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Record W4401690508 · doi:10.4103/cjrm.cjrm_34_24

Electronic medical records: Transitions

2024· editorial· en· W4401690508 on OpenAlexvenueno aff
Peter Hutten-Czapski

Bibliographic record

VenueCanadian Journal of Rural Medicine · 2024
Typeeditorial
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsnot available
Fundersnot available
KeywordsMedical recordMedicineInternal medicine

Abstract

fetched live from OpenAlex

The world is changing. You are probably practising next to a computer, and then it changes. Yes, an incremental change within a version, is no problem. However, if you change groups, or even within a group, external or internal forces will lead to changes, in which programmes you are using and how. The new rural medical resident becomes an expert at changing systems as each rotation may start with the challenge of learning how to use new informatics. Such a recent change at our rural hospital has led me to think how fortunate we can be in rural settings when dealing with change management. It has never been pleasant to change, but a supportive environment is essential. One of the advantages of rural is that dealing with occasional stuff that we are not experts in is a strength of the rural generalist physician. Frankly, it is easier to do these transitions rurally where people, as an abstract concept, do not really exist and instead, you have interaction with a collection of persons. You must have some buy-in. Even if it is a decision imposed from the city, starting the day with wanting to make the best of it is a place you want to be. Then you need the support. You need physical support (many more computers and paraphernalia) and they need to be there before you need them. This is hard to do at a hospital. Space needs to be allocated and walls moved, desks installed with phone and Internet connections. Management needs to realise that the transition will fail if this does not happen on time. Let us not forget that you need training on the new software. I grant that most training ahead of time will be insufficient as you will be looking at an interface without the context of real-life tasks and workflow. This brings up the training at the school of hard knocks. What worked with us is a small cadre of volunteer helpers. You can call them ‘super users’, but the concept is understood. They are nothing more special than people whose presence is entirely to support you. These are your peers who may have received a few hours more training in the test environment and help you muddle through right then and there. Needless to say, you also need a real ‘super user’ consultant or two to pull in when two heads and some clicking are not getting anywhere. That is it for a successful transition. Pity on those who are in the city.

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)0.008
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.483
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0030.018
Insufficient payload (model declined to judge)0.0110.001

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 head0.023
GPT teacher head0.405
Teacher spread0.383 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEditorial

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
Published2024
Admission routes1
Has abstractyes

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