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Record W4404394336 · doi:10.1186/s41687-024-00795-9

How patient-reported outcomes and experience measures (PROMs and PREMs) are implemented in healthcare professional and patient organizations? An environmental scan

2024· article· en· W4404394336 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Patient-Reported Outcomes · 2024
Typearticle
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsUniversité LavalMcGill UniversityUniversité de MontréalUniversité de Sherbrooke
Fundersnot available
KeywordsThematic analysisHealth carePatient experiencePatient-reported outcomeNursingMedicinePsychologyQualitative researchSociologyQuality of life (healthcare)Political science

Abstract

fetched live from OpenAlex

BACKGROUND: Patient-reported outcome measures (PROMs) and patient-reported experience measures (PREMs) are becoming essential parts of a learning health system, and using these measures is a promising approach for value-based healthcare. However, evidence regarding healthcare professional and patient organizations' knowledge, use and perception of PROMs and PREMs is lacking. OBJECTIVES: The objectives of the study were to: 1- Describe the current knowledge and use of PROMs and PREMs by healthcare professional and patient organizations, 2- Describe the determinants of PROMs and PREMs implementation according to healthcare professional and patient organizations. METHODS: We conducted an environmental scan using semi-structured interviews with representatives from healthcare professional and patient organizations. Interviews were recorded and live coded based on the Franklin framework. We used inductive and deductive thematic analysis to extract information about the main themes addressed during the interview (awareness of PROMs and PREMs, examples of implementation and use of PROMs and PREMs, tools used, vision for future implementation, barriers and facilitators to implementation and the best way to collect PROMs and PREMs data). RESULTS: 63% of healthcare professional organizations (n = 19) and 41% of patient organizations (n = 9) that were contacted agreed to have a representative interviewed. The representatives from both the healthcare professional and patient organizations acknowledged the importance of assessing patients' experience and outcomes. However, they considered the implementation of PROMs and PREMs tools to be scarce within their organizations, in clinical practice and in the education system. Patient organizations were worried that overuse of PROMs and PREMs could lead to depersonalization of practice. Barriers to implementing PROMs and PREMs included lack of awareness of tools, resistance to change and lack of motivation to complete or explain the questionnaire. Barriers also included factors such as lack of financial, technological and human resources and issues with integration of data and inconsistency of digital platforms. CONCLUSIONS: This environmental scan revealed a lack of awareness of tools by healthcare professional and patient organizations' representatives and limited implementation. Adequate training, technological integration, and demonstration of PROMs and PREMs benefits to foster broader adoption in clinical and organizational settings is dearly needed. Addressing these challenges is essential for enhancing value-based care.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.050
GPT teacher head0.389
Teacher spread0.340 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it