Patient segmentation and resource allocation for tailored healthcare delivery
Bibliographic record
Abstract
Healthcare systems often face the challenge of providing quality care to a diverse patient population while effectively utilizing limited resources. While some patients with complex conditions may require specialized care at dedicated clinics, others could benefit from receiving treatments at home, rehabilitation centers, community health centers, or other tailored service programs. Evaluating resource allocation and directing patients to appropriate care settings are particularly important when planning new healthcare initiatives with limited prior data or operational experience. This work introduces a methodology that uses patient segmentation techniques to address the challenges of resource allocation in healthcare settings, specifically focusing on planning new programs that lack prior data or clinical experience by leveraging existing electric patient records. First, we use unsupervised learning, specifically ensemble clustering, to identify distinct groups of patients. Next, an algorithm for rule-based representation of clusters is proposed to generate simple data-driven recommendations that can be applied to practical settings for selecting target patient groups, and lastly, we introduce a resource delivery priority score function that can guide decision-making and patient prioritization under resource constraints. Our methodology is applied to a case study of home transfusion delivery of Red Blood Cell (RBC) products, a proposed program for patients who are required to regularly visit outpatient clinics for receiving transfusions. The results highlight the potential of our methodology in efficient resource allocation and improving patient care outcomes beyond the current heuristic-based approaches in clinical practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".