An Ethnographic Study of Health Information Technology Use in Three Intensive Care Units
Bibliographic record
Abstract
OBJECTIVES: To identify the impact of a full suite of health information technology (HIT) on the relationships that support safety and quality among intensive care unit (ICU) clinicians. DATA SOURCES: A year-long comparative ethnographic study of three academic ICUs was carried out. A total of 446 hours of observational data was collected in the form of field notes. A subset of these observations-134 hours-was devoted to job-shadowing individual clinicians and conducting a time study of their HIT usage. PRINCIPAL FINDINGS: Significant variation in HIT implementation rates and usage was noted. Average HIT use on the two "high-use" ICUs was 49 percent. On the "low-use" ICU, it was 10 percent. Clinicians on the high-use ICUs experienced "silo" effects with potential safety and quality implications. HIT work was associated with spatial, data, and social silos that separated ICU clinicians from one another and their patients. Situational awareness, communication, and patient satisfaction were negatively affected by this siloing. CONCLUSIONS: HIT has the potential to accentuate social and professional divisions as clinical communications shift from being in-person to electronically mediated. Socio-technically informed usability testing is recommended for those hospitals that have yet to implement HIT. For those hospitals already implementing HIT, we suggest rapid, locally driven qualitative assessments focused on developing solutions to identified gaps between HIT usage patterns and organizational quality goals.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".