The Impact of Electronic Medical Records Systems on Healthcare Subunit Performance: A Dynamic Capabilities Study
Bibliographic record
Abstract
This thesis focuses on the impact of electronic medical records (EMR) systems on healthcare subunit performance.EMR systems are a relatively new artifact in the healthcare industry and have come under a great deal of attention lately, seen as a way of reducing healthcare costs and improving healthcare quality.We have taken a dynamic capabilities perspective for this research.We developed a model that suggests that two dynamic capabilities: process management and change management, will positively impact usage of EMR systems, and that EMR use is the key driver of operational performance at the subunit level.We tested our theory with a sequential mixed method Change Management Scale CM1: I clearly understand the purpose and objectives of the EMR system.CM2: The benefits of the EMR system have been clearly identified and communicated.CM3: I have not received sufficient training to learn to use the EMR system.CM4: Hospital management effectively communicated the need for a new EMR system.CM5: Hospital management was highly supportive of and committed to the EMR related change process.CM6: No efforts were made to involve affected hospital employees in designing the EMR related changes. CM7:The hospital implemented an explicit change management plan related to the EMR implementation.CM8: Hospital management kept us informed of EMR related changes through frequent communication.CM9: Responsibilities to be assumed by hospital employees during and after the change were assigned.Process Management Scale PM1: Emergency department processes have been re-designed to align with the new EMR system.PM2: The redesigned processes meet the needs of the emergency department and the requirements of the EMR system.PM3: Hospital management assembled a cross-functional team to support EMR related process re-design.If "yes", please describe your role in selecting the EMR system.
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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 | 0.011 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".