THE ROLE OF WORKAROUNDS DURING AN OPENSOURCE ELECTRONIC MEDICAL RECORD SYSTEM IMPLEMENTATION
Bibliographic record
Abstract
A significant degree of customization of medical information technology is required to effectively integrate the promise of IT with the diversity and complexity of medical work. In the absence of such customizations, dissatisfaction and resistance toward the system arise. Indeed, the complexity of the medical work and the inability of software to tailor to the diverse medical practices may explain the limited diffusion of health information systems especially in North America. We study the role of workarounds during an open-source Electronic Medical Record System (EMR) implementation at a medium-size urgent care clinic in a major Canadian city. We found that the technology appropriation process involved the evolving of number of non-trivial workarounds in order to match the EMR to medical work. The emergence of workarounds is conceptualized as a knowledge creation and integration process. This perspective allows us to look at the antecedents and the change dynamics of workarounds in the clinic. Furthermore diverging from the negative view toward workarounds, we discuss the importance of incorporating workarounds during and following system development. The workaround perspective shed the light on how users’ behavior can be channeled into a constructive development effort. This paper contributes by examining the workaround of medical practitioners using an open-source electronic medical record system as well as offering a knowledge perspective for the study of EMR appropriation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".