THE IMPACT OF HEALTH INFORMATION TECHNOLOGY ON FAMILY PHYSICIAN’S PRACTICE PATTERNS: A CROSS-SECTIONAL STUDY USING DATA FROM THE 2007 NATIONAL PHYSICIAN SURVEY
Bibliographic record
Abstract
Objective: To evaluate the impact of health information technology on primary care physicians in Canada by the number of medical services and clinical procedures they offer, total direct patient care hours provided, and visit duration. Methods: We used nationally representative data from the 2007 National Physician Survey to examine the extent of Health Information Technology (HIT count and HIT type) on the scope of practice, total direct patient care hours, and length of office visits among family physicians/general practitioners. HIT count is defined as the number of HIT the physician uses and HIT type is categorical variable based on its features. Negative binomial regression models were used to assess the number of medical services and clinical procedures offered. Linear regression models were used to assess the total direct patient care hours and visit duration. Results: Multivariate analyses show a significant increase in the expected mean number of medical services offered, clinical procedures offered, direct patient care hours, and visit duration with each additional HIT the physician uses in his/her practice. The greatest positive impact was found among physicians who uses EMR-Plus HIT for medical services (8.8 percent, p<0.01) and clinical procedures (8.7 percent, p<0.01) when compared to non-HIT users. Physicians were found to increase their time spent per patient visit who use EMR HIT by 7.8 percent (p<0.05) and EMR-Plus HIT by 6.8 percent (p<0.01) when compared to non-HIT users. No significant association was found between the different HIT types physician uses and total direct patient care hours. Conclusion: The use of health information technology is found to be associated with an increase of medical services and clinical procedures offered, and duration of visit. Limited impact of HIT was found to be associated with total direct patient care hours. Further investigation of what components in HIT affects the workflow of family physicians is needed. Overall, careful consideration needs to be taken when investing and implementing HIT in Canada because there may be unintended consequences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".