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Record W2116170773 · doi:10.3122/jabfm.2015.03.140246

Health Information Technology Needs Help from Primary Care Researchers

2015· article· en· W2116170773 on OpenAlexaff
Alex H. Krist, L. A. Green, Robert L. Phillips, John W. Beasley, Jennifer E. DeVoe, Michael S. Klinkman, John Hughes, Jon Puro, Chester H. Fox, Timothy E. Burdick

Bibliographic record

VenueThe Journal of the American Board of Family Medicine · 2015
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsMcGill UniversityUniversity of Alberta
FundersNational Cancer Institute
KeywordsMedicinePrimary careHealth information technologyInformation technologyHealth careBest practiceKnowledge managementNursingMedical educationProcess managementFamily medicineBusinessComputer science

Abstract

fetched live from OpenAlex

While health information technology (HIT) efforts are beginning to yield measurable clinical benefits, more is needed to meet the needs of patients and clinicians. Primary care researchers are uniquely positioned to inform the evidence-based design and use of technology. Research strategies to ensure success include engaging patient and clinician stakeholders, working with existing practice-based research networks, and using established methods from other fields such as human factors engineering and implementation science. Policies are needed to help support primary care researchers in evaluating and implementing HIT into everyday practice, including expanded research funding, strengthened partnerships with vendors, open access to information systems, and support for the Primary Care Extension Program. Through these efforts, the goal of improved outcomes through HIT can be achieved.

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)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.525
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.129
GPT teacher head0.451
Teacher spread0.323 · 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 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

Citations18
Published2015
Admission routes1
Has abstractyes

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