Using social knowledge networking technology to enable meaningful use of electronic health record technology in hospitals and health systems
Bibliographic record
Abstract
Despite the federal policy momentum towards “meaningful use” of Electronic Health Records (EHRs), the healthcare organizational literature remains replete with reports of unintended adverse consequences of implementing EHRs, including: increased work for clinicians, unfavorable workflow changes, and unexpected changes in communication patterns & practices. In addition to being costly and unsafe, these unintended adverse consequences may pose a formidable barrier to “meaningful use” of EHRs. Correspondingly, it is essential for hospital administrators to understand and detect the causes of unintended adverse consequences, to ensure successful implementation of EHRs. The longstanding Technology-in-Practice framework emphasizes the role of human agency in enacting structures of technology use or “technologies-in-practice”. Given a set of unintended adverse consequences from health information technology implementation, this framework could help trace them back to specific actions (types of technology-in-practice) and institutional conditions (social structures). On the other hand, the more recent Knowledge-in-Practice framework helps understand how information and communication technologies (e.g., social knowledge networking systems) could be implemented alongside existing technology systems, to create new social structures, generate new knowledge-in-practice, and transform technology-in-practice. Therefore, integrating the two literature streams could serve the dual purpose of understanding and overcoming unintended adverse consequences of EHR implementation. This paper seeks to: (1) review the theoretical literatures on technology use & implementation, and identify a framework for understanding & overcoming unintended adverse consequences of implementing EHRs; (2) outline a broad project proposal to test the applicability of the framework in enabling “meaningful use” of EHRs in a healthcare context; and (3) identify strategies for successful implementation of EHRs in hospitals & health systems, based on the literature review and application.
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 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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".