Affiliation:
1. Canadian Center for Health Economics Toronto Canada
2. Hospinnomics (PSE – École d’Économie de Paris, Assistance Publique Hôpitaux de Paris – AP‐HP) 1 Parvis Notre Dame Paris France
3. Centre de Recherche en Économie et Statistique Institut National de la Statistique et des Etudes Economiques Palaiseau France
Abstract
AbstractStudying quasi‐experimental data from French hospitals from 2010 to 2013, we test the effects of a substantial diagnosis‐related group (DRG) tariff refinement that occurred in 2012, designed to reduce financial risks of French maternity wards. To estimate the resulting DRG incentives with regard to the choice between scheduled C‐sections and other modes of child delivery, we predict, based on pre‐admission patient characteristics, the probability of each possible child delivery outcome and calculate expected differences in associated tariffs. Using patient‐level administrative data, we find that introducing additional severity levels and clinical factors into the reimbursement algorithm had no significant effect on the probability of a scheduled C‐section being performed. The results are robust to multiple formulations of DRG financial incentives. Our paper is the first study that focuses on the consequences of a DRG refinement in obstetrics and develops a probabilistic approach suitable for measuring the expected effects of DRG fee incentives in the presence of multiple tariff groups.
Cited by
1 articles.
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1. A Comprehensive Analysis on Various Machine Learning Algorithms for Child Birth Mode Prediction;2023 First International Conference on Advances in Electrical, Electronics and Computational Intelligence (ICAEECI);2023-10-19