Documentation of best interest by intensivists: a retrospective study in an Ontario critical care unit
Bibliographic record
Abstract
BACKGROUND: Intensive care physicians often must rely on substitute decision makers to address all dimensions of the construct of "best interest" for incapable, critically ill patients. This task involves identifying prior wishes and to facilitate the substitute decision maker's understanding of the incapable patient's condition and their likely response to treatment. We sought to determine how well such discussions are documented in a typical intensive care unit. METHODS: Using a quality of communication instrument developed from a literature search and expert opinion, 2 investigators transcribed and analyzed 260 handwritten communications for 105 critically ill patients who died in the intensive care unit between January and June 2006. Cohen's kappa was calculated before analysis and then disagreements were resolved by consensus. We report results on a per-patient basis to represent documented communication as a process leading up to the time of death in the ICU. We report frequencies and percentages for discrete data, median (m) and interquartile range (IQR) for continuous data. RESULTS: Our cohort was elderly (m 72, IQR 58-81 years) and had high APACHE II scores predictive of a high probability of death (m 28, IQR 23-36). Length of stay in the intensive care unit prior to death was short (m 2, IQR 1-5 days), and withdrawal of life support preceded death for more than half (n 57, 54%). Brain death criteria were present for 18 patients (17%). Although intensivists' communications were timely (median 17 h from admission to critical care), the person consenting on behalf of the incapable patient was explicitly documented for only 10% of patients. Life support strategies at the time of communication were noted in 45% of charts, and options for their future use were presented in 88%. Considerations relevant to determining the patient's best interest in relation to the treatment plan were not well documented. While explicit survival estimates were noted in 50% of charts, physicians infrequently documented their own predictions of the patient's functional status (20%), anticipated need for chronic care (0%), or post ICU quality of life (3%). Similarly, documentation of the patient's own perspectives on these ranged from 2-18%. CONCLUSIONS: Intensivists' documentation of their communication with substitute decision makers frequently outlined the proposed plan of treatment, but often lacked evidence of discussion relevant to whether the treatment plan was expected to improve the patient's condition. Legislative standards for determination of best interest, such as the Health Care Consent Act in Ontario, Canada, may provide guidance for intensivists to optimally document the rationales for proposed treatment plans.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".