The effect of a state health care consent law on patient care in hospitals: A survey of physicians
Bibliographic record
Abstract
Objective: When a patient cannot make medical decisions for him or herself, and has not appointed a healthcare representative, default state healthcare consent laws determine who is able to make healthcare decisions for the patient. The narrow construction of some state laws leaves many patients in situations where the closest person to the patient does not qualify as a representative under the law, or where the patient has too many representatives and a consensus cannot be reached on the patient’s medical care.Methods: In order to determine how state healthcare consent laws affect patient care in hospitals, a survey of 412 Indiana physicians was conducted.Results: The data shows 53.8% of physicians experienced a delay in patient care because they were unable to identify a legally appropriate health care representative. Almost half (46.01%) of physicians experienced delay of patient care due to the inability to identify a final decision maker when disputes arose between multiple legal representatives.Conclusions: The results of this study have important implications for hospital administrators as a delay in patient care can be costly and unnecessarily utilizes hospital resources. Additionally, the results of this study have important implications for the status of state surrogate decision making laws. Amending state laws to include more potential surrogates, has the potential to minimize delays in patient care and ensure that appropriate surrogates are making medical care decisions for patients without the undue burden of court intervention.
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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 | 0.002 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".