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Record W136886189 · doi:10.14236/jhi.v17i1.713

Effectiveness of an HbA1c tracking tool on primary care managementof diabetes mellitus: glycaemic control, clinical practice andusability

2009· article· en· W136886189 on OpenAlexaffabout
Jane Howard, Ryan Sommers, Odette N. Gould, Michelina Mancuso

Bibliographic record

VenueJournal of Innovation in Health Informatics · 2009
Typearticle
Languageen
FieldMedicine
TopicDiabetes, Cardiovascular Risks, and Lipoproteins
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicineDiabetes mellitusPrimary careUsabilityDiabetes managementTracking (education)Intervention (counseling)CohortFamily medicineEmergency medicineType 2 diabetesInternal medicineNursing

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine if a laboratory data report (the HbA1c Tracking Tool) could be used as an effective intervention to improve diabetes management. DESIGN: A longitudinal quasi-experimental cohort design was used to test the effectiveness of an HbA1c summary report sent to primary care physicians for all patients having HbA1c levels greater than 7%. SETTING: Moncton, New Brunswick, Canada. SAMPLE SELECTION: Administrative data from all adult patients with diabetes who had had at least two HbA1c measurements within the year prior to the initiation of the HbA1c Tracking Tool, and who had had five years of HbA1c measurements (2002-2007) overall was included. INTERVENTIONS: In March 2006 all primary care physicians began receiving HbA1c summary reports (through the HbA1c Tracking Tool) as a means to improving the management of diabetes. MAIN OUTCOME MEASURES: (a) patient glycaemic control as indicated by HbA1c levels, (b) physician adherence to practice guidelines as indicated by measuring the mean number of HbA1c tests ordered per patient per year, and (c) physician usage rates of the HbA1c Tracking Tool in clinical practice. RESULTS: The sample (n=955) was divided into three subgroups based on flagged HbA1c level (7-<8%, 8-9%, >9%). The strongest effect of the intervention was found in the two groups with the poorest glycaemic control. The effect was stronger in the >9% group (from 10.1 to 9.3%), than in the 8-9% group (a drop of 8.5 to 8.3%). Longitudinal analyses over a five-year period indicated the same findings. Patients were also found to receive more tests across time (from 2.45 tests per year to 3.0 across five years). In terms of usage, 92.1% of the physicians surveyed used the tool in their practice. CONCLUSION: Routinely collected hospital laboratory data can be used both as the basis for an information-based intervention and as a tool to monitor quality of diabetes 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)0.023
metaresearch head score (Gemma)0.081
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.081
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.026
GPT teacher head0.361
Teacher spread0.335 · 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

Citations8
Published2009
Admission routes2
Has abstractyes

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