Quality improvement strategies in medical oncology: A qualitative analysis from a scoping review.
Bibliographic record
Abstract
203 Background: In 2001, the Institute of Medicine (IOM) outlined imperatives to improve quality of care. Quality improvement (QI) has since become essential to cancer care but barriers still exist to the publication of and participation in QI initiatives, including limited recognition for QI and uncertainty with methodologies. We sought to identify strategies used in QI in scholarly medical oncology literature to provide practical guidance for QI. Methods: We conducted a scoping review using Arksey and O’Malley’s framework. A search of EMBASE and MEDLINE databases found 48,186 unique English citations published between January 2001 and August 2014. We utilized an iterative process to refine the inclusion criteria and two reviewers independently reviewed abstracts, resulting in the inclusion of 270 articles. The reviewers then extracted text segments relevant to QI strategies. A qualitative content analysis approach was used to accurately analyze and summarize this process-oriented data. Results: Fifty-four unique QI strategies identified were used alone or in combination to improve structures or processes of care. Five content categories of strategies that targeted structures of care emerged: 1) more methodical approaches (eg, lean thinking, supply-demand analyses), 2) participatory action research and similar strategies, 3) infrastructure to promote health care provider collaboration, 4) application or improvement of information technology (IT), and 5) progression towards a systematic assessment of all patients’ needs. We identified three categories of QI strategies for processes of care: 1) improving patient-clinician relationships or communications, 2) care navigation, and 3) telehealth. Conclusions: Our review identifiedQI strategies in published literature. Strategies were consistent with and expanded on the IOM’s redesign imperatives such as effective use of IT, development of better teams, and care coordination. Identification of strategies provides professionals with tools to engage in QI and may encourage support and recognition for QI. Future studies should examine the impact of different QI strategies on outcomes of care in oncology.
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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 | 0.109 | 0.202 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.028 | 0.042 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".