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From downcoding to upcoding: DRG based payment in hospitals
1Health Economics, Center for National Scientific Research, CNRS - Paris School of Economics - PSE, 48 Bd Jourdan, 75014, Paris, France. milcent@pse.ens.fr.
Insights
Disease group-based payment systems, while common, can be manipulated. A French study found that refining payment classifications led to upcoding, shifting budgets from public to for-profit hospitals.
Area of Science:
- Health Economics
- Healthcare Policy
- Health Services Research
Background:
- Disease group-based payment systems are widely adopted internationally.
- These systems aim for efficient hospital reimbursement by linking fees to patient groups and hospital activities.
- A prevailing assumption is that these payment rules are resistant to manipulation by stakeholders.
Purpose of the Study:
- To investigate the potential for data manipulation within disease group-based payment systems.
- To assess the impact of implementing a finer classification system on hospital reimbursement and resource allocation.
- To determine if changes in payment classification lead to alterations in healthcare provision.
Main Methods:
- Utilized a unique French longitudinal database encompassing 145 million patient stays.
- Analyzed the effects of implementing a more granular disease classification system.
- Examined changes in hospital billing and resource allocation patterns before and after policy implementation.
Main Results:
- Demonstrated an 'upcoding-learning effect' following the implementation of a finer classification system.
- Observed a significant budget transfer from public non-research hospitals to for-profit hospitals.
- Found that the 2009 policy change resulted in upcoding unrelated to actual changes in healthcare production.
Conclusions:
- Disease group-based payment systems are susceptible to manipulation through data classification changes.
- Finer classification can incentivize upcoding, leading to inequitable budget distribution.
- Policy changes in payment classification require careful consideration of potential unintended consequences on hospital finances and care delivery.
Abstract:
A prospective disease group-based payment is a reimbursement rule used in a wide array of countries. It turns to be the hospital's payment rule to imply. The secret of this payment is a fee payment as well as a hospital's activity based payment. There is a consensus to consider this rule of payment as the least likely to be manipulated by the actors. However, the defined fee per group depends on recorded information that is then processed using complex algorithms. What if the data itself can be manipulated? The result would be a fee per group based on manipulated factors that would lead to an inefficient budget allocation between hospitals. Using a unique French longitudinal database with 145 million stays, I unambiguously demonstrate that the implementation of a finer classification led to an upcoding-learning effect. The end result has been a budget transfer from public non-research hospitals to for-profit hospitals. The 2009 policy lead to upcoding disconnected from any changes in the trend of production of care.
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