Physician Orders for Life-Sustaining Treatment (POLST) Forms in a Primary Care Setting
Bibliographic record
Abstract
Purpose: To increase completed POLST forms among elderly patients aged seventy-seven and older with chronic/debilitating illnesses, by at least 50% in a Primary Care clinic setting, from September 2021 to January 2022. Background: The government pays for over two-thirds of healthcare costs for the elderly aged sixty-five and older. Furthermore, by ages seventy to ninety, medical spending more than doubles. Many elderly and chronically ill patients report that they would prefer less aggressive treatment and more comfort care measures. However, patients also report a lack of knowledge about their end-of-life (EOL) wishes particularly when it comes to completing the Physicians Orders for Life Sustaining Treatment (POLST) form. Completing the POLST form within the Primary Care setting, rather than in the Hospital setting, allows for a calm discussion in the clinic rather than in the hectic environment of the hospital. EBP Model/Framework: This project utilized the Ottawa Model of Research Use (OMRU). Practice change and Implementation Strategies: Pre-implementation data was collected, which included the percentage of completed and up-to-date POLST forms. After screening for age and diagnosis on each patient’s chart, a discussion occurred with the patient about the various end-of-life medical preferences listed on the POLST form. Next, each section of the POLST form was explained to the patient and they assisted in completing the form within the primary care setting. Once completed, the POLST form was given to the Medical Assistant who arranged for the Provider to sign by the end of the day. Once signed, the secretary uploaded the POLST form into the patient’s chart. This processes ensured every healthcare provider has access to and is aware of the patient’s end-of-life wishes. Data analysis was completed at the end of the measurement period by comparing the percentage of pre and post-implementation POLST forms. Results: According to pre-implementation data, thirty-five percent of patients had a completed POLST form and that percentage increased to seventy-two percent post-implementation. Thus, the project demonstrated a fifty-two-percent increase from baseline, demonstrating that the goal was met. Conclusions and implications for practice: Having a target age group and identified workflow supports sustainability and cost-effectiveness and should become standard practice within the clinic setting.
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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 | 0.016 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".