A Digital Communication Intervention to Support Older Adults and Their Care Partners Transitioning Home After Major Surgery: Protocol for a Qualitative Research Study
Bibliographic record
Abstract
BACKGROUND: Older adults (aged ≥65 years) account for approximately 30% of inpatient procedures in the United States. After major surgery, they are at high risk of a slow return to their previous functional status, loss of independence, and complications like delirium. With the development and refinement of Enhanced Recovery After Surgery protocols, older patients often return home much earlier than historically anticipated. This put a larger burden on care partners, close family or friends who partner with the patient and guide them through recovery. Without adequate preparation, both patients and their care partners may experience poor long-term outcomes. OBJECTIVE: This study aimed to improve and streamline recovery for patients aged ≥65 years by exploring the communication needs of patients and their care partners. Information from this study will be used to inform an intervention developed to address these needs and define processes for its implementation across surgical clinics. METHODS: This qualitative research protocol has two aims. First, we will define patient and care partner needs and perspectives related to digital health innovation. To achieve this aim, we will recruit dyads of patients (aged ≥65 years) who underwent elective major surgery 30-90 days prior and their respective care partners (aged ≥18 years). Participants will complete individual interviews and surveys to obtain demographic data, characterize their perceptions of the surgical experience, identify intervention targets, and assess for the type of intervention modality that would be most useful. Next, we will explore clinician perspectives, tools, and strategies to develop a blueprint for a digital intervention. To achieve this aim, clinicians (eg, geriatricians, surgeons, and nurses) will be recruited for focus groups to identify current obstacles affecting surgical outcomes for older patients, and we will review current assessments and tools used in their clinical practice. A hybrid deductive-inductive approach will be undertaken to identify relevant themes. Insights from both clinicians and patient-care partners will guide the development of a digital intervention strategy to support older patients and their care partners after surgery. RESULTS: This study has been approved by the Massachusetts General Hospital and Harvard Institutional Review Boards. Recruitment began in December 2023 for the patient and care partner interviews. As of August 2024, over half of the interviews have been performed, deidentified, and transcribed. Clinician recruitment is ongoing, with no focus groups conducted yet. The study is expected to be completed by fall 2024. CONCLUSIONS: This study will help create a scalable digital health option for older patients undergoing major surgery and their care partners. We aim to enhance our understanding of patient recovery needs; improve communication with surgical teams; and ultimately, reduce the overall burden on patients, their care partners, and health care providers through real-time assessment. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/59067.
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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 | 0.065 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.040 | 0.006 |
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".