Examining the Factors Affecting PDA Acceptance among Physicians: An Extended Technology Acceptance Model

Intention to Use Mobile-Based Partograph and Its Predictors Among Obstetric Health Care Providers Working at Public Referral Hospitals in the Oromia Region of Ethiopia in 2022: Cross-Sectional Questionnaire Study

BACKGROUND

A partograph is a pictorial representation of the relationship between cervical dilatation and the time used to diagnose prolonged and obstructed labor. However, the utilization of paper-based partograph is low and it is prone to documentation errors, which can be avoided with the use of electronic partographs. There is only limited information on the proportion of intention to use mobile-based partographs and its predictors.

OBJECTIVE

The objective of this study was to determine the proportion of obstetric health care providers at public referral hospitals in Oromia, Ethiopia, in 2022 who had the intention to use mobile-based partographs and to determine the predictors of their intention to use mobile-based partographs.

METHODS

We performed an institution-based cross-sectional study from June 1 to July 1, 2022. Census was conducted on 649 participants. A self-administered structured English questionnaire was used, and a 5% pretest was performed. Data were entered into EpiData version 4.6 and exported to SPSS version 25 for descriptive analysis and AMOS (analysis of moment structure; version 23) for structural and measurement model assessment. Descriptive and structural equation modeling analyses were performed. The hypotheses developed based on a modified Technology Acceptance Model were tested using path coefficients and P values <.05.

RESULTS

About 65.7% (414/630; 95% CI 61.9%-69.4%) of the participants intended to use mobile-based electronic partographs, with a 97% (630/649) response rate. Perceived usefulness had a positive influence on intention to use (β=.184; P=.02) and attitude (β=.521; P=.002). Perceived ease of use had a positive influence on attitude (β=.382; P=.003), perceived usefulness (β=.503; P=.002), and intention to use (β=.369; P=.001). Job relevance had a positive influence on perceived usefulness (β=.408; P=.001) and intention to use (β=.185; P=.008). Attitude positively influenced intention to use (β=.309; P=.002). Subjective norms did not have a significant influence on perceived usefulness (β=.020; P=.61) and intention to use (β=-.066; P=.07).

CONCLUSIONS

Two-thirds of the obstetric health care providers in our study intended to use mobile-based partographs. Perceived usefulness, perceived ease of use, job relevance, and attitude positively and significantly influenced their intention to use mobile-based electronic partographs. The development of a user-friendly mobile-based partograph that meets job and user expectations can enhance the intention to use.

'Digitising health protection schemes in Ghana': An enquiry into factors associated with the use of a mobile phone-based health insurance contribution payment system among tertiary students

In 2018, Ghana's National Health Insurance Scheme (NHIS) introduced a mobile money payment system for membership renewal and premium payments to enhance enrolment and retention rates. However, the adoption of such innovations depends on various factors, including personal traits and public perceptions. This study aims to explore the determinants of NHIS membership renewal and premium payment via the mobile renewal system. Conducted at Kwame Nkrumah University of Science and Technology (KNUST) in Kumasi, Ghana, the study used a survey design to gather data from 951 KNUST students. Employing logistic regression analysis, the study identified key factors influencing the use of the NHIS mobile renewal service. The findings revealed that individuals aged 19-21, 25-27 or above 27, without mobile money accounts, and those with no history of online purchases were less likely to adopt the mobile renewal system (P < .05). Conversely, those perceiving the system as useful and easy to use were more likely to utilise it for NHIS membership renewal (P < .05). In conclusion, policymakers should prioritise system quality, accessibility, perceived ease of use, and usefulness to facilitate the adoption and usage of the NHIS mobile payment system. These findings contribute valuable insights for enhancing the effectiveness of health insurance innovations.

Trajectory tracking of changes digital divide prediction factors in the elderly through machine learning

RESEARCH MOTIVATION

Recently, the digital divide problem among elderly individuals has been intensifying. A larger problem is that the level of use of digital technology varies from person to person. Therefore, a digital divide may even exist among elderly individuals. Considering the recent accelerating digital transformation in our society, it is highly likely that elderly individuals are experiencing many difficulties in their daily life. Therefore, it is necessary to quickly address and manage these difficulties.

RESEARCH OBJECTIVE

This study aims to predict the digital divide in the elderly population and provide essential insights into managing it. To this end, predictive analysis is performed using public data and machine learning techniques.

METHODS AND MATERIALS

This study used data from the '2020 Report on Digital Information Divide Survey' published by the Korea National Information Society Agency. In establishing the prediction model, various independent variables were used. Ten variables with high importance for predicting the digital divide were identified and used as critical, independent variables to increase the convenience of analyzing the model. The data were divided into 70% for training and 30% for testing. The model was trained on the training set, and the model's predictive accuracy was analyzed on the test set. The prediction accuracy was analyzed using logistic regression (LR), support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT), and eXtreme gradient boosting (XGBoost). A convolutional neural network (CNN) was used to further improve the accuracy. In addition, the importance of variables was analyzed using data from 2019 before the COVID-19 outbreak, and the results were compared with the results from 2020.

RESULTS

The study results showed that the variables with high importance in the 2020 data predicting the digital divide of elderly individuals were the demographic perspective, internet usage perspective, self-efficacy perspective, and social connectedness perspective. These variables, as well as the social support perspective, were highly important in 2019. The highest prediction accuracy was achieved using the CNN-based model (accuracy: 80.4%), followed by the XGBoost model (accuracy: 79%) and LR model (accuracy: 78.3%). The lowest accuracy (accuracy: 72.6%) was obtained using the DT model.

DISCUSSION

The results of this analysis suggest that support that can strengthen the practical connection of elderly individuals through digital devices is becoming more critical than ever in a situation where digital transformation is accelerating in various fields. In addition, it is necessary to comprehensively use classification algorithms from various academic fields when constructing a classification model to obtain higher prediction accuracy.

CONCLUSION

The academic significance of this study is that the CNN, which is often employed in image and video processing, was extended and applied to a social science field using structured data to improve the accuracy of the prediction model. The practical significance of this study is that the prediction models and the analytical methodologies proposed in this article can be applied to classify elderly people affected by the digital divide, and the trained models can be used to predict the people of younger generations who may be affected by the digital divide. Another practical significance of this study is that, as a method for managing individuals who are affected by a digital divide, the self-efficacy perspective about acquiring and using ICTs and the socially connected perspective are suggested in addition to the demographic perspective and the internet usage perspective.

Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students' behavioral intentions

Background

Artificial intelligence (AI) technology has recently seen rapid advancement, with an expanding role and scope in nursing education and healthcare. This study identifies the influence of AI ethics awareness, attitude toward AI, anxiety, and self-efficacy on nursing students’ behavioral intentions to use AI-based healthcare technology.

Methods

The participants included 189 nursing students in Gyeonggi-do, with data collected from November to December 2021 using self-reported questionnaires. We analyzed the data using the SPSS/WIN 26.0 program, including a t-test, Pearson’s correlation coefficient, and hierarchical multiple linear regression.

Results

The results revealed that AI ethical awareness (t = − 4.32, p < .001), positive attitude toward AI (t = − 2.60, p = .010), and self-efficacy (t = − 2.65, p = .009) scores of the third and fourth-year nursing students were higher, while their anxiety scores were lower (t = 2.30, p = .022) compared to the scores of the first and second-year nursing students. The factors influencing behavioral intention included a positive attitude toward AI (β = 0.58) and self-efficacy (β = 0.22). The adjusted R² was 0.42.

Conclusion

It is necessary to inculcate a positive attitude toward AI and self-efficacy by providing educational programs on AI-based technology in healthcare settings.

Exploring the acceptance for e-learning among higher education students in India: combining technology acceptance model with external variables

The concept of e-learning has now become fundamental in student learning process. This concept becomes even more relevant in situations of global crisis such as that arising from COVID-19. Since this pandemic there have been tectonic shifts in the education sector. Effective implementation of e-learning in higher education depends on students' adoption of this technology. So, this study aimed to identify the factors influencing the behavioral intentions and actual usage of students in adopting e-learning. Additionally, it also examined the mediation effects among different latent constructs. Based on technology acceptance model (TAM), an explanatory structural model of technology acceptance was tested along with introduction of three external variables. To do this, a quantitative investigation was conducted using an online survey of higher education students in India, obtaining 570 responses. The structural model was examined through the partial least square structural equation modeling. Results obtained make it possible to validate the proposed model as findings explains the 56.2% variance of actual usage. In addition, it shows the direct and indirect effect of all three selected external variables of personal innovativeness, social factors and self-efficacy on the main constructs of TAM. The findings of this study are relevant for the higher education management, administration, e-learning system developers, marketers and researchers for improving the effective usage of e-learning by developing more focused and customized learning solutions.

Affecting factors of cloud computing adoption in public hospitals affiliated with Zahedan University of Medical Sciences: A cross-sectional study in the Southeast of Iran

Objective

Health care organizations require cloud computing to remain efficient and cost-effective, and provide high-quality health care services. Adoption of this technology by users plays a critical role in the success of its application. This study aimed to determine factors affecting cloud computing adoption in public hospitals affiliated with Zahedan University of Medical Sciences.

Methods

A cross-sectional descriptive and analytic study was performed in 2017. The study population comprised information technology and hospital information system authorities and hospital information system users. The sample consisted of 573 participants. The data were collected using a questionnaire and analyzed with the Statistical Package for Social Sciences software using descriptive and analytical statistics.

Results

The mean score of environmental, human, organizational, technological, and intention dimensions of cloud computing adoption was 3.39 ± 0.81, 3.27 ± 0.63, 3.19 ± 0.71, 3 ± 0.43, and 3.55 ± 1.10, respectively. Furthermore, a significant positive relationship was found between intention of cloud computing adoption and environmental (R = 0.521, p = 0.000), organizational (R = 0.426, p = 0.000), human (R = 0.492, p = 0.000), and technological dimensions (R = 0.157, p = 0.000).

Conclusions

Benefits of cloud computing adoption, relative advantage, and competitive pressure were identified as the most influential factors in accepting cloud computing. Simplifying the users’ understanding of this technology and its application, improving the staff's technical capabilities, promoting executive managers’ understanding of the nature and functions of cloud computing, and fully supporting and increasing governmental mandates for adoption of new technologies are necessary for facilitating the adoption of cloud computing in given hospitals.

Technology Acceptance in Healthcare: A Systematic Review

Understanding the factors affecting the use of healthcare technologies is a crucial topic that has been extensively studied, specifically during the last decade. These factors were studied using different technology acceptance models and theories. However, a systematic review that offers extensive understanding into what affects healthcare technologies and services and covers distinctive trends in large-scale research remains lacking. Therefore, this review aims to systematically review the articles published on technology acceptance in healthcare. From a yield of 1768 studies collected, 142 empirical studies have met the eligibility criteria and were extensively analyzed. The key findings confirmed that TAM and UTAUT are the most prevailing models in explaining what affects the acceptance of various healthcare technologies through different user groups, settings, and countries. Apart from the core constructs of TAM and UTAUT, the results showed that anxiety, computer self-efficacy, innovativeness, and trust are the most influential factors affecting various healthcare technologies. The results also revealed that Taiwan and the USA are leading the research of technology acceptance in healthcare, with a remarkable increase in studies focusing on telemedicine and electronic medical records solutions. This review is believed to enhance our understanding through a number of theoretical contributions and practical implications by unveiling the full potential of technology acceptance in healthcare and opening the door for further research opportunities.

A Technology Acceptance Model for Deploying Masks to Combat the COVID-19 Pandemic in Taiwan (My Health Bank): Web-Based Cross-sectional Survey Study

BACKGROUND

The successful completion of medical practices often relies on information collection and analysis. Government agencies and medical institutions have encouraged people to use medical information technology (MIT) to manage their conditions and promote personal health. In 2014, Taiwan established the first electronic personal health record (PHR) platform, My Health Bank (MHB), which allows people to access and manage their PHRs at any time. In the face of the COVID-19 pandemic in 2020, Taiwan has used MIT to effectively prevent the spread of COVID-19 and undertaken various prevention measures before the onset of the outbreak. Using MHB to purchase masks in an efficient and orderly way and thoroughly implementing personal protection efforts is highly important to contain disease spread.

OBJECTIVE

This study aims to understand people's intention to use the electronic PHR platform MHB and to investigate the factors affecting their intention to use this platform.

METHODS

From March 31 to April 9, 2014, in a promotion via email and Facebook, participants were asked to fill out a structured questionnaire after watching an introductory video about MHB on YouTube. The questionnaire included seven dimensions: perceived usefulness, perceived ease of use, health literacy, privacy and security, computer self-efficacy, attitude toward use, and behavioral intention to use. Each question was measured on a 5-point Likert scale ranging from "strongly disagree" (1 point) to "strongly agree" (5 points). Descriptive statistics and structural equation analysis were performed using SPSS 21 and AMOS 21 software.

RESULTS

A total of 350 valid questionnaire responses were collected (female: 219/350, 62.6%; age: 21-30 years: 238/350, 68.0%; university-level education: 228/350, 65.1%; occupation as student: 195/350, 56.6%; average monthly income

CONCLUSIONS

From the perspective of the populace, this study explored the factors affecting the use of MHB and constructed an interpretation model with a strong goodness of fit. The results of our analysis are consistent with the technology acceptance model. Through the diverse value-added services of MHB, Taiwan's experience in pandemic prevention with smart technology can facilitate future responses to unknown, emerging infectious diseases.

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Key Words

• COVID-19
• electronic health record
• electronic medical record
• intention
• literacy
• model
• my health bank
• personal health record
• privacy
• protection
• security
• structural equation model
• survey
• technology acceptance model
• usage

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MESH Headings

• Adult
• COVID-19/epidemiology
• COVID-19/prevention & control
• COVID-19/transmission
• Cross-Sectional Studies
• Disease Transmission, Infectious/prevention & control
• Female
• Humans
• Internet
• Male
• Masks
• Pandemics
• SARS-CoV-2
• Surveys and Questionnaires
• Taiwan/epidemiology
• Technology
• Young Adult

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Affiliation(s)

Wen-Hsun Tsai Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei, Taiwan Medical Administration Office, Beitou Branch, Tri-Service General Hospital, Taipei, Taiwan
Yi-Syuan Wu Graduate Institute of Life Sciences, National Defense Medical Center, Taipei, Taiwan
Chia-Shiang Cheng Graduate Institute of Life Sciences, National Defense Medical Center, Taipei, Taiwan
Ming-Hao Kuo Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei, Taiwan
Yu-Tien Chang School of Public Health, National Defense Medical Center, Taipei, Taiwan
Fu-Kang Hu Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei, Taiwan Medical Informatics Office, Tri-Service General Hospital, Taipei, Taiwan
Chien-An Sun Big Data Research Center, College of Medicine, Fu-Jen Catholic University, New Taipei City, Taiwan
Chi-Wen Chang School of Nursing, College of Medicine, Chang-Gung University, Taoyuan, Taiwan Division of Pediatric Endocrinology & Genetics, Department of Pediatrics, Chang-Gung Memorial Hospital, Taoyuan, Taiwan
Ta-Chien Chan Research Center for Humanities and Social Sciences, Academia Sinica, Taipei, Taiwan
Chao-Wen Chen Trauma and Critical Care Service Department of Surgery, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
Chia-Cheng Lee Medical Informatics Office, Tri-Service General Hospital, Taipei, Taiwan Division of Colorectal Surgery, Department of Surgery, Tri-Service General Hospital, Taipei, Taiwan
Chi-Ming Chu Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei, Taiwan Graduate Institute of Life Sciences, National Defense Medical Center, Taipei, Taiwan School of Public Health, National Defense Medical Center, Taipei, Taiwan Big Data Research Center, College of Medicine, Fu-Jen Catholic University, New Taipei City, Taiwan Department of Public Health, China Medical University, Taichung, Taiwan Department of Public Health, College of Health Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan Department of Healthcare Administration and Medical Informatics, College of Health Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan

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Mobile app development in health research: pitfalls and solutions

Mobile app health research presents myriad opportunities to improve health, and simultaneously introduces a new set of challenges that are non-intuitive and extend beyond typical training received by researchers. Informed by our experiences with app development for health research, we discuss some of the most salient pitfalls when working with emerging technology as well as potential strategies to avoid or resolve these challenges. To address challenges at the project level, we suggest strategies that researchers can use to future-proof their research, such as using theory and involving those with app development expertise as part of a research team. At the structural level, we include a new model to characterize the relationship between technology- and research-timelines, and provide ideas regarding how to best address this challenge. Given that screen-based time now predominates our lived experiences, it is important that health researchers have the capacity and structural support to develop interventions that utilize these technologies, assess them rigorously, and ensure their timely and equitable dissemination.

Exploring critical factors influencing nurses' intention to use tablet PC in Patients' care using an integrated theoretical model

The use of health information technology (HIT) is expected to deliver benefits for patients, nurses, physicians, and organizations, but the benefits of HIT can only be attained if nurses accept and intend to use it as they are the leading user-group. The use of the tablet is becoming commonplace in healthcare organizations to improve patient care. The current study incorporates Technology Acceptance Model2 (TAM2) with two antecedents, facilitating condition and personal, to identify and understand the factors that influence nurses' intention to use the Tablet PC. The survey methodology was used to collect data from the nurses working in a regional healthcare center in Taiwan. The structural equation modeling (SEM) technique was employed to analyze the research framework. A total of 110 valid responses for analysis. The results suggest that the modified proposed research framework explains about 41.7% of the variance of nurses' behavioral intention. The partial least squares (PLS) regression indicated that perceived usefulness, subjective norm, and personal a positive and significant influence on nurses' intention to use the Tablet PC. But concerning the perceived ease of use, the insignificant path coefficient was reported. The finding also indicated that personal on the research model is much stronger than the subjective norm on Tablet PC performance. The proposed research framework contributes to the conclusive explanation for understanding nurses' intention to use. The current study brings perspectives from the technological and attitudinal differences that have largely been missing in the existing literature of the nurses' intention to use HIT. Thus, health care providers must take these factors into consideration as the findings of the current study advance theory and contribute to the basis for future study intended for enhancing our understanding of nurses' adoption behavior regarding HIT.

Visualizing Co-citations of Technology Acceptance Models in Education

With the rapid development of information and communication technologies, studies devoted to technology-assisted education have been soaring up. Nevertheless, the studies on technology acceptance model (TAM) appear relatively fewer. This study, through reviewing high quality papers, analyzed the co-citations of TAM on the basis of the basic TAM and numerous extended TAMs. Co-citations of TAM were revealed and discussed in terms of citation counts, bursts, betweenness centrality, and sigma. It also reviews the basic concept underlying user acceptance models, as well as its extended TAMs in details. Future research into the TAM may resort to statistical support, as well as critical analysis. The interdisciplinary research design is also needed between computer science, education, psychology, statistics and mathematics.

A Systematic Review of the Technology Acceptance Model in Health Informatics

BACKGROUND

One common model utilized to understand clinical staff and patients' technology adoption is the technology acceptance model (TAM).

OBJECTIVE

This article reviews published research on TAM use in health information systems development and implementation with regard to application areas and model extensions after its initial introduction.

METHOD

An electronic literature search supplemented by citation searching was conducted on February 2017 of the Web of Science, PubMed, and Scopus databases, yielding a total of 492 references. Upon eliminating duplicates and applying inclusion and exclusion criteria, 134 articles were retained. These articles were appraised and divided into three categories according to research topic: studies using the original TAM, studies using an extended TAM, and acceptance model comparisons including the TAM.

RESULTS

The review identified three main information and communication technology (ICT) application areas for the TAM in health services: telemedicine, electronic health records, and mobile applications. The original TAM was found to have been extended to fit dynamic health service environments by integration of components from theoretical frameworks such as the theory of planned behavior and unified theory of acceptance and use of technology, as well as by adding variables in specific contextual settings. These variables frequently reflected the concepts subjective norm and self-efficacy, but also compatibility, experience, training, anxiety, habit, and facilitators were considered.

CONCLUSION

Telemedicine applications were between 1999 and 2017, the ICT application area most frequently studied using the TAM, implying that acceptance of this technology was a major challenge when exploiting ICT to develop health service organizations during this period. A majority of the reviewed articles reported extensions of the original TAM, suggesting that no optimal TAM version for use in health services has been established. Although the review results indicate a continuous progress, there are still areas that can be expanded and improved to increase the predictive performance of the TAM.

Nurses' satisfaction with use of a personal digital assistants with a mobile nursing information system in China

BACKGROUND

Personal digital assistants, technology with various functions, have been applied in international clinical practice. Great benefits in reducing medical errors and enhancing the efficiency of clinical work have been achieved, but little research has investigated nurses' satisfaction with the use of personal digital assistants.

AIM

To investigate nurses' satisfaction with use of personal digital assistants, and to explore the predictors of this.

DESIGN

This is a cross-sectional descriptive study.

METHODS

We conducted a cross-sectional survey targeting nurses who used personal digital assistants in a comprehensive tertiary hospital in Beijing.

RESULTS

A total of 383 nurses were recruited in this survey in 2015. The total score of nurses' satisfaction with use of personal digital assistants was 238.91 (SD 39.25). Nurses were less satisfied with the function of documentation, compared with the function of administering medical orders. The time length of using personal digital assistants, academic degree, and different departments predicted nurses' satisfaction towards personal digital assistant use (all P < 0.05).

CONCLUSION

Nurses were satisfied with the accuracy of administering medical orders and the safety of recording data. The stability of the wireless network and efficiency related to nursing work were less promising. To some extent, nurses with higher education and longer working time with personal digital assistants were more satisfied with them.

The use of social media among health professionals to facilitate chronic disease self-management with their patients: A systematic review

OBJECTIVE

The objective of this systematic review was to summarize the evidence pertaining to the use of social media by health professionals to facilitate chronic disease self-management with their patients.

METHODS

A systematic approach was used to retrieve and extract relevant data. A total of 5163 citations were identified, of which seven unique studies met criteria for inclusion; one was a randomized controlled trial, two were prospective cohort studies, and four were qualitative studies. The following social media platforms were evaluated: discussion forums (6 studies) and collaborative project (1 study).

RESULTS

The available evidence suggests that health professionals perceived discussion forums and collaborative projects to be useful social media platforms to facilitate chronic disease self-management with patients. No relevant evidence was found regarding the use of other social media platforms. Most studies indicated positive findings regarding health professionals' intention to use discussion forums, while the one study that used a collaborative project also indicated positive findings with its perceived ease of use as health professionals felt that it was useful to facilitate chronic disease self-management with patients. Mixed findings were seen in regards to health professionals' perceived ease of use of discussion forums. The most common barrier to using social media platforms was the lack of time in health professionals' schedules.

CONCLUSIONS

Discussion forums and collaborative projects appear to be promising resources for health professionals to assist their patients in self-managing their chronic conditions; however, further research comparing various social media platforms is needed.

Information and Communication Technologies for the Dissemination of Clinical Practice Guidelines to Health Professionals: A Systematic Review

BACKGROUND

The transfer of research knowledge into clinical practice can be a continuous challenge for researchers. Information and communication technologies, such as websites and email, have emerged as popular tools for the dissemination of evidence to health professionals.

OBJECTIVE

The objective of this systematic review was to identify research on health professionals' perceived usability and practice behavior change of information and communication technologies for the dissemination of clinical practice guidelines.

METHODS

We used a systematic approach to retrieve and extract data about relevant studies. We identified 2248 citations, of which 21 studies met criteria for inclusion; 20 studies were randomized controlled trials, and 1 was a controlled clinical trial. The following information and communication technologies were evaluated: websites (5 studies), computer software (3 studies), Web-based workshops (2 studies), computerized decision support systems (2 studies), electronic educational game (1 study), email (2 studies), and multifaceted interventions that consisted of at least one information and communication technology component (6 studies).

RESULTS

Website studies demonstrated significant improvements in perceived usefulness and perceived ease of use, but not for knowledge, reducing barriers, and intention to use clinical practice guidelines. Computer software studies demonstrated significant improvements in perceived usefulness, but not for knowledge and skills. Web-based workshop and email studies demonstrated significant improvements in knowledge, perceived usefulness, and skills. An electronic educational game intervention demonstrated a significant improvement from baseline in knowledge after 12 and 24 weeks. Computerized decision support system studies demonstrated variable findings for improvement in skills. Multifaceted interventions demonstrated significant improvements in beliefs about capabilities, perceived usefulness, and intention to use clinical practice guidelines, but variable findings for improvements in skills. Most multifaceted studies demonstrated significant improvements in knowledge.

CONCLUSIONS

The findings suggest that health professionals' perceived usability and practice behavior change vary by type of information and communication technology. Heterogeneity and the paucity of properly conducted studies did not allow for a clear comparison between studies and a conclusion on the effectiveness of information and communication technologies as a knowledge translation strategy for the dissemination of clinical practice guidelines.