Expert consensus on implementing patient-reported outcomes in telehealth: findings from an international Delphi study
Bibliographic record
Abstract
BACKGROUND: Using Patient Reported Outcomes (PROs) in clinical care can reduce healthcare service utilization by improving the quality of care. Telehealth, defined by WHO, as the use of "telecommunications and virtual technology to deliver healthcare outside of traditional healthcare facilities", can facilitate a dynamic dialogue between patients and healthcare providers for timely interventions. With the increased use of telehealth facilitated by the infrastructure development during the COVID-19 pandemic, there is an opportunity to utilize telehealth for PRO implementation and a need for guidelines for using PROs via telehealth. This study aimed to generate expert consensus on the utilization of PROs in telehealth. METHODS: Delphi methodology was used to achieve consensus among international experts with a predetermined consensus threshold of 70%. Experts were mainly identified through the ISOQOL Clinical Practice SIG. Surveys asked a combination of structured and open-ended questions about the conceptualization of PROs in telehealth, its applicability, target population, implementation challenges and successful strategies, evaluation approaches, and the essential stakeholders. Data from each round were iteratively analyzed using descriptive statistics (quantitative data) and content analysis (qualitative data). RESULTS: Out of 24 invitations sent, 17 completed the first round, and 11 completed all three rounds. Respondents were equally distributed between clinicians and researchers and 70% had used PROs via telehealth before the pandemic. Consensus was achieved and some of the relevant aspects are monitoring patients for applicability; individuals with chronic diseases as the target population; resources, staff buy-in, and clinical workflow as the implementation challenges and strategies; utilization metrics for evaluation; and clinicians and patients as essential stakeholders. Though consensus was not reached for the conceptualization of PROs using telehealth, the modified FDA definition of telehealth with the addition of its purpose, and the mode of administration was the most acceptable version. See attached table. CONCLUSION: The expert consensus achieved provides important insights from an international perspective on how PROs are currently used via telehealth and the needed implementation support to advance their expansion in research and practice. Lack of consensus on the definition of PROs in telehealth signals the continued rapid evolution of their use and the need for additional research.
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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 | 0.189 | 0.193 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".