A narrative review of current evidence supporting the implementation of electronic patient-reported outcome measures in the management of chronic diseases
Bibliographic record
Abstract
An application of telemedicine of growing interest and relevance is the use of personal computers and mobile devices to collect patient-reported outcomes (PROs). PROs are self-reports of patients' health status without interpretation by anyone else. The tools developed to assess PROs are known as patient-reported outcomes measures (PROMs). The technological innovations that have led to an increased ownership of electronic devices have also facilitated the development of electronic PROMs (ePROMs). ePROMs are a conduit for telemedicine in the care of patients with chronic diseases. Various studies have demonstrated that the use of ePROMs in routine clinical practice is both acceptable and feasible with patients increasingly expressing a preference for an electronic mode of administration. There is increasing evidence that the use of electronic patient-reported outcome (ePROMs) could have significant impacts on outcomes valued by patients, healthcare providers and researchers. Whilst the development and implementation of these systems may be initially costly and resource-intensive, patient preferences and existing evidence to support their implementation suggests the need for continued research prioritisation in this area. This narrative review summarises and discusses evidence of the impact of ePROMs on clinical parameters and outcomes relevant to chronic diseases. We also explore recently published literature regarding issues that may influence the robust implementation of ePROMs for routine clinical practice.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".