Evaluation of a Quality Improvement Program to improve the detection of Alzheimer’s disease and related dementias
Bibliographic record
Abstract
Context Early detection of cognitive impairment can improve health outcomes and delay the onset of Alzheimer’s disease and related dementias (ADRD). Primary care providers (PCPs) are at the forefront of evaluating cognitive concerns and detecting ADRD, but they often lack training and tools to do so. Objective To evaluate the outcomes of a system-wide primary care quality improvement intervention to improve PCPs’ ability to evaluate and detect ADRD. Study Design and Analysis Implementation project and outcomes evaluation comparing outcome measures before and after implementation. Setting Large primary care system comprising 14 community-based primary care clinics. Population Primary care providers Intervention An education series integrated with workflows and tools in the electronic health record (EHR). Outcome Measures Number of cognitive assessments recorded in the EHR by PCPs and the number of patients newly diagnosed with ADRD by PCPs. Results A total of 94 PCPs participated in the program. In the 9 months preceding the intervention, the average number of Montreal Cognitive Assessments (MoCA) recorded in the EHR by PCPs was 2.8 (SD 1.9) per month. Following the intervention, in the 9 months post, this average increased to 19.8 (SD 6.1) MoCAs per month (p < 0.001). Additionally, during this time frame, the average number of new ADRD diagnoses made in primary care rose from 6.2 (SD 2.4) per month before the intervention to 14.6 (SD 5.96) per month after the intervention (p = 0.012). Conclusions Training for PCPs, workflow adjustments, and the use of EHR tools increased cognitive testing and diagnoses of ADRD in primary care clinics. These resources are essential for enabling PCPs to take targeted actions to support patients’ brain health and help them access better ADRD care.
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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 | 0.046 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".