Weighted activity unit effect: evaluating the cost of diagnosis‐related group coding

Tan, Joanne Yue‐Ai; Senko, Clare; Hughes, Brett; Lwin, Zarnie; Bennett, Richard J.; Power, John A.; Thomson, Leah

doi:10.1111/imj.14373

Cited by 9 publications

(11 citation statements)

References 9 publications

(12 reference statements)

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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%

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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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Section: Introductionmentioning

confidence: 99%

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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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“…6 In this way hospitals are more appropriately compensated, tailoring funding to case volume and complexity, encouraging efficiency to accommodate increased service provision. 1,2,6,7…”

Section: Introductionmentioning

confidence: 99%

“…1 Implemented in Australia in 2012, ABF awards funding based on services (activity) provided to patients, and the complexity of the activity. 6 In this way hospitals are more appropriately compensated, tailoring funding to case volume and complexity, encouraging efficiency to accommodate increased service provision. 1,2,6,7 ABF and the wider healthcare system rely on accurate clinical documentation of activity, which is then coded for billing.…”

Section: Introductionmentioning

confidence: 99%

Demystifying the role of anaesthetists in clinical coding in the Australian healthcare system

Smith

Mockeridge

Zundert

2022

Anaesth Intensive Care

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Despite the self-evident importance of hospital funding, many anaesthetists remain unsure of exactly how their daily work relates to hospital reimbursement. A lack of awareness of the nuances of the Australian hospital activity–based funding system has the potential to affect anaesthetic department reimbursement and thus resourcing. Activity-based funding relies on clinical coders reviewing clinical documentation and quantifying the care given to a patient during an admission. Errors in funding allocation may arise when there is a disconnect between the work performed and the information coded. In anaesthesia, there are several factors impeding this process, including clinical understanding of coding, system setup and coders’ understanding of anaesthesia. This article explores these factors from the clinical anaesthetist’s point of view and suggests solutions, such as awareness and education, clinician–coder cooperation and redesign of documentation systems at a systems level that anaesthetic departments can incorporate.

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“…Tan et al ( 22 ) put forward a medical and health care compensation method combined with activity weight, which allocates funds to hospitals according to the number and combination of clinical activities. This method is completely based on the compensation classification method of Australian refining DRG.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Obstetric Patient Flow and Horizontal Allocation of Medical Resources Based on Time Series Analysis

Wang

et al. 2021

Front. Public Health

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Objective: Given the ever-changing flow of obstetric patients in the hospital, how the government and hospital management plan and allocate medical resources has become an important problem that needs to be urgently solved. In this study a prediction method for calculating the monthly and daily flow of patients based on time series is proposed to provide decision support for government and hospital management.Methods: The historical patient flow data from the Department of Obstetrics and Gynecology of the First Hospital of Jilin University, China, from January 1, 2018, to February 29, 2020, were used as the training set. Seven models such as XGBoost, SVM, RF, and NNAR were used to predict the daily patient flow in the next 14 days. The HoltWinters model is then used to predict the monthly flow of patients over the next year.Results: The results of this analysis and prediction model showed that the obstetric inpatient flow was not a purely random process, and that patient flow was not only accompanied by the random patient flow but also showed a trend change and seasonal change rule. ACF,PACF,Ljung_box, and residual histogram were then used to verify the accuracy of the prediction model, and the results show that the Holtwiners model was optimal. R2, MAPE, and other indicators were used to measure the accuracy of the 14 day prediction model, and the results showed that HoltWinters and STL prediction models achieved high accuracy.Conclusion: In this paper, the time series model was used to analyze the trend and seasonal changes of obstetric patient flow and predict the patient flow in the next 14 days and 12 months. On this basis, combined with the trend and seasonal changes of obstetric patient flow, a more reasonable and fair horizontal allocation scheme of medical resources is proposed, combined with the prediction of patient flow.

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Weighted activity unit effect: evaluating the cost of diagnosis‐related group coding

Cited by 9 publications

References 9 publications

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

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

Demystifying the role of anaesthetists in clinical coding in the Australian healthcare system

Prediction of Obstetric Patient Flow and Horizontal Allocation of Medical Resources Based on Time Series Analysis

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