Importance of coding co-morbidities for APR-DRG assignment: Focus on cardiovascular and respiratory diseases

• Type: Article in International Scientific Journal
• Date: 2020

Authors

Souza, J (Author) Other; Santos, JV (Author) Other; Bolon Canedo, VB (Author) Other; Betanzos, A (Author) Other; Alves, D (Author) Other; Freitas A (Author) FMUP

Journal

Title: Health Information Management Journal

Vol. 49

Pages: 47-57

ISSN: 1833-3583

Publisher: SAGE

Indexing

ISI Web of Knowledge - 18 Citations

Scopus - 23 Citations

Other information

Authenticus ID: P-00Q-KE6

DOI: 10.1177/1833358319840575

Abstract (EN): Background: The All Patient-Refined Diagnosis-Related Groups (APR-DRGs) system has adjusted the basic DRG structure by incorporating four severity of illness (SOI) levels, which are used for determining hospital payment. A comprehensive report of all relevant diagnoses, namely the patient's underlying co-morbidities, is a key factor for ensuring that SOI determination will be adequate. Objective: In this study, we aimed to characterise the individual impact of co-morbidities on APR-DRG classification and hospital funding in the context of respiratory and cardiovascular diseases. Methods: Using 6 years of coded clinical data from a nationwide Portuguese inpatient database and support vector machine (SVM) models, we simulated and explored the APR-DRG classification to understand its response to individual removal of Charlson and Elixhauser co-morbidities. We also estimated the amount of hospital payments that could have been lost when co-morbidities are under-reported. Results: In our scenario, most Charlson and Elixhauser co-morbidities did considerably influence SOI determination but had little impact on base APR-DRG assignment. The degree of influence of each co-morbidity on SOI was, however, quite specific to the base APR-DRG. Under-coding of all studied co-morbidities led to losses in hospital payments. Furthermore, our results based on the SVM models were consistent with overall APR-DRG grouping logics.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11