Methods Inf Med 1999; 38(02): 140-143
DOI: 10.1055/s-0038-1634173
Original Article
Schattauer GmbH

Distinction between Planned and Unplanned Readmissions following Discharge from a Department of Internal Medicine

M. P. Kossovsky
1   Department of Internal Medicine, Geneva, Switzerland
2   Medical Director‘s office, Geneva, Switzerland
3   Groupe de recherche et d‘analyse en systèmes et soins hospitaliers (GRASSH), and Geneva, Switzerland
,
F. P. Sarasin
1   Department of Internal Medicine, Geneva, Switzerland
3   Groupe de recherche et d‘analyse en systèmes et soins hospitaliers (GRASSH), and Geneva, Switzerland
,
F. Bolla
1   Department of Internal Medicine, Geneva, Switzerland
3   Groupe de recherche et d‘analyse en systèmes et soins hospitaliers (GRASSH), and Geneva, Switzerland
,
J.-M. Gaspoz
1   Department of Internal Medicine, Geneva, Switzerland
3   Groupe de recherche et d‘analyse en systèmes et soins hospitaliers (GRASSH), and Geneva, Switzerland
,
F. Borst
3   Groupe de recherche et d‘analyse en systèmes et soins hospitaliers (GRASSH), and Geneva, Switzerland
4   Division of Medical Computing, Geneva University Hospitals, Geneva, Switzerland
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Publikationsdatum:
08. Februar 2018 (online)

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Abstract

Readmission rate is often used as an indicator for the quality of care. However, only unplanned readmissions may have a link with substandard quality of care. We compared two databases of the Geneva University Hospitals to determine which information is needed to distinguish planned from unplanned readmissions. All patients readmitted within 42 days after a first stay in the wards of the Department of Internal Medicine were identified. One of the databases contained encoded information needed to compute DRGs. The other database consisted of full-text discharge reports, addressed to the referring physician. Encoded reports allowed the classification of 64% of the readmissions, whereas full-text reports could classify 97% of the readmissions (p <0.001). The concordance between encoded reports and full-text reports was fair (kappa = 0.40). We conclude that encoded reports alone are not sufficient to distinguish planned from unplanned readmissions and that the automation of detailed clinical databases seems promising.