Survey of information technology in Intensive Care Units in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: The Intensive Care Unit (ICU) is a data-rich environment where information technology (IT) may enhance patient care. We surveyed ICUs in the province of Ontario, Canada, to determine the availability, implementation and variability of information systems. METHODS: A self-administered internet-based survey was completed by ICU directors between May and October 2006. We measured the spectrum of ICU clinical data accessible electronically, the availability of decision support tools, the availability of electronic imaging systems for radiology, the use of electronic order entry and medication administration systems, and the availability of hardware and wireless or mobile systems. We used Fisher's Exact tests to compare IT availability and Classification and Regression Trees (CART) to estimate the optimal cut-point for the number of computers per ICU bed. RESULTS: We obtained responses from 50 hospitals (68.5% of institutions with level 3 ICUs), of which 21 (42%) were university-affiliated. The majority electronically accessed laboratory data and imaging reports (92%) and used picture archiving and communication systems (PACS) (76%). Other computing functions were less prevalent (medication administration records 46%, physician or nursing notes 26%; medication order entry 22%). No association was noted between IT availability and ICU size or university affiliation. Sites used clinical information systems from15 different vendors and 8 different PACS systems were in use. Half of the respondents described the number of computers available as insufficient. Wireless networks and mobile computing systems were used in 23 ICUs (46%). CONCLUSION: Ontario ICUs demontrate a high prevalence of the use of basic information technology systems. However, implementation of the more complex and potentially more beneficial applications is low. The wide variation in vendors utilized may impair information exchange, interoperability and uniform data collection.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".