Getting physicians to accept new information technology: insights from case studies
Bibliographic record
Abstract
BACKGROUND: The success or failure of a computer information system (CIS) depends on whether physicians accept or resist its implementation. Using case studies, we analyzed the implementation of such systems in 3 hospitals to understand better the dynamics of physicians' resistance to CIS implementation. METHODS: We selected cases to maximize variation while allowing comparison of CIS implementations. Data were collected from observations, documentation and interviews, the last being the main source of data. Interviewees comprised 15 physicians, 14 nurses and 14 system implementers. Transcripts were produced; 45 segments of the transcripts were coded by several judges, with an appropriate level of intercoder reliability. We conducted within-case and cross-case analyses of the data. RESULTS: Initially, most staff were neutral or enthusiastic about the CIS implementations. During implementation, the level of resistance varied and in 2 instances became great enough to lead to major disruptions and system withdrawal. Implementers' responses to physicians' resistance behaviours played a critical role. In one case, the responses were supportive and addressed the issues related to the real object of resistance; the severity of resistance decreased, and the CIS implementation was ultimately successful. In the other 2 cases, the implementers' responses reinforced the resistance behaviours. Three types of responses had such an effect in these cases: implementers' lack of response to resistance behaviours, antagonistic responses, and supportive responses aimed at the wrong object of resistance. INTERPRETATION: The 3 cases we analyzed showed the importance of the roles played by implementers and users in determining the outcomes of a CIS implementation.
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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 | 0.039 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".