Factors Associated With Adoption of the Electronic Health Record System Among Primary Care Physicians
Bibliographic record
Abstract
BACKGROUND: A territory-wide Internet-based electronic patient record allows better patient care in different sectors. The engagement of private physicians is one of the major facilitators for implementation, but there is limited information about the current adoption level of electronic medical record (eMR) among private primary care physicians. OBJECTIVE: This survey measured the adoption level, enabling factors, and hindering factors of eMR, among private physicians in Hong Kong. It also evaluated the key functions and the popularity of electronic systems and vendors used by these private practitioners. METHODS: A central registry consisting of 4324 private practitioners was set up. Invitations for self-administered surveys and the completed questionnaires were sent and returned via fax, email, postal mail, and on-site clinic visits. Current users and non-users of eMR system were compared according to their demographic and practice characteristics. Student's t tests and chi-square tests were used for continuous and categorical variables, respectively. RESULTS: A total of 524 completed surveys (response rate 524/4405 11.90%) were collected. The proportion of using eMR in private clinics was 79.6% (417/524). When compared with non-users, the eMR users were younger (users: 48.4 years SD 10.6 years vs non-users: 61.7 years SD 10.2 years, P<.001); more were female physicians (users: 80/417, 19.2% vs non-users: 14/107, 13.1%, P=.013); possessed less clinical experience (with more than20 years of practice: users: 261/417, 62.6% vs non-user: 93/107, 86.9%, P<.001); fewer worked under a Health Maintenance Organization (users: 347/417, 83.2% vs non-users: 97/107, 90.7%, P<.001) and more worked with practice partners (users: 126/417, 30.2% vs non-users: 4/107, 3.7%, P<.001). Efficiency (379/417, 90.9%) and reduction of medical errors (229/417, 54.9%) were the major enabling factors, while patient-unfriendliness (58/107, 54.2%) and limited consultation time (54/107, 50.5%) were the most commonly reported hindering factors. The key functions of computer software among eMR users consisted of electronic patient registration system (376/417, 90.2%), drug dispensing system (328/417, 78.7%) and electronic drug labels (296/417, 71.0%). SoftLink Clinic Solution was the most popular vendor (160/417, 38.4%). CONCLUSIONS: These findings identified several physician groups who should be targeted for more assistance on eMR installation and its adoption. Future studies should address the barriers of using Internet-based eMR to enhance its adoption.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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; a candidate call from one teacher head, not a consensus.
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".