Performance Evaluation of Public Non-Profit Hospitals Using a BP Artificial Neural Network: The Case of Hubei Province in China

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1 Int. J. Envron. Res. Publc Health 2013, 10, ; do: /jerph OPEN ACCESS Artcle Internatonal Journal of Envronmental Research and Publc Health ISSN Performance Evaluaton of Publc Non-Proft Hosptals Usng a BP Artfcal Neural Network: The Case of Hube Provnce n Chna Chunhu L 1 and Chuanhua Yu 1,2, * 1 2 School of Publc Health, Wuhan Unversty, 115 Donghu Road, Wuhan , Chna; E-Mal: chl0201@hotmal.com Global Health Insttute, Wuhan Unversty, 115 Donghu Road, Wuhan , Chna * Author to whom correspondence should be addressed; E-Mals: yuchua@163.com or yuchua@whu.edu.cn; Tel./Fax: Receved: 5 June 2013; n revsed form: 1 August 2013 / Accepted: 5 August 2013 / Publshed: 15 August 2013 Abstract: To provde a reference for evaluatng publc non-proft hosptals n the new envronment of medcal reform, we establshed a performance evaluaton system for publc non-proft hosptals. The new nput-output performance model for publc non-proft hosptals s based on four prmary ndexes (nput, process, output and effect) that nclude 11 sub-ndexes and 41 tems. The ndcator weghts were determned usng the analytc herarchy process (AHP) and entropy weght method. The BP neural network was appled to evaluate the performance of 14 level-3 publc non-proft hosptals located n Hube Provnce. The most stable BP neural network was produced by comparng dfferent numbers of neurons n the hdden layer and usng the Leave-one-out Cross Valdaton method. The performance evaluaton system we establshed for publc non-proft hosptals could reflect the basc goal of the new medcal health system reform n Chna. Compared wth PLSR, the result ndcated that the BP neural network could be used effectvely for evaluatng the performance publc non-proft hosptals. Keywords: publc non-proft hosptals; health care reform; ndcator system; performance evaluaton; BP neural network; cross valdaton

2 Int. J. Envron. Res. Publc Health 2013, Introducton Snce 2009, new health reforms have entered the mplementaton stage n Chna. Changes to publc non-proft hosptals are an mportant part of the health reform process. In the past three years, reform has had postve effects, but t has also encountered some problems. The only way to evaluate the effects of publc non-proft hosptal reform and to solve the problems accurately s to establsh a performance evaluaton system. An evaluaton system could help managers make decsons and determne how to mprove hosptal-performance [1 3]. A performance evaluaton system for publc non-proft hosptals wll not only establsh a better system for supervsng performance but also facltate evdence-based health polcymakng and the regulaton of publc nonproft hosptals. In recent years, as publc non-proft hosptal reform has been mplemented, many studes have comprehensvely evaluated the operatons of domestc publc non-proft hosptals. However, the research on the performance of publc non-proft hosptals s currently lmted to the qualty of medcal servces, servce effcency, servce ablty and management ablty, whch are not suffcent to fully evaluate the performance for publc non-proft hosptals. Tang et al. [4] have used the balanced score card to establsh the performance evaluaton system for publc non-proft hosptals. In Cu et al. [5], the evaluaton system ncluded nvestment assets, servce qualty, fnancal management and external evaluaton. Wang et al. [6] have selected several key performance ndcators by key success factor. They gnored the embodment of commonweal n ther studes. In addton, the present evaluaton system lacks a satsfacton ndcator for evaluatng medcal performance, such as patent satsfacton and medcal safety. Thus, the evaluaton results wll not support the sustanable development of publc non-proft hosptals, and they cannot accurately measure the effectveness of publc non-proft hosptal reform. The most mportant feature of an deal performance evaluaton system s the accuracy of the evaluaton results. Thus, t s mportant to choose reasonable ndcators that reflect the purpose of the performance evaluaton and to use the proper methods to evaluate performance. Recently, the lterature on performance evaluatons of medcal servces has dramatcally ncreased n Chna. Key Performance Indcators (KPIs) have been used wdely; the most common ndcators are medcal costs and medcal qualty [7]. Subsequently, many methods (ncludng the Balanced Score Card (BSC)) have been ntroduced nto the performance evaluatons of hosptals [8,9]. Furthermore, fuzzy comprehensve evaluatons, fuzzy gray relatonal analyses and TOPSIS have been appled for evaluatng hosptals or makng predctons. However, there are some dsadvantages to conductng of fuzzy comprehensve evaluatons and fuzzy gray relatonal analyses. In fuzzy comprehensve evaluatons, the weghts of the factors are subjectve and the membershp functon s hard to defne [10,11]. In addton, because of the narrow theoretcal bass of the grey correlaton quanttatve model, the postve correlaton result s contradctory to the actual relatonshp between the factors [12 14]. Consderng the questons above, we chose artfcal neural networks (ANNs). The neural network more closely represents human thnkng. Based on the expert evaluatons of the gven sample and the knowledge ganed from experence, the neural network can be used to compute complex nonlnear relatonshps, just lke human bran [15,16]. Thus, both qualtatve analyss and quanttatve analyss can be used, and the

3 Int. J. Envron. Res. Publc Health 2013, objectvty of the evaluaton results wll be ensured. In recent years, artfcal neural networks have emerged as tools for clncal decson-makng [17], and they may be more successful than tradtonal statstcal models n predctng clncal outcomes [18,19]. ANNs can acqure experental knowledge expressed through nternal connectons n a manner smlar to the way natural neurons functon n the bran, and ths knowledge can be made avalable for use [20]. ANNs, whch demonstrate excellent performance n modelng nonlnear relatonshps that nvolve a multtude of varables, can potentally be useful tools to allevate nonlnear problems. However, applcatons of ANNs n publc health montorng and evaluaton are uncommon. The present evaluaton manly depends on the experence and knowledge of experts, and ths nformaton s dffcult to express n mathematcal formulas. In addton, t s more dffcult to accumulate experences. Artfcal neural networks can capture self-study and self-evaluaton, and they have hgh overall collateral and a hgh capacty for nonlnear relatonshps. These models can use hdden knowledge expresson to ntegrate knowledge nto the nterlnked concepts and the lnked weghts n the network, makng t easer to realze the relatonshp between experence and knowledge [21]. An ANN model wth an nput layer, an output layer and one or more hdden layers could be an adequate unversal approxmate of any nonlnear functon [22,23]. The nput layer comprses the data avalable for the analyss, and the output layer comprses the outcome (e.g., a predcton, prognoss or evaluaton). Based on the current stuaton n Chna and the am of the performance evaluaton, ths study used an nput-output model to establsh proper evaluaton ndcators. The weghted TOPSIS method and BP artfcal neural networks were used to conduct a comprehensve evaluaton. We chose 14 level-3 publc non-proft hosptals located n Hube Provnce for a performance evaluaton. 2. Methods 2.1. Data Collecton Hube Provnce les n central Chna, and t was one of the frst provnces to mplement health reform. Ths evaluaton was conducted at large publc non-proft hosptals (14 hosptals) n Hube Provnce. At the begnnng of 2009, the Hube Health Mnstry (HHM) conducted a program to facltate reforms of publc non-proft hosptals. All of the publc non-proft hosptals were requred to report servce and management data to the Hube Medcal Servce Informaton Qualty Control Center (HMSIQCC) va Hube Publc Hosptal Informaton Software. The Informaton Software nvolves 117 varables, ncludng general nformaton (the number of doctors, beds, etc.), hosptal management (ncludng tranng tmes and nfectous dsease control), medcal servce quantty (ncludng nformaton on all servces performed n the hosptal), medcal servce qualty (ncludng hosptalzaton outcomes, e.g., dagnoss rates, postve rates, etc.), nursng qualty control, laboratory qualty control, economc effcency (ncludng ncome, expendtures, etc.), medcal safety (ncludng medcal neglgence and compensaton nformaton) and patent satsfacton. The standardzed reportng system was developed by HHM, and the doctors and admnstrators from all of the publc non-proft hosptals n Hube were traned to report the data n a standard format. After the data had been uploaded to the software, the experts n HMSIQCC could exclude abnormal data and check the accuracy of the data, whch could then be downloaded for our study. The data used n our study came

4 Int. J. Envron. Res. Publc Health 2013, from the HMSIQCC n the frst half of The ntegrty and the accuracy of the data were checked by HMSIQCC Establshng the Evaluaton System Indcator Selecton Gven our understandng of performance, the nput-output model was used as the framework for evaluatng publc non-proft hosptals. The effcency and the qualty of servces hosptals are key concerns for consumers and managers and are wdely used n performance evaluatons [24 26]. Therefore, ths study began from these startng ponts to establsh the evaluaton ndcators. Frst, we used experts suggestons and the crtera from the lterature to select the evaluaton ndcators. Experts and grassroots workers were nvted to complete a questonnare based on four scentfc prncples (orentaton, comparablty, operablty and representatveness). After several rounds of expert scorng, we reached a fnal selecton of reasonable, hgh senstve, typcal ndcators that could meet the performance evaluaton needs of government managers and publc non-proft hosptals Tendency Treatment If a hgher value of an ndcator ndcates better performance, the normalzed value can be calculated by Equaton (1): y j xj mn[ xj ] max[ x ] mn[ x ] If a smaller value ndcates better performance, the normalzed value can be calculated by Equaton (2): y j If a value n a fxed nterval ndcates better performance, the normalzed value can be calculated by Equaton (3): where x j s the value of ndcator j for the evaluaton subject. mn[ x ] s the mnmum value of ndcator j for all of the evaluaton subjects, and max[ x ] s the maxmum value of ndcator j for all of the evaluaton subjects. If the best standard value was not provded, we used X S (e.g., the daly number of clnc patents for each doctor). j max[ xj ] xj max[ x ] mn[ x ] j xj mn[ xj ], mn[ xj ] xj m1 max[ ] mn[ ] xj xj yj 1, m1 xj m2 max[ xj ] xj, m2 xj max[ xj ] max[ x ] mn[ ] j xj j j j j (1) (2) (3) as the best value

5 Int. J. Envron. Res. Publc Health 2013, Weght Defnton Frst, accordng to the mportance of each ndcator, we nvted the experts to score the ndcators, and we then determned level l and 2 ndex weghts usng an analytc herarchy process (AHP). The AHP s a decson-makng tool developed by Satty to handle complex, unstructured and mult-factor problems [27]. The AHP nvolves rankng a set of ndces wth respect to an overall goal, whch s broken down nto a set of crtera and ndces [28]. The weghts of ndcators were calculated by conductng parwse comparsons between the relatve mportance of the lower evaluaton ndcator and the relatve mportance of the upper ndcator. The entropy weght method was used to determne the weghts of the level 3 ndcators. The weght of ndcator j can be calculated by Equaton (4): where entropy h j as n 1 h p ln p, j 1,2,, m. In addton, pj ln p j s defned as 0 f j j j ln n 1 n pj 0. pj yj / yj, 1,2,, n; j 1,2,, m. 1 The comprehensve ndcator weghts were calculated by determnng the product of the weghts of the level 1, 2 and 3 evaluaton ndcators Artfcal Neural Networks j m j1 1 h (1 h ) Artfcal neural networks can be defned as a parallel dstrbuted processng method wth a large number of processng elements and neurons connected to one another wth dfferent connecton strengths. The strength of a connecton between neurons s the weght. In the begnnng of the neural development process, these weghts are ntally random. They are adjusted n a model calbraton phase (called tranng ) to mnmze the MSE between the calculated outputs and the correspondng target output values for the partcular tranng data set. The testng subset s used to check the performance of the developed network. Varous types of ANNs are used for dfferent applcatons [29]. BP (back propagaton) artfcal neural networks (BP-ANN) are typcally used for amendng errors. BP-ANNs set each quantfable ndcator as the network s nput (X) and the result as the output (Y). After tranng enough samples and repeatedly amendng the connecton weght values (W, V) and the threshold values among the neurons, the fnal weght values and threshold values were obtaned to ndcate correct knowledge (Fgure 1). In the three-layer BP-ANN n our study, the ndcators for the performance evaluaton system for publc non-proft hosptals were used as nput varables, and each publc non-proft hosptal s performance score as the output. Accordng to the evaluaton ndex system, there were 41 thrd-level evaluaton ndcators. The outcome was the performance evaluaton; thus, the output layer ncluded only one varable. Therefore, the numbers of neurons n the nput and output layers were 41 and 1, respectvely. The number of hdden layer neurons can be calculated by Equaton (5). The number of neurons n the hdden layer ranged from j j (4)

6 Int. J. Envron. Res. Publc Health 2013, Fgure 1. Three-layer BP artfcal neural network framework. nh n m a (5) where n s the number of neurons n the nput layer, m s the number of neurons n the output layer, a s the constant, and 1 < a < 10. When BP neural networks are used for publc hosptal performance evaluaton, the data should be normalzed before they are traned. In ths paper, the MATLAB software normalzaton functon mapmnmax was adopted, and the normalzaton nterval was 0 1. The mean squared error (MSE) can be used to determne how well the network output fts the desred output. MSE s defned as follows: n 1 MSE ( y yˆ ) n 1 where y s the observed value, y ˆ s the network output value, and smaller values ndcate better performance. To avod the effects of usng fewer samples for tranng and to nfluence the generalzaton ablty of network, the Leave-one-out Cross Valdaton (LOOCV) method was used to tran and test the BP neural network n our study. In LOOCV, f the raw data set has N samples, the model s traned and tested N tmes. Each tme, one sample s selected as the valdaton sample, and the remanng samples are used as tranng samples. The cross-valdaton estmate of the overall accuracy s smply calculated as the average of the N ndvdual accuracy measures. Wth ths method, we attempted to derve relable results and ncrease the generalzaton ablty of network [30,31]. The performance of the model can be evaluated usng certan statstcal ndcators, ncludng the coeffcent of determnaton (R 2 ), the root mean squared error (RMSE) and the mean absolute percentage error (MAPE). These ndcators are mathematcally defned as follows: 2 (6)

7 Int. J. Envron. Res. Publc Health 2013, R 2 n n n n yˆ ˆ y y y n n 2 n n n yˆ ˆ y n y y n 1 RMSE ( y yˆ ) n (7) (8) 1 MAPE n where y s the observed value and y ˆ s the network output value. n 1 y ˆ y y 100% Accordng to Table 1, the most sutable neural network was model wth 10 neurons n the hdden layer. Table 1. Statstcal ndcators for varous numbers of neurons n the hdden layer. Neuron number R 2 RMSE MAPE Thus, a BP neural network was obtaned. The learnng speed, the maxmum numbers of epochs, the target error goal MSE and the mnmum performance gradent were set at 0.05, 3,000, 10 5, and 10 5, respectvely. The Levenberg-Marquardt (LM) algorthm was chosen to avod the tme-consumng one-dmenson searchng [21]. Tranng stopped when any of these condtons occurred. All of the calculatons were performed usng MATLAB software (MathWorks, Inc., Natck, MA, USA). To explan the good performance of BP neural network, we compared BP neural network wth partal least-squares regresson (PLSR). PLSR s a new multvarate statstcal analyss method. The advantages of PLSR are exhbted n dealng wth the problems: low sample, more ndependent varables and mult-correlaton [32]. The coeffcent of determnaton (R 2 ) was used to evaluate the performance of the model [33]. (9)

8 Int. J. Envron. Res. Publc Health 2013, Table 2. Publc non-proft hosptals performance evaluaton system. Level 1 Weght a Level 2 Weght a Level 3,reference value Weght b Comprehensve weght Index attrbute Human Resources 0.40 Percentage of health techncans (%), 75% Doctors-nurses rato, 1: Input 0.20 Beds-nurses rato, 1: Equpment and facltes 0.60 Percentage of fxed assets n total assets (%) Average number of open beds Nursng Management 0.30 The percentage of approprate wrtten nursng documents (%) Percentage of passng student n nurses tranng (%) Percentage of passng student n doctors tranng (%) Percentage of class A medcal records n all medcal records Physcan management 0.50 (%), 95% The percentage of approprate prescrptons (%) Process 0.15 Percentage of antbacteral prescrpton (%), 30 45% Rate of CT nspecton (%), 70% Rate of MRI nspecton (%), 70% Rate of X-ray nspecton (%), 70% Medcal technology 0.20 Clncal chemstry laboratory scorng Management Hematology laboratory scorng Immunology laboratory scorng bacterologcal laboratory scorng Therapeutc response rate (%) Output 0.45 Qualty 0.40 Proporton of npatents dagnosed wthn 3 days (%) Mortalty (%) Proporton of nurses wth basc qualfcaton (%), 90%

9 Int. J. Envron. Res. Publc Health 2013, Table 2. Cont. Level 1 Weght a Level 2 Weght a Level 3,reference value Weght b Comprehensve weght Index attrbute Success rate of rescue (%) Incdence of nosocomal nfecton (%), 10% Qualty 0.40 Percentage of agreement between admsson and dscharge dagnoses (%), 95% Medcal nsttuton bed utlzaton rato (%), 90% Medcal nsttuton bed turnover rato, 19 tmes per year Effcency 0.25 Daly number of clnc patents for each doctor Daly number of hosptalzaton bed-days for each doctor Output 0.45 Average number of days n hosptal, 15 days Average outpatent expendtures (Yuan) Cost control 0.15 Average hosptalzaton expendtures (Yuan) Average expendtures per bed per day (Yuan) Percentage of medcne ncome of the total ncome, 45% The asset-lablty rato (%) Fnancal balances 0.20 Percentage of expendtures n servce revenue (Yuan) Income generated by each staff member (Yuan) Medcal ncome per 100 Yuan of fxed assets (Yuan) Satsfacton 0.35 Patent satsfacton (%) Effect 0.20 Compensaton as a percentage of total ncome (%) Medcal Safety 0.65 Medcal accdent rate per 10,000 npatents a Analytc herarchy process (AHP) was used to determne the weghts of level 1and 2 ndcators; b The entropy weght method was used to determne the weghts of level 3 ndcators; The reference values were from Hosptal management evaluaton gudelnes; +: Hgher ndcator values ndcate better performance, -: Smaller ndcator values ndcate better performance, 0: values n one nterval ndcate better performance.

10 Int. J. Envron. Res. Publc Health 2013, Results The nput-output model was used as the framework for establshng the performance evaluaton system for publc non-proft hosptals. Experts suggeston and crtera from the lterature were used to select ndcators. The weghts of the ndcators were determned by AHP and the entropy weght method (as shown n Table 2). Based on the publc non-proft hosptals performance evaluaton system, the TOPSIS evaluaton method was adopted to calculate the relatve scores for the evaluaton standards, as shown n Table 3. Table 3. The weghted TOPSIS results for 14 level 3 hosptals n the frst half of Hosptal code The 1st half of 2012 C Rank H H H H H H H H H H H H H H C s relatve approach degree n the TOPSIS method; The hgher the value of C, the better the rank. The 14 publc non-proft hosptals we chose are almost the same level (level 3, class A) and scale. The value of C (relatve approach degree) ndcated the performance of the hosptal. The hgher the value of C, the better the performance was. Thus, C was the output varable for tranng and testng BP neural network. Because the sample was small (14 hosptals), LOOCV was used to tran and test the BP neural network to derve relable results and ncrease the generaton ablty. The smulaton of the tranng error s shown n Fgure 2. The MSE value decreased as the number of teratve steps ncreased. Begnnng wth the 441th teratve step, the MSE was 9.95e 6, whch was smaller than target error goal MSE (10 5 ). Consequently, the tranng was fnshed. The MATLAB software functon cputme was adopted to record the computatonal cost of the model tranng, and the cpu tme used to run the program s s.

11 Tranng-Blue Goal-Black Int. J. Envron. Res. Publc Health 2013, Fgure 2. Tranng convergence curve Performance s e-006, Goal s 1e Epochs The statstcal ndcators of network performance are shown n Table 4. The network was traned by usng LOOCV, and after 14 experments, the average RMSE was The coeffcent of determnaton (R 2 ) was The closer that the value of R 2 s to 1, the better the network performance s [34]. Table 4. The statstcal ndcators of net performance. Model Publc Hosptal Performance Structure RMSE R The error analyses of partal least-squares regresson are shown n Table 5. The coeffcent of determnaton (R 2 ) of PLSR model was Obvously, the value of R 2 s smaller than BP neural network model. Thus, BP neural network provded the better results n performance evaluaton for publc non-proft hosptal. Table 5. The error analyses of partal least-squares regresson. Hosptal code Observed value Predcton value Absolute error Relatve error (%) H H H

12 Int. J. Envron. Res. Publc Health 2013, Table 5. Cont. Hosptal code Observed value Predcton value Absolute error Relatve error (%) H H H H H H H H H H H Conclusons The performance evaluaton system for publc non-proft hosptals covers almost all aspects of qualty for publc non-proft hosptals. The system could reflect the gudance of publc non-proft hosptals reform. The ndcator weghts were scentfc and reasonable, based on both the objectve and the subjectve ponts of vew. Developng the ndcator weghts usng both the subjectve (experts scorng and AHP) and objectve (entropy method) methods ensured that both experence and objectvty were consdered. The ntegrty and the accuracy of the data were also mportant and, may have nfluenced the results. A small mstake could potentally lead to naccurate estmates of the hosptal's performance. HMSIQCC has a specal qualty control measure for the ntegrty and accuracy of the management data for publc non-proft hosptals. All of the data used n ths study were verfed. A relatvely stable BP neural network model was obtaned usng the Leave-one-out Cross-Valdaton method and adjustng the network parameters. Accordng to the results, the structure of the BP neural network was , R 2 was and RMSE was Compared wth PLSR model, the value of R 2 (R 2 = ) for BP neural network s larger than PLSR model (R 2 = ). Thus, the proposed model could be used for publc non-proft hosptal performance evaluatons. The new health reform polces n our country state that three key problems should be consdered: accessblty, equty and prce. Accessblty refers to basc medcal nsttutons. Equty refers to the efforts to narrow the gap between urban and rural areas and between regons, and the prce s a consderaton so that people can afford to consult a doctor or purchase medcnes when they get sck. Thus, a performance evaluaton system for publc non-proft hosptals should have certan characterstcs (.e., the weghts of the socal beneft ndex n the evaluaton system should be ncreased). Consequently, the commonweal goal of publc non-proft hosptals can be reflected fully. One of the purposes of ths evaluaton s to help the government understand the performance of publc non-proft hosptals and to ad n decsons related to the development and reform of publc non-proft hosptals; another goal s to help hosptals mprove ther performance. Whether or not performance evaluaton mproves performance depends on whether and how the evaluaton results are appled [35]. Frst, the evaluaton results should be used approprately. Each publc non-proft hosptal

13 Int. J. Envron. Res. Publc Health 2013, wll fnd the problems or weak lnks that may nfluence the evaluaton results. Then, they may resolve or mprove them, but they may accomplsh these results falsfyng data or acceptng only low-rsk patents. Second, the evaluaton system and methodology should be same, so that the performance evaluaton results from dfferent hosptals wll be comparable. Thrd, the performance of hosptals at dfferent levels and n dfferent categores should be evaluated ndvdually. Acknowledgments Ths work was funded by the Mnstry of Health of the People s Republc of Chna, Natonal Natural Scence Foundaton of Chna (No ). The authors would lke to thank Jngchen Hu at the Hube Medcal Servce Informaton Qualty Control Center (HMSIQCC) for the data preparaton. Conflct of Interest The authors declare no conflcts of nterest. References 1. L, L.X.; Benton, W.C. Performance measurement crtera n health care organzatons: Revew and future research drectons. Eur. J. Oper. Res. 1996, 93, Elzabeth, A.M.; Steven, M.A. Developng a clncal performance measure. Am. J. Prev. Med. 1998, 14, Mara, G.; Russell, M.; Peter, C.S. Assessng the performance of NHS hosptal trusts: The role of hard and soft nformaton. Health Polcy 1999, 48, Tang, Y.H.; Xue, X.; Chen, J.C.; Jan, D.S.; Pan, L.J.; Qu, F.; Cao, M.Q.; Wang, P.; Jang, X.M.; Wang, J.J.; et al. The performance apprasal ndcator system of publc general hosptals based on balanced scorecard. Chn. Hosp. Manag. 2008, 28, Cu, S.; Yang, J.L.; L, Z.S.; L, C.R. The study on performance evaluaton of publc hosptal: The evaluaton methods and ndcator system. Chn. Health Econ. 2008, 27, Wang, H.; Ln, Q.Y.; Xe, G. Dscusson on establshment of key performance evaluaton system for publc hosptal. Mod. Hosp. Manag. 2009, 5, Wang, X.J. Theory and practce of KPI utlty for hosptal performance evaluaton. Chn. J. Hosp. Adm. 2006, 6, Zhao, J.S.; L, L.; Zhu, Q. Study of usng BSC for hosptal management n Chna. Chn. Hosp. Manag. 2007, 27, Ren, R. Theory and practce of hosptal performance evaluaton. Chn. Hosp. Manag. 2005, 25, Wang, Y.L.; Zheng, J.G.; Wang, X. The summary of the domestc study on assessng knowledge management performance. J. Intell. 2010, 29, Lang, J.; Jang, W.; L, X.H. An mprovement on fuzzy comprehensve evaluaton method and ts use n urban traffc plannng. J. Traffc Transp. Eng. 2002, 2, Cao, M.X. Research on Grey Incdence Analyss Model and ts Applcaton. Master Thess, Nanjng Unversty of Aeronautcs and Astronautcs, Nanjng, Chna, January 2007.

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