105: EMR Readiness Assessment at a Tertiary Care Paediatric Hospital
Bibliographic record
Abstract
Electronic medical record (EMR) use is increasing with 57% of physicians using an EMR in 2013. Successful adoption of an EMR is dependent on many factors including the type, practice setting, interface design and usability. Many providers express discomfort and concern in adapting from a paper-based documentation system to an EMR. At our institution we completed an assessment of our readiness to implement an ambulatory care EMR. To understand provider concerns In preparation for the EMR implementation and to respond to their needs to increase implementation success. An EMR Readiness Assessment (RA) and Technical Adoption questionnaire was distributed electronically to end users through Electronic Data Capture software (Version 5.6.1 – © 2013 Vanderbilt University). The questions were derived from three sources previously validated in a physician based healthcare setting; a questionnaire used by Morton (2008) to determine factors that contribute to physician EMR acceptance; an Organizational RA questionnaire for targeted follow-up; and a Benefits Evaluation and Technology Acceptance Model (Davis, 1989; Chutter, 2009). All three sources were compared to eliminate duplication. The final questionnaire was pilot tested for face-validity. Participation was voluntary. The questionnaire was administered for three consecutive weeks prior to the EMR go-live; two separate email reminders were sent. A total of 167 (48%) providers completed the RA questionnaire; 21% were <30 years and 60% 30 to 50 years of age; 57% had worked in health care for >10 years. 99% used a computer as part of their daily work with 59% stating general and 37% advanced proficiency. Overall attitude regarding the EMR is given in the table. We have seen provider anticipation, engagement and acceptance of the EMR implementation. Providers are aware of the need for an EMR and the benefits for patient care. The majority of providers use computers in their daily work and are proficient in its use. We plan to perform a post go-live RA questionnaire to ensure ongoing provider engagement.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".