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Record W4407567897 · doi:10.1200/op-24-00551

Real-World Patient-Reported Outcome Measure Implementation: Challenges and Successes of a Pan-Canadian Initiative to Improve the Future of Patient-Centered Cancer Care

2025· article· en· W4407567897 on OpenAlexaffabout
Sylvie Lambert, Michael McKenzie, Andrea C. Coronado, Amanda Caissie, Linda Watson, Andrea DeIure, Raquel Shaw-Moxam, Jean Ann Ryan, Marianne Arab, Bryan Jorgensen, Ashley Crump, Mireille Lecours, Peter Shaw Howatt

Bibliographic record

VenueJCO Oncology Practice · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsHealth PEISaskatchewan Cancer AgencyCancer Care Nova ScotiaDalhousie UniversityCanadian Partnership Against CancerBC Cancer AgencyAlberta Health ServicesMcGill University
Fundersnot available
KeywordsPromBest practiceBusinessProcess managementRestructuringHealth careStakeholder engagementQuality managementContingency planStakeholderSustainabilityLeverage (statistics)Patient-reported outcomeKnowledge managementOperations managementMedicinePublic relationsComputer scienceNursingPolitical scienceMarketingEngineeringFinance

Abstract

fetched live from OpenAlex

PURPOSE: Implementation of patient-reported outcome measures (PROMs) in routine care continues to be limited, despite their demonstrated efficacy and substantial investments. We report on the lessons learned and the challenges of the concerted implementation of the same PROMs across teams in nine provinces/territories (jurisdictions) in Canada, as well as the solutions to move implementation forward despite cost containments and the COVID-19 pandemic. METHODS: Each team from nine jurisdictions submitted a final report describing their PROM implementation project. Reports were analyzed for themes on lessons learned, challenges, and solutions. Themes were compared for similarities and differences. The Standards for Quality Improvement Reporting Excellence (SQUIRE) guidelines were used. RESULTS: Six key lessons learned were identified from eight challenges. To address these challenges, 27 solutions were used. The six lessons learned were as follows: Harness the power of change management, ensure consistent stakeholder engagement at all levels for success, establish buy-in as soon as possible, plan to maintain buy-in through changing circumstances, identify ways to make technology the solution, and optimal implementation includes a sustainability plan. Examples of solutions included the following: develop a multipronged, multilevel communication plan; include change management experts on the team; identify champions; restructure and reprioritize as needed; leverage existing technology; and leave a permanent trace of the project. CONCLUSION: To our knowledge, this is the first analysis to synthesize lessons learned from real-world PROM implementation across geographically diverse jurisdictions. We identified generalizable solutions that other health care managers and policymakers can use to accelerate PROM implementation, despite pervasive implementation barriers. Future studies can integrate these solutions with methods and tools from implementation science (eg, theoretical frameworks, implementation strategies) for more successful spread and scale of PROMs.

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 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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.647
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.354
GPT teacher head0.620
Teacher spread0.266 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2025
Admission routes2
Has abstractyes

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