Remote Symptom Monitoring Using Patient-Reported Outcomes in Patients With Chronic Kidney Disease: Process Evaluation of a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: In Denmark, outpatient follow-up for patients with chronic kidney disease (CKD) is changing from in-hospital visits toward more remote health care delivery. The nonuse of remote patient-reported outcomes (PROs) is a well-known challenge, and it can be difficult to explain which mechanisms of interventions influence the outcome. Process evaluation may, therefore, be used to answer important questions on how and why interventions work, aiming to enhance the implications for clinical practice. OBJECTIVE: This study aimed to provide insight into the intervention process by evaluating (1) the representativity of the study population, (2) patient and physician use patterns, (3) patient adherence to the intervention, and (4) clinical engagement. METHODS: A process evaluation determining the reach, dose, fidelity, and clinical engagement was carried out, alongside a multicenter randomized controlled trial (RCT). We developed and implemented an intervention using PRO measures to monitor outpatients remotely. Data were collected for the PRO intervention arms in the RCT from 4 sources: (1) PRO data from the participants to determine personal factors, (2) the web-based PRO system to identify key usage intervention patterns, (3) medical records to identify clinical factors relating to the use of the intervention, and (4) semistructured interviews conducted with involved physicians. RESULTS: Of the 320 patients invited, 152 (47.5%) accepted to participate. The study population reflected the target population. The mean adherence rate to the PRO intervention arms was 82% (95% CI 76-87). The questionnaire response rate was 539/544 (99.1%). A minority of 13 (12.9%) of 101 patients needed assistance to complete study procedures. Physicians assessed 477/539 (88.5%) of the questionnaires. Contact was established in 417/539 (77.4%) of the cases, and 122/539 (22.6%) of the patients did not have contact. Physicians initiated 288/417 (69.1%) and patients requested 129/417 (30.9%) of all the contacts. The primary causes of contact were clinical data (242/417, 58%), PRO data (92/417, 22.1%), and medication concerns and precautionary reasons (83/417, 19.9%). Physicians found the use of PRO measures in remote follow-up beneficial for assessing the patient's health. The inclusion of self-reported clinical data in the questionnaire motivated physicians to assess patient responses. However, some barriers were emphasized, such as loss of a personal relationship with the patient and the risk of missing important symptoms in the absence of a face-to-face assessment. CONCLUSIONS: This study demonstrates the importance and practical use of remote monitoring among patients with CKD. Overall, the intervention was implemented as intended. We observed high patient adherence rates, and the physicians managed most questionnaires. Some physicians worried that distance from the patients made it unfeasible to use their "clinical glance," posing a potential risk of overlooking crucial patients' symptoms. These findings underscore key considerations for the implementation of remote follow-up. Introducing a hybrid approach combining remote and face-to-face consultations may address these concerns. TRIAL REGISTRATION: ClinicalTrials.gov NCT03847766; https://clinicaltrials.gov/study/NCT03847766.
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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 | 0.071 | 0.091 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".