Participation in Advance Care Planning Among Medically At-Risk Rural Veterans: Protocol for a Personalized Engagement Model
Bibliographic record
Abstract
BACKGROUND: Many of the challenges in advanced care planning (ACP) conversations are linked to the waxing and waning progress of serious illnesses. Conversations with patients about future medical care decisions by a surrogate decision maker have historically been left until late in the patient's disease trajectory. These conversations often happen at a time when the patient is already very ill. The challenge in effective early ACP and serious illness conversations is to create a situation where patients appreciate the link between current and future medical care. Setting the stage to make these conversations more accessible includes using telehealth to have conversations at the patient's place of choice. The personalization used includes addressing the current medical and social needs of the patient and ensuring that expressed needs are addressed as much as possible. Engaging patients in these conversations allows the documentation of patient preferences in the electronic health record (EHR), providing guidelines for future medical care. OBJECTIVE: The objective of our telehealth serious illness care conversations program was to successfully recruit patients who lacked up-to-date documentation of ACP in their EHR. Once these patients were identified, we engaged in meaningful, structured conversations to address the veterans' current needs and concerns. We developed a recruitment protocol that increased the uptake of rural veterans' participation in serious illness care conversations and subsequent EHR documentation. METHODS: The recruitment protocol outlined herein used administrative data to determine those patients who have not completed or updated formal ACP documentation in the EHR and who are at above-average risk for death in the next 3-5 years. The key features of the telehealth serious illness care conversations recruitment protocol involve tailoring the recruitment approach to address current patient concerns while emphasizing future medical decision-making. RESULTS: As of September 2022, 196 veterans had completed this intervention. The recruitment method ensures that the timing of the intervention is patient driven, allowing for veterans to engage in ACP at a time and place convenient for them and their identified support persons. CONCLUSIONS: The recruitment protocol has been successful in actively involving patients in ACP conversations, leading to an uptick in completed formal documentation of ACP preferences within the EHR for this specific population. This documentation is then available to the medical team to guide future medical care. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/55080.
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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 | 0.104 | 0.086 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.079 | 0.013 |
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".