Identifying, Engaging, and Supporting Care Partners in Clinical Settings: Protocol for a Patient Portal–Based Intervention
Bibliographic record
Abstract
BACKGROUND: In the United States, the landscape of unpaid care delivery is both challenging and complex, with millions of individuals undertaking the vital role of helping families (broadly defined) manage their health care and well-being. This includes 48 million caregivers of adults, 42 million of whom are caregivers of adults aged 50 years or older. These family care partners provide critical and often daily support for tasks such as dressing and bathing, as well as managing medications, medical equipment, appointments, and follow-up care plans. OBJECTIVE: This study aimed to implement a novel patient portal-based intervention to identify, engage, and support care partners in clinical settings. METHODS: The project team collaborated with 3 health care organizations (6 primary care practices in total) to design and implement a patient portal-based intervention. Three days in advance of a visit, patients were invited to log on to their patient portal account and answer a brief questionnaire as part of the routine electronic check-in process asking them to (1) identify themselves as the patient or someone answering for the patient, (2) report major life changes, (3) set the agenda for the upcoming visit, and (4) report on care partner responsibilities. Respondents' answers to this brief questionnaire were available to providers ahead of the visit. Patients with care partner responsibilities, as well as care partners answering the questionnaire on behalf of patients, were provided a link to the ARCHANGELS Caregiver Intensity Index to measure the intensity of their caregiving role and motivate care partners to connect with suggested state and local resources. RESULTS: The intervention was launched in September 2022 at Organization A. Organization B launched in May 2023 in one clinic and June 2023 in the other. In focus groups, staff and clinicians reported that the intervention was easy to implement and did not cause workflow disruption. At 6 months post implementation, across both organizations, a total of 22,152 patients had received questionnaires and 13,825 (62.4%) had submitted completed questionnaires. Full data will be reported at the completion of the intervention period. CONCLUSIONS: Early results suggest that the intervention could be an easily scalable and adaptable method of identifying and supporting care partners in clinical settings. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/66708.
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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 | 0.045 | 0.033 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.075 | 0.014 |
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".