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Record W267629933

The realities of implementing health information legislation: the Manitoba experience, 1997-2004.

2005· article· en· W267629933 on OpenAlexaffabout
Renata Neufeld

Bibliographic record

VenuePubMed · 2005
Typearticle
Languageen
FieldHealth Professions
TopicMedical Research and Practices
Canadian institutionsManitoba Health
Fundersnot available
KeywordsLegislationHealth carePublic relationsHealth policyContext (archaeology)Internet privacyPersonally identifiable informationBusinessPolitical scienceLawComputer science
DOInot available

Abstract

fetched live from OpenAlex

In the late 1990s, an increasing global emphasis on data protection and the expansion of provincial health information systems provided the optimal context for developing health information access and privacy legislation. Manitoba's Personal Health Information Act (PHIA) was thus enacted on December 11, 1997. (1) PHIA was the first law of its kind in Canada and was developed with a view to interpreting internationally accepted fair information principles for application within the local health care system. PHIA requires health information trustees (public bodies and health care providers) to grant individuals access, upon request, to personal health information about themselves. (2) It also requires trustees to protect the personal health information they maintain from inappropriate collection, use, disclosure, retention and destruction. (3) Manitoba's experience with PHIA over the past six years has included some interesting (and a few unforeseen) implementation challenges and has raised several demanding policy questions. This experience has also offered some useful lessons regarding the administration of health information access and privacy legislation. Some of these challenges, policy questions, and lessons are explored below. Implementation Challenges An implementation challenge that emerged early on was the complexity of the legislation. Although PHIA was deliberately drafted in plain language, and although the basic requirements are easily understood, the exceptions to these requirements are sometimes not. Yet an understanding of these exceptions is essential to effective implementation. An employee charged with the responsibility of responding to an access request must understand when it is appropriate or even necessary to sever certain information. (4) Likewise, an employee given the responsibility of discussing treatment and care with a patient's family must understand the amount of information it is appropriate to release without the patient's express consent. (5) While understanding the exceptions can be as important as understanding the rules, these details can sometimes be lost in the myriad of other legal requirements and operational demands trustees must respond to on any given working day. Demands on the time of health providers and health facility staff can also lead to another challenge in implementation: the time necessary to seek and obtain consent. Although PHIA does not currently define consent, common law holds that must be informed to be valid. (6) Obtaining informed may require a discussion with the patient, which in turn requires time and opportunity. Where these are scarce, there is a danger that the principle of no disclosure without consent may, in practice, simply mean no disclosure. Where to disclose is not sought and information is withheld, misunderstandings about the purpose of the legislation may ensue. This, in turn, may further impede appropriate communication. One such misunderstanding is that PHIA assures absolute privacy. Like all information protection legislation, PHIA provides for reasonable confidentiality with exceptions where the right of privacy conflicts with an overriding societal interest. The legislation thus enables reporting to appropriate authorities where there is a child protection issue, communicable disease outbreak, or serious and immediate threat to public safety. (7) Another misunderstanding is that the legislation is a barrier to health care. PHIA permits the disclosure of personal health information to persons providing health care, even without express from the patient, provided that the patient does not specifically object. (8) In doing so, the legislation also recognizes the importance of information sharing within the circle of care insofar as it is necessary for that purpose. Another challenge to effective implementation that occasionally arises is an unwarranted fear of prosecution under the legislation. …

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.902
Threshold uncertainty score0.713

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.016
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.005
Science and technology studies0.0260.010
Scholarly communication0.0090.003
Open science0.0040.008
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0070.001

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 head0.150
GPT teacher head0.459
Teacher spread0.309 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2005
Admission routes2
Has abstractyes

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