Hossein Ahmadi et al. - Ranking the Meso Level Critical Factors of EMR Adoption Using Fuzzy Topsis Method

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1 Hossen Ahmad et al. - Rankng the Meso Level Crtcal Factors of EMR Adopton Usng Fuzzy Topss Method Orgnal Paper Rankng the Meso Level Crtcal Factors of Electronc Medcal Records Adopton Usng Fuzzy Topss Method HOSSEIN AHMADI, MEHRBAKHSH NILASHI, OTHMAN IBRAHIM, KOMEIL RAISIAN Faculty of Computng, Unverst Technolog Malaysa, Johor, Malaysa ABSTRACT. As Electronc Medcal Records (EMRs) have a great possblty for rsng physcan s performance n ther daly work whch mproves qualty, safety and effcency n healthcare, they are mplemented throughout the world (Boonstra and Broekhus, 00). In physcan practces the rate of EMRs adopton has been slow and restrcted (around 5%) accordng to Endsley, Baker, Kershner, and Curtn (005) n spte of the cost savngs through lower admnstratve costs and medcal errors related wth EMRs systems. The core objectve of ths research s to dentfy, categorze, and analyse meso-level factors ntroduced by Lau et al, 0, perceved by physcans to the adopton of EMRs n order to gve more knowledge n prmary care settng. Fndng was extracted through questonnare whch dstrbuted to 350 physcans n prmary cares n Malaysa to assess ther percepton towards EMRs adopton. The fndngs showed that Physcans had postve percepton towards some features related to technology adopton success and emphaszed EMRs had helpful mpact n ther offce. The fuzzy TOPSIS physcan EMRs adopton model n meso-level developed and ts factors and sub-factors dscussed n ths study whch provde makng sense of EMRs adopton. The related factors based on meso-level perspectve prortzed and ranked by usng the fuzzy TOPSIS. The purpose of rankng usng these approaches s to nspect whch factors are more mperatve n EMRs adopton among prmary care physcans. The result of performng fuzzy TOPSIS s as a novelty method to dentfy the crtcal factors whch assst healthcare organzatons to nspre ther users n acceptng of new technology. KEYWORDS: EMRs, Adopton, Fuzzy TOPSIS, Meso-Level Adopton Factors, CA Introducton As Electronc Medcal Records (EMRs) have a great possblty for rsng physcan s performance n ther daly work whch mproves qualty, safety and effcency n healthcare, they are mplemented throughout the world []. In physcan practces the rate of EMRs adopton has been slow and restrcted (around 5%) accordng to Endsley, Baker, Kershner, and Curtn [] n spte of the cost savngs through lower admnstratve costs and medcal errors related wth EMRs systems [3]. These days, there s a vast nvestment of Informaton Technology (IT) by healthcare provders that looked at development and mplementaton of clncal nformaton systems for nstance Electronc Medcal Records (EMRs) [4]. IT s utlzed by physcans offces for bllng purposes, but unfortunately the number ncorporatng IT nto ther practces for clncal purposes such as EMRs are low [4]. It s estmated that the healthcare ndustry s at least ten years behnd other ndustres n terms of IT nvestment [5]. Despte ITs ncreasng ubquty, decreasng costs, and the potental for benefts n the clncal decson-makng process, the low rate of adopton occurs. The reason s, due to the dstnctve structure of the healthcare ndustry. Healthcare organzatons are dssmlar from organzatons operatng wthn other busness contexts, specally, about ndvdual autonomy and operatonal ndependence [6]. EMRs adopton has been attracted by lttle nterest n the management nformaton systems (MIS) Lterature [7]. In ths research, An EMR explaned as computerzed health nformaton system where provder s record detaled encounter nformaton such as patent demographcs, encounter summares, medcal hstory, allerges, ntolerances, and lab test hstores. Some may support order entry, results management and decson support and some may also contan features or be ntegrated wth software that can schedule appontments, perform bllng tasks, and generate reports. Prmary care s becomng a core part of healthcare communty. The term general practce was consdered to refer to the same care settng as the term prmary care. Prmary care s defned as the frst pont of contact a person has wth the health system and usually refers to famly practce. Ths s the pont where people receve care for most of ther everyday health needs [8,9]. In ths research, the meso-level factors has been nvestgated that have more effect on EMRs adopton whch has been developed by [0] n hs study revew of Clncal Adopton (CA) framework accordng to three dmensons. 8 DOI: 0.865/CHSJ.4.0.

2 Current Health Scences Journal Thus, the purpose of ths study s to develop and valdate the Meso-level physcan EMRs adopton model n the context of prmary care unts. In addton, ths study provdes contextual analyses of the factors contrbutng to the EMRs adopton. The remander of ths paper s structured as follows. Secton ntroduces the proposed research model. In Secton 3, the research methodology has been descrbed step by step. Secton 4 and 5 allocated to the background mathematcal of the data collecton and fuzzy TOPSIS, respectvely. Fnally, we present the results of fuzzy TOPSIS and conclusons n sectons 6 and 7, respectvely. Proposed Research Model The adopton model of physcan n prmary care provdes a conceptual model to dentfy the factors that have more nfluence on adopton of EMRs. It extends Clncal Adopton framework by Lau et al [0] n hs study revew whch was based on three dmensons. In hs revew, DeLone and McLean [] nformaton system Vol. 4, No., 06 January-March success model was followed. Lau s CA framework comprsed of mcro, meso and macro-level dmensons. Each dmenson has ts own factors and sub-factors whch could nfluence physcans n EMRs adopton. In ths research t has been concentrated on meso-level factors. At the meso-level, the adopton framework of prmary care physcan explans clncal nformaton system success nclude EMRs system. In ths study, EMRs adopton has been examned n practce of physcan n prmary care settng through the lens of clncal adopton framework. EMRs adopton defned based on evaluaton measures, related to the factors that rendered to ths mpact. Hence, ths study concentrated on meso-level factors that nfluence on EMR adopton. At the end the proposed model of fuzzy topss physcan adopton model n meso-level developed and shown n Fg.. At meso-level, there are three man factors ncludng people, organzaton and mplementaton. The followng has descrbed each of the man factors n detal and ts sub factors respectvely. Personal Chacterstcs computerexperence People PersonalExpectat ons Tme nvestment task-shft RolesResponsblt es champon conflct Info-nfrastructure Partcpaton EMR Adopton Organzaton Culture Structure processes postve culture process change Value Return on value Practce sze substtuton effect Implementaton Project Resource/Tranng Plannng Hybrd system HIS-practce ft Screen/room Workflow Fg.. Fuzzy TOPSIS Physcan EMRs Adopton Model n Meso-Level People are the ntegral part of the system success that may adopt or refuse the new technology based on ther characterstcs, expectatons and responsbltes. People factors covers personal characterstcs and expectatons lke pror EMRs experence of the users [], DOI: 0.865/CHSJ

3 Hossen Ahmad et al. - Rankng the Meso Level Crtcal Factors of EMR Adopton Usng Fuzzy Topss Method and ther personal tme nvestment n exchange for the benefts expected from the system [8,9]. Roles/responsbltes ncluded the need for champons and staff partcpaton [3], and shft n tasks (documentaton by staff vs. physcans) [5,6]. That could lead to role ambguty and conflct [4]. Organzaton factors covered structure/processes and culture that emphaszed EMRs adopton/use [4], EMRs-practce ft (hybrd EMRs/paper systems), and EMRssupported offce and workflow desgn [4] such as the placement of computer screens n consult rooms. Return-on value concentrated on verfed value at the practce level such as replacement effect from gudelne drven test orders and prescrbng, and tangble cost-effcency gan wth larger practce sze and patent volume [5]. Implementaton factors covered the area that the ntroducton of EMRs nto the practce was desgned and conducted as a prorty project wth devoted tme and resources [6]. The servce support provded durng mplementaton was essental [7], snce they nfluenced the dsruptons that physcans and offce staff had to defeat whle learnng to use the EMRs and redesgn ther work routnes. Indvduals-Groups Table. Meso-Level factors nfluenced EMRs adopton People People sub-factors References Personal characterstcs Computer experence Personal expectatons Tme nvestment Roles-responsbltes Task shft Champon Conflct Partcpaton Organzaton Organzaton Sub-factors References Strategy Culture Structure-processes Van Wjk, M. A., et al (00) [] Keshavjee, K., et al. (00) [8], Ludwck and Doucette (009) [9], Robnson, A. (003) [9] Keshavjee, K., et al. (00) [8], Tamblyn,R., et al. (003) [0], Mller, R. H., et al (005) [], Crosson, J. C., et al. (005) [4], Bassa, A., et al. (005) [3]. Crosson, J. C., et al. (005) [4], Baron, R. J. (007) [], Info-nfrastructure Ludwck and Doucette (009) [8] Return on value Value Mtchell, E., et al. (003) [5]. Practce Substtuton effect Implementaton Implementaton Sub-factors References Stage Project Resource/Tranng Plannng HIS-Practce Ft Hybrd system Screen/room Workflow Samouts, G., et al. (008) [6], Randeree, E. (007) [7], Wager, K. A., et al. (000) [3], Cauldwell, M. R., et al. (007) [4], Crosson, J. C., et al. (007) [5]. Research Methodology EMRs n ths study have been focused as a new technology n prmary care whch has been tred to descrbe the factors whch have the more prorty n ts adopton. A quanttatve, surveybased research study was carred out and analyzed to descrbng the factors that have an mpact on EMRs adopton. Eght Malaysa prmary care clncs n dfferent specalty have been chosen to conduct ths research. Survey was emaled n electronc webste to 350 physcans who work n offces n the context of prmary care. 300 physcans fulflled the questonnare n ths study and the rest dd not complete. The survey contans number of questons that were desgn to capture nformaton about the constructs n the research model. The questons that measured were people, organzaton and mplementaton besdes ther sub-factors. Fuzzy TOPSIS was used to obtan the ranks of parameters n meso-level EMRs adopton. Fg. contans a descrpton of each step n ths study. 84 DOI: 0.865/CHSJ.4.0.

4 Current Health Scences Journal Vol. 4, No., 06 January-March Fg.. Research Methodology Data Collecton In ths study, the prmary data were collected through questonnare whch delvered to the physcans through ther emal. One of the ways n whch questonnare can be admnstered s the emaled questonnare; one of the most general approach to collectng nformaton s to send the questonnare to prospectve respondents by emal. Obvously ths approach presupposes that should have access to ther addresses. Kumar [6], n hs study justfed usng of the questonnare through emal based on the crtera of the geographcal dstrbuton of the study populaton n whch he sad f potental respondents are scattered over a wde geographcal area, you have no choce but to use a questonnare. Accrodngly, n ths research, the questonnare by emal has been used by researcher as an effcent and effectve nstrument to collect data from the respondents. For ths study, a number of respondents, were approxmately 350 (n=350) physcans. Seventy eght (85.7%) of the respondents provded answers to all the questons n the nstrument. The frst secton comprses of nformaton on respondent demographc profle, twelve sectons on the ndependent varable namely, ndvdual groups, personal characterstcs, personal expectatons, roles responsbltes, strategy, culture, structure-process, nfo nfrastructure, return on value, stage, project, HIS practce ft. Fve optons (ndex) ranked by -5 for the rased questons as: = very low mportant =low mportant 3=moderately mportant 4= hgh mportant 5= very hgh mportant. Table provdes the respondents demographc profle. About sxty fve percent of physcans were male and thrty four percent were female, generalst physcans n one to over ten years of experence wth EMRs technology. Table. The respondents demographc profle Aspects Category Respondents (n) Respondents (%) Gender Male % Female % Age Years of electronc medcal records experence % 30% 55% % % Over % Medcal specalzaton Generalst % Specalst 40.66% DOI: 0.865/CHSJ

5 Hossen Ahmad et al. - Rankng the Meso Level Crtcal Factors of EMR Adopton Usng Fuzzy Topss Method Background of Fuzzy Topss TOPSIS, one of the known classcal MCDM methods, was frst developed by Hwang and Yoon [7] that can be used wth both normal numbers and fuzzy numbers. In addton, TOPSIS s attractve n that lmted subjectve nput s needed from decson makers. The only subjectve nput needed s weghts. Snce the preferred ratngs usually refer to the subjectve uncertanty, t s natural to extend TOPSIS to consder the stuaton of fuzzy numbers. Fuzzy TOPSIS can be ntutvely extended by usng the fuzzy arthmetc operatons as follows [8]. Gven a set of alternatves, A= { A =,, n}, and a set of crtera, C = { Cj j =,, m}, where X = { x j =,, n; j =, m} denotes the set of fuzzy ratngs and W = { w,, } j j = m s the set of fuzzy weghts. The frst step of TOPSIS s to calculate normalzed ratngs by x j r j ( x ) =, =,, n; j =,, m n x = j and then to calculate the weghted normalzed ratngs by v ( x) = wr ( x ), =,, n; j=,, m. () j j j () Next the postve deal pont (PIS) and the negatve deal pont (NIS) are derved as PIS = A = { v ( x), v ( x),, v ( x),, v ( x)} = {( max v ( x) j J ), ( mn v ( x ) j J ) =,, n} j j j m (3) PIS = A = { v ( x), v ( x),, v j ( x),, v m( x )} = {( mn v ( x) j J ), ( max v ( x) j J ) =,, n} j j. Smlar to the crsp stuaton, the followng step s to calculate the separaton from the PIS and the NIS between the alternatves. The separaton values can also be measured usng the Eucldean dstance gven as: (4) + m = [ j ( ) + j ( )], =,, j= S v x v x n. (5) And m = [ j ( ) j ( )], =,, j= S v x v x n. (6) Where + max{ v ( x)} v ( x) = mn{ v ( x)} v ( x) = 0 j j j j (7). Then, the defuzzfed separaton values should be derved usng one of defuzzfed methods, such as CoA to calculate the smlartes to the PIS. Next, the smlartes to the PIS s gven as. DS ( ) C = +, =,, n [ DS ( ) + DS ( )] where C [0,] =,, n. (8) 86 DOI: 0.865/CHSJ.4.0.

6 Current Health Scences Journal Vol. 4, No., 06 January-March Fnally, the preferred orders are ranked accordng to n descendng order to choose the best alternatves. Fuzzy-TOPSIS method s another type of fuzzfcaton for the TOPSIS method n fuzzy envronment that s defned and nvestgated by credblty measure. In ths method, trapezod -fuzzy numbers are used for rankng all sub-crtera of webste qualty. Therefore, usng fuzzy trapezod numbers enabled us to change normal TOPSIS nto fuzzy TOPSIS whch s more precsely as the result shows n the next paragraph. One of the characterstc of fuzzy numbers s fuzzy sets wth specal consderaton for easy ~ calculatons. Trapezod Fuzzy Numbers Let A = ( a, b, c, d), a<b<c<d, be a fuzzy set on R = (, ). It s called a trapezod fuzzy number, f ts membershp functon s x a, f a x b b a, f b x c µ ~ ( x) = A (9) d x, f c x d d c 0, otherwse. Fg.3 shows the shape of a fuzzy trapezod number: C Fg.3. Fuzzy trapezod number All process of fuzzy TOPSIS wll be calculated upon three of trapezod numbers that average numbers of experts are shown n Table 3 and Fg.4: Table 3. Fuzzy trapezod numbers for fuzzy TOPSIS method Lngustc Varable Range of Fuzzy trapezod number Non Important [0.6, 0.8,.6,.8] Low Important [.4,.6,.5,.7] Moderate [.3,.5, 3.8, 4] Important [3.6, 3.8, 4.6, 4.8] Very Important [4.4, 4.6, 5., 5.4] DOI: 0.865/CHSJ

7 Hossen Ahmad et al. - Rankng the Meso Level Crtcal Factors of EMR Adopton Usng Fuzzy Topss Method Fg.4. Fuzzy trapezod numbers for fuzzy TOPSIS method Rankng Parameters Usng Fuzzy Topss For applyng fuzzy TOPSIS method after gatherng data from the respondents, Table 4 was organzed. In Table 4, fuzzy trapezod numbers have been multpled to base the fundamental of the fuzzy TOPSIS. Table 4. Applyng fuzzy number on questonnare data R j Selected Opton Fuzzy Number Selected Opton Fuzzy Number Selected Opton Fuzzy Number3 Selected Opton Fuzzy Number4 Selected Opton Fuzzy Number5 Q.No 0.6, 0.8,.6,.8.4,.6,.5,.7 3.3,.5, 3.8, , 3.8, 4.6, , 4.6, 5., A calculaton between two fuzzy trapezod numbers can be defned as: D = ( a, b, c, d D = ( a, b, c ), d ) D + D = ( a + a, b + b, c + c, d + d ). (0) Therefore, Table 5 was calculated from Table 4 by summng of four trapezod numbers. 88 DOI: 0.865/CHSJ.4.0.

8 Current Health Scences Journal Vol. 4, No., 06 January-March Table 5. The sum of four trapezod numbers Sum of Trapezod Numbers In the next step, each cell of Table 5 wll be dvded by 300 n order to make the 6 fuzzy numbers for startng fuzzy TOPSIS. Table 6. Sxteen fuzzy non trapezod numbers ( R j ) Q.No a L L b c d R R Sum SQRT /SQRT Therefore trapezod number wll be (d,c,b,a) =( , ,0.0655,0.0634). Afterward, each cell n Table 6 should be multpled by ( , ,0.0655,0.0634) that s trapezod. Table 7 demonstrates result of ths multplcaton. DOI: 0.865/CHSJ

9 Hossen Ahmad et al. - Rankng the Meso Level Crtcal Factors of EMR Adopton Usng Fuzzy Topss Method Table 7. The 4 fuzzy trapezod numbers for fuzzy TOPSIS processes Q.No n j a b c d Area In ths step, for fndng mnmum and maxmum fuzzy trapezod number for A+ and A-, was tred to calculate the area under each of the curve. Each curve forms a trapezod shape. Table 8 shows mnmum and maxmum trapezod numbers wth ther membershp functons. Therefore, the maxmum and mnmum vectors are for queston number 6 and 5, respectvely. Table 8. Maxmum and mnmum of fuzzy trapezod numbers for A+ and A- Max V No.6 A Mn V No.5 A In Table 9 the square of dstance between the fuzzy number and the Ideal number, ( v ), has been + calculated. Table 9. The square of dstance between maxmum pont and each pont (v j v j+ ) (v j v j+ ) (v j v j+ ) (v j v j+ ) v j j 90 DOI: 0.865/CHSJ.4.0.

10 Current Health Scences Journal Vol. 4, No., 06 January-March In the smlar way, the square of dstance between mnmum pont and each pont was calculated that has been shown n Table 0. Table 0. The square of dstance between mnmum pont and each pont (v j v j- ) (v j v j- ) (v j v j- ) (v j v j- ) Therefore, d+ and d- can be as n Table : Table. The square dstance between mnmum and maxmum for d+ and d- d+ d As can be seen n Table, frst rank goes to the queston number wth the area under the curve.39, the second rank s for queston number 5 wth the.3 area under the curve and so on. DOI: 0.865/CHSJ

11 Hossen Ahmad et al. - Rankng the Meso Level Crtcal Factors of EMR Adopton Usng Fuzzy Topss Method Table. Ranked parameters by fuzzy TOPSIS Parameters rankng by Fuzzy TOPSIS Area Queston No Concluson The present study provdes contextual analyses of the meso-level factors contrbutng to the EMRs adopton. In addton to add knowledge concernng technology adopton wthn a physcan practces through prmary care. In ths study, meso-level factors have been focused whch nfluenced on EMRs adopton based on Lau et al.[9]. The fndngs of the present study were used to address the adopton of EMRs technology wthn the physcan communty n prmary care settng. The fndngs ndcated that Physcans had postve percepton towards some features related to technology adopton success and emphaszed EMRs had postve mpact n ther offce. The fuzzy TOPSIS physcan EMRs adopton model n mesolevel has been developed and ts factors and sub-factors dscussed n ths study whch provde makng sense of EMRs adopton. References. Boonstra, A. and M. Broekhus (00). "Barrers to the acceptance of electronc medcal records by physcans from systematc revew to taxonomy and nterventons." BMC health servces research 0(): 3.. Endsley, S., et al. (006). "What famly physcans need to know about pay for performance." Famly practce management 3(7): Lau, F., et al. (006). "A proposed benefts evaluaton framework for health nformaton systems n Canada." Healthcare quarterly (Toronto, Ont.) 0(): -6, Burt, C. W. and E. Hng (005). "Use of computerzed clncal support systems n medcal settngs: Unted States, " Advance data(353): Sknner, R. I. (003). "The value of nformaton technology n healthcare." Fronters of health servces management 9(3): Hu, P. J., et al. (999). "Examnng the technology acceptance model usng physcan acceptance of telemedcne technology." Journal of management nformaton systems 6(): Hennngton, A. H. and B. D. Janz (007). "Informaton Systems and healthcare XVI: physcan adopton of electronc medcal records: applyng the UTAUT model n a healthcare context." Communcatons of the Assocaton for Informaton Systems 9(5): Ludwck, D. A. and J. Doucette (009). "Adoptng electronc medcal records n prmary care: lessons learned from health nformaton systems mplementaton experence n seven countres." Internatonal Journal of Medcal Informatcs 78(): Ludwck, D. and J. Doucette (009). "Prmary care physcans' experence wth electronc medcal records: barrers to mplementaton n a fee-for-servce envronment." Internatonal Journal of Telemedcne and Applcatons 009:. 0. Lau, F., Prce, M., Boyd, J., Partrdge, C., Bell, H., & Raworth, R. (0). Impact of electronc medcal record on physcan practce n offce settngs: a systematc revew. BMC medcal nformatcs and decson makn. (), 0.. DeLone, W. H. and E. R. McLean (99). "Informaton systems success: the quest for the dependent varable." Informaton Systems Research 3(): van Wjk, M. A., et al. (00). "Assessment of Decson Support for Blood Test Orderng n Prmary CareA Randomzed Tral." Annals of Internal Medcne 34(4): DOI: 0.865/CHSJ.4.0.

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