Clinicians' perceptions of organizational readiness for change in the context of clinical information system projects: insights from two cross-sectional surveys
Bibliographic record
Abstract
BACKGROUND: The adoption and diffusion of clinical information systems has become one of the critical benchmarks for achieving several healthcare organizational reform priorities, including home care, primary care, and integrated care networks. However, these systems are often strongly resisted by the same community that is expected to benefit from their use. Prior research has found that early perceptions and beliefs play a central role in shaping future attitudes and behaviors such as negative rumors, lack of involvement, and resistance to change. In this line of research, this paper builds on the change management and information systems literature and identifies variables associated with clinicians' early perceptions of organizational readiness for change in the specific context of clinical information system projects. METHODS: Two cross-sectional surveys were conducted to test our research model. First, a questionnaire was pretested and then distributed to the future users of a mobile computing technology in 11 home care organizations. The second study took place in a large teaching hospital that had approved a budget for the acquisition of an electronic medical records system. Data analysis was performed using partial least squares. RESULTS: Scale items used in this study showed adequate psychometric properties. In Study 1, four of the hypothesized links in the research model were supported, with change appropriateness, organizational flexibility, vision clarity, and change efficacy explaining 75% of the variance in organizational readiness. In Study 2, four hypotheses were also supported, two of which differed from those supported in Study 1: the presence of an effective project champion and collective self-efficacy. In addition to these variables, vision clarity and change appropriateness also helped explain 75% of the variance in the dependent variable. Explanations for the similarities and differences observed in the two surveys are provided. CONCLUSIONS: Organizational readiness is arguably a key factor involved in clinicians' initial support for clinical information system initiatives. As healthcare organizations continue to invest in information technologies to improve quality and continuity of care and reduce costs, understanding the factors that influence organizational readiness for change represents an important avenue for future research.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".