Increasing Colorectal Cancer Screening in the Primary Care Setting
Bibliographic record
Abstract
abstract: Purpose: The purpose of this project was to implement a change in workflow to increase colorectal cancer (CRC) screening rates and improve Meaningful Use scores in a primary care setting.\n\nBackground and Significance: CRC is the second leading cause of cancer-related deaths in the United States among men and women. Current CRC screening rates remain low, even with advanced screening options available. Meaningful Use sets specific objectives for health care providers to achieve. Documenting CRC screening status and recommending CRC screenings to patients is one of the objectives of Meaningful Use and is considered a Clinical Quality Measure (HealthIT.gov). Factors that lead to CRC screening include primary care providers (PCPs) raising the topic, involving support staff, involving patients in the decision-making process, and setting alerts in electronic health records (EHRs).\n\nMethods: The Health Belief Model and Ottawa Model of Research Use helped guide this project. The project took place at a private primary care practice. The focus was on patients between the ages of 50 and 75 years old meeting criteria for CRC. Five PCPS and five medical assistants (MAs) chose to participate in the study. Participants were given pre and post Practice Culture Assessment (PCA) surveys to measure perceptions of the practice culture. The project included a three-part practice change: PCP and MA education about CRC screening guidelines, EHR documentation and reminders, and a change of patient visit workflow which included having MAs review patient's CRC screening status before they were seen by the PCP and handing out CRC screening brochures when appropriate. PCPs then ordered the appropriate CRC screening, and the MA documented the screening in the EHR under a designated location. CRC Screening Project Evaluation Forms were completed by MAs after each patient visit.\n\nOutcomes: No significant difference from pre to post survey satisfaction scores were found (t (8) = - 1.542, p= = .162). Means of quantitative data were reported from the CRC screening evaluation forms; N=91. The most common method of screening chosen was colonoscopy, 87%. A strong correlation was found (r (-.293) = .01, p<.05) between receiving a CRC brochure and choosing a form of screening. Meaningful Use scores pre and post project are pending.\n\nConclusion: Patients are more likely to choose a screening method when the topic is raised in a primary care setting. Continued staff education on workflow is important to sustain this change. Further research is needed to evaluate cost effectiveness and sustainability of this practice change.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".