Improving Patient Access through Value-Based CAR T Delivery: Examples Using a Micro-costing Tool
Bibliographic record
Abstract
The true costs of chimeric antigen receptor T-cell (CAR T) delivery (i.e., all costs except acquisition costs) remain uncertain, with little guidance available to guide accurate estimation. Providers therefore may face challenges in estimating the costs of establishing or expanding CAR T services, particularly if they have limited business expertise or CAR T experience. This risks over- or under-estimation of CAR T delivery costs, with potential for inadequate provider reimbursement, resource challenges, and restricted patient access. Accurate estimation of CAR T delivery costs is an essential component of ensuring value-based healthcare that achieves the best possible patient outcomes, while also ensuring that providers are adequately compensated for service delivery. The purpose of this paper is to demonstrate how value-based CAR T service delivery can be facilitated using an Excel-based micro-costing framework ("tool") to derive accurate and transparent cost estimates that consider the full patient pathway from initial assessment through to 100 days post CAR T infusion. Micro-costing can be used to support robust CAR T business case development and ensure all individual components of the treatment pathway have been accounted for. The tool's flexibility allows providers to model different future scenarios (e.g., changes in the case mix) and test the impact of resource changes (e.g., adjusting staff experience levels) to make informed resourcing decisions. This facilitates identification of opportunities for efficiencies, resource reallocation, and service improvements, and supports providers in delivering CAR T therapy to as many eligible patients as possible. A clear and detailed understanding of CAR T delivery costs can also support validation of reimbursement levels, such as CAR T tariffs, mitigating the risk of provider underpayment for services. The publicly available micro-costing tool is the first to support costing across the full CAR T treatment pathway from initial patient assessment to long-term follow-up. It has the potential to transform provider approaches to CAR T program development, reducing reliance on assumptive costs and uncertainty, and instead offering an evidence-driven approach. Overall, the tool supports value-based and sustainable CAR T therapy delivery, with the aim of ensuring adequate reimbursement for providers while facilitating patient access to this potentially life-saving therapy.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".