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Record W2149816395

Multispecialty physician networks in Ontario.

2013· article· en· W2149816395 on OpenAlexaffabout
Thérèse A. Stukel, Richard H. Glazier, Susan Schultz, Jun‐Lin Guan, Brandon Zagorski, Peter Gozdyra, David Henry

Bibliographic record

VenuePubMed · 2013
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsInstitute for Clinical Evaluative SciencesUniversity of Toronto
Fundersnot available
KeywordsMedicineFamily medicinePrimary careAmbulatory carePrimary care physicianHealth carePopulationEmergency medicine
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Large multispecialty physician group practices, with a central role for primary care practitioners, have been shown to achieve high-quality, low-cost care for patients with chronic disease. We assessed the extent to which informal multispecialty physician networks in Ontario could be identified by using health administrative data to exploit natural linkages among patients, physicians, and hospitals based on existing patient flow. METHODS: We linked each Ontario resident to his or her usual provider of primary care over the period from fiscal year 2008/2009 to fiscal year 2010/2011. We linked each specialist to the hospital where he or she performed the most inpatient services. We linked each primary care physician to the hospital where most of his or her ambulatory patients were admitted for non-maternal medical care. Each resident was then linked to the same hospital as his or her usual provider of primary care. We computed "loyalty" as the proportion of care to network residents provided by physicians and hospitals within their network. Smaller clusters were aggregated to create networks based on a minimum population size, distance, and loyalty. Networks were not constrained geographically. RESULTS: We identified 78 multispecialty physician networks, comprising 12,410 primary care physicians, 14,687 specialists, and 175 acute care hospitals serving a total of 12,917,178 people. Median network size was 134,723 residents, 125 primary care physicians, and 143 specialists. Virtually all eligible residents were linked to a usual provider of primary care and to a network. Most specialists (93.5%) and primary care physicians (98.2%) were linked to a hospital. Median network physician loyalty was 68.4% for all physician visits and 81.1% for primary care visits. Median non-maternal admission loyalty was 67.4%. Urban networks had lower loyalties and were less self-contained but had more health care resources. INTERPRETATION: We demonstrated the feasibility of identifying informal multispecialty physician networks in Ontario on the basis of patterns of health care-seeking behaviour. Networks were reasonably self-contained, in that individual residents received most of their care from providers within their respective networks. Formal constitution of networks could foster accountability for efficient, integrated care through care management tools and quality improvement, the ideas behind "accountable care organizations."

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)0.000
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.174

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.055
GPT teacher head0.334
Teacher spread0.279 · 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

Citations51
Published2013
Admission routes2
Has abstractyes

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