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Record W7052423474

Regional Integration: Physician Perceptions on Electronic Medical Record Use and Impact in South West Ontario

2020· article· en· W7052423474 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship@Western (Western University) · 2020
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Electronic medical recordMedical recordHealth careIntegrated careElectronic health recordMEDLINEPerceptionPrimary care
DOInot available

Abstract

fetched live from OpenAlex

Regional initiatives in the health care context in Canada are typically organized and administered along geographic boundaries or operational units. Regional integration of Electronic Medical Records (EMR) has been continuing across Canadian provinces in recent years, yet the use and impact of regionally integrated EMRs are not routinely assessed and questions remain about their impact on and use in physicians’ practices. Are stated goals of simplifying connections and sharing of electronic health information collected and managed by many health services providers being met? What are physicians’ perspectives on the use and impact of regionally integrated EMR? In this thesis, I examined how primary health care and family physicians use electronic medical records and associated electronic health information resources in South West Ontario, the challenges they face in doing so, as well as the impact of an integrated EMR. A mixed methods-grounded theory research approach was employed to explore physician EMR use, and data acquired using participant consultation, observership and shadowing, semi-structured interviews, and a self-administered questionnaire. The study revealed that there are clear and present challenges to regional integration of EMR. Although regional integration initiatives such as implementation of ClinicalConnect, a regional EMR clinical viewer, continue to expand, physicians face challenges related to implementation, support and advanced use of electronic records. Not every patient has data access, patient portals are often not fully integrated, and the impact of EMR transitioning can reshape a primary care physician practice. A comprehensive model of physician integrated EMR use and a six-stage maturity model were developed from this study: The comprehensive model conceptualizes how the experience of EMR transitioning, managing patient expectation, meeting information needs, engaging regional entities, support and practice context, influence physician perception of EMR integration, and often resulted in practice changing moments. It further describes influences on physician perception of EMR use by EMR offering, EMR content, integration tools, information attributes, practice type, and patient and physician characteristics. The six-stage maturity model provides a framework that describes key elements of operative EMR use within the context of regional integration of electronic health information resources. It enhances understanding of EMR maturity by shifting orientation from theoretical evolutionary improvement path, which characterized prior maturity models, to assessment of EMR maturity based on how practicing physicians actually use EMR in primary health care. Insights from this study will advance understanding of regional integration of electronic medical records and serve as additional resource for individuals interested in assessment of the use and impact of electronic health information resources in primary health care.

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)0.004
metaresearch head score (Gemma)0.013
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 score0.057
Threshold uncertainty score0.254

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0060.003
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.073
GPT teacher head0.279
Teacher spread0.205 · 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
Published2020
Admission routes1
Has abstractyes

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