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Record W4312946085 · doi:10.2196/39417

Adapting an Advance Care Planning Intervention Delivered via Telehealth for Older Patients With Acute Myeloid Leukemia and Myelodysplastic Syndromes

2022· article· en· W4312946085 on OpenAlexvenueno aff
Chandrika Sanapala

Bibliographic record

VenueIproceedings · 2022
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTelehealthAdvance care planningPalliative careMyeloid leukemiaMyelodysplastic syndromesIntervention (counseling)PopulationHealth literacyHealth careInternal medicineFamily medicinePhysical therapyTelemedicineNursing

Abstract

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Background Older patients with acute myeloid leukemia (AML) and myelodysplastic syndromes (MDS) experience high-intensity care (eg, chemotherapy, hospitalization, and life-sustaining treatments) during the end of life. Early advance care planning (ACP) may promote end-of-life care that is more consistent with patients’ values and goals. As the COVID-19 pandemic has resulted in a rapid shift to telehealth, the use of such methods may improve access to ACP among this vulnerable population. Objective In this qualitative study, we aimed to adapt an evidence-based ACP intervention, the Serious Illness Care Program (SICP), to be delivered via telehealth for older adults with AML and MDS. Methods We conducted semistructured interviews with 14 oncology clinicians and 10 palliative care clinicians (physicians, advanced practitioners, and nurses), as well as 15 patients and 4 caregivers. Oncology and palliative care clinicians were recruited if they had cared for at least one patient with AML or MDS in the past year. Eligible patients were aged ≥60 years and had a diagnosis of AML or MDS, and their caregivers, if available, were recruited. Interviews were transcribed and qualitatively coded by 2 independent coders using MAXQDA (VERBI GmbH). We used directed content analyses focused on the content and delivery (telehealth vs in-person ACP) of the SICP. Results The mean ages of clinicians, patients, and caregivers were 48, 71, and 66 years, respectively. Health literacy, which was measured using the 6-item Cancer Health Literacy Test, was high in both patients (score: mean 6; range 0-6) and caregivers (score: mean 6). The majority of participants liked the intent and content of the SICP, with suggestions mainly on wording changes. One patient stated, “I wish I’d had a little of this back in the beginning, it would’ve eased my way through….” Oncologists expressed positive feedback for the SICP language “planting the seeds” of the ACP conversation, emphasizing that “it doesn’t mean that it’s going to happen.” Oncology and palliative care clinicians were comfortable with conducting ACP discussions via telehealth. Providers felt that the use of telehealth in ACP conversations would allow them to “deliver care with less burden.” Most patients and caregivers however were comfortable with conducting ACP conversations via telehealth “after the first couple of appointments [being] in-person” to first establish care. Lastly, providers felt that including a geriatric assessment summary prior to ACP conversations “helps to ground and anchor the discussion,” as it provides a “sense of baseline functionality…[and] quality of life.” Conclusions Overall, the SICP was well received by clinicians, patients, and caregivers. This stakeholder feedback will help us to better understand current barriers to ACP conversations and gauge whether telehealth may be utilized to help improve access to ACP. This feedback will be used to further refine the SICP intervention for a future single-arm pilot study. Trial Registration ClinicalTrials.gov NCT04745676; https://clinicaltrials.gov/ct2/show/NCT04745676 Acknowledgements Funding: R00CA237744, 5UG1CA189961, and R33AG059206. Conflicts of Interest None declared.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.600

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.036
GPT teacher head0.349
Teacher spread0.314 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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