159 Perceptions of Health Care Quality in an Emergency Department During a Planned Electronic Health Record Downtime

2014 ◽  
Vol 64 (4) ◽  
pp. S58
Author(s):  
A.M. Mehta ◽  
N. Okafor
Medical Care ◽  
2010 ◽  
Vol 48 (3) ◽  
pp. 203-209 ◽  
Author(s):  
Eric G. Poon ◽  
Adam Wright ◽  
Steven R. Simon ◽  
Chelsea A. Jenter ◽  
Rainu Kaushal ◽  
...  

2009 ◽  
Vol 2 (1) ◽  
pp. 23-26
Author(s):  
Julie A. Lindenberg

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.


2019 ◽  
Vol 58 (02/03) ◽  
pp. 063-070 ◽  
Author(s):  
Saja A. Al-Rayes ◽  
Arwa Alumran ◽  
Weam AlFayez

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.


Author(s):  
Hadi Kharrazi ◽  
Claudia P Gonzalez ◽  
Kevin B Lowe ◽  
Timothy R Huerta ◽  
Eric W Ford

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.


2011 ◽  
Vol 21 (1) ◽  
pp. 18-22
Author(s):  
Rosemary Griffin

National legislation is in place to facilitate reform of the United States health care industry. The Health Care Information Technology and Clinical Health Act (HITECH) offers financial incentives to hospitals, physicians, and individual providers to establish an electronic health record that ultimately will link with the health information technology of other health care systems and providers. The information collected will facilitate patient safety, promote best practice, and track health trends such as smoking and childhood obesity.


2021 ◽  
Vol 1 (1) ◽  
pp. 6-17
Author(s):  
Andrija Pavlovic ◽  
Nina Rajovic ◽  
Jasmina Pavlovic Stojanovic ◽  
Debora Akinyombo ◽  
Milica Ugljesic ◽  
...  

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.


2010 ◽  
Vol 17 (8) ◽  
pp. 824-833 ◽  
Author(s):  
Gregory W. Daniel ◽  
Edward Ewen ◽  
Vincent J. Willey ◽  
Charles L. Reese IV ◽  
Farshad Shirazi ◽  
...  

Author(s):  
Claire M. Campbell ◽  
Daniel R. Murphy ◽  
George E. Taffet ◽  
Anita B. Major ◽  
Christine S. Ritchie ◽  
...  

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