Can Physician Education and Support Improve Patient Management
Bibliographic record
Abstract
Background: Despite widespread use of statins, it is estimated that 40 -50% of Canadian patients with known atherosclerotic cardiovascular disease (ASCVD) do not achieve recommended LDL-C level.We aimed to ascertain the care gap and whether the use of physician reminders imbedded in the electronic medical record (EMR) can optimize the use of second-and thirdline therapy as recommended by guidelines and its impact on LDL-C goal achievement in a real-world experience.Methods: We invited 300 physicians from our list of those known to be using Telus EMR in order to participate and share their practice level data.Physicians were asked whether they aimed to prescribe guidelines' recommended therapy in patients with LDL-C above recommended level.Results: Of the invited physicians, 159 were recruited to participate and 140 activated their dashboard and 97 shared their practice results.There were 7,647 patients coded as ASCVD or familial hypercholesterolemia (FH) of whom 63% were male and 81% were older than 60 years of age and 49% had history of hypertension (HT), 29% diabetes, and 19% CKD.Approximately half (51%) of patients did not have the cholesterol panel results documented in EMR in the past two years.Of those with documented LDL-C, the value was above the recommended level of <2.0 mmol/L in 33% of patients; 22% had LDL-C between 2.0 and 3.0 mmol/L and 11% above 3.0 mmol/L.Among patients with LDL-C > 2.0 mmol/L, 35% were receiving no treatment, 32% were on sub-optimal dose of statin, 22% were on high intensity statin but no ezetimibe, 10% were on statin and ezetimibe, and only 1% were on PCSK9i.The most common reason for not being on any lipid lowering was patient refusal or intolerance in 47% followed by "my management is appropriate" in 32%; only in 15% of patients were there a plan to modify therapy at the next visit.Conclusion: significant care gaps exist among primary care practices with respect to lipid lowering management with half of the patients not having the LDL-C level on the chart and of those with LDL-C, a third of patients not achieving guidelines recommended LDL-C level.Programs designed to overcome treatment inertia are needed to improve LDL-C control and achieve reduction in cardiovascular morbidity and mortality of high-risk patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".