Systems-based hematology: highlighting successes and next steps
Bibliographic record
Abstract
Systems-based hematology is dedicated to improving care delivery for patients with blood disorders. First defined by the American Society of Hematology in 2015, the idea of a systems-based hematologist arose from evolving pressures in the health care system and increasing recognition of opportunities to optimize the quality and cost effectiveness of hematologic care. In this review, we begin with a proposed framework to formalize the discussion of the range of initiatives within systems-based hematology. Classification by 2 criteria, project scope and method of intervention, facilitates comparison between initiatives and supports dialogue for future efforts. Next, we present published examples of successful systems-based initiatives in the field of hematology, including efforts to improve stewardship in the diagnosis and management of complex hematologic disorders (eg, heparin-induced thrombocytopenia and thrombophilias), the development of programs to promote appropriate use of hematologic therapies (eg, blood products, inferior vena cava filters, and anticoagulation), changes in care delivery infrastructure to improve access to hematologic expertise (eg, electronic consultation and disorder-specific care pathways), and others. The range of projects illustrates the broad potential for interventions and highlights different metrics used to quantify improvements in care delivery. We conclude with a discussion about future directions for the field of systems-based hematology, including extension to malignant disorders and the need to define, expand, and support career pathways.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".