Formal Model for e-healthcare Readiness Assessment in Developing Country Context

Formal Model for e-healthcare Readiness Assessment in Developing Country Context Ojo S.O. 1, Olugbara O.O. 2, Ditsa G. 3, Adigun M. O. 2 and Xulu S.S. 2 1 Department of Computer Science, University of Botswana, 2 Department of Computer Science, University of Zululand, South Africa, 3 College of Information Technology, United Arab Emirates University Email: ojoso@gmail.com, olu_olugbara@gmail.com, georged@uaeu.ac.ae Abstract The main objective of this paper is to report on a pilot test for a proposed formal model for e-healthcare readiness assessment. The model provides a tool for determining critical factors of e-healthcare readiness such as need-change readiness, engagement readiness, structural readiness and, acceptance and use readiness. These factors constitute the main constructs of the model which are formalized as Hierarchical e-healthcare Readiness Index System. The model was operationalized and pilot tested to determine the e- Healthcare readiness status of healthcare practitioners, the public and patients from communities associated with two healthcare facilities in the Uthungulu Health District of KwaZulu/Natal province of Republic of South Africa (RSA). The result of the pilot testing shows that (i) readiness with acceptance and use appeared to be the most important attribute, followed by structural and then engagement while need-change is the least important, (ii) healthcare practitioners agreed to be e-healthcare ready while the public and patients fairly agreed and (iii) the attitude of healthcare practitioners can be determined as a function of their preference for technology usefulness to ease of use. The theoretical framework for the model is drawn from change and change management theories, and IT acceptance and use, and innovation adoption theories. 1. Introduction There is great potential in e-healthcare to address a number of pressing challenges facing healthcare systems in developing countries, including clear inequalities in health status, quality of care and access. With healthcare equity divide which is skewed against rural communities, these challenges are more pronounced in rural areas in developing countries. Information & Communication Technology (ICT) projects in developing countries are also generally associated with failures. E-Healthcare represents a substantial ICT investment and as such, its failure can result in huge losses in time, money and effort [1]. 978-1-4244-1841-1/08/$25.00 2008 IEEE Established innovation adoption and change theories suggest that multiple factors are at play when an innovation is successful or failing [2]. The successful introduction of e- Healthcare requires the examination of such complex social, political, organizational and infrastructure factors, which include the readiness factor [3]. That is, the degree to which a community is ready to participate and succeed in e-healthcare adoption. The assessment of readiness for an innovation in healthcare can reduce the risk of its failure after introduction [3]. It is therefore, imperative that all e-healthcare stakeholders have tools and mechanisms to understand the readiness concept and to determine the readiness status of rural communities before implementing e-healthcare innovations. The remainder of the paper is organized as follows. Section 2 provides the background for this study. Section 3 reviews literature and Section 4 presents the theoretical framework for the study Section 5 presents the research methodology and a discussion of the model. Section 6 presents analysis and results of the pilot study. The paper finally concludes in Section 7. 2. Background Major socio-economic development challenges facing most African countries include economic diversification, poverty and unemployment, diseases and unsustainable use of natural resources. The challenge of quality healthcare service provisions is compounded by the HIV/AIDS pandemic, notably in the sub-saharan Africa. The pandemic has huge strain on the national healthcare system, increased number of orphans which stretches family support systems, reduced productive human capital & productivity, eroding knowledge and skills, pressure on the national budget, increased povertystricken populace and reduced quality of life. Attainment of socio-economic objectives is also greatly affected [4, 5]. The usual socio-economic & infrastructural divide between rural and urban communities makes the effects of these challenges to be more pronounced in the rural areas. Hence, bridging the rural-urban equity divide in healthcare service provisions has become a key issue of concern in developing countries. In RSA context, a Govt White Paper on the transformation of the public health system reveals that: (i) majority of RSA population has inadequate access to basic health services, (ii) larger percentage of the population live in rural communities, the vast majority of whom are poor, (iii) poverty is widely 1 41

recognized as a major determinant of health status of individuals, households and communities and (iv) gross disparity between health status and nation s economy. The country spends 8.5% of her Gross Domestic Product (GDP), which is greater than the European Union (EU) average of 8% on health [6]. However, according to Thomas et al [7], the most deprived health districts, that is, those with highest deprivation indices have the lowest per capital expenditure on Primary Health Care (PHC) services. A constant theme of the health policy document [6] is the recognition of the need to reduce the large level of social inequality in healthcare service provisions towards overcoming the inadequacies of hospitals and clinics in rural areas. The challenges of healthcare needs in the developing countries in general underscore the fact that healthcare administrators need to increase their operations, efficiency and effectiveness in healthcare provisions. Therefore, appropriate ICT solution adoption for strengthening various activities such as information and communication exchange between healthcare managers, providers and the community, optimal allocation of resources and managing the roll out of the chronic diseases programs has been advocated. According to Brewer et al [8], assuming researchers can find a suitable and appropriate healthcare management solutions for developing regions, then technology and specifically ICT can play enabling roles in disease control, improving doctor's efficiency, offering low cost diagnostics, improving data collection and providing patient management tool. E- Healthcare is increasingly considered an important tool for enhancing healthcare service provisions, particularly in rural communities [9]. E-Healthcare represents a substantial ICT investment and as such, its failure can result in huge losses in resources, which are scarce in developing countries. Hence, there is the need to provide a tool for handling factors that provide successful e-healthcare adoption, including e- Readiness. 3. Literature review 3.1 E-Readiness and e-healthcare Multiple factors are at play when an innovation is successful or failing [2]. Readiness is one such factor and it is the cognitive precursor to behaviors to either resist or support a change effort [10]. Readiness for change is an integral and preliminary step in the successful adoption of innovation [11]. E-Readiness is defined as the degree to which a community is prepared to participate in the networked world [2]. It is usually measured by assessing the community s relative advancement in the areas that are most critical for ICT the adoption and most important applications of ICT [12]. This notion is different from classical needs assessment, which identifies the real problems. E-Readiness is a strategy to identify fissures in the ability of a community or organization to implement essential ICT solution to pre-identified problems. Extrapolating from the general concept of e-readiness, e- Healthcare Readiness is the degree to which a community is ready to participate and succeed in e-healthcare adoption. Fundamentally, e-healthcare involves moving information without moving the information owner using ICTs to deliver and support healthcare services. A successful introduction of e-healthcare requires examination of the readiness of the community among other complex organizational, social, political, and infrastructure factors. 3.2 E-Readiness assessment models and approaches The goal of this study is to develop a formal model for e- Healthcare Readiness Assessment (ehra) appropriate for developing countries. This section examines some of the exiting e-readiness assessment models. There are numerous case studies that aim at rating countries on various measures held to indicate e-readiness. Based on their underlying goals, existing assessment tools and models can generally be divided into three main categories: E- Economy Readiness Assessment models, E-Society Readiness models, & E-Systems Readiness Assessment models [12]. E- Economy Readiness Assessment models focus on basic infrastructure or a nation s readiness for ICT to enable economic activities towards economic growth. Examples are WITSA e-commerce survey, Mosaic s Global Diffusion of Internet Framework and EIU s e-business readiness rankings. E-Society Readiness Assessment models, focus on the ability of the overall society to benefit from ICT at work and personal lives. Examples are CSPP s e-readiness Assessment Guide, ASEAN Readiness Assessment and World Bank s Knowledge Assessment Methodology (KAM). E-Systems Readiness Assessment models, examine the underlying technology infrastructure that is a prerequisite for both e-economy and e- Society. Example includes ITU s World Telecom Indicators. Some researchers [11, 13] have critique these models. There are only very limited ready-to-use tools and models available to assess e-readiness. Each assessment tool or model has a different goal and definition of e-readiness. The first generation of e-readiness models assumes a fixed, onesize-fits-all set of requirements, regardless of the characteristics of individual countries, the investment context, or the demands of specific applications. Many e-readiness models provide little information on how their indices were constructed, or how they might be adjusted to analyze particular e-opportunities. The details and methodologies of assessment, if any, are not always publicly available and there is a general tendency to provide single standard views and values. Ambiguities in methodology compound uncertainties of analyses and results. More to the point, the prevailing one size fits all feature obscures the very differences that investors or policy analysts require in order to reduce uncertainties or, possibly even make more educated decisions. Finally, there is no attention to the most fundamental questions, namely: e- Readiness for what? Furthermore, these earlier e-readiness 2 42

assessment models all focus on too general measures for assessing a nation s e-readiness without particular focus on rural communities. We believe that efforts to assess overall society s e-readiness need to focus specific attention on rural communities given their particularities with regards to lack of equity in rural-urban infrastructural provisions. In particular, any attempt to address the digital divide problem must place strong emphasis on rural communities. Recently, rural community focused healthcare related e- Readiness assessment tools have emerged [11, 14]. Additionally, some studies look at telehealth readiness assessment prior to implementation as being an important consideration [15]. However, existing e-healthcare readiness assessment tools are in the context of the developed world. Given the contextual differences between the two worlds, this raises the possibility of model mismatch in e-readiness model adoption. Also, the models and tools are limited in e- Healthcare adoption scope as they are specifically designed for telehealth technology adoption. There is the necessity therefore to develop a model for ehra in a developing world context with potential for extrapolation to countries with contextual similarities. 4. Theoretical framework for ehra model The theoretical framework for the ehra model draws on the ideas from change and change management theories, information technology acceptance and use, and innovation adoption theories. The principal constructs of the model are Need-change readiness (NCR) and Engagement readiness (ER) both of which have their foundation in change theories [10], Structural readiness (SR) which has its root from perceived behavioral control element of the theory of planned behavior [23], and Acceptance and Use readiness (AUR) which is an adaptation of the technology acceptance and use theories [18, 19, 20]. The NCR, ER and SR are adaptations of the concepts of Core readiness, Engagement Readiness and Structural readiness respectively defined in [21], though their work did not link these concepts with any established theories. The AUR component draws on elements of the Technology Acceptance and Use (TAU) model introduced in [18], and which has been subjected to rigorous theoretical and empirical validation [19, 20]. Figure 1 shows the principal components (constructs) of the model with their definitions and characteristics. Figure 2 shows a refinement of this model with these constructs and the sub-constructs depicted. 5. Research methodology and pilot testing This study took a phenomenological approach that allows one to discover the semantics for people in situations by interpreting their self-descriptions. This allowed the exploration of semantics of e-healthcare readiness from the following domains: practitioner, public, patient and management using discourse as the meaningful articulation of the understandable structure of being-in-the-world [16]. The model accepted that human beings are self-interpreting and recognized that meaning can be limited by language, culture and history [17]. The analysis of the transcripts from each of the domains led to the construction of the essence of the ehra model. A questionnaire instrument was designed containing four principal model constructs with their 94 measurement items. The Hierarchical e-healthcare Readiness Index System (He- HRIS) Models 1 and 2 shown in Figures 2 and 3 depict the general structure of the instrument with the distribution of the 94 measurement items among the four principal constructs. Response to these measurement items are designed based on five-point Likert scale: strongly disagree (1), disagree (2), neural (3), agree (4) and strongly agree (5). The communities around some healthcare facilities within the Uthungulu Health District of the Kwazulu/Natal Province of RSA were randomly chosen as the sample population areas for the pilot testing of the model. The questionnaire instrument complemented with interviews was then administered. The population sampling frame comprises healthcare practitioners, public, patients and managers associated with healthcare facilities in the selected communities. Figure 1: Model construct and characteristics To facilitate revision of indexes, the He-HRIS Models 1 and 2 shown in Figures 2 and 3 respectively were developed. Figure 2 is a six-level more complex model, which describes the relationship between constructs in terms of opinions evaluated for healthcare practitioners. Figure 3 is a three-level simpler model that describes opinions evaluated for public, patient and management respectively. The items at level 3 of the He-HRIS in Figure 3 indicate the number of measurement predictors for public, patient and management respectively. 3 43

For example (5, 4, 2) means 5, 4 and 2 predictors are used to measure the opinion of individuals drawn from public, patient and management group respectively for the readiness construct indicated. measurement variables. Level 4 comprises 8 items and perceived ease of use, which is determined by 7 items in level 6. Details of all 57 items (x 1 x 57 ) used in He-HRIS model 1 and 37 items (x 58 x 94 ) in He-HRIS model 2 is not released in this paper for the sake of publication space requirement. 6. Data analysis, results and discussion Figure 2: Hierarchical e-healthcare readiness index system (Model 1) Figure 3: Hierarchical e-healthcare readiness index system (Model 2) In the He-HRIS model 1, e-healthcare readiness depends on four principal constructs: need-change readiness, engagement readiness, structural readiness and acceptance and use readiness as shown in level 2. The attributes of level 2 depend on opinions measured using 21 items, attitude, social influence and facilitating condition as shown in level 3. According to the model, acceptance and use readiness can be measured by 4 items, attitude, social influence and facilitating condition or directly measured by perceived ease of use as in level 5. Since attitude in level 3 can be measured in terms of 4 items and perceived usefulness or directly in terms of perceived ease of use then acceptance and use readiness has three alternative A total number of 500 questionnaires were administered out of which 323 valid responses were received for analysis, giving a response rate of 64.6%. Evaluations were given in terms of opinion elicited on Likert scale. The result of the overall evaluation of all the samples for He-HRIS model 1 revealed that healthcare practitioners in Uthungulu Health District of RSA agreed with e-healthcare readiness, with the mean score (standard deviation) of 3.85(0.31), which is far from the midpoint of the five-point scale. Similarly, the overall evaluation for He-HRIS model 2 revealed that both public and patients in Uthungulu Health District of RSA fairly agreed with e-healthcare readiness with mean scores slightly above the midpoint of the scale as evidence in Table 1. The result for management was not concluded because fewer data were available to ascertain the effectiveness of the model. Figures 2 and 3 provide the readiness model for the hierarchical multiple regression analysis by means of commercial SPSS 14 software package. Evaluations of higher level attributes were regressed by the evaluations of the lower attributes. For the He-HRIS model 1, the resulting equations system was obtained using a 5-level formula (excluded due to space limitation- details are obtainable from the authors). Table 1: Model scores Domain Construct Mean Score Practitioner Public Patient e-healthcare 3.85 0.31 Need-change 3.60 0.77 Engagement 4.05 0.50 Structural 3.58 0.67 Acceptance and Use 3.84 0.38 Attitude 4.33 0.40 Social Influence 3.35 0.47 Facilitating 3.49 0.66 Condition Perceived Usefulness 4.08 0.50 Perceived Ease of 3.82 0.62 Use e-healthcare 3.18 0.47 Need-change 2.98 0.42 Engagement 3.25 0.92 Structural 3.38 0.70 e-healthcare 3.28 0.44 Need-change 3.26 0.60 Engagement 3.32 0.72 Structural 3.26 1.02 Standard Deviation The relative importance of each readiness attribute was given by standardized regression coefficient β. The readiness with acceptance and use appeared to be the most important attribute (β=0.82), then came the attributes of structural, 4 44

engagement, and need-change readiness with β=0.33, β=0.24 and β=0.10 respectively. The regression parameters of all 94 attributes to explain e-healthcare readiness were obtained following the American Psychological Association guidelines for reporting multiple regressions [22]. Table 2 shows the constructs regression parameters with repeated values denoted by an entry. The model fitness was determined by the proportion of explained variance r 2, which can vary from 0-100%, with 100% indicating perfect fitness. The analysis results show that the models perfectly fit well and the hierarchical multiple attributes model applied in this study offer a promising and valuable theoretical framework for modeling e-healthcare readiness assessment. Table 2: Model regression parameters Domain Construct B S.E.B. β e-healthcare 0.04, 0.15, 0.03, 0.10, 0.24, 0.15, 0.67 0.03, 0.04, 0.04 0.33, 0.82 Need-change 0.20 0.00 0.24, 0.16, 0.34, 0.32, Practitioner 0.32 Engagement 0.15, 0.15, 0.14, 0.28, 0.15, 0.14 0.00 0.28, 0.13, 0.19, 0.28, 0.18, 0.20 Structural 0.20 0.00 0.47, 0.47, 0.25, 0.21, 0.21 Acceptance and Use 0.28, 0.27, 0.19, 0.04, 0.12, - 0.05, 0.06 Attitude 0.50, 0.13, 0.13, 0.25 Social Influence Facilitating Condition Perceived Usefulness Perceived Ease of Use 0.13, 0.13, 0.13, 0.13, 0.12, 0.13, 0.13, 0.13 0.14, 0.21, 0.10, 0.15, 0.07, 0.04, 0.19 0.14, 0.15, 0.13, 0.14, 0.15, 0.14, 0.15 0.51, 0.06, 0.06, 0.13, 0.12, 0.07, 0.06 0.0 0.38, 0.34, 0.34, 0.10, 0.17, -0.02, 0.07, 0.07 0.00 0.62, 0.16, 0.17, 0.31 0.00 0.25, 0.24, 0.24, 0.26, 0.21, 0.25, 0.32, 0.14 0.00 0.20, 0.38, 0.18, 0.25, 0.02, 0.10, 0.06, 0.29 0.25, 0.17, 0.09, 0.24, 0.21, 0.12, 0.17 0.02, 0.01 0.63, 0.06, 0.07, 0.12, 0.13, 0.10, 0.09 Constant -0.01 0.18 - e-healthcare 0.37, 0.20, 0.04, 0.33, 0.40, 0.41 0.02, 0.03 0.61 Public Need-change 0.20 0.00 0.53, 0.52, 0.45, 0.56, 0.45 Engagement 0.33 0.00 0.42, 0.44, 0.47 Structural 0.20 0.00 0.35, 0.37, 0.33, 0.34, 0.28 Constant 0.03 0.15 - e-healthcare 0.40, 0.40, 0.00 0.55, 0.66, 0.20 0.46 Patient Need-change 0.25 0.00 0.44, 0.47, 0.41, 0.49 Engagement 0.25 0.00 0.43, 0.38, 0.42, 0.44 Structural 0.50 0.00 0.55, 0.58 Constant - - - Finally, the determination of AUR and Attitude were based on equations containing perceived usefulness as variable since error attained the minimum value for these equations. The result shows that attitude of healthcare practitioners in Uthungulu Health District can be determined as a function of their preference for technology usefulness to ease of use. 7. Future work Further study to involve wider population of study from a range of healthcare facilities in different national contexts will be conducted to further test the theoretical validity and empirical applicability of the model. Further test of the reliability of the measurement scales will be carried out using relevant statistical reliability measures. 8. Conclusion In this research, a comprehensive formal model for e- Healthcare readiness assessment is developed. The model was operationalized into a battery of questionnaire instrument and pilot tested through the case study of some selected healthcare communities. The model includes hierarchical index systems that were established by means of questionnaire survey and statistical analysis. This model is expected to provide a formal approach for determining the e-healthcare readiness status of a rural community in a developing country. 9. References [1]. Lyytinen K, Hirschheim R. IS failure - a survey and classification of empirical literature. Oxford Surveys IT 1987;4:257-309. [2]. Information Technologies Group, Center for International Development at Harvard University. Readiness for the Networked World: A Guide for Developing Countries; 2002. Accessed at http://cyber.law.harvard.edu/readinessguide/ [3]. Jennett P, Jackson A, Healy T, Ho K, Kazanjian A, Woollard R,. A study of a rural community's readiness for telehealth. J Telemed Telecare 2003;9:259-63. [4]. Adigun M O, Ojo S O, Emuoyibofarhe O. J, & Dehinbo. e- Healthcare Mgt : A Partnership & Collaboration Model In Developing Countries. Proceedings of the 1 st All African Conf on Technology Diffusion. Johannesburg, June 2006. 5 45