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Record W2990702731 · doi:10.3389/fpsyt.2019.00917

Easy Access, Difficult Consequences? Providing Psychiatric Patients With Access to Their Health Records Electronically

2019· article· en· W2990702731 on OpenAlexaff
Gillian Strudwick, Anthony T. Yeung, David Gratzer

Bibliographic record

VenueFrontiers in Psychiatry · 2019
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsCentre for Addiction and Mental HealthUniversity of Toronto
Fundersnot available
KeywordsHealth recordsPsychiatryMental healthInternet privacyAccess to informationPsychologyMedicineData scienceComputer scienceWorld Wide WebHealth careInformation accessPolitical science

Abstract

fetched live from OpenAlex

Should psychiatric patients have access to their clinical notes (often referred to as OpenNotes)?For decades, mental health providers have debated this.Some have argued that open access to notes will undermine the therapeutic rapport, and possibly undermine care itself (1).Others have suggested that involving patients in their care decisions must include access to their records; in 2014, an editorial called on providers to "show patients their mental health records" (2).Following this publication, several psychiatrists opened up their clinical notes to patients electronically through patient portals and have been providing access ever since (3).Despite initial concerns from providers about what opening up their notes might to do therapy and care, these concerns have generally not been realized (4, 5).We believe that more effort needs to be made to support providers in feeling more comfortable opening up their notes.Although patient portals are increasingly found in other areas of medicine, they are not available to the same extent in psychiatry.Progress has been made both in terms of: 1) the number of organizations that have opened up their psychiatric notes (6); and, 2) our understanding of the impact of opening up notes to both patients and providers (7, 8).Yet, in our opinion, the uptake of "opening up" mental health clinical notes remains slow.In large part, we believe that this is due to the ongoing discomfort and concerns that providers have had (9).A recent study has shown that working in psychiatry is a predictor of discomfort in providing clinical notes to patients, with psychiatrists and those working in acute settings being particularly concerned (9).This does not come as a surprise as we are aware of the sensitive topics discussed during clinical encounters, and the stigma often associated with mental illness.In our experience talking to providers about this topic, we have received mixed feedback from those that have not yet opened up their notes.There are providers who see the merit in opening up clinical notes to support patient empowerment, and providers who are opposed to the idea (often adamantly) due to a number of clinical concerns.These concerns include possible documentation changes that influence the clinical utility of the notes (note "sanitization"); patients reading their notes in unsupported environments and becoming upset; providers answering more questions after hours; patients not understanding the content of their notes; clinical interactions taking more time as questions related to note content are discussed; concerns about providers being subjected to violence; and, the list goes on (5, 8-10).Since there have now been implementations of psychiatric patient portals with OpenNotes in numerous organizations, the question is now: what have the experiences been of providers?Generally speaking the literature has shown that the concerns of providers who are less

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)0.006
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.062
Threshold uncertainty score0.208

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.044
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0080.009
Open science0.0010.005
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0620.009

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 head0.022
GPT teacher head0.370
Teacher spread0.348 · 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 designObservational
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

Citations20
Published2019
Admission routes1
Has abstractyes

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