The Adoption of the Electronic Health Record by Physicians

2019 ◽  
Vol 58 (02/03) ◽  
pp. 063-070 ◽  
Author(s):  
Saja A. Al-Rayes ◽  
Arwa Alumran ◽  
Weam AlFayez
Keyword(s):  
Social Influence ◽  
Electronic Health Record ◽  
Technology Acceptance ◽  
Health Care Quality ◽  
Resistance To Change ◽  
Systems Design ◽  
Care Quality ◽  
Perceived Usefulness ◽  
Health Record ◽  
Electronic Health

Abstract Background Health information technology, especially the electronic health record (EHR) systems, improves health care quality and patient safety. Objectives This study's objectives are as follows: first, to explore the adoption of EHR systems among physicians in Saudi Arabia (with King Fahd Military Medical Complex as the location of the pilot study), and second, to identify the factors that influence these physicians' adoption of such systems. Methods This cross-sectional quantitative study is based on a paper survey that was administered to a sample of 213 physicians. The theoretical model is a version of the Technology Acceptance Model (TAM) that features the following additional variables: resistance to change, training, and social influence. Results The sample includes 133 (62%) physicians who used EHRs and 80 (38%) who did not. The main findings show that users and nonusers of the EHR system differ significantly for several factors such as perceived usefulness, perceived ease of use, social influence, and resistance to change. In addition, age, work experience, and medical specialty are significantly associated with physicians' use of the EHR system. Conclusion To increase EHR systems' adoption rate, the following elements should be improved: the systems' design, the social environments, and the physicians' awareness of the systems' benefits. This is the first study to produce a valid and reliable instrument for measuring the factors that influences physicians' use of the EHR system at a Saudi hospital in the Eastern Province. Further studies are needed to measure how these factors influence physicians' use of EHRs in other settings.

Relationship Between Use of Electronic Health Record Features and Health Care Quality

Medical Care ◽  
2010 ◽  
Vol 48 (3) ◽  
pp. 203-209 ◽  
Author(s):  
Eric G. Poon ◽  
Adam Wright ◽  
Steven R. Simon ◽  
Chelsea A. Jenter ◽  
Rainu Kaushal ◽  
...  
Keyword(s):  
Health Care ◽  
Electronic Health Record ◽  
Health Care Quality ◽  
Care Quality ◽  
Health Record ◽  
Electronic Health

Benchmarking: Extracting Clinical Measures and Practice Performance From the Electronic Health Record

2009 ◽  
Vol 2 (1) ◽  
pp. 23-26
Author(s):  
Julie A. Lindenberg
Keyword(s):  
Health Care ◽  
Chronic Disease ◽  
Disease Management ◽  
Electronic Health Record ◽  
Health Care Quality ◽  
Care Quality ◽  
Health Record ◽  
Practice Performance ◽  
Electronic Health ◽  
Near Term

Adoption of electronic health record (EHR) systems can lead to health care savings, reduction in medical errors, and improvement in health. The intersection of health information technology (HIT) and health care quality is a result of the following two developments: the quality improvement movement and the maturation of HIT. The potential safety benefits of EHR systems focus largely on alerts, reminders, and other components of ambulatory computerized provider order entry. EHR systems are integral throughout the disease management process. Using HIT for near-term chronic disease management programs has the benefits of identifying people with a potential or active chronic disease, targeting services based on their level of risk, and monitoring their risk. The purposes of benchmarking and survey reports are to provide a quantifiable measure of performance and to quantify gaps between your practice and best practices.

Factors Associated with Nurses' User Resistance to Change of Electronic Health Record (Preprint)

2020 ◽  
Author(s):  
Younghee Cho ◽  
Mihui Kim ◽  
Mona Choi
Keyword(s):  
Electronic Health Record ◽  
Organizational Support ◽  
Resistance To Change ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Health Record ◽  
Factors Affecting ◽  
User Resistance ◽  
Factors Associated ◽  
Electronic Health

BACKGROUND Electronic health record (EHR) systems often face user resistance in hospitals, which results in a failure to acquire the full benefits of EHR systems. It is crucial to reduce nurses’ resistance to use the EHR to implement the system successfully. OBJECTIVE This study aimed to investigate the factors associated with nurses’ resistance to use the EHR system. METHODS A descriptive correlational study was conducted on nurses working at four university hospitals implementing a new EHR within 2 years. Path analysis was performed with seven factors affecting user resistance behavior. RESULTS All seven factors were found to be significantly associated with user resistance, whether directly or indirectly. The total effect on user resistance behavior was highest in resistance to change (0.65), followed by perceived usefulness (-0.33), which both have direct but no indirect effects. Conversely, Self-efficacy (-0.25), perceived value (-0.21), colleagues’ opinion (-0.16), perceived ease of use (-0.16), and organizational support (-0.05) were found to have indirect but no direct effects. CONCLUSIONS The study explored the factors affecting nurses’ user resistance behavior after the implementation of a new EHR system. These findings could help hospitals develop better EHR implementation strategies to reduce user resistance behavior among the nursing staff.

scholarly journals Forecasting the Maturation of Electronic Health Record Functions Among US Hospitals: A Retrospective Analysis and Predictive Model (Preprint)

2018 ◽  
Author(s):  
Hadi Kharrazi ◽  
Claudia P Gonzalez ◽  
Kevin B Lowe ◽  
Timothy R Huerta ◽  
Eric W Ford
Keyword(s):  
Electronic Health Record ◽  
Technology Diffusion ◽  
Health Care Quality ◽  
Care Delivery ◽  
Care Quality ◽  
Direct Care ◽  
Health Record ◽  
Bass Model ◽  
Ehr Adoption ◽  
Electronic Health

BACKGROUND The Meaningful Use (MU) program has promoted electronic health record (EHR) adoption among US hospitals. Studies have shown that EHR adoption has been slower than desired in certain types of hospitals; but generally, the overall adoption rate has increased among hospitals. However, these studies have neither evaluated the adoption of advanced functionalities of EHRs (beyond MU) nor forecasted EHR maturation over an extended period in a holistic fashion. Additional research is needed to prospectively assess US hospitals’ EHR technology adoption and advancement patterns. OBJECTIVE This study forecasts the maturation of EHR functionality adoption among US hospitals through 2035. METHODS The Healthcare Information and Management Systems Society (HIMSS) Analytics’ Electronic Medical Record Adoption Model (EMRAM) dataset was used to track historic uptakes of various EHR functionalities considered critical to improving health care quality and efficiency in hospitals. The Bass model was used to predict the technological diffusion rates for repeated EHR adoptions where upgrades undergo rapid technological improvements. The forecast used EMRAM data from 2006 to 2014 to estimate adoption levels to the year 2035. RESULTS In 2014, over 5400 hospitals completed HIMSS’ annual EMRAM survey (86%+ of total US hospitals). In 2006, the majority of the US hospitals were in EMRAM Stages 0, 1, and 2. By 2014, most hospitals had achieved Stages 3, 4, and 5. The overall technology diffusion model (ie, the Bass model) reached an adjusted R-squared of .91. The final forecast depicted differing trends for each of the EMRAM stages. In 2006, the first year of observation, peaks of Stages 0 and 1 were shown as EHR adoption predates HIMSS’ EMRAM. By 2007, Stage 2 reached its peak. Stage 3 reached its full height by 2011, while Stage 4 peaked by 2014. The first three stages created a graph that exhibits the expected “S-curve” for technology diffusion, with inflection point being the peak diffusion rate. This forecast indicates that Stage 5 should peak by 2019 and Stage 6 by 2026. Although this forecast extends to the year 2035, no peak was readily observed for Stage 7. Overall, most hospitals will achieve Stages 5, 6, or 7 of EMRAM by 2020; however, a considerable number of hospitals will not achieve Stage 7 by 2035. CONCLUSIONS We forecasted the adoption of EHR capabilities from a paper-based environment (Stage 0) to an environment where only electronic information is used to document and direct care delivery (Stage 7). According to our forecasts, the majority of hospitals will not reach Stage 7 until 2035, absent major policy changes or leaps in technological capabilities. These results indicate that US hospitals are decades away from fully implementing sophisticated decision support applications and interoperability functionalities in EHRs as defined by EMRAM’s Stage 7.

Why Do Physicians Reject the Electronic Health Record: A Theoretical Perspective? (Preprint)

2018 ◽  
Author(s):  
Weam Alfayez ◽  
Arwa Alumran ◽  
Dr Saja A. Al-Rayes
Keyword(s):  
Information Technology ◽  
Electronic Health Record ◽  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Health Record ◽  
Record System ◽  
Factors Influencing ◽  
Tam Model ◽  
Electronic Health ◽  
Acceptance Model

BACKGROUND Many theories/ models adopted from behavioral sciences literature or developed within the field of information technologies could help in understanding the technology acceptance, usage, and effective adoption. OBJECTIVE The main aim of this paper is to review the different theories/ models that can help in understanding information technology/system acceptance and use, and to choose the most appropriate theoretical framework that could be applied to understand the factors influencing physicians’ use of the Electronic Health Record system (EHR) at King Fahd Military Medical Complex (KFMMC) in Dhahran city, Saudi Arabia. METHODS The theories/ models were reviewed using scientific databases. The inclusion criteria were if the theories/ models used to explain individual behaviors toward accepting and using of information technology including the once conducted within the healthcare. RESULTS The review showed that there were five theories/ models were used within information technology studies to understand the technology acceptance and used. There were Theory of Reasoned Action, Theory of Planned Behaviour, Innovation Diffusion Theory, Unified theory of acceptance and use of technology, and Technology Acceptance Model. Each has different explanatory power of technology use. The most appropriate theoretical framework to understand the reason behind physician use of the EHR at KFMMC would be the Technology Acceptance Model (TAM). TAM model could explain up to 75% of the variation in the behavioral intention (acceptance), and up to 62% of the variation in the actual use. It is the gold standard for assessing the usage of health technologies and systems. In fact, the TAM model is one of the core models used to explore the physician’s perceptions of the Electronic Health Record system adoption. CONCLUSIONS This review showed that there are different theories available in the literature can be used to justify the reason behind electronic health record acceptance. TAM is one of the effective, simplest models used to understand the factors influencing physicians to use the EHR-system. Further studies need to apply the TAM model to check its ability in explaining the reason behind EHR within different hospitals in Saudi Arabia

Electronic Health Records Implementation in Jordan: Perceptions of Healthcare Providers and Determinant Factors (Preprint)

2020 ◽  
Author(s):  
Tamadur Shudayfat ◽  
Çağdaş Akyürek ◽  
Noha Al-Shdayfat ◽  
Hatem Alsaqqa
Keyword(s):  
Electronic Health Records ◽  
Electronic Health Record ◽  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Healthcare Providers ◽  
Health Record ◽  
Health Records ◽  
Context Factors ◽  
Electronic Health ◽  
Acceptance Model

BACKGROUND Acceptance of Electronic Health Record systems is considered an essential factor for an effective implementation among the Healthcare providers. In an attempt to understand the healthcare providers’ perceptions on the Electronic Health Record systems implementation and evaluate the factors influencing healthcare providers’ acceptance of Electronic Health Records, the current research examines the effects of individual (user) context factors, and organizational context factors, using Technology Acceptance Model. OBJECTIVE The current research examines the effects of individual (user) context factors, and organizational context factors, using Technology Acceptance Model. METHODS A quantitative cross-sectional survey design was used, in which 319 healthcare providers from five public hospital participated in the present study. Data was collected using a self-administered questionnaire, which was based on the Technology Acceptance Model. RESULTS Jordanian healthcare providers demonstrated positive perceptions of the usefulness and ease of use of Electronic Health Record systems, and subsequently, they accepted the technology. The results indicated that they had a significant effect on the perceived usefulness and perceived ease of use of Electronic Health Record, which in turn was related to positive attitudes towards Electronic Health Record systems as well as the intention to use them. CONCLUSIONS User attributes, organizational competency, management support and training and education are essential variables in predicting healthcare provider’s acceptance toward Electronic Health records. These findings should be considered by healthcare organizations administration to introduce effective system to other healthcare organizations.

scholarly journals Electronic Health Record Acceptance by Physicians: A Single Hospital Experience in Daily Practice

2021 ◽  
Vol 1 (1) ◽  
pp. 6-17
Author(s):  
Andrija Pavlovic ◽  
Nina Rajovic ◽  
Jasmina Pavlovic Stojanovic ◽  
Debora Akinyombo ◽  
Milica Ugljesic ◽  
...  
Keyword(s):  
Health Care ◽  
Logistic Regression ◽  
Electronic Health Record ◽  
Technology Acceptance ◽  
Daily Practice ◽  
Ease Of Use ◽  
Future Research ◽  
Health Record ◽  
Cross Sectional ◽  
Electronic Health

Introduction: Potential benefits of implementing an electronic health record (EHR) to increase the efficiency of health services and improve the quality of health care are often obstructed by the unwillingness of the users themselves to accept and use the available systems. Aim: The aim of this study was to identify factors that influence the acceptance of the use of an EHR by physicians in the daily practice of hospital health care. Material and Methods: The cross-sectional study was conducted among physicians in the General Hospital Pancevo, Serbia. An anonymous questionnaire, developed according to the technology acceptance model (TAM), was used for the assessment of EHR acceptance. The response rate was 91%. Internal consistency was assessed by Cronbach’s alpha coefficient. A logistic regression analysis was used to identify the factors influencing the acceptance of the use of EHR. Results: The study population included 156 physicians. The mean age was 46.4 ± 10.4 years, 58.8% participants were female. Half of the respondents (50.1%) supported the use of EHR in comparison to paper patient records. In multivariate logistic regression modeling of social and technical factors, ease of use, usefulness, and attitudes towards use of EHR as determinants of the EHR acceptance, the following predictors were identified: use of a computer outside of the office for reading daily newspapers (p = 0.005), EHR providing a greater amount of valuable information (p = 0.007), improvement in the productivity by EHR use (p < 0.001), and a statement that using EHR is a good idea (p = 0.014). Overall the percentage of correct classifications in the model was 83.9%. Conclusion: In this research, determinants of the EHR acceptance were assessed in accordance with the TAM, providing an overall good model fit. Future research should attempt to add other constructs to the TAM in order to fully identify all determinants of physician acceptance of EHR in the complex environment of different health systems.

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