Factors influencing health care consumer adoption of electronic health records: An empirical investigation
Bibliographic record
Abstract
An electronic health record (EHR) can be defined as the digital version of an individual’s medical history. EHRs are intended to improve the quality and efficiency of health care, decrease costs and prevent medical errors. Previous studies have shown that achievement of the potential benefits from EHRs depends largely upon the adoption and continued use of EHR services by health care consumers (Esmaeilzadeh & Sambasivan, 2017; Hanna et al., 2017). Further research, therefore, is necessary to better understand the factors that influence consumer EHR adoption. This study aims to investigate the factors influencing consumer adoption of EHRs. A model based on the Decomposed Theory of Planned Behaviour (DTPB) (Taylor & Todd, 1995) provides the theoretical framework for the research. The goal of the research is to improve understanding of how health care consumers perceive this technology and the factors that influence their intentions to use it. \n \nThe study used a mainly quantitative approach, conducting an online cross-sectional survey to collect data. The target population for this research study is health care consumers in Australia and the proposed model was tested using partial least squares structural equation modelling (PLS-SEM). Using the responses collected, the research model was validated in terms of overall fit and explanatory power. The study identified the following key factors that influence health care consumer adoption of EHRs: attitude, subjective norm, perceived usefulness, perceived ease of use, and privacy and security concerns. The findings support seven of the nine proposed hypotheses. Overall, the model explained 65% of the variation in intention to use and the results revealed that the proposed model exhibited good overall model fit with relatively high explanatory power. The outcomes of this research provide new knowledge that provides increased understanding of the factors influencing health care consumer adoption of EHRs. This knowledge should be valuable to health care educators, health care professionals and government policy makers as they develop strategies designed to inform potential consumers and educate people as to how to use EHR systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".