Information Technology Ambidexterity, Digital Dynamic Capability, and Knowledge Processes as Enablers of Patient Agility: Empirical Study
Bibliographic record
Abstract
Background There is a limited understanding of information technology’s (IT) role as an enabler of patient agility and the department’s ability to respond to patients’ needs and wishes adequately. Objective This study aims to contribute to the insights of the validity of the hypothesized relationship among IT resources, practices and capabilities, and hospital departments’ knowledge processes, and the department’s ability to adequately sense and respond to patient needs and wishes (ie, patient agility). Methods This study conveniently sampled data from 107 clinical hospital departments in the Netherlands and used structural equation modeling for model assessment. Results IT ambidexterity positively enhanced the development of a digital dynamic capability (β=.69; t4999=13.43; P<.001). Likewise, IT ambidexterity also positively impacted the hospital department’s knowledge processes (β=.32; t4999=2.85; P=.005). Both digital dynamic capability (β=.36; t4999=3.95; P<.001) and knowledge processes positively influenced patient agility (β=.33; t4999=3.23; P=.001). Conclusions IT ambidexterity promotes taking advantage of IT resources and experiments to reshape patient services and enhance patient agility.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".