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Study of Hospitalization Costs in Patients with Cerebral Ischemia Based on E-CHAID Algorithm

Study of Hospitalization Costs in Patients with Cerebral Ischemia Based on E-CHAID Algorithm Hindawi Journal of Healthcare Engineering Volume 2022, Article ID 3978577, 8 pages https://doi.org/10.1155/2022/3978577 Research Article Study of Hospitalization Costs in Patients with Cerebral Ischemia Based on E-CHAID Algorithm 1 2 1 1,3,4 Jing Gong , Ying Wang , Siou-Tang Huang , and Herng-Chia Chiu Institute for Hospital Management, Tsinghua University, Shenzhen, Guangdong, China Department of Medical Administration, West China Hospital, Sichuan University, Chengdu, Sichuan, China School of Public Health, Johns Hopkins University, Baltimore, USA Department of Health Care Management and Medical Informatics, Kaohsiung Medical University, Kaohsiung, Taiwan Correspondence should be addressed to Herng-Chia Chiu; chiuhc@sz.tsinghua.edu.cn Received 10 February 2022; Accepted 6 April 2022; Published 2 May 2022 Academic Editor: Qiang Wang Copyright © 2022 Jing Gong et al. �is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background. �e aging of the population has led to a rapid increase in the prevalence of most neurological diseases between 1990 and 2016, with a growth rate of up to 117%, which has put enormous pressure on medical insurance funds. As one of the core diseases of disease diagnosis grouping, the hospitalization cost composition and grouping research of patients with cerebral ischemic disease can help to determine scienti‰c payment standards and reduce the economic burden of patients. Aim. We aimed to understand the cost composition and inŠuencing factors of hospitalized patients with cerebral ischemic diseases and to identify a reasonable cost grouping scheme. Methods. �e data come from the homepage of medical records of inpatients with cerebral ischemia in a tertiary hospital in Sichuan Province from 2018 to 2020. After cleaning the data, a total of 5,204 pieces of data were obtained. Nonparametric tests and gamma regression models were used to explore the inŠuencing factors of hospitalization costs. Taking the inŠuencing factors as the predictor variables and the hospitalization cost as the target variable, the exhaustive Chi- squared automatic interaction detector (E-CHAID) algorithm was used to form the costs grouping, and the payment standard of the hospitalization cost for each group was determined. �e rationality of cost grouping was evaluated by coe›cient of variation (CV) and Kruskal–Wallis H test. Results. From 2018 to 2020, the average hospital stay of 5,204 inpatients with cerebral ischemic disease was 10.70 days, and the average hospitalization cost was 17,206.09 RMB yuan. Among the hospitalization costs, diagnosis costs and drug costs accounted for the highest proportion, accounting for 41.18% and 22.38%, respectively, in 2020. Gender, age, admission route, comorbidities and complications, super length of stay (>30 days), and discharge mode had signi‰cant e¦ects on hospitalization costs (P < 0.05). Patients were divided into 10 cost groups, and the grouping nodes included comorbidities and complications, discharge mode, age, gender, and admission route. �e CV of 9 of the 10 cost groups is less than or equal to 1. �e Kruskal–Wallis H test showed that the di¦erence between groups was statistically signi‰cant (P < 0.05). Conclusion. �e cost grouping of patients with cerebral ischemic diseases based on the E-CHAID algorithm is reasonable. �is study examined the e¦ects of super length of stay (>30 days), comorbidities and complications, and age on hospitalization cost in patients with cerebral ischemic disease. �is study can provide a theoretical basis for advancing the China Healthcare Security Diagnosis Related Groups (CHS- DRG) grouping program and medical expense payment, thereby reducing the disease burden of patients. and 114.8 per 100,000 people, respectively [2]. Studies have 1. Introduction shown that the proportion of people discharged with neu- Cerebral ischemic disease is brain tissue damage caused by rological diseases in general hospitals in China is close to vascular obstruction, including neuronal cell death and 11% [3], and cerebrovascular disease has become the leading cerebral infarction, mainly manifested as ischemic stroke [1]. cause of death among Chinese residents. Ischemic cere- A survey of about 480,000 people in China showed that the brovascular disease accounts for 87% of new or recurring prevalence and mortality of cerebral ischemia were 1114.8 cerebrovascular diseases each year, and cerebral ischemic 2 Journal of Healthcare Engineering finally tries to determine the cost standard of each cost disease is also the fifth leading cause of death and disability in the United States [4]. grouping, so as to reduce the disease burden and economic pressure of patients. In 2021, WHO released the Cross-departmental Global Action Plan for Epilepsy and Other Neurological Disorders [5], requiring countries to develop sustainable interventions 2. Material and Methods for the prevention and management of neurological diseases based on local best practices, to ensure that patients with 2.1. Setting. )is study is a single-center retrospective study neurological diseases receive timely, affordable, and high- with data from a large tertiary hospital in Sichuan Province, quality service. Patient hospital length of stay is one the most China. )e medical center has more than 4,000 acute care beds. In 2021, the hospital has 7.75 million outpatient visits direct embodiment on efficiency as well as bed turnover rate, which consequently enhance patient access to service [6]. (included emergency), and more than 283,000 admissions, with an average length of stay of 6.80 days. )e hospital has Hospitals have been using clinical management, such as the organizational structure, discharge planning, clinical path- been among the best for many years which can represent the medical level of China’s general acute hospital and has way to shorten hospital length of stay, and reduce the medical cost [7]. DRG is believed to have a positive impact in certain reference significance for other countries in the optimizing hospital management, shortening the day of world. hospitalization. )e appropriate bed management is not only to control medical cost, indirectly but also to reduce the 2.2. Data Source and Processing. )e data come from the financial burden for patients and third-party payers. information system of a tertiary hospital in Sichuan Prov- )erefore, DRG is widely used among many medical systems ince, including the basic information and cost data of pa- as payment methods to achieve the objectives. tients with cerebral ischemic diseases. In the CHS-DRG )e reform of medical insurance payment includes two grouping scheme (Figure 1), patients with cerebral ischemic aspects: cost grouping and cost payment. )e growing el- disease were divided into the BR2 ADRG group, and no derly population puts more and more pressure on the DRG group subdivision was performed. A total of 6214 cases medical insurance fund pool [8, 9], so the reform of medical of inpatients with cerebral ischemic diseases from 2018 to insurance payment methods will be the focus of long-term 2020 were collected, and 5204 cases remained after data research in the future. According to the United Nations processing. Data exclusion criteria: (1) cases with blank/ definition, China has entered an aging society at the be- missing items; (2) cases in which the major diagnosis or ginning of the twenty-first century, and the proportion of the major operation code is not included in the CHS-DRG elderly population in China is expected to exceed 30% by grouping scheme; (3) cases in which the length of stay is 2050 [10]. longer than 60 days; (4) cases in which hospitalization cost Reasonable cost grouping is the premise of scientific cost are less than 1 percentile or more than 99 percentiles. payment. Based on BJ-DRG, CN-DRG, CR-DRG, and According to the MCC&CC inclusion and exclusion C-DRG, which are the most widely used and authoritative in tables in the CHS-DRG grouping scheme, we sorted out the China, CHS-DRG grouping program [11] was formulated comorbidities and complications of patients with cerebral and released by the National Healthcare Security Admin- ischemic diseases. If a patient had any secondary diagnosis istration in 2019. CHS-DRG mainly uses the national health on the MCC&CC inclusion table and the primary diagnosis insurance code, including “Medical security disease diag- was not on the exclusion table, the case had MCC or CC. )e nosis classification and code (ICD-10)” and “Medical se- comorbidities and complications of the patients were di- curity surgery classification and code (ICD-9-CM-3).” As vided into three groups, the first group had major comor- one of the 376 adjacent diagnosis related groups (ADRG) in bidities and complications (MCC), the second group had the CHS-DRG grouping scheme, the cerebral ischemic comorbidities and complications (CC), and the third group disease group has research value. had no comorbidities and complications (Non-CC). Due to the skewed distribution of hospitalization cost, it is usually necessary to logarithmically transform it to use multiple linear regression [12]. Although Logistic regression 2.3. Classification and Description of Hospitalization models do not require data distribution, cost data integrity Expenses. )e classification of the total hospitalization cost after classification is reduced [13]. Gamma regression model is based on the homepage of Chinese hospitals [20], of which is a type of generalized linear model that can process data the diagnosis cost and drug cost account for the highest through logarithmic links to reduce data loss and ensure proportion. Diagnosis cost items include pathological di- data integrity [14], so it is gradually applied to medical cost agnosis, laboratory diagnosis, imaging diagnosis, and clin- analysis [15, 16]. ical diagnosis. Drug cost includes cost items in western )ere have been previous studies on hospitalization cost medicine, Chinese patent medicine, and Chinese herbal [17–19], but not many studies on cerebral ischemic diseases. medicine. Comprehensive medical service cost includes cost Based on the newly released CHS-DRG grouping scheme in items of physician fee, nursing care, bed, and others. China, we firstly calculated the CV of cerebral ischemic diseases ADRG group, which is the premise of cost grouping. )en we analyze the composition, influencing factors, and 2.4. Statistical Analysis. Because the hospitalization cost cost grouping of cerebral ischemic disease patients, and does not conform to the normal distribution, the Journal of Healthcare Engineering 3 Cases Patients with Cerebral Ischemia Major diagnosis CHS-DRG grouping scheme Major diagnostic categories Major diagnosis and major operation Major operation Major diagnosis BR2 group Internal Medicine Non-operating room Surgery ADRG ADRG operation ADRG Yes Subdivide groups considering individual and disease characteristics of cases CV<1 DRGs (eg: age, comorbidities and complications and so on) No Internal Medicine Non-operating room Surgery DRG Personal characteristics and disease characteristics DRG operation DRG Figure 1: )e grouping process of CHS-DRG grouping scheme. Yes CV<1 DRGs nonparametric test was used to carry out univariate analysis of the hospitalization cost, and the Gamma regression model No was used to carry out the multivariate analysis of the hos- Clinical judgement DRGs pitalization cost and calculate the cost ratio (CR). In uni- variate analysis, the Mann–Whitney U test was used for cost Figure 2: )e grouping process of sample patients with cerebral comparisons between two groups of variables, and the ischemia. Kruskal–Wallis H test was used for cost comparisons among multiple groups. Taking the hospitalization cost as the de- comprehensive medical service cost has decreased year by pendent variable, and the factors that have a high degree of influence on the hospitalization cost as the grouping node, year, and the proportion of treatment cost, and blood and blood products cost has increased year by year. the E-CHAID algorithm is used to group the cost. CV [21] and Kruskal–Wallis H test [22] were used to evaluate the reasonableness of cost grouping. Excel 2019 software was 3.4. Factors Affecting Inpatient Hospitalization Expenditure used for data entry and SPSS 26.0 and SPSS Modeler 18.0 were used for statistical analysis. 3.4.1. Univariate Analysis. As shown in Table 2, in addition to allergy, gender, age, social insurance, admission route, 3. Results comorbidities and complications, discharge mode, and su- per length of stay (>30 days) have statistically significant 3.1. 1e Premise of Cost Grouping. )e document shows [23] effects on hospitalization cost (P< 0.05). that ADRG group with CV greater than 1 can be subdivided, and the CV of hospitalization cost in the cerebral ischemic disease ADRG group is calculated to be 1.18. )e cost 3.4.2. Multivariate Analysis Using Gamma Model. grouping process of this study is shown in Figure 2. Results (Table 3) showed that gender, age, comorbidities and complications, admission route, super length of stay (>30 days), and discharge mode all had an impact on hospitali- 3.2. General Information. From 2018 to 2020, there were zation cost. )rough the cost ratio, it can be seen that the 5204 patients in the cerebral ischemia disease ADRG group, super length of stay (>30 days), age, and comorbidities and including 3215 male patients (61.78%) and 1989 female complications have a greater impact on the hospitalization patients (38.22%). )e age of the patients ranged from 0 to cost. Compared with patients whose hospitalization days 99 years old, with an average age of 65.30 years. )e length of were less than or equal to 30 days, patients with super length hospital stay ranged from 1 to 60 days, with an average of stay (>30 days) spent more medical costs (CR � 4.23); hospital stay of 10.70 days. compared with patients aged 0–17 years old, patients older than 65 years old spent more medical costs (CR � 2.63). 3.3. Composition of Inpatient Hospitalization Expenditure. As shown in Table 1, the total hospitalization cost of the cerebral ischemic disease ADRG group from 2018 to 2020 3.5. Grouping and Verification of Inpatient Hospitalization was 89.54 million RMB yuan, and the average hospitali- Cost. Since the length of hospital stay in China is affected by zation cost was 17,206.09 RMB yuan, of which the di- many factors, and there are large disparities between dif- agnosis cost and drug cost accounted for the highest ferent hospitals, the super length of stay (>30 days) is not proportion, accounting for 41.18% and 22.38%, respec- used as a grouping variable. Selecting the meaningful factors tively, in 2020. In the past three years, the proportion of of multivariate analysis as grouping nodes, using CART and 4 Journal of Healthcare Engineering Table 1: Composition of hospitalization cost from 2018–2020 (N � 5204). 2018 (N � 1892) 2019 (N � 1909) 2020 (N � 1403) Year Cost category (RMB yuan) Mean cost % Mean cost % Mean cost % Diagnosis cost 7002.85 42.69 6944.94 39.86 7409.75 41.18 Drug cost 3762.32 22.93 4341.63 24.92 4026.44 22.38 Comprehensive medical service cost 2580.27 15.73 2514.80 14.43 2491.84 13.85 Rehabilitation cost 1065.62 6.50 988.00 5.67 1191.05 6.62 Consumables cost 780.23 4.76 746.37 4.28 909.14 5.05 Treatment cost 575.62 3.51 698.46 4.01 782.53 4.35 Blood and blood product cost 18.82 0.11 88.96 0.51 176.71 0.98 Other cost 619.28 3.77 1098.63 6.31 1005.46 5.59 Overall 16405.00 17421.78 17992.90 Table 2: Univariate analysis of cerebral ischemic disease ADRG group (N � 5204). Variable Assignments N % Rank mean in RMB yuan Z/H/F P Male 3,215 61.78 2,645.55 Sex −2.628 0.009 Female 1,989 38.22 2,532.91 1–17 68 1.31 677.17 Age 18–65 2,271 43.64 2,316.16 288.525 <0.001 >65 2,865 55.05 2,875.17 No 2,672 51.35 2,606.35 Allergic −0.190 0.849 Yes 2,532 48.65 2,598.44 No 4599 88.37 2,602.05 Social insurance 41.131 <0.001 Yes 605 11.63 2,433.93 MCC 1,167 22.43 3,431.38 Comorbidities and complications CC 3,002 57.69 2,633.63 838.131 <0.001 Non-CC 1,035 19.89 1,577.61 Emergency 2,853 54.82 2,836.93 Admission route −12.400 <0.001 Outpatient and others 2,351 45.18 2,318.01 Discharged home 5,030 96.66 2,561.76 Transferred to another hospital 63 1.21 3,880.44 Discharge mode 121.377 <0.001 Death 91 1.75 3,938.09 Others 20 0.38 2,746.35 No 4,991 95.91 2,503.22 LOS >30 days −23.076 <0.001 Yes 213 4.09 4,928.87 LOS (Mean± SD) 10.70 ± 8.61 106.688 <0.001 Table 3: Multivariate analysis of cerebral ischemic disease ADRG group (N � 5204). Marginal mean in Marginal mean’s difference in Cost Variables Assignments 95% CI P RMB yuan RMB yuan ratio Ref: female 27496.58 Sex Male 28940.61 1444.04 1.05 1.02 1.09 0.004 Ref:0–17 15666.35 Age 18–65 34761.31 19094.96 2.22 1.91 2.58 <0.001 >65 41220.71 25554.36 2.63 2.27 3.06 <0.001 Ref: yes 27604.41 Social insurance No 28827.55 1223.14 1.04 0.99 1.10 0.108 Ref: outpatient and 25710.88 Admission route others Emergency 30950.62 5239.73 1.20 1.16 1.25 <0.001 Ref: non-CC 18895.52 Comorbidities and CC 27889.88 8994.36 1.48 1.41 1.54 <0.001 complications MCC 42596.49 23700.97 2.25 2.14 2.38 <0.001 Ref: no 13711.32 LOS >30 days Yes 58037.29 44325.97 4.23 3.89 4.61 <0.001 Ref: discharged home 20378.89 Transferred to another 35905.05 15526.16 1.76 1.51 2.05 <0.001 Discharge mode hospital Death 31563.01 11184.12 1.55 1.36 1.76 <0.001 Others 27419.41 7040.52 1.35 1.03 1.76 0.031 Journal of Healthcare Engineering 5 E-CHAID algorithms to construct cost groups, and form 2 disease, selection of treatment options, and assessment of and 10 cost groups respectively. As shown in Table 4, taking prognostic status involve imaging studies [27, 28], which may result in high diagnostic costs. )e high proportion of into account the number of groups and the mean absolute error, we finally chose the E-CHAID model for subsequent diagnostic costs is in line with the trend of medical reform, analysis. and early diagnosis is of great significance for the prevention As shown in Table 5, 5204 patients were divided into 10 and treatment of cerebral ischemic diseases. )e use of cost groups, and the grouping nodes included comorbidities imported drugs (the thrombolytic agent alteplase (rt-PA) and complications, discharge mode, age, gender, and ad- [29]) and the nonreimbursement of certain drugs by medical mission route. )e fourth group had the largest number of insurance are possible reasons for the high cost of drugs patients with 1823 patients (35.03%), these patients had [30, 31]. )e proportion of drug costs in the total cost in general comorbidities and complications, and were older China is significantly higher than the international pro- than 65 years. )e seventh group had the smallest number of portion [32, 33], so reducing the drug costs of patients with patients, with only 56 patients (2.09%), who had no cerebral ischemic disease has positive significance for re- ducing the disease burden of such patients [34]. comorbidities and complications, were admitted through outpatient and other routes, and were younger than 18 years Age has important implications for patient classification old. Among the 10 cost groups, the CV of nine groups are all in many countries [35, 36]. )e research results show that less than or equal to 1, and the grouping is reasonable. )e the average age of patients with cerebral ischemia is 65.30 Kruskal–Wallis H test was performed on the cost groups, years old, and the hospitalization cost of patients over 65 and the difference between the groups was statistically years old is 2.63 times that of patients aged 0–17 years old, significant (P< 0.001). which is in line with the natural pathological characteristics of the disease. Meanwhile, the elderly tend to have more comorbidities and complications because of the decline of 3.6. Cost Payment Standard. As shown in Table 5, the physical function and thus spend more medical expenses median medical cost of each group is taken as the standard [36]. cost. )e 75th percentile of cost per group plus 1.5 times the )e homogeneity of patients in the DRG group is a interquartile range was used as the upper limit of medical prerequisite for reasonable reimbursement by medical costs for that group, and cases above the upper limit were insurance. In order to ensure the homogeneity of patients defined as excess amount [24, 25]. )e second group (MCC, in each group, it is necessary to carefully select categorical transferred to another hospital, death and others) had the variables [37]. )rough the cost ratio, it can be found that highest standard cost, about 25,000 RMB yuan; the seventh super length of stay (>30 days), comorbidities and com- group (Non-CC, outpatient and other admission,<18 years plications, and discharge mode have a greater impact on old) had the lowest standard cost, close to 1,500 RMB yuan. hospitalization cost. )e hospitalization cost of patients )e fourth group (CC, ≤65 years old) had the highest excess with super length of stay (>30 days) is significantly higher rate at 10.43%, and the ninth group (Non-CC, outpatient than that of patients with hospitalization days less than or and other admission, 18–65 years old, female) had the lowest equal to 30 days (CR � 4.23), which requires hospitals to excess rate at 0.56%. follow evidence-based guidelines, strengthen clinical pathway management, and standardize similar patients’ treatments and costs, thereby reducing the disease and 4. Discussion economic burden of individual patients. Comorbidities and complications are important influencing factors in hospi- According to the CHS-DRG grouping scheme, the CV of the cerebral ischemic disease ADRG group is 1.18, and the talization costs. It can be seen from the grouping results patient’s personal characteristics and disease characteristics that the standard cost of the second group (MCC, trans- have a certain influence on the hospitalization cost, so it is ferred to another hospital, death and others) is the highest, reasonable to group the cost for the ADRG group. At the about 25,000 RMB yuan, which may be related to the in- creased difficulty of treatment due to the higher severity of same time, it also shows that the applicability of the grouping scheme of CHS-DRG needs to be improved. the patients included in this grouping [18]. Discharge mode is also influenced to some extent by the severity of the )e prevalence of most neurological diseases increased rapidly from 1990 to 2016, with a growth rate of 117% due to disease, for example, patients who are transferred to an- other hospital on advice or who die tend to have more an aging population [26]. As a common neurological dis- ease, cerebral ischemic disease has the characteristics of high severe disease. Computational cost grouping usually includes three mortality and high disability rate [5], which will cause a heavy economic and labor burden to patients, families, and methods: CART, CHAID, and E-CHAID, of which society. )e results of the study showed that from 2018 to E-CHAID is an improved method of CHAID [19]. We found 2020, the average length of hospital stay for patients with that E-CHAID has smaller model error and higher grouping cerebral ischemic disease was 10.70 days, and the average performance than CART, which is consistent with other research results [38]. )e results of CV and Kruskal–Wallis hospitalization cost was 17,206.09 RMB yuan. Among the hospitalization cost, diagnosis cost and drug cost accounted H test show that the cost grouping in this study is reasonable, and the grouping scheme is meaningful, which can provide a for the highest proportion, accounting for 41.18% and 22.38% in 2020, respectively. Diagnosis of cerebral ischemic feasible method for the medical insurance department to 6 Journal of Healthcare Engineering Table 4: Comparison of CART and E-CHAID models (N � 5204). Model Linear correlation Mean absolute error Standard deviation Number of groups CART 0.309 10426.817 19296.419 2 E-CHAID 0.352 10209.751 18985.752 10 Table 5: Grouping results and the predicted medical costs (N � 5204). Group Median in RMB Upper Excess amount Classification description N CV IQR P75 number yuan limit N (%) 1 MCC, discharged home 1046 1.13 16861.55 20478.52 30826.32 61544.09 107 (10.23) MCC, transferred to another hospital, 2 121 1.11 24554.26 32125.81 46340.91 94529.62 11 (9.09) death and others 3 CC, ≤65 years old 1179 0.84 10527.18 6991.76 14977.50 25465.14 123 (10.43) 4 CC, >65 years old 1823 0.92 11928.30 8956.58 17892.89 31327.76 177 (9.71) 5 Non-CC, emergency admission, male 269 0.60 9875.89 5329.05 13127.80 21121.38 20 (7.43) 6 Non-CC, emergency admission, female 169 0.45 9414.20 4938.46 12336.98 19744.67 8 (4.73) Non-CC, outpatient and other admission, 7 56 2.09 1484.30 51.62 1527.48 1604.91 11 (19.64) <18 years old Non-CC, outpatient and other admission, 8 212 0.94 6811.34 8170.03 9705.90 21960.94 11 (5.19) 18–65 years old, male Non-CC, outpatient and other admission, 9 179 0.74 5584.74 6597.86 8098.58 17995.37 1 (0.56) 18–65 years old; female Non-CC, outpatient and other admission, 10 150 0.66 8502.10 4613.23 11123.75 18043.59 15 (10.00) >65 years old improve the disease grouping scheme and pay for diseases Consent [39]. Since the data obtained after each patient’s written consent to treatment were anonymous, patient consent was not 5. Conclusion required. Based on the CHS-DRG grouping scheme, this study analyzed the composition and influencing factors of medical costs for Conflicts of Interest inpatients with cerebral ischemic diseases, and used the )e authors declare that they have no conflicts of interest. E-CHAID algorithm to group costs. )is study further verifies the applicability of the CHS-DRG grouping scheme and helps to optimize the DRG grouping system. )is study also provides Authors’ Contributions a theoretical basis for cost control of cerebral ischemic diseases, which is beneficial to reduce the economic burden of patients, JG contributed to the study design, data analysis, drafting the and provides suggestions for other developing countries to major portion of the manuscript, and article revisions. HCC improve the disease diagnosis grouping system. contributed to the conceptual framework and article revi- sions. YW contributed to data collection and analysis. STH contributed to writing the manuscript. All authors critically 5.1. Limitations of the Study. )e advantage of this study lies reviewed the manuscript and approved the final version. in the rich sample size and the data from representative general hospitals. However, this study also has certain Acknowledgments limitations. Due to the availability of data, the data in this study are only from one hospital, and multicenter studies )e authors would like to thank all the experts involved in need to be added in the future to make the findings more the research of the investigated hospital in Sichuan province generalizable. In addition, the hospitalization cost measured for their willingness to cooperate. in this article is only a part of the direct economic burden [39], so the actual cost of the patient may be higher. 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M. van den Bos, “International comparison of stroke cost studies,” Stroke, vol. 35, no. 5, pp. 1209–1215, 2004. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Healthcare Engineering Hindawi Publishing Corporation

Study of Hospitalization Costs in Patients with Cerebral Ischemia Based on E-CHAID Algorithm

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Hindawi Journal of Healthcare Engineering Volume 2022, Article ID 3978577, 8 pages https://doi.org/10.1155/2022/3978577 Research Article Study of Hospitalization Costs in Patients with Cerebral Ischemia Based on E-CHAID Algorithm 1 2 1 1,3,4 Jing Gong , Ying Wang , Siou-Tang Huang , and Herng-Chia Chiu Institute for Hospital Management, Tsinghua University, Shenzhen, Guangdong, China Department of Medical Administration, West China Hospital, Sichuan University, Chengdu, Sichuan, China School of Public Health, Johns Hopkins University, Baltimore, USA Department of Health Care Management and Medical Informatics, Kaohsiung Medical University, Kaohsiung, Taiwan Correspondence should be addressed to Herng-Chia Chiu; chiuhc@sz.tsinghua.edu.cn Received 10 February 2022; Accepted 6 April 2022; Published 2 May 2022 Academic Editor: Qiang Wang Copyright © 2022 Jing Gong et al. �is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background. �e aging of the population has led to a rapid increase in the prevalence of most neurological diseases between 1990 and 2016, with a growth rate of up to 117%, which has put enormous pressure on medical insurance funds. As one of the core diseases of disease diagnosis grouping, the hospitalization cost composition and grouping research of patients with cerebral ischemic disease can help to determine scienti‰c payment standards and reduce the economic burden of patients. Aim. We aimed to understand the cost composition and inŠuencing factors of hospitalized patients with cerebral ischemic diseases and to identify a reasonable cost grouping scheme. Methods. �e data come from the homepage of medical records of inpatients with cerebral ischemia in a tertiary hospital in Sichuan Province from 2018 to 2020. After cleaning the data, a total of 5,204 pieces of data were obtained. Nonparametric tests and gamma regression models were used to explore the inŠuencing factors of hospitalization costs. Taking the inŠuencing factors as the predictor variables and the hospitalization cost as the target variable, the exhaustive Chi- squared automatic interaction detector (E-CHAID) algorithm was used to form the costs grouping, and the payment standard of the hospitalization cost for each group was determined. �e rationality of cost grouping was evaluated by coe›cient of variation (CV) and Kruskal–Wallis H test. Results. From 2018 to 2020, the average hospital stay of 5,204 inpatients with cerebral ischemic disease was 10.70 days, and the average hospitalization cost was 17,206.09 RMB yuan. Among the hospitalization costs, diagnosis costs and drug costs accounted for the highest proportion, accounting for 41.18% and 22.38%, respectively, in 2020. Gender, age, admission route, comorbidities and complications, super length of stay (>30 days), and discharge mode had signi‰cant e¦ects on hospitalization costs (P < 0.05). Patients were divided into 10 cost groups, and the grouping nodes included comorbidities and complications, discharge mode, age, gender, and admission route. �e CV of 9 of the 10 cost groups is less than or equal to 1. �e Kruskal–Wallis H test showed that the di¦erence between groups was statistically signi‰cant (P < 0.05). Conclusion. �e cost grouping of patients with cerebral ischemic diseases based on the E-CHAID algorithm is reasonable. �is study examined the e¦ects of super length of stay (>30 days), comorbidities and complications, and age on hospitalization cost in patients with cerebral ischemic disease. �is study can provide a theoretical basis for advancing the China Healthcare Security Diagnosis Related Groups (CHS- DRG) grouping program and medical expense payment, thereby reducing the disease burden of patients. and 114.8 per 100,000 people, respectively [2]. Studies have 1. Introduction shown that the proportion of people discharged with neu- Cerebral ischemic disease is brain tissue damage caused by rological diseases in general hospitals in China is close to vascular obstruction, including neuronal cell death and 11% [3], and cerebrovascular disease has become the leading cerebral infarction, mainly manifested as ischemic stroke [1]. cause of death among Chinese residents. Ischemic cere- A survey of about 480,000 people in China showed that the brovascular disease accounts for 87% of new or recurring prevalence and mortality of cerebral ischemia were 1114.8 cerebrovascular diseases each year, and cerebral ischemic 2 Journal of Healthcare Engineering finally tries to determine the cost standard of each cost disease is also the fifth leading cause of death and disability in the United States [4]. grouping, so as to reduce the disease burden and economic pressure of patients. In 2021, WHO released the Cross-departmental Global Action Plan for Epilepsy and Other Neurological Disorders [5], requiring countries to develop sustainable interventions 2. Material and Methods for the prevention and management of neurological diseases based on local best practices, to ensure that patients with 2.1. Setting. )is study is a single-center retrospective study neurological diseases receive timely, affordable, and high- with data from a large tertiary hospital in Sichuan Province, quality service. Patient hospital length of stay is one the most China. )e medical center has more than 4,000 acute care beds. In 2021, the hospital has 7.75 million outpatient visits direct embodiment on efficiency as well as bed turnover rate, which consequently enhance patient access to service [6]. (included emergency), and more than 283,000 admissions, with an average length of stay of 6.80 days. )e hospital has Hospitals have been using clinical management, such as the organizational structure, discharge planning, clinical path- been among the best for many years which can represent the medical level of China’s general acute hospital and has way to shorten hospital length of stay, and reduce the medical cost [7]. DRG is believed to have a positive impact in certain reference significance for other countries in the optimizing hospital management, shortening the day of world. hospitalization. )e appropriate bed management is not only to control medical cost, indirectly but also to reduce the 2.2. Data Source and Processing. )e data come from the financial burden for patients and third-party payers. information system of a tertiary hospital in Sichuan Prov- )erefore, DRG is widely used among many medical systems ince, including the basic information and cost data of pa- as payment methods to achieve the objectives. tients with cerebral ischemic diseases. In the CHS-DRG )e reform of medical insurance payment includes two grouping scheme (Figure 1), patients with cerebral ischemic aspects: cost grouping and cost payment. )e growing el- disease were divided into the BR2 ADRG group, and no derly population puts more and more pressure on the DRG group subdivision was performed. A total of 6214 cases medical insurance fund pool [8, 9], so the reform of medical of inpatients with cerebral ischemic diseases from 2018 to insurance payment methods will be the focus of long-term 2020 were collected, and 5204 cases remained after data research in the future. According to the United Nations processing. Data exclusion criteria: (1) cases with blank/ definition, China has entered an aging society at the be- missing items; (2) cases in which the major diagnosis or ginning of the twenty-first century, and the proportion of the major operation code is not included in the CHS-DRG elderly population in China is expected to exceed 30% by grouping scheme; (3) cases in which the length of stay is 2050 [10]. longer than 60 days; (4) cases in which hospitalization cost Reasonable cost grouping is the premise of scientific cost are less than 1 percentile or more than 99 percentiles. payment. Based on BJ-DRG, CN-DRG, CR-DRG, and According to the MCC&CC inclusion and exclusion C-DRG, which are the most widely used and authoritative in tables in the CHS-DRG grouping scheme, we sorted out the China, CHS-DRG grouping program [11] was formulated comorbidities and complications of patients with cerebral and released by the National Healthcare Security Admin- ischemic diseases. If a patient had any secondary diagnosis istration in 2019. CHS-DRG mainly uses the national health on the MCC&CC inclusion table and the primary diagnosis insurance code, including “Medical security disease diag- was not on the exclusion table, the case had MCC or CC. )e nosis classification and code (ICD-10)” and “Medical se- comorbidities and complications of the patients were di- curity surgery classification and code (ICD-9-CM-3).” As vided into three groups, the first group had major comor- one of the 376 adjacent diagnosis related groups (ADRG) in bidities and complications (MCC), the second group had the CHS-DRG grouping scheme, the cerebral ischemic comorbidities and complications (CC), and the third group disease group has research value. had no comorbidities and complications (Non-CC). Due to the skewed distribution of hospitalization cost, it is usually necessary to logarithmically transform it to use multiple linear regression [12]. Although Logistic regression 2.3. Classification and Description of Hospitalization models do not require data distribution, cost data integrity Expenses. )e classification of the total hospitalization cost after classification is reduced [13]. Gamma regression model is based on the homepage of Chinese hospitals [20], of which is a type of generalized linear model that can process data the diagnosis cost and drug cost account for the highest through logarithmic links to reduce data loss and ensure proportion. Diagnosis cost items include pathological di- data integrity [14], so it is gradually applied to medical cost agnosis, laboratory diagnosis, imaging diagnosis, and clin- analysis [15, 16]. ical diagnosis. Drug cost includes cost items in western )ere have been previous studies on hospitalization cost medicine, Chinese patent medicine, and Chinese herbal [17–19], but not many studies on cerebral ischemic diseases. medicine. Comprehensive medical service cost includes cost Based on the newly released CHS-DRG grouping scheme in items of physician fee, nursing care, bed, and others. China, we firstly calculated the CV of cerebral ischemic diseases ADRG group, which is the premise of cost grouping. )en we analyze the composition, influencing factors, and 2.4. Statistical Analysis. Because the hospitalization cost cost grouping of cerebral ischemic disease patients, and does not conform to the normal distribution, the Journal of Healthcare Engineering 3 Cases Patients with Cerebral Ischemia Major diagnosis CHS-DRG grouping scheme Major diagnostic categories Major diagnosis and major operation Major operation Major diagnosis BR2 group Internal Medicine Non-operating room Surgery ADRG ADRG operation ADRG Yes Subdivide groups considering individual and disease characteristics of cases CV<1 DRGs (eg: age, comorbidities and complications and so on) No Internal Medicine Non-operating room Surgery DRG Personal characteristics and disease characteristics DRG operation DRG Figure 1: )e grouping process of CHS-DRG grouping scheme. Yes CV<1 DRGs nonparametric test was used to carry out univariate analysis of the hospitalization cost, and the Gamma regression model No was used to carry out the multivariate analysis of the hos- Clinical judgement DRGs pitalization cost and calculate the cost ratio (CR). In uni- variate analysis, the Mann–Whitney U test was used for cost Figure 2: )e grouping process of sample patients with cerebral comparisons between two groups of variables, and the ischemia. Kruskal–Wallis H test was used for cost comparisons among multiple groups. Taking the hospitalization cost as the de- comprehensive medical service cost has decreased year by pendent variable, and the factors that have a high degree of influence on the hospitalization cost as the grouping node, year, and the proportion of treatment cost, and blood and blood products cost has increased year by year. the E-CHAID algorithm is used to group the cost. CV [21] and Kruskal–Wallis H test [22] were used to evaluate the reasonableness of cost grouping. Excel 2019 software was 3.4. Factors Affecting Inpatient Hospitalization Expenditure used for data entry and SPSS 26.0 and SPSS Modeler 18.0 were used for statistical analysis. 3.4.1. Univariate Analysis. As shown in Table 2, in addition to allergy, gender, age, social insurance, admission route, 3. Results comorbidities and complications, discharge mode, and su- per length of stay (>30 days) have statistically significant 3.1. 1e Premise of Cost Grouping. )e document shows [23] effects on hospitalization cost (P< 0.05). that ADRG group with CV greater than 1 can be subdivided, and the CV of hospitalization cost in the cerebral ischemic disease ADRG group is calculated to be 1.18. )e cost 3.4.2. Multivariate Analysis Using Gamma Model. grouping process of this study is shown in Figure 2. Results (Table 3) showed that gender, age, comorbidities and complications, admission route, super length of stay (>30 days), and discharge mode all had an impact on hospitali- 3.2. General Information. From 2018 to 2020, there were zation cost. )rough the cost ratio, it can be seen that the 5204 patients in the cerebral ischemia disease ADRG group, super length of stay (>30 days), age, and comorbidities and including 3215 male patients (61.78%) and 1989 female complications have a greater impact on the hospitalization patients (38.22%). )e age of the patients ranged from 0 to cost. Compared with patients whose hospitalization days 99 years old, with an average age of 65.30 years. )e length of were less than or equal to 30 days, patients with super length hospital stay ranged from 1 to 60 days, with an average of stay (>30 days) spent more medical costs (CR � 4.23); hospital stay of 10.70 days. compared with patients aged 0–17 years old, patients older than 65 years old spent more medical costs (CR � 2.63). 3.3. Composition of Inpatient Hospitalization Expenditure. As shown in Table 1, the total hospitalization cost of the cerebral ischemic disease ADRG group from 2018 to 2020 3.5. Grouping and Verification of Inpatient Hospitalization was 89.54 million RMB yuan, and the average hospitali- Cost. Since the length of hospital stay in China is affected by zation cost was 17,206.09 RMB yuan, of which the di- many factors, and there are large disparities between dif- agnosis cost and drug cost accounted for the highest ferent hospitals, the super length of stay (>30 days) is not proportion, accounting for 41.18% and 22.38%, respec- used as a grouping variable. Selecting the meaningful factors tively, in 2020. In the past three years, the proportion of of multivariate analysis as grouping nodes, using CART and 4 Journal of Healthcare Engineering Table 1: Composition of hospitalization cost from 2018–2020 (N � 5204). 2018 (N � 1892) 2019 (N � 1909) 2020 (N � 1403) Year Cost category (RMB yuan) Mean cost % Mean cost % Mean cost % Diagnosis cost 7002.85 42.69 6944.94 39.86 7409.75 41.18 Drug cost 3762.32 22.93 4341.63 24.92 4026.44 22.38 Comprehensive medical service cost 2580.27 15.73 2514.80 14.43 2491.84 13.85 Rehabilitation cost 1065.62 6.50 988.00 5.67 1191.05 6.62 Consumables cost 780.23 4.76 746.37 4.28 909.14 5.05 Treatment cost 575.62 3.51 698.46 4.01 782.53 4.35 Blood and blood product cost 18.82 0.11 88.96 0.51 176.71 0.98 Other cost 619.28 3.77 1098.63 6.31 1005.46 5.59 Overall 16405.00 17421.78 17992.90 Table 2: Univariate analysis of cerebral ischemic disease ADRG group (N � 5204). Variable Assignments N % Rank mean in RMB yuan Z/H/F P Male 3,215 61.78 2,645.55 Sex −2.628 0.009 Female 1,989 38.22 2,532.91 1–17 68 1.31 677.17 Age 18–65 2,271 43.64 2,316.16 288.525 <0.001 >65 2,865 55.05 2,875.17 No 2,672 51.35 2,606.35 Allergic −0.190 0.849 Yes 2,532 48.65 2,598.44 No 4599 88.37 2,602.05 Social insurance 41.131 <0.001 Yes 605 11.63 2,433.93 MCC 1,167 22.43 3,431.38 Comorbidities and complications CC 3,002 57.69 2,633.63 838.131 <0.001 Non-CC 1,035 19.89 1,577.61 Emergency 2,853 54.82 2,836.93 Admission route −12.400 <0.001 Outpatient and others 2,351 45.18 2,318.01 Discharged home 5,030 96.66 2,561.76 Transferred to another hospital 63 1.21 3,880.44 Discharge mode 121.377 <0.001 Death 91 1.75 3,938.09 Others 20 0.38 2,746.35 No 4,991 95.91 2,503.22 LOS >30 days −23.076 <0.001 Yes 213 4.09 4,928.87 LOS (Mean± SD) 10.70 ± 8.61 106.688 <0.001 Table 3: Multivariate analysis of cerebral ischemic disease ADRG group (N � 5204). Marginal mean in Marginal mean’s difference in Cost Variables Assignments 95% CI P RMB yuan RMB yuan ratio Ref: female 27496.58 Sex Male 28940.61 1444.04 1.05 1.02 1.09 0.004 Ref:0–17 15666.35 Age 18–65 34761.31 19094.96 2.22 1.91 2.58 <0.001 >65 41220.71 25554.36 2.63 2.27 3.06 <0.001 Ref: yes 27604.41 Social insurance No 28827.55 1223.14 1.04 0.99 1.10 0.108 Ref: outpatient and 25710.88 Admission route others Emergency 30950.62 5239.73 1.20 1.16 1.25 <0.001 Ref: non-CC 18895.52 Comorbidities and CC 27889.88 8994.36 1.48 1.41 1.54 <0.001 complications MCC 42596.49 23700.97 2.25 2.14 2.38 <0.001 Ref: no 13711.32 LOS >30 days Yes 58037.29 44325.97 4.23 3.89 4.61 <0.001 Ref: discharged home 20378.89 Transferred to another 35905.05 15526.16 1.76 1.51 2.05 <0.001 Discharge mode hospital Death 31563.01 11184.12 1.55 1.36 1.76 <0.001 Others 27419.41 7040.52 1.35 1.03 1.76 0.031 Journal of Healthcare Engineering 5 E-CHAID algorithms to construct cost groups, and form 2 disease, selection of treatment options, and assessment of and 10 cost groups respectively. As shown in Table 4, taking prognostic status involve imaging studies [27, 28], which may result in high diagnostic costs. )e high proportion of into account the number of groups and the mean absolute error, we finally chose the E-CHAID model for subsequent diagnostic costs is in line with the trend of medical reform, analysis. and early diagnosis is of great significance for the prevention As shown in Table 5, 5204 patients were divided into 10 and treatment of cerebral ischemic diseases. )e use of cost groups, and the grouping nodes included comorbidities imported drugs (the thrombolytic agent alteplase (rt-PA) and complications, discharge mode, age, gender, and ad- [29]) and the nonreimbursement of certain drugs by medical mission route. )e fourth group had the largest number of insurance are possible reasons for the high cost of drugs patients with 1823 patients (35.03%), these patients had [30, 31]. )e proportion of drug costs in the total cost in general comorbidities and complications, and were older China is significantly higher than the international pro- than 65 years. )e seventh group had the smallest number of portion [32, 33], so reducing the drug costs of patients with patients, with only 56 patients (2.09%), who had no cerebral ischemic disease has positive significance for re- ducing the disease burden of such patients [34]. comorbidities and complications, were admitted through outpatient and other routes, and were younger than 18 years Age has important implications for patient classification old. Among the 10 cost groups, the CV of nine groups are all in many countries [35, 36]. )e research results show that less than or equal to 1, and the grouping is reasonable. )e the average age of patients with cerebral ischemia is 65.30 Kruskal–Wallis H test was performed on the cost groups, years old, and the hospitalization cost of patients over 65 and the difference between the groups was statistically years old is 2.63 times that of patients aged 0–17 years old, significant (P< 0.001). which is in line with the natural pathological characteristics of the disease. Meanwhile, the elderly tend to have more comorbidities and complications because of the decline of 3.6. Cost Payment Standard. As shown in Table 5, the physical function and thus spend more medical expenses median medical cost of each group is taken as the standard [36]. cost. )e 75th percentile of cost per group plus 1.5 times the )e homogeneity of patients in the DRG group is a interquartile range was used as the upper limit of medical prerequisite for reasonable reimbursement by medical costs for that group, and cases above the upper limit were insurance. In order to ensure the homogeneity of patients defined as excess amount [24, 25]. )e second group (MCC, in each group, it is necessary to carefully select categorical transferred to another hospital, death and others) had the variables [37]. )rough the cost ratio, it can be found that highest standard cost, about 25,000 RMB yuan; the seventh super length of stay (>30 days), comorbidities and com- group (Non-CC, outpatient and other admission,<18 years plications, and discharge mode have a greater impact on old) had the lowest standard cost, close to 1,500 RMB yuan. hospitalization cost. )e hospitalization cost of patients )e fourth group (CC, ≤65 years old) had the highest excess with super length of stay (>30 days) is significantly higher rate at 10.43%, and the ninth group (Non-CC, outpatient than that of patients with hospitalization days less than or and other admission, 18–65 years old, female) had the lowest equal to 30 days (CR � 4.23), which requires hospitals to excess rate at 0.56%. follow evidence-based guidelines, strengthen clinical pathway management, and standardize similar patients’ treatments and costs, thereby reducing the disease and 4. Discussion economic burden of individual patients. Comorbidities and complications are important influencing factors in hospi- According to the CHS-DRG grouping scheme, the CV of the cerebral ischemic disease ADRG group is 1.18, and the talization costs. It can be seen from the grouping results patient’s personal characteristics and disease characteristics that the standard cost of the second group (MCC, trans- have a certain influence on the hospitalization cost, so it is ferred to another hospital, death and others) is the highest, reasonable to group the cost for the ADRG group. At the about 25,000 RMB yuan, which may be related to the in- creased difficulty of treatment due to the higher severity of same time, it also shows that the applicability of the grouping scheme of CHS-DRG needs to be improved. the patients included in this grouping [18]. Discharge mode is also influenced to some extent by the severity of the )e prevalence of most neurological diseases increased rapidly from 1990 to 2016, with a growth rate of 117% due to disease, for example, patients who are transferred to an- other hospital on advice or who die tend to have more an aging population [26]. As a common neurological dis- ease, cerebral ischemic disease has the characteristics of high severe disease. Computational cost grouping usually includes three mortality and high disability rate [5], which will cause a heavy economic and labor burden to patients, families, and methods: CART, CHAID, and E-CHAID, of which society. )e results of the study showed that from 2018 to E-CHAID is an improved method of CHAID [19]. We found 2020, the average length of hospital stay for patients with that E-CHAID has smaller model error and higher grouping cerebral ischemic disease was 10.70 days, and the average performance than CART, which is consistent with other research results [38]. )e results of CV and Kruskal–Wallis hospitalization cost was 17,206.09 RMB yuan. Among the hospitalization cost, diagnosis cost and drug cost accounted H test show that the cost grouping in this study is reasonable, and the grouping scheme is meaningful, which can provide a for the highest proportion, accounting for 41.18% and 22.38% in 2020, respectively. Diagnosis of cerebral ischemic feasible method for the medical insurance department to 6 Journal of Healthcare Engineering Table 4: Comparison of CART and E-CHAID models (N � 5204). Model Linear correlation Mean absolute error Standard deviation Number of groups CART 0.309 10426.817 19296.419 2 E-CHAID 0.352 10209.751 18985.752 10 Table 5: Grouping results and the predicted medical costs (N � 5204). Group Median in RMB Upper Excess amount Classification description N CV IQR P75 number yuan limit N (%) 1 MCC, discharged home 1046 1.13 16861.55 20478.52 30826.32 61544.09 107 (10.23) MCC, transferred to another hospital, 2 121 1.11 24554.26 32125.81 46340.91 94529.62 11 (9.09) death and others 3 CC, ≤65 years old 1179 0.84 10527.18 6991.76 14977.50 25465.14 123 (10.43) 4 CC, >65 years old 1823 0.92 11928.30 8956.58 17892.89 31327.76 177 (9.71) 5 Non-CC, emergency admission, male 269 0.60 9875.89 5329.05 13127.80 21121.38 20 (7.43) 6 Non-CC, emergency admission, female 169 0.45 9414.20 4938.46 12336.98 19744.67 8 (4.73) Non-CC, outpatient and other admission, 7 56 2.09 1484.30 51.62 1527.48 1604.91 11 (19.64) <18 years old Non-CC, outpatient and other admission, 8 212 0.94 6811.34 8170.03 9705.90 21960.94 11 (5.19) 18–65 years old, male Non-CC, outpatient and other admission, 9 179 0.74 5584.74 6597.86 8098.58 17995.37 1 (0.56) 18–65 years old; female Non-CC, outpatient and other admission, 10 150 0.66 8502.10 4613.23 11123.75 18043.59 15 (10.00) >65 years old improve the disease grouping scheme and pay for diseases Consent [39]. Since the data obtained after each patient’s written consent to treatment were anonymous, patient consent was not 5. Conclusion required. Based on the CHS-DRG grouping scheme, this study analyzed the composition and influencing factors of medical costs for Conflicts of Interest inpatients with cerebral ischemic diseases, and used the )e authors declare that they have no conflicts of interest. E-CHAID algorithm to group costs. )is study further verifies the applicability of the CHS-DRG grouping scheme and helps to optimize the DRG grouping system. )is study also provides Authors’ Contributions a theoretical basis for cost control of cerebral ischemic diseases, which is beneficial to reduce the economic burden of patients, JG contributed to the study design, data analysis, drafting the and provides suggestions for other developing countries to major portion of the manuscript, and article revisions. HCC improve the disease diagnosis grouping system. contributed to the conceptual framework and article revi- sions. YW contributed to data collection and analysis. STH contributed to writing the manuscript. All authors critically 5.1. Limitations of the Study. )e advantage of this study lies reviewed the manuscript and approved the final version. in the rich sample size and the data from representative general hospitals. However, this study also has certain Acknowledgments limitations. Due to the availability of data, the data in this study are only from one hospital, and multicenter studies )e authors would like to thank all the experts involved in need to be added in the future to make the findings more the research of the investigated hospital in Sichuan province generalizable. In addition, the hospitalization cost measured for their willingness to cooperate. in this article is only a part of the direct economic burden [39], so the actual cost of the patient may be higher. 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Journal of Healthcare EngineeringHindawi Publishing Corporation

Published: May 2, 2022

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