Information technology has been used in healthcare delivery systems to improve patient safety and patient care outcomes worldwide.1-3 The importance of information technology was recognized by the Institute of Medicine4 in 2000 when they released the report entitled, To Err Is Human; the report recommended increased efforts to incorporate information technology into the delivery of patient care, and since that time, there has been a remarkable effort on the part of many organizations (Leapfrog Group, the National Patient Safety Foundation, the Institute for Healthcare Improvement, and the Joint Commission) to improve patient safety by supporting the use of information technology. All of these organizations have encouraged the implementation of information technology to prevent human error.5
A high incidence of adverse events and medical errors has been found in critical care settings (ICUs).6-8 The Leapfrog Group, the National Quality Forum, and the Agency for Healthcare Research and Quality have all recommended that the ICUs be staffed exclusively with board-certified critical care physicians (intensivists) who will respond immediately to provide patient management, thus decreasing medical errors and adverse events and reducing hospital mortality rates.7,8
A shortage of critical care physicians and nurses makes it difficult to comply with the recommendation. During the night shift and weekend hours, it is more difficult to have critical care physicians covering for the ICU patients.8 Therefore, it has been proposed that telemedicine technology, eICU (remote ICU or electronic ICU) be used as a possible alternative solution that allows critical care nurses and physician intensivists to monitor ICU patients from off-site locations. The rationale is that patients can be more intensively monitored, thus decreasing adverse events and improving patient outcomes.5,6,8 As of 2008, this technology, eICU, was implemented in 28 states covering more than 200 hospitals and 40 healthcare systems.9 According to several studies,6,8-11 the eICU technology system has many advantages over the traditional ICU systems that exist today, such as decreasing in-hospital mortality, decreasing ICU length of stay, decreasing cost, and increasing high quality of care.
The eICU unit is a secure telemedicine center where a team of critical care physicians and nurses provides oversight surveillance for the patients in off-site intensive care units. This monitoring utilizes various technologies such as video assessment, two-way communication directly into the patient's room, access to hospital information systems, and the use of eCareManagement (VISICU Inc, Baltimore, MD). Data, including vital signs from bedside monitors, intake/output, blood glucose, laboratory results, and current medications are all interfaced with the computer database system. Thus, the eICU team can review all of the medical data through the computer system and have immediate communication with and access to the on-site nurses and physicians. Bedside nurses have the role of closely monitoring the patient and cooperating with the eICU team in assessment and management.
The intent of this study was to determine significant factors and predictors of nurses' intention to use (ITU) the telemedicine technology (eICU) and to provide psychometric data, further supporting the evidence for the reliability and content validity of the measurement scale strategy. Nurses have been identified as computer end users in healthcare settings. In the 1980s, they were often apprehensive about integrating computer systems into their nursing practice.12 Recently, a common fear of nurses is that the information technology will take over their roles, and their roles might be replaced by machines.1 Also, nurses' frustrations when using a new technology system have been associated with the fears of increasing workload and an unfriendly technical system.13 Stafford et al14 conducted an ethnography study and examined the collaborative communication between on-site (bedside) and off-site (eICU) nurses. These researchers found that the on-site nurses felt uncomfortable, as if they were being watched; some were resentful, and these nurses questioned the eICU nurses about their commitment to nursing practice.14
Based on the author's critical care experiences, nurses complained when they received a telephone call from the eICU nurses. On-site nurses felt as though someone was watching over their shoulder. They complained about having to do extra work, losing autonomy, receiving contradictory orders from two different doctors (from on-site and off-site), and creating duplicate documentation. While several nurses had positive attitudes toward eICU, they struggled when they had to deal with technical problems with the computer program. As a result, some nurses were not willing to communicate with the eICU nurse and refused to take advice from the eICU team.
The successful implementation of clinical information technology systems is highly dependent on user acceptance.15 Patient safety and quality will not be achieved if nursing staff are not willing to use the technologies.15 Therefore, it is critical to understand the readiness and willingness of hospital nurses to implement new information technology and efficiently integrate it into their practice.16
One model that has attempted to explain acceptance of technology systems is the Technology Acceptance Model (TAM); according to TAM, the intention generates the actual behavior.17 Thus, behavior should be predictable from measures of behavioral intention and other factors that influence intention directly and indirectly.18 Since recognizing that the ITU new technology has become a very significant issue, it is conjectured that TAM may be a useful conceptual framework for examining nurses' ITU telemedicine technology.19
CONCEPTUAL FRAMEWORK/RESEARCH GAP
The TAM was initially developed by Davis et al in 1986.17 The TAM provides a framework for understanding the determinants of computer acceptance that explain user behavior with a variety of end-user populations. The theoretical framework has the potential to identify, explain, and predict the factors, such as internal beliefs and attitudes, which have an effect on the intentions of technology end users.17,18
The original TAM has five constructs: perceived usefulness (PU), perceived ease of use (PEOU), attitude toward using, ITU, and actual system use.17 The key determinants of computer acceptance in TAM are the belief that the computer system will help to improve job performance (PU) and the belief that using the computer system requires only a minimal level of mental effort; in other words, it is easy to use.17 Those two determinants are considered to be the basis for evaluating the attitudes toward using particular computer systems and ultimately generating the ITU. The ITU a particular system then leads to actual end-user behavior.
The TAM has evolved over time, being used with different populations and various technology systems; the framework has been used extensively in information technology, education, and business.20-29 However, there have been few studies in healthcare settings,20,22,25,27 and even fewer with regard to nursing practice.16,25,30 While nurse researchers have focused on developing and testing instruments that measure nurses' attitudes toward using new computer technology,1,2,12,13,31-33 very few studies have examined the ITU the technology in the practice of nursing.16,30
PROPOSED CONCEPTUAL MODEL
A number of nursing studies have demonstrated that the primary factors influencing nurses' attitudes toward using computers are age, years of nursing experience, and years of experience with computers.3,16,33 Based on interviews with five nursing experts in the field and previous studies, three additional potential factors that influence the individual's belief and attitudes toward using telemedicine technology (eICU) were the number of years working in the hospital, support from administrators, and support from physicians.16,34 According to Shoham and Gonen,16 support from the nursing administrator is a significant factor of influence on the nurses' intentions to use the computers. Taken together, these six factors were selected as the external variables in the proposed conceptual model that underpins the current study.
The TAM provides the related constructs of PU, PEOU, attitude toward using, and ITU. PU and PEOU are the known primary key determinants for computer acceptance behaviors. Both determinants influence attitudes, leading to ITU and ultimately to actual individual usage behavior.17
Interestingly, according to the Pearson correlation results in this study, there were no statistically significant correlations with the three constructs that were drawn from the original TAM: age, years of experience in nursing, and years of experience with computers. The literature reveals that the relationships between the latter variables and attitudes toward computerization were unclear with most of the studies,3,15,32 having been done in different settings and with different computer programs. The eICU technology has been implemented in nursing practice for only a few years.9,14 It would appear that as technology changes and the demands of practice change, the attitudes of nurses will be shaped and formed by their earlier interface with technology, as well as their experiences with technology in nursing education.
In the current study, the author sought and received permission from the creators of the TAM to revise the original model, and renamed it the Telemedicine TAM (TTAM; Figure 1). The TTAM utilizes four constructs drawn from the original TAM (PU, PEOU, attitude toward using, and ITU) and the three previously identified external variables (years working in the hospital, support from administrators, and support from physicians). Age, years of experience in nursing, and years of experience with computers were omitted from the model because they were not statistically supported by the data.
1. What are the relationships among the external variables, the key constructs of TTAM, nurses' attitude toward computerization, and the ITU telemedicine technology (eICU)?
2. Which variables are most influential in predicting ITU telemedicine technology (eICU)?
3. Is the proposed hypothesized model consistent with the empirical data in the study (in other words, goodness-of-fit)?
The eICU Acceptance Survey was used as an instrument for the study. The instrument was modified from three original instruments, which are (1) PU and PEOU,35 (2) Nurses' Computer Attitudes Inventory,31 and (3) ITU.20 The content validity was examined by five nursing technology experts. The questionnaire for the experts consisted of 10 questions. The experts were asked about the content of each construct for clarity, appropriateness, the relationship of overall items, and whether those items measured the constructs. The experts were given 1 week to complete the questionnaire, and then the researcher discussed the results with each expert. The results from five experts showed that they all agreed or strongly agreed on each item. Therefore, overall results of content validity for this study were satisfied.
The internal consistency of the instrument constructs was evaluated using coefficient α (Cronbach α), which showed ranges of .91 to .96 with a total coefficient α of .96 (Table 1).
Potential nursing participants were RNs, employed in critical care units, where the eICU technology had not yet been implemented. The nurses employed in these units had a nurse-to-patient ratio of 1:2. Nurses in this study were caring for patients who were in critical conditions that might require life support, such as a ventilator or vasopressive medications. Nurse managers or directors of the critical care units and the nurses who had worked in the unit that has implemented eICU technology before were excluded.
There were 139 potential participants in two metropolitan healthcare systems; of these, 131 nurses participated for a 94% response. Of the 131 responses, three (2.16 %) were excluded because of incomplete answers, and 11 (7.19%) did not meet the criteria (two participants were managers, and nine participants were not employed in the critical care units). The final sample was composed of 117 nurses. Given a moderate effect size (0.13) with an α of .05 and a power level of 0.80, the sample of 117 was adequate for multiple regression and path analysis based on Marsh and colleagues (1988) as cited in Kelloway,36 Bollen,37 and Harris and Schaubroek.38
Data Collection/Data Analysis
The study was implemented after approval by the human subjects review boards of the participating university and the two healthcare systems. Nurses who indicated a willingness to participate were provided a cover letter, an informed consent form, and the study questionnaire in a personal meeting with the researcher. During the meeting, the researcher explained the purpose of the study and reviewed the informed consent form and the questionnaire. Signed participant consent forms were placed in an envelope that was kept separate from the questionnaire. Participants were instructed to complete the questionnaire and return it to the researcher in a sealed envelope. Once collected, both consent forms and questionnaires were stored separately in a locked drawer.
Descriptive statistics, regressions, and multiple regressions (for path analysis) were accomplished with the SPSS version 15.0 data analysis software (SPSS Inc, Chicago, IL). LISREL 8.8 (Scientific Software International, Lincolnville, IL) was used to test the goodness of fit with the proposed model (TTAM). Statistical significance for all of the analyses was set as P < .05. Data screening was performed for missing data and outliers, and the assumptions of multiple regression analysis methods were considered.39,40
The average age of the participants was 35.4 (SD, 9.37) years. There were 107 women (91.5%) and 10 men (8.5%). Most of the participants were employed on the day shift (56 [47.9%]), whereas 35 (29.9%) were employed on the night shift, and 26 (22.25%) worked both day and night shifts. Sixty-four of the participants (54.7%) had bachelor degrees in nursing, 31 (26.5%) had associate degrees in nursing, and three (2.6%) had graduate degrees in nursing. Most of the participants (54 [46.2%]) had heard about eICU from nurses who had prior experience with eICU technology; however, 39 (33.3%) had heard about eICU from both nurses who had prior experience and nurses who had not used the technology. Only one participant had learned about eICU technology from the Internet.
Most participants (101 [86.3%]) had never attended a conference on eICU technology, and 98 (83.35%) had never been trained to use eICU technology. There were only 16 participants (13.7%) who reported that they had attended a conference on eICU technology and 19 (16.2%) who had been trained to use the eICU technology before they worked in their current units. Most of the participants (110 [94%]) reported that there were technology support personnel available in their units, while seven (6.0%) reported there were no available support personnel.
The participants had worked in nursing an average of 10.44 (SD, 9.13) years, and in the present hospital for 6.83 (SD, 6.98) years. The average number of years for working in the critical care units was 7.64 (SD, 7.82) years. The average number of years that nurses had known about eICU was 2.41 (SD, 1.45) years, and the average number of years working with any type of computer technology was 14.77 (SD, 5.97) years.
A path analysis was performed to determine the causal effects among the variables in the proposed model, TTAM. Multiple regressions identified four paths based on the assumptions of the causal closure of the path diagram (Figure 1).
According to the results of regression 1, the following path coefficients were statistically significant: years working in the hospital to PU (β = −.200, P = .010), support from physicians to PU (β = .270, P = .003), and PEOU to PU (β = .420, P = .000). The path coefficient of support from administrators to PU was not significant (β = .051, P = .576).
The results of regression 2 provided a path coefficient from support from administrators to PEOU that was statistically significant (β = .242, P = .009).
The results of regression 3 provided two path coefficients that were statistically significant: from PU to attitudes toward using (β = .297, P = .000) and from ease of use to attitudes toward using (β = .466, P = .000).
Finally, the results of regression 4 provided two path coefficients that were statistically significant: from PU to ITU (β = .506, P = .000) and from attitudes toward using to ITU (β = .364, P = .000).
LISREL provided an analysis of "fit" index values that was used to examine and determine the model fit for the data collected in this study. The overall model fit was guided by using multiple fit indices as suggested in the literature20,36,41-43 and is presented in Table 2. The results show that the model fit was reasonably adequate. Furthermore, the LISREL program provided the results of squared multiple correlations for structural equations (R2) that explain the power of the model for individual constructs.
Together, years working in the hospital, support from physicians, support from administrators, and PEOU explained 35% of the variance observed in PU. Support from administrators explained only 6% of the variance observed in PEOU. However, PU and PEOU were able to explain 44% of the variance observed in nurses' attitudes toward the eICU technology system. Finally, PU and nurses' attitudes toward using eICU technology were able to explain 58% of the variance observed in ITU in the eICU technology.
This study evaluated the usefulness of the TTAM in explaining nurses' ITU eICU technology systems. The TTAM was able to explain 58% of the variance in ITU eICU, and the ITU was predicted by PU and attitude toward using. Compared with a prior TAM study in nursing settings,30 the TTAM in this study appeared to be more useful in explaining the intention of nurses to use the technology.
According to the model fit results, the model fit indices were within the reference range. Therefore, the TTAM in this study appears to adequately specify the intention of nurses to use the eICU technology system and has the ability to reproduce a correlation matrix with this nursing sample as did the original TAM. The suitability and applicability of TTAM in the nursing sample were confirmed as indicated by reasonable model fit indices. Nevertheless, "it is important for researchers to recognize that 'model fit' does not equate to 'truth' or 'validity.' Finding the expected pattern of correlation is a necessary but not sufficient condition for the validity of the theory that generated the model predictions."36(p40)
In terms of prediction, in agreement with the original TAM and previous studies20,30,35,43 PU was found to be a key determinant that has a statistically significant and strong influence on nurses' intentions to use the eICU technology system. This suggests that nurses in critical care units tend to focus on the usefulness of this technology itself. In this study, PU was significantly impacted by PEOU as TAM hypothesizes, contrary to what Hu and colleagues20 found, namely, that PEOU had no significant effect on PU of the telemedicine technology. However, the population of the study of Hu et al20 was not nurses, but physicians. The nature of the population might well play a role for this contradictory result.
According to the results of this study, PEOU was found to have more significant effect on nurses' attitudes toward using the eICU technology system than PU. This result could reflect that most of the critical care nurses in the current study were familiar with using computer technology equipment (average number of years using computers was 14.77). They had already been charging, ordering, documenting, and using computers with various types of software programs. Moreover, 94% of nurses in this study reported that they had personnel support in their facilities to help them solve technical problems whenever they were struggling with new technology features or operations.
Nurses' attitudes toward the eICU technology system were also a significant factor that predicted the ITU in this technology, even though it contributed less to predicting the ITU in this technology than PU. The results demonstrated that nurses' attitudes were also relatively important to nurses' ITU in the eICU technology system. Nursing practice requires particular knowledge and skill in dealing with patient care within the critical care environment. Nurses are very focused on their patients because those patients are acutely ill. Any new technology that appears to take the nurses away from patient care would lead to the belief that it would not be useful or helpful. The TTAM does appear to explain the factors that influenced nurses' attitudes toward using the eICU technology system in a manner different from the original TAM. This may be due to the unique characteristics of the healthcare setting and the nature of the nursing profession.
According to the results, the numbers of years working in the hospital had a negative statistically significant correlation with PU, suggesting that nurses who worked in the hospital longer might believe less in the usefulness of the technology. Nurses who worked longer might need to receive information at the early stage of implementation. Therefore, an information and training program that outlines the purpose, usefulness, and features of eICU technology should be provided. This program would outline effective communication strategies among healthcare providers to foster better communication and avoid conflicts. The information and training program would primarily focus on how the eICU technology system can help improve patients' safety and outcomes.
PU was also influenced by support from physicians. Physicians had a great impact on nurses' belief about how this technology would be beneficial for their patients. Therefore, to increase nurses' ITU in the eICU technology system, it is necessary to have support and cooperation from the physicians as suggested by the findings. Communication between physicians and nurses needs to be clear and have precise direction. A physician who embraces computer technology must be selected as the one in charge of patient care management and should be identified before the eICU technology system is implemented. Protocols that outline the plan of escalation of treatment must be clearly stated, thus enabling nurses to deliver quality care and promote patient safety.
The support from administrators, an external variable from this study, showed a significant influence on PEOU, but not a significant influence on PU. Most of the administrators or directors in critical care units were RNs. They have similar backgrounds with nurses who work at the patient's bedside. However, the nursing administrators had a different focus on the eICU technology system. Their focus was on how to provide all nurses with user support and proper training before the technology was implemented. The administrators often reassure the nurses that using this technology would not be difficult and that it would improve patient outcomes.
Prior to introducing the new eICU technology system to intensive care units, administrators can increase the ITU the technology system by assessing nurses' and physicians' perceptions. Nurses and physicians should be involved in the planning and implementation process. Since PEOU is a main factor in predicting attitude toward using, nurse administrators could support this factor by having on-site user training and reassuring the staff that they will have personnel support at the units at all times.
LIMITATIONS AND RECOMMENDATIONS
Participants in this study were volunteers and subject to self-selection biases. Additional research is needed to address construct validity with a larger sample size and improved model fit. The indirect path coefficient (ease of use and ITU), which is statistically significant, and the direct path coefficient (support from administrator and usefulness), which is not statistically significant, may need to have further investigation for model modification to improve the "fit" of the research model.
The external variables (years working in the hospital, support from administrators, and support from physicians) were the primary factors that influenced the two key determinants (PU and PEOU). In the healthcare setting, there might have been more than three factors that influenced those two key determinants specified in the TTAM. More investigation on external variables, such as knowledge, participation in the decision-making process, peer support, and individual awareness, is needed.
To improve the nurses' ITU the eICU technology system, cultivating PU and attitudes toward using this technology are important. In critical care units, nurses have high autonomy and are competent in patient care. Since patients' outcomes are of utmost concern, it behooves administrators to support the autonomy and competency of the nurses by providing them with the educational opportunities that allow them to adapt to new technology and new environments.
In addition, this nursing study used and adopted a theoretical model and instruments that have been developed from the discipline of social psychology. The TTAM shows promise as a valuable model for predicting nurses' ITU in the eICU technology in healthcare as demonstrated by the fact that 58% of the variance (R2 = 0.58) in ITU the technology is explained by the model in this study. Furthermore, the study suggests that the TTAM has applicability in identifying, explaining, and predicting the ITU telemedicine technology in nursing practice. The replication of this study is also highly recommended.
The author thanks Dr Catharine A Kopac, associate professor at the George Washington University, and Dr George Crossman for editing the article. Also, the author thanks Dr Jean Burley Moore, Dr Heibatollah Baghi, and Dr Gregory Guagnano, George Mason University, who have assisted me throughout the study.
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