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Record W2135003013 · doi:10.12927/hcq.2005.17680

Legal Issues in Patient Safety: The Example of Nosocomial Infection

2005· article· en· W2135003013 on OpenAlexaffabout
Tracey M. Bailey, Nola M. Ries

Bibliographic record

VenueHealthcare Quarterly · 2005
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsInstitute of Health EconomicsUniversity of Alberta
Fundersnot available
KeywordsPatient safetyBest practiceMedicineNursingMedical emergencyBusinessIntensive care medicineHealth carePolitical scienceLaw

Abstract

fetched live from OpenAlex

“Preventable infections are out of control in Canadian hospi-tals,” declared an April 2005 headline in the British Medical Journal. Hospitals face less stringent infection-control monitoring than do restaurants, warned a CBC news investiga-tion. Recent events in Canada have indeed highlighted concern with infectious disease exposure through the healthcare system: the SARS outbreak led to criticism of lax hospital infection-control practices; various Canadian hospitals discovered that improper sterilization of equipment may have exposed patients to HIV, hepatitis and other diseases; virulent C. difficile infec-tions claimed patient lives; and a Montreal children’s hospital faced public concern in spring 2004 following disclosure that one of its former surgeons had died from AIDS. In an era of growing concern with patient safety in the healthcare system, these events raise important legal issues regarding liability, disclosure of information to patients and reporting to regulatory bodies, government agencies and others that have a paramount duty to protect the public from harm. In this article, we review several key legal issues related to patient safety. Using the example of nosocomial infection, we begin by summarizing recent lawsuits that have stemmed from alleged lapses in infection-control practices. We then identify legal duties that healthcare providers and facilities owe to patients to ensure their safety. Next, we discuss disclosure quandaries that may arise in the patient safety context. If a patient has been harmed, or exposed to risk of harm, do providers have a duty to disclose that information to the patient? What about the situation of remote or theoretical risks? When errors have occurred, or where some risk of harm exists, what information must be disclosed to regulatory authorities such as professional colleges or government agencies? We describe several new legal requirements that mandate disclosure of errors and conclude by offering some thoughts on the role of law in promoting patient safety. Readers are advised that this article does not constitute legal advice and are urged to consult with legal counsel regarding specific questions or concerns.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.898
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.050
GPT teacher head0.420
Teacher spread0.370 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2005
Admission routes2
Has abstractyes

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