The use of patient-reported outcome measures to improve patient-related outcomes – a systematic review
Bibliographic record
Abstract
BACKGROUND: Patient-reported outcome measures (PROMs) provide invaluable information on patients' health outcomes and can be used to improve patient-related outcomes at the individual, organizational and policy levels. This systematic review aimed to a) identify contemporary applications and synthesize all evidence on the use of PROMs in these contexts and b) to determine characteristics of interventions associated with increased effectiveness. METHODS: Five databases were searched for studies providing quantitative evidence of the impact of PROM interventions. Any study design was permitted. An overall benefit (worsening) in outcome was defined as a statistically significant improvement (deterioration) in either a PROM, patient-reported experience measure or clinical outcome. Study quality was assessed using the Effective Public Healthcare Panacea Project's Quality Assessment Tool for Quantitative Studies. A narrative synthesis was conducted. RESULTS: Seventy-six studies of the 11,121 articles identified met the inclusion criteria. At the individual level, 10 (43%) of 23 studies that fed back PROMs to the patient or healthcare provider showed an improvement in outcome. This percentage increased in studies which used PROMs to monitor disease symptoms and linked these to care-pathways: 17 (68%) of 25 studies using this mechanism showed an improvement. Ten (71%) of 14 studies using PROMs to screen for disease found a benefit. The monitoring and screening approach was most effective using PROMs covering cancer-related, depression and gastro-intestinal symptoms. Three studies found that the mere collection of PROMs resulted in improved outcomes. Another three studies used PROMs in decision aids and found improved decision quality. At the organizational/policy level, none of the 4 studies that used PROMs for benchmarking found a benefit. The three studies that used PROMs for in-depth performance analyses and 1 study in a plan-do-study-act (PDCA) cycle found an improvement in outcome. Studies employing disease-specific PROMs tended to observe improved outcomes more often. There are concerns regarding the validity of findings, as studies varied from weak to moderate quality. CONCLUSIONS: The use of PROMs at the individual level has matured considerably. Monitoring/screening applications seem promising particularly for diseases for which treatment algorithms rely on the experienced symptom burden by patients. Organizational/policy-level application is in its infancy, and performance evaluation via in-depth analyses and PDCA-cycles may be useful. The findings of this review may aid stakeholders in the development and implementation of PROM-interventions which truly impact patient outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".