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Published on: December 1, 2023
Forecasting Single Disease Cost of Cataract Based on Multivariable Regression Analysis and Backpropagation Neural
Fang Qing1, Chuang Liu1,2
1Business School, Sichuan University, Chengdu, China.
Insights
Forecasting medical costs for cataract surgery helps control expenses. Intraocular lens implantation, doctor level, and payment source significantly influence costs, with neural networks offering more accurate predictions than regression analysis.
Area of Science:
- Medical Economics
- Health Services Research
- Ophthalmology
Background:
- Accurate medical cost forecasting is crucial for healthcare pricing reform and managing patient expenses.
- Single disease cost prediction aids in proactive estimation and control of medical expenditures.
Purpose of the Study:
- To identify factors influencing single disease cataract medical costs.
- To compare multivariable regression analysis (MRA) and backpropagation neural network (BPNN) for cataract cost forecasting.
Main Methods:
- Utilized data from 483 cataract surgery patients at West China Hospital (May-October 2015).
- Applied multivariable regression analysis (MRA) and backpropagation neural network (BPNN) for cost forecasting.
- Analyzed data using SPSS21.0 and MATLAB2014a.
Main Results:
- Key cost-influencing factors identified: intraocular lens (IOL) implantation, doctor level, payment source, admission status, additional diagnosis, surgery type, and anesthesia type.
- Backpropagation neural network (BPNN) achieved a lower average error (2.81%) compared to multivariable regression analysis (MRA) (5.79%).
- Both MRA and BPNN demonstrated technical and economic feasibility for cataract cost forecasting.
Conclusions:
- Identified significant drivers of cataract surgery costs, including IOL type and provider level.
- BPNN provides more accurate cost forecasts than MRA, though MRA offers greater interpretability.
- Forecasting models offer insights for controlling and reducing cataract-related medical costs.
Abstract:
In medical services, charge according to the disease is an important way to promote the reform of pricing mechanism, control the unreasonable growth of medical expenses, as well as reduce the burden on patients. Single disease cost forecasting that both identify potential influencing or driving factors and enable better proactive estimation of costs can guide the management and control of medical costs. This study aimed to identify the factors that affect the medical costs of single disease cataract and compare 2 regression models for anticipating acceptable medical cost forecasts. For this purpose, 483 patients with cataract surgery completed in West China Hospital from May 1, 2015, to October 1, 2015, were selected from hospital information system. For cost forecasting, multivariable regression analysis (MRA) and backpropagation neural network (BPNN) were used. Analysis of data was performed with SPSS21.0 and MATLAB2014a software. Total medical costs of patients with cataract (n = 483) ranged from 2015.00 to 13 359.00 CNY, and the mean ± standard deviation is 6292.29 ± 2639.43 CNY. Factors influencing costs of cataract in the MRA include, in importance order, intraocular lens (IOL) implantation (|r|: 0.805, P < .01), doctor level (|r|: 0.644, P < .01), payment source (|r|: 0.554, P < .01), admission status (|r|: 0.326, P < .01), additional diagnosis (|r|: 0.260, P < .01), type of surgery (|r|: 0.127, P < .05), and type of anesthesia (|r|: 0.126, P < .05). In terms of forecasting performance, BPNN (average error: 2.81%) outperforms, yet is less interpretable than MRA (average error: 5.79%). Both MRA and BPNN are technically and economically feasible in generating medical costs of cataract. And some insights on using results of the forecasting model in controlling and reducing disease costs are obtained.
