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Record W2028829563 · doi:10.1186/1471-2296-13-7

Describing the content of primary care: limitations of Canadian billing data

2012· article· en· W2028829563 on OpenAlexaffabout
Alan Katz, Gayle Halas, Michael P. Dillon, Jordan Sloshower

Bibliographic record

VenueBMC Family Practice · 2012
Typearticle
Languageen
FieldHealth Professions
TopicMedical Coding and Health Information
Canadian institutionsManitoba HealthUniversity of Manitoba
Fundersnot available
KeywordsMedicinePrimary careCoding (social sciences)Family medicineContent analysisHealth careNursingMEDLINEData collection

Abstract

fetched live from OpenAlex

BACKGROUND: Primary health care systems are designed to provide comprehensive patient care. However, the ICD 9 coding system used for billing purposes in Canada neither characterizes nor captures the scope of clinical practice or complexity of physician-patient interactions. This study aims to describe the content of primary care clinical encounters and examine the limitations of using administrative data to capture the content of these visits. Although a number of U.S studies have described the content of primary care encounters, this is the first Canadian study to do so. METHODS: Study-specific data collection forms were completed by 16 primary care physicians in community health and family practice clinics in Winnipeg, Manitoba, Canada. The data collection forms were completed immediately following the patient encounter and included patient and visit characteristics, such as primary reason for visit, topics discussed, actions taken, degree of complexity as well as diagnosis and ICD-9 codes. RESULTS: Data was collected for 760 patient encounters. The diagnostic codes often did not reflect the dominant topic of the visit or the topic requiring the most amount of time. Physicians often address multiple problems and provide numerous services thus increasing the complexity of care. CONCLUSION: This is one of the first Canadian studies to critically analyze the content of primary care clinical encounters. The data allowed a greater understanding of primary care clinical encounters and attests to the deficiencies of singular ICD-9 coding which fails to capture the comprehensiveness and complexity of the primary care encounter. As primary care reform initiatives in the U.S and Canada attempt to transform the way family physicians deliver care, it becomes increasingly important that other tools for structuring primary care data are considered in order to help physicians, researchers and policy makers understand the breadth and complexity of primary 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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
grokno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: yes
Observationalhigh
opusMetaresearch
Domain: Methods · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: yes
Observationallow
models splitAgreement compares identical category sets and study designs across arms.

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)0.095
metaresearch head score (Gemma)0.314
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 score0.925
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0950.314
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0180.048
Science and technology studies0.0100.004
Scholarly communication0.0100.003
Open science0.0070.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.925
GPT teacher head0.476
Teacher spread0.449 · 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

Labeled directly by 3 models reading the full record.

Metaresearch

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational
DomainMethods
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

Citations35
Published2012
Admission routes2
Has abstractyes

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