Abstract 268: Using Lean Methodology to Reduce Variation in Care of Acute Coronary Syndrome Patients
Bibliographic record
Abstract
Background: Regional variation in the utilization of health services is a well-documented phenomenon in health care with numerous studies reporting substantial and unexplained variations in coronary revascularization. In the Canadian province of British Columbia (BC), five cardiac centers provide coronary revascularization services. In 2011 Cardiac Services BC (CSBC) undertook a study that identified substantial regional variation in coronary revascularization that could not be explained by patient characteristics or risk factors. Following this initial project, CSBC launched an initiative to help better understand the regional variations and possibly devise and implement strategies to reduce them. Methods: Using Lean methodology, we are mapping the key processes of care for ACS patients across BC (initially excluding emergent STEMI and cardiogenic shock) at each cardiac centre. The ACS patient journey will be mapped from admission to discharge through several key decision points that determine whether they will continue through to diagnostic catheterization and revascularization or to be medically managed alone. The key decision points are: 1. Decision to refer to diagnostic catheterization and subsequent transfer if necessary. 2. Decision to continue to a revascularization procedure (PCI or CABG) after diagnostic catheterization. The map will summarized these key decision points using multiple sources of data: 1. Flow and patient volumes into and out of each of these decision 2. Times between decision points and key care processes 3. Clinical influencers (ex: standard orders, best practice, established patterns of referral, and consultations) and non-clinical influencers (ex: resource capacity, transportation) that are considered at each decision point (process mapping and interview data). Discussion: BC is attempting to reduce unexplained variation in coronary revascularization using the Lean methodology to take a systematic approach to the analysis of the process of ACS care across the province. Involving physicians and point of care staff in the detailed mapping process has proven to be a significant step in engaging key stakeholders in the project by allowing input into the process of describing the factors affecting variation of practice at each site. The next step is to convene provincially to determine where to improve standardized practice in order to improve patient outcomes at key points along the value stream.
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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 | 0.030 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| 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".