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Incorporation of expert knowledge in the statistical detection of diagnosis related group misclassification myDRG Stellenmarkt Gesundheitswesen

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Incorporation of expert knowledge in the statistical detection of diagnosis related group misclassification

Incorporation of expert knowledge in the statistical detection of diagnosis related group misclassification (Science Direct).

Weakly informative Bayesian models are effective in detecting DRG misallocation. Prediction is improved by incorporating subjective opinion elicited from experts as modelling inputs. A hybrid prior model using elicited expert...

Weakly informative Bayesian models are effective in detecting DRG misallocation. Prediction is improved by incorporating subjective opinion elicited from experts as modelling inputs. A hybrid prior model using elicited expert guesses is proposed. Expert guesses are only used if their accuracy is better than random.
The hybrid prior is best in 14 out of 20 trials, outperforming benchmark
models.


Abstract
Background
In activity based funding systems, the misclassification of inpatient episode
Diagnostic Related Groups (DRGs) can have significant impacts on the revenue of
health care providers. Weakly informative Bayesian models can be used to
estimate an episode's probability of DRG misclassification.

[...]

Conclusions
The incorporation of elicited expert guesses via a Hybrid prior produced a
significant improvement in DRG error detection; hence, it has the ability to
enhance the efficiency of clinical coding audits when put into practice at a
health care provider.

Quelle: Science Direct, 05.02.2020

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erschienen am Dienstag, 11.02.2020
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