Health Information Systems: Investigating Greek hospital employees intention to use electronic health records

Transcription

1 GREENWICH UNIVERSITY MSc IN FINANCE & FINANCIAL INFORMATION SYSTEMS Health Information Systems: Investigating Greek hospital employees intention to use electronic health records Argiro Nestoridou Supervisor: Dr Prodromos D. Chatzoglou Kavala, December 2009

2 Special thanks to Dr P.D. Chatzoglou 2

3 Table of Contents ABSTRACT INTRODUCTION LITERATURE REVIEW Introduction Definition of EHR Context and goals of EHR Paper records Advantages of paper records Disadvantages of paper records Electronic health records (EHR) Advantages of electronic health records Disadvantages of electronic health records Global efforts for the implementation of EHR The Greek health system Technology acceptance model TAM in healthcare General TAM literature Summary Chapter RESEARCH METHODOLOGY Introduction Methods Research questionnaire The raw data DATA ANALYSIS Proposed analysis The results Result discussion and summary CONCLUSIONS AND LIMITATIONS Review Limitations and further research REFERENCES APPENDIX QUESTIONNAIRE

4 Abstract Information systems have drawn the attention due to their efficiency and importance to the new business world. However, cannot be effective unless they are used properly by employees. Electronic Health Records (EHR) are health care data kept in digital form, containing information about a person s life with the aim of treatment, education and research. We shed light on the factors that affect Greek hospital employees intention to use EHR. The results indicate that the intention to use electronic health records by Greek hospital employees is a function of many variables interconnected to one another. Keywords: Paper records, Health information systems (HIS), Electronic health records (EHR), Technology acceptance model (TAM), 4

5 Chapter 1 Introduction The continuing enormous progress in medical research has led to a growing demand of the implementation of IT in the health sector (Knaup et al., 2006). Although, the need for innovative health information systems is a fact, Greek hospitals are still on a primary stage regarding the use of electronic health records. EHR are health care data kept in digital form, containing information about a person s life with the aim of treatment, education and research, while keeping this information confidential; a tool for ensuring the continuity of the patient s medical care (Iakovidis, 1998). Despite the fact that there are serious global efforts made for the implementation of EHR, Greek hospitals do not keep up with this trend. Through this study, we intended to shed some fresh light on the factors that affect Greek hospital employees intention to use electronic health records. For that purpose a version of the TAM (Technology Acceptance Model), differentiated to fit the needs of computer acceptance in Greek hospitals has been used. The findings indicate that perceived ease of use and perceived usefulness are very significant drivers in the adoption of the EHR. Moreover, the intention to use EHR by employees is also affected by management support and subjective norms. The present study is consisted of five chapters. Chapter one introduces the reader to the subject by making a brief presentation of electronic health records. Moreover in this chapter the main findings of this research are presented. Finally, this chapter provides with an overview of how this dissertation is structured, which is like a map to the reader. The second chapter is the literature review, were all the relevant literature that surrounds health information systems is presented. Firstly, chapter two provides with the definitions of HIS and EHR. The context and the goals of EHR are also illustrated in section 2.3, while in sections 2.4 and 2.5 we attempted a comparison between paper and electronic health records. In the following sections of chapter two, the technology 5

6 acceptance model is analyzed both in general and in the health sector, by presenting related studies. In the third chapter the research question and the methodological approach are presented. In section 3.1 a set of 16 hypotheses are developed and tested. Additionally, in section 3.2 there is an analysis of the research questionnaire and the items that are examined. The sample is also presented in section 3.4. The fourth chapter deals with the statistical analysis of data conducted both by using LISREL and SPSS 16. Section 4.2 presents the results based on the tables of the reliability statistics, descriptive statistics, factor analysis, KMO and Bartlett s test of sphericity, construct reliability and variance, goodness of fit, model fit statistics, hypotheses testing and Pearson s correlation. Finally, section 4.3 is the most crucial as it discusses the findings and their implications. The fifth chapter presents the concluding remarks of this study. It discusses the findings, the limitations of the research and proposes issues of further research associated with the present one. 6

7 Chapter 2 Literature review 2.1 Introduction Information Technology (IT) is considered to be a second industrial revolution after that of the 19 th century (Sistrom, 2004). And even though it has brought about many changes in various fields of the daily life, in finance and banking sector and billing for services, in the field of healthcare there has been a delay in the adoption of the IT (Ash and Bates, 2005 cited in Callen, 2007). Computers have been used in the health industry for many decades, helping the automation of many administrative procedures at first and, then, entering to other departments such as laboratories and pharmacies (Van der Meijden, 2000). However, most of the medical work from nurses and doctors is still kept in paper records, resulting in a delay in the provision of health services. According to Ovretveit et al. (2007), despite the fact that many countries and health service providers have established policies for health information systems and electronic medical records (EMR), there is still a large distance to be covered from policy theory to practice. The rapid growth of IT, in combination with the complexity of healthcare, does not help implementers and decision makers to the creation of the appropriate system. To support this, Wyatt (2003) adds an important factor for the implementation of an information system: money that should be wisely and efficiently spent by the decision makers in health care organizations, not to mention the pressure that is undergone from doctors, vendors and public to adopt the latest clinical information systems. Information systems that are complex and can serve the same time many different departments of a health enterprise. These information systems however, despite their pros and cons, consist also an important tool for the people in health departments. It is a way of monitoring their work accurately and cost effectively and, at the same time, improves the care delivered from physicians to patients. Moreover, it provides evidence of the medical work to the trust board and the 7

8 state that the provided health services fill the needs and are according to the standards that have been set (Sanderson, 2000). As it is understood from the above, the implementation of IT in health sector is partial and on a primary stage, due to organizational, cultural and legal reasons. Even though some tasks in hospitals are being performed with the help of PCs, still, an important field of health computer systems-ehrs (Electronic Health Records) - is being merely used as it should be. Before analyzing the benefits and the barriers in the development of these electronic records, it is important to give a specific definition and distinction of EPR and EMR - or (EHR) electronic health records. 2.2 Definition of EHR According to Iakovidis (1998), EHR can be defined as health care data kept in digital form, containing information about a person s life with the aim of treatment, education and research, while keeping this information confidential; a tool for ensuring the continuity of the patient s medical care. Hayrinen et al. (2008) gives another definition for EHR according to the International Organization for Standardization (ISO); it is a repository of patient data in digital form, stored and exchanged securely, and accessible by multiple authorized users. These records can be found both in hospitals and in general practice. In the same 2004 ISO Technical Report, there is also a description of shareable and non-shareable EHRs, as well as integrated EHR and EHR. Shareable EHR refers mostly in networks, either local or internet, with the advantage of finding and exchanging data; non-shareable are the data that can be kept at a single computer. As for integrated EHRs, their main goal is to support a fulfilled treatment of a patient across health institutes (Hovenga, 2005). Atreja et al. (2008) agrees that EHR are computerized health records and processes; however, they consider that there are minor differences between the terms EMR and EHR. EMR is referred to the data kept within a health institution, while EHR refer to the care provided to a patient in many health facilities. 8

9 Finally, another term that is commonly used is EPR. According to Elberg (2001), it is not clearly distinct whether EPR is just an application or the entire computer system containing applications, data flow and the structure of the health organization. If EPR is considered as a tool transforming manual scripts in digital form, then its goal is only the automation of paperwork. In this paper, the term EHR which seems to have a wider meaning is going to be used. In table 2.1 below the variety of EHR is given according to the International Organization for Standardization (ISO). Table 2.1: Types and definitions of EHR 9

10 Source: Hayrinen et al. (2008) p Context and goals of EHR From some of the definitions above, it is clearly stated that the main goal of an EHR is to keep all the data referring to the health condition of a person. This includes diagnoses, care delivery, laboratory and microbiology exams, allergies, medications and treatment of a patient that has been received not only in health institutions and hospitals but also in individual doctors as well. Rigby and Robins (1996), consider that a patient record should not be just a historical document describing past findings and medical exams. It should offer a plan of treatment and what the forthcoming results would be, as well as monitoring later what were the results from the patient care and the deviation from what was expected. They also divide a patient record into four sections according to their index: 1. Patient registration: in this part general data of the patient such as name, age, sex, permanent or temporary address are being asked 2. Patient Profile: in this part there are usually registered patients preferences for appointments date and time, the sex and age of the health specialists that would treat them and the language that should be spoken. This part however has been used in records in the UK healthcare than in other countries. 3. Treatment requirements: it is the part where a professional judgment is made about the patient s health condition, or a diagnosis. After that, the creation of a Careplan is needed (Rigby et al. 1994, cited in Rigby et al.1996) containing the aim, problem and diagnosis, the objective, goal and intervention. In the case of aim however, it is not only the case of a quick rehabilitation of the patient but, also, cases like a soothing treatment in patients with cancer at the last stage, or preparation for a child with special needs to be placed in a special treatment centre. 4. Resource availability: it refers to the availability of health professionals, the creation of a program and the appointments that should be made according to the Care plan. In that way, commitments towards patients are kept and the 10

11 same time, health professionals are not forced to keep these commitments by extending their working hours unless they want to. According to Kukafka et al. (2007), these EHR systems can provide important data for public health surveillance and prevention of diseases, by using data from anonymous records. 2.4 Paper records Advantages of paper records Even though EHRs have been implemented in many hospitals around the globe, still, personnel such as doctors and nurses continue to take handwritten notes for the observation and treatment of the patient, writing prescriptions and radiology orders (Atreja et al., 2008). This is mainly justified by the fact that the majority of these people find it easier and more time saving from using a computerized system during the clinical sessions and after that- exceeding sometimes their daily schedule. After all, when someone is used to doing a task in one way for years or even decades, then he is not willing enough to change it from one day to another. Another important reason that the supporters of paper-based records are opposing is the durability of them and the accessibility by sight. As Lin et al. (2003) mention, the data that are collected on paper records are kept on media or simple devices that can be easily seen. Once they are archived, they are kept in special locations that can retain their usability for many years and there is no need of taking extra measures for their preservation. Electronic records on the other hand, are not directly accessible to read, unless the appropriate software is used to transform digital data to readable form. Furthermore, by looking at the general characteristics of the paper records (organized by source, different colors of ink used for each person and shift, different handwriting) it is easily understandable why it gives a special advantage to the paper 11

12 forms in the security issue (Van der Meidjen, 2000). Moreover, in cases where there are legal issues, for example in cases of medical errors, justice may rise from the observation of these records Disadvantages of paper records However, there are many negative aspects of the paper records that gradually lead to the use of the new form of health record keeping; electronic health records. Most of the patient s records that are kept in paper usually stay within the healthcare facility. So, if the patient visits another hospital or healthcare facility, the personnel there will not be able to treat him right because they will not be able to retrieve his medical history and the medication that he has received till then. This, of course, has another negative aspect: if the patient s life is in danger, precious for his life time will be wasted and could even lead to death. What can also be added to that is the fact that these records are not kept properly in certain places; as the number of patients within a healthcare facility rises, the archive of the facility grows more and more leading the personnel to chaotic circumstances, files set without an order and in different places around the facility. This situation on its turn leads to important security issues; the archive rooms can be accessed by anyone without being noticed by the staff, not to mention files being missed either by being stolen or destroyed because of the inappropriate environmental keeping. And when paper records are being lost temporarily or even completely, it is impossible then to keep a record of these periods that are quite important for each patient, especially to patients with chronic illnesses. Also, in cases of patients with chronic illnesses that usually carry with them a large amount of laboratory exams, prescriptions and different kind of medical exams, paper records are not easily accessible and consume much time for a doctor to have a clear picture of the patient s history. 12

13 Last but not least, raises the problem of the quality of care being delivered to the patient when using a paper record. Most of the patients that have more than one health problem end up most of the times with a large pile of medication that could sometimes interact. Or even, in cases where the patient decides to visit another physician or healthcare institution for a second opinion on his problem, most of the times unintentionally doesn t reveal his full patient history and medication that he has received till then, leading the doctors to false estimation and even dangerous mixtures of medicines (Bakker, 2007). 2.5 Electronic health records (EHR) A dvantages of electronic health records For most of the professionals in the healthcare departments, the majority of the problems in paper records were considered to fade away with the creation and use of the EHRs. The existence of such a system could benefit in multiple ways both large health institutions and small practices. Starting up with the health institutions, the installation and use of an EHR helps the staff reduce the time spent for many actions of their daily routine (Hier, 2005). This could include the retrieval of a patient s data and history, immediate access to his laboratory or other exams needed and the entry of new medical data during his treatment inside the facility. Electronic records become a really useful tool for each user when used properly; imagine a hospital which accepts a large number of patients daily and at the same time the personnel has shifts changing within a 24hour.The doctors and nurses would have not only to treat the patients, but also to inform their colleagues-even some times exceeding their shifts- about some of the patients condition and treatment. On the other hand, imagine the patient s psychological condition when having to deal with different doctors -for some days or even hours- and being asked for information that the previous shift forgot to write down on the patient s folder (for example tests that were made or results waiting to be received). Therefore, the use of electronic records results in better and faster treatment of the patients and better relationships between them, since conflicts and irritations from delays in treatment are being avoided. 13

14 Something that is also avoided in such cases is errors caused by misunderstandings and misinterpretations of the handwritten notes of doctors and nurses by their colleagues (Hughes et al, 2005; Van de Castle et al., 2004 cited in Jylha et al., 2008) Most of the systems that are available nowadays offer some standard forms to be filled in (general data of the patient) in combination with free text platforms where doctors and nurses can type up their notes and comments. According to Smith (2008), it is also necessary for the EHRs the use of archetypes in order to standardize fields in the system that could be used for further research and exchange of data. Archetypes help a system locate different terms that might be used in various EHRs and value if some terms are equivalent or of a different meaning and importance. As Bakker (2007) points out, the fundamental difference between the electronic systems and the commonly used paper records is the field of security. Not only EHRs offer different levels of authorization to their multiple users, but also, it is possible to know which user had access to where and when through the electronic trails at the system. This on its turn helps insurance companies and the management of each healthcare institute finding medical errors and assuring that justice will be posed to the accused. Institutions seem to benefit also from EHRs and in other ways: management receives more accurate data for its resources, the care delivered from the medical and nursing staff, financial monitoring and decisions. Electronic records help in reduction of various costs in the facility, while the same time increases the number of patients that are daily treated by doctors. This of course, according to Pizziferri (2005), seems to benefit patients and institutions and not doctors that end up with more job demands. Another important field of EHRs is their ability to provide patients with health advices by distance-when the system offers a web usage. Most important, patients that live in distant areas can reach a physician with the help of telemedicine and also patients with chronic diseases manage better their illnesses and communicate with their doctors without having to visit frequently practices or institutions. EHRs however, don t refer only to treatment of each patient separately, but as a whole. Public health can be served and improved through the use of EHRs in many 14

15 fields (Kukafka et al., 2007; Atreja et al., 2008). First of all, they help in observation of the health status of people in a community and locate any health problems that might arise. At the same time, diseases and health hazards can be diagnosed and treated in a due time, as well as cooperation and international relationships can be empowered for the salvation of such problems. Apart from that, health staff can be monitored and evaluated for the quality of services they offer in public health services. Last but not least, EHRs can offer some important, accurate and in-time data for research purposes. Researchers can access data of patients of their research area and get the information they want, saving time and money that can be spent elsewhere in a community. Also, patients can become more energetic for their personal treatment and be informed more quickly for any research development on their illness Disadvantages of electronic health records Despite the fact that EHR improve the healthcare delivered to the patients and help staff in the exercise of their daily tasks, still different kind of problems can be located in cost, security, cultural and technical level from the installation of such an information system in a health institution or department. Higher cost More explicitly, the creation of the appropriate EHR that would reach the demands of the facility in combination with the equipment that is needed for the better usage of it (computers, printers e.t.c.), lead to a high cost that has to be undertaken by the institution. Moreover, to many institutions extra personnel were employed for the better use of the system, increasing more the operative costs of the facility. Therefore, the belief of the institution management that the installation of an information system would lead to lower costs for the organization might have a reverse result in practice. Unfortunately, as Ball (2003) mentions, most of the systems that have been used in healthcare institutions couldn t reach the healthcare managers expectations when they asked for a return on investment analysis (ROI). And as Wyatt (2003) states, someone could still oppose to the cost of an information system even after it s implementation 15

16 because these money could be spent elsewhere in a facility like better patient care or frequent training of the staff. Preservation Another important issue in EHRs is the preservation of these records and other techno logical problems that rise during the long-year usage of the systems. Firstly, the nature of technology itself is a major barrier for the continuity of EHRs. Technology improvisations appear day by day, making expensive systems be out-of date and pushing administrative staff in buying new systems to fulfill their needs. Also, it is quite difficult for the healthcare staff to retrieve past data of patients when the systems have changed over years, not to mention the short life expectancy that both software and hardware of these systems appear to have (Lin et al., 2003). Therefore, the preservation of these records becomes more difficult as years pass by, creating doubts about their integrity and authenticity. Imagine what problems could be created by the deterioration of the archives or sloppy entries of the staff, not to mention double entries of many data (medication entered both in doctors orders and nursing notes) on the patient s record due to insufficiency of the system. This problem on the other hand, depends in a great extent to the decisions the administrative staff takes when choosing an information system without predicting their future preservation and its cost. Gap between users and technicians Something that should be also mentioned about the technological problems that occur in an EHR is the fact that most of these systems are created by technicians that are unaware of the needs of a healthcare facility and create programs that are impractical, complicated and time wasting. Most doctors that have a busy schedule consider that there is a delay in response time when asking the program for a task (Yusof. et al., 2007). Also, as Kinkhorst et al. (1996) illustrate, it is really difficult for a system to fulfill all the needs of their users, since each one of them may have different needs about the information provided and even data that could be important for one user, someone else could not have register them in the patient s file. Many systems also, fail to bring positive results to the users and the facilities in general, because their creation was based in transforming paper records into digital format. 16

17 Security issues Security issues seem to concern both technicians and users of these systems. As Lin (2003) points out, a change in the content and format of the records can not be easily detected unless the appropriate procedures and security measures have been undertaken. Most users have their own personal username and password in order to retrieve specific data for the patients they treat. And even though, intruders from outside seem to be avoided, still in a department of a hospital it is quite common for the staff to know and other passwords from their colleagues for practical reasons mostly. Also, something that is quite common in large healthcare facilities is the frequent change of the personnel and, consequently the change in the authorization of the information system. Authorization changes however might also occur to the remaining staff in a hospital, which has to keep up with different passwords frequently in their workplace (Louwerse, 1998). Another common phenomenon in healthcare institutions is to have a patient which is difficult to detect its health problem and, therefore, has to be examined by doctors of different specialty. And in this case, authority issues will rise because either all doctors that have treated the patient will have access, or, there will be a delay on the patient s treatment in order to be a change in the access for each doctor. Organizational culture The implementation of a state-of-the-art EHR, with a variety of applications and a remarkable design are not the key factors to guarantee the success of this system. As long as humans operate these systems, then, problems are likely to appear due to organizational culture and user acceptance. According to Nowinski et al. (2007), it is a common phenomenon for an organization the appearance of different cultures in various departments of the facility. What is also necessary to notice for these culture groups is the fact that each department has its own culture type -beliefs and valuesthat might conflict with the culture of other departments or the leadership of the organization. 17

18 Moreover, these cultures seem to affect the development and implementation of information systems while the same times influence the use or not of an EHR according to their beliefs. As Wyatt et al. (2003) points out, the introduction of a new information system in the working field disturbs the employees because its appearance in the majority of cases leads to a change in their working routine, enforcing them indirectly to consume more time and personal effort to adjust to the new working conditions. And despite the fact that the staff can be well trained and informed for the new system, they still hesitate to use it in fear of not succeeding in their new tasks and underestimating their ability in using a computer. In some cases the managers and the health authorities are those who burden the development of new technology due to their lack of foreseeing future needs in health industry and their unwillingness to drastically change the processes in healthcare (Iakovidis, 1998). Users on the other hand, have a negative opinion about the implementation of a new information system due to problems that appear at the first implementation of the system. Such problems include delay in response time, complexity of the system, delay in the care delivered in order to input new data on the system, lack of time for training as well as many technical problems that appear when typing data to the system. Care delivered Even though EHRs have been created for improving the care delivered to the patients, Lamberg (2008) noticed that most physicians seem to keep a distance from the patients when they use an electronic record. They mainly focus on entering the data to the computer, rather than focusing to the patient s problem. Also, in many records the information entered is more general and cannot provide information for special cases such as emotional issues. Electronic records end up looking all the same to the doctors, while information entered in the record might be more than is required. Or, as Kinkhorst et al. (1996) notices, some data can be left out from the health professionals in order to avoid criticism from their partners. 18

19 2.6 Global efforts for the implementation of EHR The important role of these records in the sector of health has been identified by many countries around the globe. According to Tsiknakis et al. (2002), the United States (US), the United Kingdom (UK), Australia, Canada and some countries that belong to the European Union (EU) are some of the countries that strive to find the appropriate model that will feed their needs in healthcare. In the same article it is also mentioned that UK had launched an integrated EHR (I-EHR) for a 7-year period that was about to end at year 2005 for the amount of two (2) billion Euros approximately. 2.7 The Greek health system The National Health System (NHS) in Greece was introduced in 1983 in order to reduce the dissatisfaction of people by the existing health services, rebuilt the health system and to give an advantage to the public hospitals by upgrading public hospitals and controlling private ones (Tountas et al., 2005). And even though some of these first goals of the NHS were fulfilled, still Greek Health System has many problems to deal with. As Angelopoulou et al. (1998) points out, for many decades even till now-, the conditions in many Greek public hospitals are not really what a patient could desire and feel comfortable with. Nursing staff and medical equipment insufficient, many ancient buildings and equipment, patients in beds in the corridors of the hospital, endless hours waiting for the results of the medical exams and most of the times staff and doctors that are rather unfriendly, mainly because of the heavy workload and the difficult conditions that have to work in. On the other hand, the private sector, instead of being controlled and reduced as was one of the aims of NHS-, bloomed the last decades and gained the preference of the patients with their facilities and High-Tec equipment. Nowadays the Greek health industry consists of: a. NHS units (128 public hospitals and 185 primary care centers in semi-urban and rural areas). 19

20 b. Health facilities that belong to social insurance funds(5 hospitals and 300 health centers and special units) like the Foundation of Social Insurance (IKA) for employees of the private sector and c. Private sector health units; 218 hospitals and 400 diagnostic centers (Tountas et al., 2005). EHR In the last decade many improvements have been made in order to follow up the needs of the patients and broaden the use of electronic health records in almost all the public health institutes. Of course, the implementation and acceptance of these records seem to be on an early stage. However, some pilot programs for integrated health records like the 2-year INTRANET HEALTH CLINIC PROJECT-supported by the European Commission as part of the Health Telematics Programme (Stalidis et al., 2001) -and the regional health information network HYGEIAnet in Crete (Tsiknakis et al., 2002), emphasize the need of implementing these records not only within a healthcare facility but expanding their use through the internet to patients all around the globe. Also, it is important to notice that HYGEIAnet is an effort to join the University hospital, the general hospitals, primary healthcare centers, doctor s offices even emergency units and private healthcare facilities within a network. 2.8 Technology acceptance model In the previous sections the presentation of both the electronic health and paper records was done. Moreover, a comparison between them was attempted. In this section we present the technology acceptance model (TAM) used by researchers for the study of electronic innovations adoption by employees. According to academics, it best describes IS usage and IS acceptance behaviors (Dasgupta et al. 2002, Holden and Karsh, 2008). TAM was first introduced by Davis (1986), in his study concerning new end-user information systems. The idea of TAM was motivated by the concern that workers 20

21 were not keen to use IT systems that were provided with. The concept of TAM is simple. By investigating employees intention to use an IT innovation, organizations can find the factors that would affect their decision and ultimately to manipulate them (Holden and Karsh, 2008). TAM followed the Theory of Reasoned Action (TRA), a behavioral theory which implies that persons will behave according to a subjective probability (Fishbein and Ajzen, 1975). Figure 2.1: Technology Acceptance Model (TAM) Source: Holden and Karsh, (2008). As can be seen in figure 2.1 above attitude and intention are influenced by Perceived usefulness and Perceived ease of use. According to Davis (1989, p.320), perceived usefulness is the degree to which a person believes that using a particular system would enhance his or her job performance. On the other hand, perceived ease of use is defined as the degree to which a person believes that by using a particular system would be free of effort. The following table (2.2), summarizes the definitions of the original TAM model. 21

22 Table 2.2: Definitions of TAM Source: Holden and Karsh, (2008) TAM in healthcare Holden and Karsh (2008) used the TAM to study health IT adoption for patient care. Their research was based on 20 studies of clinicians concerning patient care with innovative IT systems. This study is an attempt to predict TAM s future in health care simply by studying its past. Their findings indicate a high predictive power of TAM concerning the use and the acceptance of health IT in future studies, though with the need of some add-ons. By the studies reviewed showed three general findings were observed: 1) all studies had high R 2 values, indicating a reasonably high proportion of variance explained, 2) frequently high effect sizes among the variables of the studies, 3) consistent high effect sizes among contracts. Apart from the standard variables of the TAM model, Holden and Karsh (2008) suggest more variables such as study quality, standardization and other theoretical additions. Moreover, they found strong evidence that perceived ease of use will result in greater acceptance and use of IT health systems by clinicians. On the other hand, ease of use may not affect acceptance in a great extent but appears to correlate with usefulness. Finally, it is stated that no 22

23 matter how useful and easy to use a health IT is, effort is needed to ensure that clinicians will be able to use it (self-efficacy), that using the system will be under their control (controllability) and they will be provided with every kind of support (facilitating conditions). Aggelidis and Chatzoglou (2007), used a modified TAM to study the acceptance of health information systems (HIS) by Greek hospital personnel. Their research was conducted involving 341 HIS users from hospitals located in the area of Eastern Macedonia and Thrace, Greece. variables were added to the original TAM. These variables were social influence Based on the finding that technology acceptance should be examined by three different aspects: 1) individual, 2) technological and 3) implementation, some exogenous, training and facilitating conditions concerning the implementation context and anxiety, self efficacy and attitude toward use concerning the individual context. Figure 2.2 below presents the hypothetical model of Aggelidis and Chatzolou s (2007) study. Figure 2.2: Hypothetical model Source: Aggelidis and Chatzoglou (2007, p. 117) 23

24 A set of 13 hypotheses was formed to examine the variables that affect behavioral intention. Finally, their findings point out that personnel s behavioral intention to use HIS is positively affected by perceived usefulness, ease of use of the system, social influence, attitude, facilitating conditions and self-efficacy. Moreover, an indirect relationship among training and behavioral intention was detected. Finally, positive effects between social influence and self-efficacy, perceived usefulness and anxiety, facilitating conditions and social influence were supported by the results General TAM literature Technology Acceptance Model was also used by Chatzoglou et al. (2009), to study Greek employee s intention to use web-based training. Many exogenous constructs were incorporated into the original model. The proposed research model examines the intention as a result of the perceived usefulness and perceived ease of use. Moreover, the indirect effect of management support, enjoyment, self efficacy, computer anxiety and learning goal orientation to the intention to use web-based training, was examined. The relationships examined were multiple. Firstly, the effect of perceived ease of use and perceived usefulness on behavioral intention and among them was examined. Furthermore, it was hypothesized that management support, as an external variable to the model, influences both the perceived usefulness and the perceived ease of use. More hypotheses were developed concerning the relations between enjoyment, computer anxiety, self efficacy, and learning goal orientation on perceived ease of use and usefulness. Finally, it was found that the intention to use web-based training is directly affected by enjoyment, perceived usefulness and perceived ease of use. It was also found that learning goal orientation factor has, among the other factors, the most significant indirect impact on employees intention. Figure 2.3 below presents the conceptual model used for their study. 24

25 Figure 2.3: Employees acceptance of web-based training Source: Chatzoglou et al. (2009, p.879) Malhotra and Galletra (1999), incorporated the psychological attachment into the technology acceptance model. Psychological attachment is referred to the social influence that the user accepts on his behavioral intentions and attitudes, when is obliged to use a technology. This influence results in the users internalization, identification, and compliance with the induced behavior. The findings of this research indicate that social influence affects significantly the acceptance and usage behavior of potential users and adopters. Furthermore, a positive influence among perceived usefulness (PU) and attitude toward using (A) to the behavioral intention was identified. On the other hand, Malhotra and Galletra s (1999) findings do not indicate any direct relationship among social influence and behavioral intention (BI). Finally, it is expected that this study will trigger a deeper analysis and research concerning social influence in a further analysis of TAM. Figure 2.4 below depicts the conceptual model of their study. 25

26 Figure 2.4: TAM extended to account for social influences Source: Malhotra and Galletra (1999) Dasgupta et al. (2002), investigated the applicability of TAM in the user acceptance of electronic collaboration technology. The e-collaboration tool examined was Prometheus, a courseware management technology which was designed in G.Washigton University (GWU) to assist professors in delivering courses to the students via the university s intranet. Prometheus gives to the instructors the ability to post lectures, notes and coursework for students on the system using file exchange. The sample of this study included 60 undergraduate students (33 males and 27 females). Five hypotheses were formed to test the individual performance. Analytically, the first two hypotheses examine the effect of perceived ease of use on perceived usefulness and on system usage. The third hypothesis tests the potential effect that perceived usefulness has on system usage. The fourth hypothesis examines the difference in system usage between novice and advanced users. Finally, the fifth hypothesis deals with effect that the use of the system has on individual performance. First of all, this research proved a wide support for TAM. Other findings revealed that perceived ease of use has a strong and positive effect on perceived usefulness of the system. Also, it was extracted that user level and past experience with group communication tools affect system usage positively. Finally, in contrast to TAM was found that perceived ease of use does not have a significant effect on system usage. Figure 2.5 below presents the research model of Dasgupta et al. s (2002) study. 26

27 Figure 2.5: Research model Source: Dasgupta et al. (2002) Yousafzai et al. (2007) attempted a meta-analysis of TAM by studying 145 published papers dealing with TAM. Meta-analysis is a technique where descriptive statistics of different studies can be analyzed without necessarily acquire access to the original data. Their approach is a literature review on TAM which, in fact, identifies gaps, provide with guidelines and propose future research. According to Yousafzai et al. s (2007) study, TAM has grown in popularity due to three factors: 1) It is IT-specific and has the potential to explain and predict the acceptance of various systems by various users within different cultural and organizational perspectives. 2) It has the advantage of a deep theoretical base which is extended to psychometric and social measurement scales. 3) It gained a wide acceptance by academics and scholars due to its explanatory power and has been presented as a pre-eminent model to study users acceptance. 27

28 2.9 Summary In this chapter we attempted an in depth analysis of electronic health records (EHR) by presenting the relevant literature. After the definition was given, we stressed on the context and the goals of EHR. Moreover, a comparison between paper records and electronic health records was done. Also, a presentation of the technology acceptance model (TAM), introduced by Davis (1986), was done. TAM has been used by numerous researchers either for the study of health IT systems or by various IT systems in general. After the presentation of TAM has been done, it is clear that TAM is the most suitable model for the study of Greek hospital employees intention to use EHR. The following chapter presents the methodology of this study. 28

29 Chapter 3 Research methodology 3.1 Introduction Having ended the part of the literature review, it is expected that the reader has an understanding of what this research is dealing with. This chapter is going to give the reader the opportunity to understand how the research has been conducted. The backbone of this research was based on a hybrid model that has reference to the articles of Chatzoglou et al. (2008) about the impact of web based training and the intention to use computers at work. It is easily understandable that such a tool is adequate to measure intention to use Computer Health records since it is a version of the TAM (Technology Acceptance Model),differentiated to fit the needs of computer acceptance in Greek companies and the intention of the Greek employees to use web based training. 3.2 Methods In order to test the model, it is required that a number of hypotheses are developed and tested. These hypotheses are based on the technology acceptance model, altered in such a way to fit the needs of the research. Different aspects of the acceptance of computers in the work environment are put to the test in order to give us a liable example. Hypotheses 1 and 2 Primarily, the intention to use the electronic patient file is, according to Chatzoglou et al., positively affected its perceived ease of use and its perceived usefulness. It makes sense to state a hypothesis like that, since it is obvious that the friendlier and easier it is for an employee to use a software in general and the electronic health records specifically, the higher the chances he/she is going to use it. On the other hand, it makes sense to say that the more useful a computer tool is to an employee for him to complete his/her task, the higher the chances that he/she uses it. Reshaping the two statements, the first two hypotheses are stated. 29

30 Finally, it makes sense to say that perceived usefulness and perceived ease of use are closely related to one another. The easier to use the software tools enhance the perception of usefulness of the tools H.1 The intention to use the electronic health records is positively affected by the perceived ease of use of it H.2 The intention to use the electronic health records is positively affected by the perceived usefulness of it H.3 The perceived usefulness of the electronic health records is positively associated with the perceived ease of use Hypotheses 4 and 5 Hypotheses 4 and 5 refer to the role played by the management support of the institutes, in this case the hospitals, for the adoption of the electronic health records. By the term management support, one understands the motivation the hospital provides to its employees, the necessary seminars for the completion of the tasks. All the above actions help the employees understand the ease of use of the tool they are called to use as well as the functionality of the tool. Summarizing, the following hypotheses are expressed H.4 The management support and the perceived usefulness of the software are positively associated H.5 The management support and the perceived ease of use of the software are positively related Hypotheses 6 and 7 The next factor that may influence the intention to use the electronic health records is computer anxiety. It makes sense to say that the more nervous someone feels towards computers, the slighter the chances he/she achieves to understand its usefulness. On the other hand, having a negative attitude towards computers will of course make the user feel uncomfortable and thus, even though he/she may want to learn how to use the software, it will be very difficult to do so. H.6 Computer anxiety has a negative impact on the perceived usefulness H.7 Computer anxiety has a negative impact on the perceived ease of use Hypotheses 8, 9 and 10 30

31 On the contrary, the computer self efficacy is a key factor that influences the intention to use electronic health records. It is logical to say that if the employee is a computer expert, he will probably be much more interested in getting to know the new software. Past experience will also help the employee to handle the new software much more easily, understand its concepts and its usefulness. It is also fully understood that self efficacy will have a negative influence on computer anxiety H.8 Self efficacy has a positive impact on perceived usefulness H.9 Self efficacy has a positive impact on perceived ease of use H.10 Self efficacy has a negative impact on computer anxiety Hypotheses 11, 12, 13 and 14 Another crucial factor for employees to have the intention to use computers is enjoyment. It has been statistically proven that the more a person enjoys using computers, the more willing he/she would be to learn new things, especially if it has to do with their occupation. Scholars also believe that there is a positive association between enjoyment and perceived usefulness (Davis et al.., 1992; Venkatesh, Speier, & Morris, 2002) and between enjoyment and perceived ease of use (Koufaris & Hampton-Sosa, 2002; Moon & Kim, 2001; Venkatesh, 1999, 2000; Yi & Hwang, 2003). These ideas can be more specific and focus on the electronic health records. Finally, the more a person enjoys using these electronic health records, the more confidence he achieves. H.11 Enjoyment of using the electronic health records and intention to use them are positively associated H.12 Enjoyment of using the electronic health records and perceived usefulness are positively associated H.13 Enjoyment of using the electronic health records and perceived ease of use are positively associated H.14 Enjoyment of using the electronic health records and self efficacy are positively associated Hypotheses15 and 16 Finally, another factor that may be proven crucial for the intention to use of the electronic health is the subjective social norms. This term refers to the opinions of 31

32 colleagues, friends and close relatives. General research has indicated that these subjective norms are not very important when there are loose relationships within the working environment. On an organization level though, things alter. As things get serious, the opinions of colleagues who have used the tool may be proven the turning point. Besides, the influence of top management may also intervene and make employees see the use of the tool as the only way to success. It could be then argued that: H.15 The opinion of top management can positively influence a person s subjective social norms about using the electronic health records H.16 The social subjective norms of a person can positively influence towards the use of electronic health records All the above statements can be depicted in the theoretical model depicted in fig.3.1 Enjoyment Subjective norms Computer anxiety Perceived usefulness Self efficacy Intention to use Management support Perceived ease of use Figure3.1 The proposed model 3.3 Research questionnaire As mentioned before, the tool that was used to measure the intention to use the electronic health records is a hybrid tool comprised of two methodologies 32

33 (Chatzoglou et al., 2008 and Chatzoglou et al., 2009). The questionnaire is split up to eight parts. Part one is a common part with general information about the participant. Parts two to eight concern questions about the questionnaire conducts. The items were all based on a 7 point Likert scale. The questionnaire is attached in Appendix A a) Intention to use More specifically, the second part of the questionnaire is designed to measure the intention to use the electronic health records and is based on the TAM (Yu et al., 2005; Premkumar and Bhattacherjee, 2008). This specific part is refers to 5 tools, more specifically: 1. I intend to use the electronic health records, if my manager asks me to 2. I intend to use the EHR in order to become more efficient 3. I intend to use the EHR continuously 4. I intend to take advantage of all the opportunities offered to me by the electronic health records 5. I would recommend the use of the EHR without second thought b) Ease of use and usefulness Parts 3 and 4 are designed to measure the perceived ease of use and the perceived usefulness of the electronic health records. These 2 parts are also based on the original TAM but they are modified in order to meet the needs of the current study. These two parts refer to 4 items each. More specifically for the ease of use: 1. Learning to use the EHR would be an easy procedure for me 2. Using the EHR in order to achieve my goals would be an easy task 3. It would be easy for me to gain special knowledge while using the EHR 4. Using the EHR would be easy for me While for measuring the perceived usefulness, the following tools are used: 1. The use of EHR would help me be more efficient 2. The use of EHR would help my productivity 3. I believe that the use of the EHR would be useful for my work 4. My work efficiency would be increased by the use of the HER 33

34 c) Management support Following, in order to measure the management support, three items were used. According to previous literature (Karahanna et al., 1999), the management support tool could be considered as an extension of the original TAM. It was also proven that organizations lacking management support wouldn t easily persuade the employees to use the technology required for the case Venkatesh et al., The three items that describe the management support are the following: 1. The management is fully aware of the potential of the EHR 2. The management encourages at maximum the use of the EHR 3. The management would be excited if the employees used the EHR d) Self efficacy Self efficacy refers to the capability of the employees to get in touch with specific software without the help of external factors. In this case, these it will be measured on the level the employees are capable to use the electronic health records. The specific construct will be measured using 11 items which are the following: I would use the EHR 1. Even though there is no one to show me how to use it 2. Even though I never used something likewise in the past 3. Even if I only had the manuals as a help 4. If I had seen someone else use it in the past 5. If I could find someone to help me in case of a problem 6. If someone helped me start 7. If I had more time for its completion 8. If someone has shown me before how to use it 9. Even if I only had the system help 10. If I had used similar technology for the same purpose in the past. e) Computer anxiety A great obstacle for the adoption and cultivation of the intention to use the EHR is the possible anxiety an employee might feel when using a computer. Computer anxiety is a factor that is measured with the aid of 4 items. 34

35 1. I feel uncomfortable in the thought of using the EHR 2. I feel as if, with the push of a button everything inside the tool is going to be erased 3. I get intimidated to use the EHR because in a case I do something wrong, I wouldn t know to correct it 4. In general, I get scared to use the EHR f) Enjoyment Previous research (Offodile and Abdel-Malek, 2002). has shown that the attitude towards computers in general can play a key role in weather to adopt new technologies within the working environment. Davis, Bagozzi, and Warshaw (1992) and Igbaria, Schiffman, and Wieckowshi (1994) were the first ones to include the term enjoyment in the generalised TAM. By the term perceived enjoyment, the pleasure of using technologies in general is highlighted, while in this specific research the term technologies is substituted by the HER. The following items describe the term perceived enjoyment at the maximum: 1. I would enjoy using the EHR 2. The use of EHR would be a pleasant procedure 3. I would find the use of EHR very enjoyable g) Social subjective norms The social subjective norms reflect the influence of important people on a person as far as the use of EHR is concerned. The specific factor is highlighted in previous literature (Fishbein and Ajzen, 1975; Ajzen, 1991; Taylor and Todd, 1995; Venkatesh and Davis, 2000; Venkatesh et al., 2003; Yu et al., 2005) as a key role player in the adoption of computerized systems. Eleven items are used to describe this construct 1. People that are important to me believe that I should use the EHR to fulfill my everyday tasks at work 2. People that influence my everyday behavior believe I should use the EHR to fulfill my everyday tasks at work 3. My colleagues believe that I should use the EHR to fulfill my everyday tasks at work 35

36 4. Generally speaking, I like to do whatever my colleagues think it s right for me at work 5. My managers believe that I should use the EHR to fulfill my everyday tasks at work 6. Generally speaking, I like to do whatever my managers think it s right for me at work 7. I am going to use the EHR because my managers demand me to do so 8. My personal beliefs about the EHR are different from the ones that I express in public 9. If I don t get rewarded I do not find any reason to use the EHR 10. If I want to get rewarded for my works I need to use the EHR 11. The amount of effort I put in using the EHR is in accord with the reward I get 3.4 The raw data The instrument was sent to 10 hospitals in Greece, nominally, the University hospitals in Alexandroupoli, Ioannina, Patra, Larissa and Chania, the Papanikolaou and Papageorgiou hospitals in Thessaloniki the 2 General hospitals in Kavala and the General hospital in Drama in electronic form. Finally, 437 responses were received. The following table indicates the variances of their gender. All the respondents were health staff in these hospitals and their average age were the 38 years Initially, the questionnaire was sent to the hospital chiefs of staff by , and then printed and handed out to the staff. A little seminar was held to explain the contents of the questionnaire to give a briefing about what the EHR is all about, so that everyone understands what the subject is. To the hospitals nearby, the questionnaires were handed out in person and little interviews were held with the permission of the chief of staff in every hospital. Initially 542 responses were gathered, 294 of which were electronic and the rest were personal. The evaluation of the questionnaire was held with the aid of a medicine PhD student, an MSc student of Computer science and an MSc psychology student. At first, the questionnaires that were less than 3/5 complete were excluded. For more reliable results, questionnaires with a small variety of answers (1-3) were excluded because it was considered that the respondents answered in a rush. This way, we ended up with the remaining questionnaires 36

37 In the SPSS file, MNG1->MNG3 symbolise the items of management support, COMPANX1 ->COMPANX4 the items of computer anxiety, ENJ1->ENJ3 for enjoyment, INT1->INT5 for the intention to use, PEU1->PEU4 for the perceived ease of use, PUF1->PUF4 for the perceived usefulness, SUBJ1->SUBJ11 for the subjective norms, SELF1->SELF10 for the self efficacy. 37

38 Chapter 4 Data analysis 4.1 Proposed analysis Having expressed the hypotheses of this research and analyzed the instrument that was handed out, it is time we proceed to the results of the study and how these results came out. In brief, the responds received in this research were evaluated by the use of SPSS 16 and LISREL. Initially, since the items were taken by other analyses familiar to our own, confirmatory factor analysis is going to be held. In order to confirm or reject the hypotheses, a structural equation model is going to be set up and the respondents answers are going to be evaluated by path analysis, correlation analysis and chi square test analysis. 4.2 The results a) Reliability analysis The first thing to do in the analysis is to run a reliability analysis in order to check the descriptive statistics and the Cronbach s alpha for each factor. The Cronbach s α provides us with a number that shows how well a group of variables comprise a unidimensional latent construct. When there is multidimensionality between items then the Cronbach s α will be low. In practise the value of Cronbach s α should be above 0.5. Technically speaking, the Cronbach measurement is not a test but a reliability indicator. Cronbach s alpha is considered to be the reliability of a measuring part of study responses which calculates the main factor of the analysis. It is also known as scale reliability coefficient. Tables 4.1 and 4.2 show the reliability statistics and the descriptive statistics 38

39 Table 4.1 Reliability statistics 39

40 Table 4.2 Descriptive statistics As seen, all Cronbach s alpha measurements are above the critical value of 0.5, which show that reliability is high. b) Factor analysis Moving on, confirmatory factor analysis is performed initially in SPSS, and then in LISREL. Since all items were taken from other researches, it is wise to use only confirmatory factor analysis and not exploratory factor analysis. When performing this type of analysis in SPSS, each and every factor is checked on its own, so as to check whether the analysis extracts one factor or more. One other aspect that should be taken into account are the values of KMO and the Bartlett s test of sphericity. The Kaiser-Meyer-Olkin measure of sampling adequacy tests whether the partial 40

41 correlations among variables are small. The KMO measures the sampling adequacy which should be greater than 0.5 for a satisfactory factor analysis to proceed. Bartlet s measure is a number that tests the null hypothesis that the correlation matrix is an identity matrix. This implies that factor analysis would work, if there is a relationship between the variables and if the R-matrix is the identity matrix then there would be no correlation between variables. Hence, it is necessary for this test to be significant (i.e. the significance value should be less than 0.05). This value indicates that the R-matrix is not the identity matrix and there is some correlation between variables which will affect the analysis. In the case of this analysis the p value is highly significant and thus, factor analysis is acceptable. Table 4.4 shows a concentrating table of the results drawn in each analysis. Table 4.2 (cont) The results of the test for the KMO and the Bartlett s test show that the indices are greater than the acceptance levels. More analytically, there should be KMO>0.5 and 41

42 the Sig<0.05, which implies that there is a relationship between the variables and the null hypothesis does not stand. Table 4.3 shows the result of the KMO and Bartlett s test of sphericity. Table.4.3 KMO and Bartlett s test of sphericity Let us now get more into detail about the goodness of fit statistics of the model. RMSEA: The RMSEA (root mean square error approximation) takes into consideration the error of approximation in the population. The question of how well would the model, with unknown, but optimally selected parameter values fit the covariance matrix of the population arises. This value is expressed per degree of freedom. A RMSEA less that 0.05 indicates good fit while values less that 0.08 show that there are reasonable errors included in the ongoing analysis. RMR: The RMR (root mean square residual) is a measure of the average residual value for the fitting of the covariance-variance measure into the model. A good fitting model would have a small RMR (RMR<0,05). GFI and AGFI: GFI (goodness of fit index) measures the relative value of variance and covariance in the S. matrix. Adjusted GFI or AGFI adjusts the number of degrees of freedom. Their values range from 0 to 1 with values close to 1 indicating good fit while the acceptable level is GFI,AGFI>

43 CFI and NFI: Comperative Fit Index and Normed Fit Index range from 0 to 1 on the basis of comparison of the model to the independence model (null model). The acceptable level here again is CFI>0.9. In order to check the validity of the confirmatory factor analysis, the data are introduced in LISREL 8.8. The validity of each and every factor is checked, although a number of items seem to have a negative impact for the goodness of the model fit. To be more specific, from the analysis were excluded all the items that had t values that created problems in the model, or because they had low error measurements close to the factor loadings.. It is important to mention that if the t-values vary between 1.96 and 1.96 then all the items statistically unimportant at the significance level of 5%. From the initial 44 items that were included, there were 32 items left in the analysis. From the analysis, the following items were excluded: PEU4, PUF1, INT4, INT5, SUBJ1, SUBJ7, SUBJ10, SUBJ11, SELF1, SELF5, SELF9. Figure 4.3 shows the goodness of fit statistics for every factor. The numbers are all above the desired level for CFI, GFI and AGFI, the values of RMR and RMSEA are all below the level of 0.1 while the ratio of chi square/df is always below the elastic level of 5. the numbers above indicate that the factors show good fit for the SEM analysis. Figure 4.3 Goodness of fit statistics for every factor Another important aspect in the factor analysis is the construct reliability and the variance extracted. The construct reliability is a measure of the internal consistency of the structure indicators, depicting the level up to which they are showing the common 43

44 structure, while the variance extracted explains the level the latent variables explain the factor constructs. Table 4.4 shows the values of the fore mentioned metrics of every factor. It should be stated that the construct reliability should exceed the value of 0.7 while the variance extracted for each factor should be greater than 50%. Table 4.6 shows the fit statistics for every factor in the confirmatory factor analysis Table 4.4 Construct reliability and variance extracted for each factor It can be noticed that construct reliability for all objects exceeds the critical value of 0.7, i.e. they should be taken into account in the analysis, while the all variance extracted values are all above 50%, which means that the latent variables explain the determination variables at an adequate level. To proceed, the theoretical model is configured on LISREL. All the relationships are built up in a structural equation model with dependant and independent variables. In general this modeling technique is used to express relationships between latent variables. Then, the LISREL file is built and run. Table 4.7 shows the goodness of fit statistics of the model. Even though the p value slightly exceeds the critical limit of 0.05, the ratio of chi square/df is greater than one and less than the very elastic limit of 3 44

45 Table 4.5 Model fit It is worthwhile noticing that all the fit indices (CFI, GFI, AGFI) are all above the desired value of 0.9, as well as the RMSEA and RMR values are below the critical limit of 0.1 c) Evaluation of the structural models The first thing to check in the evaluation of the metric models is the loadings of the determining variables of every factor as well as their statistical significance. It is clear from figure 4.1 that that all the typical loadings of both the independent (X) and the dependent (Y) variables are all above 0.5 and can be considered satisfactory at the significance level of 5%. All signs agree to the ones of the hypotheses while table 4.6 presents all the impact values between the structures, as they have been declared by the research hypotheses. The same table also indicates the t and p values which show if the hypotheses are going to be accepted or rejected. In practice, a hypothesis will be rejected if the t values, which are the result of the division between the impact values and the typical error, fall within the range of 1.96<t< In that case, they are considered insignificant at the level of significance of 5%. In total, all hypotheses are accepted, since at the level of significance of 5%, the p-values of the different relationships are all less than the critical level of

46 Table 4.6 Hypothesis testing Figure 4.1: final structural model 46