-01-06 Last updated: 2015-01-29Bibliographically approved

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Johansson, Lars Age
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