Research on diagnosis-related group grouping of inpatient medical expenditure in colorectal cancer patients based on a decision tree model

Wu, Suo-Wei; Pan, Qi; Chen, Tong

doi:10.12998/wjcc.v8.i12.2484

Cited by 20 publications

(28 citation statements)

References 28 publications

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“…Inpatient hospitalization costs were taken as the dependent variable, and age, gender, operation times, LOS, payment method, wound position, wound type and operation type were used as classification nodes. The parameters used in this study were as follows: The maximum depth of the decision tree was 5; the minimum sample number of the parent node was 200; the minimum sample number of the child node was 100; and the significance level of the splitting node was α = 0.05[ 17 ]. The grouping results are shown in Figure 3 .…”

Section: Resultsmentioning

confidence: 99%

Analysis of hospitalization costs related to fall injuries in elderly patients

Su¹,

Fu²,

Zhao³

et al. 2021

WJCC

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BACKGROUND With the aging world population, the incidence of falls has intensified and fall-related hospitalization costs are increasing. Falls are one type of event studied in the health economics of patient safety, and many developed countries have conducted such research on fall-related hospitalization costs. However, China, a developing country, still lacks large-scale studies in this area. AIM To investigate the factors related to the hospitalization costs of fall-related injuries in elderly inpatients and establish factor-based, cost-related groupings. METHODS A retrospective study was conducted. Patient information and cost data for elderly inpatients (age ≥ 60 years, n = 3362) who were hospitalized between 2016 and 2019 due to falls was collected from the medical record systems of two grade-A tertiary hospitals in China. Quantile regression (QR) analysis was used to identify the factors related to fall-related hospitalization costs. A decision tree model based on the chi-squared automatic interaction detector algorithm for hospitalization cost grouping was built by setting the factors in the regression results as separation nodes. RESULTS The total hospitalization cost of fall-related injuries in the included elderly patients was 180479203.03 RMB, and the reimbursement rate of medical benefit funds was 51.0% (92039709.52 RMB/180479203.03 RMB). The medical material costs were the highest component of the total hospitalization cost, followed (in order) by drug costs, test costs, treatment costs, integrated medical service costs and blood transfusion costs The QR results showed that patient age, gender, length of hospital stay, payment method, wound position, wound type, operation times and operation type significantly influenced the inpatient cost ( P < 0.05). The cost grouping model was established based on the QR results, and age, length of stay, operation type, wound position and wound type were the most important influencing factors in the model. Furthermore, the cost of each combination varied significantly. CONCLUSION Our grouping model of hospitalization costs clearly reflected the key factors affecting hospitalization costs and can be used to strengthen the reasonable control of these costs.

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Section: Resultsmentioning

confidence: 99%

Analysis of hospitalization costs related to fall injuries in elderly patients

Su¹,

Fu²,

Zhao³

et al. 2021

WJCC

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“…At present, research on DRG mainly focuses on building statistical models to improve DRG rules [1][2] , using DRG data for medical care quality evaluation [3][4][5] , performance appraisal [6] , disease cost measurements [7] , medical behavior research [8][9] , and hospital healthcare [10] . However, there are few studies on compiling the uni ed DRG scheme into executable programs for establishing DRG simulation grouping.…”

Section: Introductionmentioning

confidence: 99%

“…It is an important tool for measuring medical quality, evaluating cost-effectiveness, and con rming medical insurance payments. To deepen the reform of medical insurance payments, the Chinese National Healthcare Security Administration promoted DRG Payment national pilot work in 30 cities in 2019 and enabled actual DRG payments in 2021.At present, research on DRG mainly focuses on building statistical models to improve DRG rules [1][2] , using DRG data for medical care quality evaluation [3][4][5] , performance appraisal [6] , disease cost measurements [7] , medical behavior research [8][9] , and hospital healthcare [10] . However, there are few studies on compiling the uni ed DRG scheme into executable programs for establishing DRG simulation grouping.…”

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confidence: 99%

A China Healthcare Security Diagnosis Related Groups Script Development Based On R Language

Zhu

Fan

et al. 2022

Preprint

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Background: To develop a set of R scripts that could efficiently and accurately identify the home page information of medical records and perform China Healthcare Security Diagnosis Related Groups (CHS-DRG) simulating grouping.Methods: Based on the CHS-DRG grouping rules, we abstracted the DRG grouping process into a standard algorithm and compiled the R script Z-DRG. The DRG simulating groupings by Z-DRG were compared with the DRG results from the regional CHS-DRG integrated service platform to evaluate the accuracy.Results: The Z-DRG includes one function module (zdrgfun. Rc), one operation module (zdrgpro. R) and one database form (zdrgcodes.RData). The function module set 7 algorithm steps and 8 custom functions. The functions were set for multiple diagnoses, multiple operations, joint diagnosis and operation. Only (17.85±0.11) milliseconds were taken for CHS-DRG simulating grouping of one case. Compared with the regional CHS-DRG results, the accuracy rate was 99.10%. The difference in the number of other diagnoses is the main reason that affected the accuracy.Conclusions: Z-DRG is easy to operate. The CHS-DRG simulating groupings were efficient and accurate. The simulation results could be effectively applied for medical institutions to carry out CHS-DRG grouping prediction and improve the implementation effect of CHS-DRG payment work.

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“…DRGs, a prospective payment system, are used to effectively classify and combine diseases with similar characteristics into different diagnostics and treatment groups. Indeed, DRGs are set to achieve greater equality of financing based on the homogeneity of the clinical process and the similarity of resource consumption [ 10 ]. The diagnostics codes should be accurately matched with DRGs codes to claim actual reimbursement, but this is a time-consuming process and requires expert knowledge to manually retrieve information from patients’ clinical records.…”

Section: Introductionmentioning

confidence: 99%

DeepDRG: Performance of Artificial Intelligence Model for Real-Time Prediction of Diagnosis-Related Groups

Islam

Li²,

Poly

et al. 2021

Healthcare

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Nowadays, the use of diagnosis-related groups (DRGs) has been increased to claim reimbursement for inpatient care. The overall benefits of using DRGs depend upon the accuracy of clinical coding to obtain reasonable reimbursement. However, the selection of appropriate codes is always challenging and requires professional expertise. The rate of incorrect DRGs is always high due to the heavy workload, poor quality of documentation, and lack of computer assistance. We therefore developed deep learning (DL) models to predict the primary diagnosis for appropriate reimbursement and improving hospital performance. A dataset consisting of 81,486 patients with 128,105 episodes was used for model training and testing. Patients’ age, sex, drugs, diseases, laboratory tests, procedures, and operation history were used as inputs to our multiclass prediction model. Gated recurrent unit (GRU) and artificial neural network (ANN) models were developed to predict 200 primary diagnoses. The performance of the DL models was measured by the area under the receiver operating curve, precision, recall, and F1 score. Of the two DL models, the GRU method, had the best performance in predicting the primary diagnosis (AUC: 0.99, precision: 83.2%, and recall: 66.0%). However, the performance of ANN model for DRGs prediction achieved AUC of 0.99 with a precision of 0.82 and recall of 0.57. The findings of our study show that DL algorithms, especially GRU, can be used to develop DRGs prediction models for identifying primary diagnosis accurately. DeepDRGs would help to claim appropriate financial incentives, enable proper utilization of medical resources, and improve hospital performance.

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Research on diagnosis-related group grouping of inpatient medical expenditure in colorectal cancer patients based on a decision tree model

Cited by 20 publications

References 28 publications

Analysis of hospitalization costs related to fall injuries in elderly patients

Analysis of hospitalization costs related to fall injuries in elderly patients

A China Healthcare Security Diagnosis Related Groups Script Development Based On R Language

DeepDRG: Performance of Artificial Intelligence Model for Real-Time Prediction of Diagnosis-Related Groups

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