Original Articles

Using DRG to analyze hospital production: a re-classification model based on a linear tree-network topology


Abstract


Background: Hospital discharge records are widely classified through the Diagnosis Related Group (DRG) system; the version currently used in Italy counts 538 different codes, including thousands of diagnosis and procedures. These numbers reflect the considerable effort of simplification, yet the current classification system is of little use to evaluate hospital production and performance.

Methods: As the case-mix of a given Hospital Unit (HU) is driven by its physicians’ specializations, a grouping of DRGs into a specialization-driven classification system has been conceived through the analysis of HUs discharging and the ICD-9-CM codes. We propose a three-folded classification, based on the analysis of 1,670,755 Hospital Discharge Cards (HDCs) produced by Lombardy Hospitals in 2010; it consists of 32 specializations (e.g. Neurosurgery), 124 sub-specialization (e.g. skull surgery) and 337 sub-sub-specialization (e.g. craniotomy).

Results: We give a practical application of the three-layered approach, based on the production of a Neurosurgical HU; we observe synthetically the profile of production (1,305 hospital discharges for 79 different DRG codes of 16 different MDC are grouped in few groups of homogeneous DRG codes), a more informative production comparison (through process-specific comparisons, rather than crude or case-mix standardized comparisons) and a potentially more adequate production planning (considering the Neurosurgical HUs of the same city, those produce a limited quote of the whole neurosurgical production, because the same activity can be realized by non-Neurosugical HUs).

Conclusion: Our work may help to evaluate the hospital production for a rational planning of available resources, blunting information asymmetries between physicians and managers. 


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NBN: http://nbn.depositolegale.it/urn%3Anbn%3Ait%3Aprex-15685

DOI: https://doi.org/10.2427/9347

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EBPH Epidemiology, Biostatistics and Public Health | ISSN 2282-0930

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