Implementing patient-reported outcomes in routine clinical care for diverse and underrepresented patients in the United States
Bibliographic record
Abstract
BACKGROUND: Patient-reported outcomes (PROs) are used increasingly in routine clinical care and inform policies, reimbursements, and quality improvement. Less is known regarding PRO implementation in routine clinical care for diverse and underrepresented patient populations. OBJECTIVE: This review aims to identify studies of PRO implementation in diverse and underrepresented patient populations, elucidate representation of clinical specialties, assess implementation outcomes, and synthesize patient needs, concerns, and preferences. METHODS: MEDLINE, Embase, Web of Science, CINAHL, and PsycINFO were searched September 2021 for studies aiming to study PRO implementation in diverse and underrepresented patient populations within the United States. Studies were screened and data extracted by three independent reviewers. Implementation outcomes were assessed according to Proctor et al. taxonomy. A descriptive analysis of data was conducted. RESULTS: The search yielded 8,687 records, and 28 studies met inclusion criteria. The majority were observational cohort studies (n = 21, 75%) and conducted in primary care (n = 10, 36%). Most studies included majority female (n = 19, 68%) and non-White populations (n = 15, 54%), while fewer reported socioeconomic (n = 11, 39%) or insurance status (n = 9, 32.1%). Most studies assessed implementation outcomes of feasibility (n = 27, 96%) and acceptability (n = 19, 68%); costs (n = 3, 11%), penetration (n = 1, 4%), and sustainability (n = 1, 4%) were infrequently assessed. CONCLUSION: PRO implementation in routine clinical care for diverse and underrepresented patient populations is generally feasible and acceptable. Research is lacking in key clinical specialties. Further work is needed to understand how health disparities drive PRO implementation outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.056 | 0.197 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".