Julio Souza1,2, João Vasco Santos1,2,3, Veronica Bolon Canedo4, Amparo Betanzos4, Domingos Alves2,5, Alberto Freitas1,2. 1. Faculty of Medicine of the University of Porto, Portugal. 2. CINTESIS - Center for Health Technology and Services Research, Portugal. 3. Public Health Unit, ACES Grande Porto VIII - Espinho/Gaia, Portugal. 4. Faculty of Informatics of the University of A Coruña, Spain. 5. Ribeirão Preto Medical School of the University of São Paulo, Brazil.
Abstract
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. CONCLUSION AND IMPLICATIONS: Comprehensive reporting of pre-existing or newly acquired co-morbidities should be encouraged in hospitals as they have an important influence on SOI assignment and thus on hospital funding. Furthermore, we recommend that future guidelines to be used by medical coders should include specific rules concerning coding of co-morbidities.
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. CONCLUSION AND IMPLICATIONS: Comprehensive reporting of pre-existing or newly acquired co-morbidities should be encouraged in hospitals as they have an important influence on SOI assignment and thus on hospital funding. Furthermore, we recommend that future guidelines to be used by medical coders should include specific rules concerning coding of co-morbidities.
Entities:
Keywords:
Diagnosis-Related Groups; clinical coding; data accuracy; hospital administration; hospitals; machine learning; medical informatics; support vector machine
Authors: João Vasco Santos; João Viana; Carla Pinto; Júlio Souza; Fernando Lopes; Alberto Freitas; Sílvia Lopes Journal: J Med Syst Date: 2022-05-06 Impact factor: 4.460
Authors: Christian Benzing; Lea Timmermann; Thomas Winklmann; Lena Marie Haiden; Karl Herbert Hillebrandt; Axel Winter; Max Magnus Maurer; Matthäus Felsenstein; Felix Krenzien; Moritz Schmelzle; Johann Pratschke; Thomas Malinka Journal: Langenbecks Arch Surg Date: 2022-03-21 Impact factor: 2.895