Factors Affecting The Adoption Of EHRs By Primary .
International Journal of e-Healthcare Information Systems (IJe-HIS), Volume 5, Issue 1, June 2018Factors Affecting the Adoption of EHRs by Primary Healthcare Physiciansin the Kingdom of Saudi Arabia: An Integrated Theoretical FrameworkAsma AlJarullah, Richard Crowder, Mike Wald, Gary WillsDepartment of Electronics and Computer Science,University of Southampton, Southampton, UKAbstractImplementing Electronic Health Records (EHRs)in primary healthcare has the potential to improve thepopulation health, and to enhance the overallhealthcare system of the country. Current policyinitiatives in the Kingdom of Saudi Arabia (KSA) areattempting major reforms in primary care with EHRsas a key component. Understanding human factorsinvolved in the implementation process of technologyis crucial for its successful implementation. The aimof this paper is to support current policy initiatives byinvestigating and identifying factors that are likely toaffect primary care physicians’ acceptance of EHRs.Factors were identified based on extensive literaturereviews and empirical findings. Three main stages ofliterature review were conducted: (1) factorsinfluencing user adoption of IT, (2) factors affectingphysician adoption of EHR, and (3) findings ofrelevant studies pertaining EHR adoption byphysicians in the KSA. As a result, we developed anintegrated framework of eight factors that wereproven to have a significant direct influence onphysicians’ acceptance of EHRs: attitude, perceivedusefulness, perceived ease of use, social influence,computer self-efficacy, perceived threat to physicianautonomy, confidentiality concerns, and physicianparticipation. The proposed framework will be ofgreat potential to policy makers to make the transitionto EHRs run smoothly.1.IntroductionAn Electronic Health Record (EHR) is consideredas the backbone integrating various information tools(e.g. computerized physician order entry, clinicaldecision-support, clinical documentation, digitalimagery, patient portals, telemedicine) [1]. Benefits ofEHRs have been well documented in the literature(e.g. optimizing the documentation of patientencounters, improving the availability and timelinessof information, reduction of errors) [1], [2]. However,in primary healthcare, an EHR has a specificimportance. It improves the management of chronicdiseases, facilitates continuity of care, enablesreporting of population health, improves preventivecare, and allows for the development of patient portals(e.g. personal health records) and adaptive plementing EHRs in primary healthcare has theCopyright 2018, Infonomics Societypotential to improve the population health, and toenhance the overall healthcare system of the country[5].Over the past several decades, many governmentshave been moving toward EHRs [6]. Particularly, theadoption of EHRs in primary healthcare centers hasbeen a priority in many countries [7]. The healthcaresystem in the Kingdom of Saudi Arabia (KSA) haslagged behind significantly in this regard [8]. Primaryhealthcare centers under the Ministry of Health(MOH) are still using paper-based records and theuptake of Information Technology (IT) is rare [9].However, recent policy initiatives are attemptingmajor reforms in primary healthcare with EHR as akey component [10].Studies on EHR implementation have shown thedifficulty of the process [11]. The literature reportsmany cases of failure in EHR implementation due tothe lack of end users’ adoption [12], [13]. Themajority of EHR projects discontinue in early stagesof implementation, with end users’ resistance usuallya major contributing factor [1]. Therefore,understanding human factors involved in theimplementation process of technology is crucial for itssuccessful implementation.Little is known about the factors that couldincrease or hinder healthcare professionals adoptionof EHRs in the KSA. To the best of our knowledge,there has been no previous study that identifies thesefactors in the KSA. Because physicians are the mainfrontline user-group of EHRs, whether or not theyadopt these systems will have a great influence onother user-groups in a primary care center [2].The focus of this paper is to investigate andidentify factors that are likely to influence theadoption of EHRs by primary healthcare physicians inthe KSA based on extensive literature reviews andprior empirical studies. The findings of this study willbe of great potential to policy makers to tailorimplementation strategies toward factors thatmotivate adoption.This paper is organized as follows: Section 2discusses the methodology used for the constructionof the proposed framework. Section 3 presents anddiscusses the proposed framework, and Section 5discusses the implications of the proposed framework.126
International Journal of e-Healthcare Information Systems (IJe-HIS), Volume 5, Issue 1, June 20182.MethodologyThe aim of this paper is to develop a framework offactors that are likely to affect the adoption of EHRsby primary healthcare physicians in the KSA. Threestages were conducted in order to construct theappropriate framework for the current study as shownin Figure 1. In stage 1, determinants of user adoptionof Information Technology (IT) were identified basedon theories and models of user adoption of IT. In stage2, determinants of physician adoption of EHRs wereidentified based on prior theoretical models ofphysician adoption of EHRs. In stage 3, barriers tophysicians’ acceptance and use of EHRs in the KSAwere identified based on relevant empirical studiesconducted in the KSA.At the end of each stage, factors identified werefiltered in order to remove those irrelevant to thepurpose of the current study. Particularly,semantically duplicates were excluded. Also, becauseEHRs have not been introduced in primary healthcarecenters, factors that are not applicable for the preimplementation phase were excluded. Therefore, theaim of this research is to identify acceptance factorsfor EHRs, not use, similar to many previous studies[1], [14]–[16]. Because acceptance is the main, andpossibly the only, predictor of IT systems use [17],[18], it is crucial to understand what influencesacceptance of EHRs.2.1. Stage 1: Determinants of user adoption ofITA number of theoretical models attempted tofacilitate explaining and predicting users’ acceptanceand use of a new IT. The most widely usedexplanatory theories are: the Theory of ReasonedAction (TRA) [19], the Theory of Planned Behavior(TPB) [20], the Technology Acceptance Model(TAM) [17], and the Unified Theory of Acceptanceand Use of technology (UTAUT) [18].Originated in social psychology, the Theory ofReasoned Action (TRA) [19], is one of the mostfundamental theories in human behaviour. The TRAposits that any behaviour of an individual isdetermined by the behavioural intention. Strongerbehavioural intention increases the likelihood ofperforming the behaviour. According to TRA,behavioural intention is determined by twoindependent factors: attitude toward the behaviourand subjective norms. Attitude toward behaviour isdefined as an individual’s positive or negativefeelings about performing the behavior in question.More positive attitude on the behaviour increases thelevel of intention to perform that behaviour.Subjective norm is defined as an individual’sperception that most people who are important to himthink he or she should perform the behaviour inCopyright 2018, Infonomics Societyquestion. Higher perceived subjective norm increasesthe level of intention to perform the behaviour. Themodel of TRA is shown in Figure 2(a). Although TRAhas been evaluated and supported in a wide range ofstudies, it has been criticized because it assumes thatbehaviour is totally under volitional control. As somespecific behaviours or actions may require specificresources, skills, or opportunities for an individual inorder to perform them, attitude and subjective normare not enough for predicting behavior [20], [21].To address the limitation of TRA, Ajzen [20]developed the Theory of Planned Behaviour (TPB) byextending TRA with a new construct, namely,perceived behavioural control. Perceived behaviouralcontrol was defined as the perceived ease or difficultyof performing the behavior in question. TPB positsthat perceived behavioural control determines bothintention and behaviour as shown in Figure 2(b). Theinclusion of perceived behavioural control in TPBdemonstrates the importance of one’s perceptionsabout his or her capabilities and resources availablefor performing the target behaviour. That is, anindividual with insufficient capabilities or resourcesmight have less intention to perform the behaviourand might not perform the behaviour even if he or sheholds a positive attitude toward the behaviour andperceives support from important others [20], [21].The TPB has been widely applied to understandbehaviour and behaviour intention in different settings[18]. However, TPB and TRA have been criticized forthe general belief measurements, which need to beadjusted according to behavioural contexts [22].The Technology Acceptance Model (TAM) [17]was designed specifically for the information systemscontext and was developed to predict user’sacceptance and use of technology on the job. TheTAM was adapted from TRA, and similarly, itpredicts technology adoption based on intention.However, it assumes that intention is determined byattitude, which is determined by two technologyrelated beliefs: perceived usefulness and perceivedease of use Figure 2(c). Perceived usefulness wasdefined as the degree to which a person believes thatusing a particular system would enhance his or her jobperformance. Perceived ease of use was defined as thedegree to which a person believes that using aparticular system will be free of effort. The TAM alsoassumes that perceived ease of use has a casual directeffect on perceived usefulness. In summary, TAMassumes that a user has a greater intention to use atechnology when he or she perceives a higher ease ofuse and usefulness. The TAM became the most widelyused model to study the adoption of varioustechnologies and has arguably become the mostinfluential theory in the information systems field. Ithas proven to be effective in predicting variance intechnology acceptance in a wide variety of contextsfor different types of users [23]. The determinants inthe TAM are easy to understand for system developers127
International Journal of e-Healthcare Information Systems (IJe-HIS), Volume 5, Issue 1, June 2018Figure 1. The protocol applied to develop the proposed framework(a) The Theory of Reasoned Action (TRA), adapted from [19](c) The Technology Acceptance Model (TAM), adapted from [17](b) The Theory of Planned Behaviour (TPB), adapted from [20](d) The Unified Theory of Acceptance and Use of Technology (UTAUT),adapted from [18]Figure 2. Theories of user adoption of information technologyCopyright 2018, Infonomics Society128
International Journal of e-Healthcare Information Systems (IJe-HIS), Volume 5, Issue 1, June 2018and can be considered during system requirementanalysis and other system development stages to solvethe acceptance problem [24]. However, the TAMmodel does not consider the social environment inwhich the technology is introduced [25]. Existentresearch indicates that while the TAM has thecapacity to generally predict variance in technologyacceptance, context-specific variables must be addedto the model to increase its explanatory power [23].The Unified Theory of Acceptance and Use ofTechnology (UTAUT) [18] was developed based onthe combination of factors of eight theories includingthe TRA, TPB, TAM, the Decomposed Theory ofPlanned Behavior (DTPB) [24], the InnovationDiffusion Theory (IDT) [26], the Motivation Model[27], the Model of PC-Utilization (MPCU) [28] andthe Social Cognitive Theory (SCT) [29]. The UTAUThypotheses that threeconstructs, namely:performance expectancy, effort expectancy, andsocial influence can explain IT usage intention.Further, a fourth construct called facilitatingconditions along with usage intention can explainactual usage of IT, as shown in Figure 2(d).Performance expectancy is similar to perceivedusefulness in TAM. It refers to the degree to which theuser expects that using the system will help him or herattain gains in job performance. Also, effortexpectancy is similar to perceived ease of use in TAMand refers to the degree of ease associated with the useof the system. Social influence is similar to socialnorms in TRA and TPB and refers to the degree towhich an individual perceives that important others Social factors, such as subjective norm in TRA[19] and TPB [20], and social influence inUTAUT [18]. Controllability factors, such as perceivedbehavioral control in TPB [20], andfacilitating conditions in UTAUT [18].Because facilitating conditions is a determinant ofIT usage, not acceptance [18], it is excluded from theproposed framework in the current study. Also, manyauthors consider facilitating conditions and perceivedbehavioral control as referring to the same concept[31]. The DTPB theory [24], decomposed perceivedbehavioral control into two factors, facilitatingconditions and computer self-efficacy (i.e. judgmentof one’s ability to use technology to accomplish aparticular task). Computer self-efficacy was adaptedfrom the SCT theory [29]. Based on DTPB’sdefinition of perceived behavioral control, andbecause facilitating conditions was excluded from theproposed framework, computer self-efficacy isincluded in the proposed framework instead ofperceived behavioral control.Copyright 2018, Infonomics Societybelieve that he or she should use the new system.Facilitating conditions is the degree to which anindividual believes that an organizational andtechnical infrastructure exists to support use of thesystem. According to their study [18], UTAUT wasable to explain approximately 70% of variance in ITusage intention, whereas the original eight modelsexplained approximately 40% of variance. However,although UTAUT successfully integrates allconstructs from eight important models, it was testedin the original study in industrial and economic areas,such as product development, sales, banking andaccounting [18]. When applied to the healthcarecontext in their later study [30], the results weredisappointing and the model only explained 21% ofvariance in usage intention. A modified UTAUT wasable to explain 44% of variance in usage intention[30].Collectively, IT adoption theories identify fivekey determinants of IT acceptance and use, as follows: Attitude toward technology, which is includedin three models, TRA [19], TPB [20], andTAM [17]. Perceived benefits of the system, such asperceived usefulness in TAM [17] andperformance expectancy in UTAUT [18]. Perceived usability of the system, such asperceived ease of use in TAM [17] and effortexpectancy in UTAUT [18].2.2. Stage 2: Prior theoretical models ofphysician adoption of EHRsTo identify prior theoretical models explainingphysician adoption of EHRs, we searched three keysearch engines: Web of Science, PubMed, andGoogle Scholar. The following search query wasused: (Physician AND (Adoption OR Acceptance ORUse) AND (“Electronic Health Record” OR“Electronic Medical Record” OR EHR OR EMR)).Titles and abstracts of the retrieved studies werescreened for relevance. Also, reference lists of theretrieved studies were screened for relevant studies. Astudy is selected for inclusion if: (1) the study focuseson EHR or EMR (i.e. not the other electronic systemsused in medical practices), (2) the study is peerreviewed (i.e. unpublished work was excluded), (3)the study is empirical, (4) the study sample iscomposed of physicians only (i.e. not the other usergroups), and (5) the study employed a theoreticalmodel. As a result, nine studies were identified [1],[14]–[16], [30], [32]–[35]. A summary of the findingsof these studies is provided in Table 1.Most determinants identified by technologyadoption theories were supported by many studies,particularly: attitude [15], [32], perceived129
International Journal of e-Healthcare Information Systems (IJe-HIS), Volume 5, Issue 1, June 2018Table 1. Findings of prior theoretical models of physicians’ acceptance and use of EHRsStudy/PublicationyearCountry of datacollectionSubjects/Analyzed responsesTheory[16]/2009USAPhysicians/239Extended TAM[32]/2009USAPhysicians/102Combined TAM TPB[30]/2011USAPhysicians/141Modified UTAUT[1]/2014CanadaPhysicians/150Extended TAM[14]/2016CanadaPhysicians/278Extended TAM[33]/2011CanadaPhysicians/185Extended UTAUT[15]/2015AustriaPhysicians/204Extended TAM[34]/2015IranPhysicians/237Extended TAM[35]/2012MalaysiaPhysicians/300Extended TAMKey determinants of EHR acceptancePerceived usefulnessPhysician involvementPerceived threat to physician autonomyAttitudePerceived behavioral controlPerformance expectancyEffort expectancySocial influencePerceived ease of useProfessional normsSocial normsDemonstrability of resultsPerceived usefulnessPerceived ease of useProfessional normsSocial normsComputer self-efficacyPersonal identityPerformance expectancy (non users)Effort expectancy (current users, non users)Perceived risk (current users)Perceived usefulnessAttitudeSocial influenceHealth IT experiencePrivacy concernsPerceived usefulnessPerceived ease of usePerceived usefulnessPerceived ease of usePerceived threat to physician autonomyTable 2. The integrated framework for the adoption of EHRs by primary healthcare physicians in the KSADefinitionSupporting modelsin IT acceptanceliteratureSupporting modelsin EHRacceptanceliteratureAttitudeAn individual’s positive or negative feelings aboutperforming the target behavior [19].TRA [19], TPB[20], TAM [17][15], [32]Perceived UsefulnessThe degree to which an individual believes that using aparticular system would enhance job performance [17]TAM [17][14]–[16], [34],[35][39]TAM [17][1], [14], [34], [35][39]UTAUT [18][15], [30]SCT [29][14]FactorPerceived Ease of UseSocial InfluenceComputer Self-EfficacyPerceived Threat toPhysician AutonomyConfidentiality ConcernsPhysician ParticipationThe degree to which an individual believes that using aparticular system will be free of effort [17]The degree to which an individual perceives that mostpeople who are important to him think he or she shoulduse the new system [18]Self-evaluation by a person of his/her capacity to use thetechnology.The degree to which an individual believes that using aparticular system would decrease his or her control overthe conditions, processes, procedures, or content of hisor her work [40]The degree to which a primary care physician believesthat using EHR would impose risk to the confidentialityof patients’ information.The degree to which a primary care physician believesthat his/her participation in the selection and planning ofEHR is important for system acceptance.Copyright 2018, Infonomics SocietySupportingstudiesconducted in theKSA[38][16], [35][15][39][16]130
International Journal of e-Healthcare Information Systems (IJe-HIS), Volume 5, Issue 1, June 2018usefulness/performance expectancy [14]–[16], [30],[33]–[35], perceived ease of use/effort expectancy [1],[14], [30], [33]–[35], computer self-efficacy/HealthIT experience [14], [15], social influence/socialnorms/professional norms [1], [14], [15], [30].However, new determinants were identified,particularly: perceived threat to physician autonomy[16], [35], physician involvement [16], and privacyconcerns [15].Two studies [1], [14] where conducted by thesame authors and applied approximately to the samepopulation, but have shown conflicting findingsregarding the significance of two factors:demonstrability of results and personal identity.Whereas demonstrability of results was found to besignificant in [1], it was insignificant in [14], and viceversa for personal identity. Because there was nosupport for the importance of the two factors in manysystematic reviews [2], [23], [36
Factors were identified based on extensive literature reviews and empirical findings. Three main stages of literature review were conducted: (1) factors influencing user adoption of IT, (2) factors affecting physician adoption of EHR, and (3) findings of relevant studies pertaining EHR adoption by physicians in the KSA.
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