Bibliographic record
Abstract
Introduction: Broad policy and local healthcare organization have recently favored computerized case notes, care charting, order writing, and specialty service consultation/referral in Critical Care Units (CCUs). Methods: Data on the impact of IT on CCUs were extracted from a multi-site comparative ethnography. 4 CCUs with varying levels of computerization located in 2 major urban areas in the US were recruited for this qualitative research. Ethnographers carried out 600+ hours of observations, and 70+ semi-structured interviews between December 2012 and December 2013. A subset of the observation time – 90+ hours – was structured to quantify how long clinicians spent working on computers to chart care, keep case notes, and issue or acknowledge prescriptions. These data were used to supplement the study’s qualitative focus on how IT influences team interactions and care delivery. The data were coded using NVivo software in an iterative analytic process. Results: Clinicians on the high computerization units spent between 35 and 50 per cent of their time on computers. Clinicians on the low computerization unit spent between 5 and 15 per cent of their time on computers. Key intended consequences of delivering care and coordinating teamwork and information with IT were achieved and these included the elimination of handwriting legibility issues, and improved access to clinical information. Unintended consequences included negative impacts on team coordination and communication. Some clinicians were ‘siloed’ by their IT work, and expressed concerns that they had less meaningful interactions with other members of their team and patients. Some clinicians felt these degraded interactions impeded their teams’ abilities to develop trust and identify and prevent errors. Conclusions: This pilot study suggests the unintended teamwork consequences of computerization in CCUs are an important object of study, with IT implementation design likely to be of central importance in the future.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.586 | 0.413 |
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".