8 Increasing capacity for autologous stem cell transplants for lymphomas: a quality improvement study
Bibliographic record
Abstract
Background Autologous Stem Cell Transplant (ASCT) patients have an inpatient length of stay (LOS) of 21–28 days. In a resource constrained environment, this leads to increasing wait times and limited ability to do transplants. We tried to create additional capacity for transplants at our institution by studying the root causes of this problem and designing improvement in our own resource constrained environment. Objectives Our main objective or Big Dot aim was to increase the capacity of ASCT by 20% by Dec 2020 (figure 1). The project aim was to decrease the LOS in ASCT patients by 2 days by May 2020. Methods A major root cause of increased LOS of ASCT patients was identified (figures 2 and 3) and was targeted for improvement. The first PDSA ramps consisted of conversion of IP PowerPlan to OP for administration of Chemotherapy. The second PDSA ramps consisted of creation of a modified CoSTARS assessment tool used to determine the risk pathway for readmissions. The subsequent PDSA ramps consisted of education of In-charge Nurses, trainees and patients. The data of LOS was plotted on a Shewart Process Control Chart “I Chart” using QI Macros software. Results The median LOS at the baseline was 24.5 days (range: 16–42) (figure 4). After the implementation of the change idea on 10 Dec 2019 till 5 Feb 2021, the LOS for ASCT decreased to 16.5 days (range: 13–21) (figure 5). The capacity for ASCT in our institution increased by 40% during the study period. We were able to save 120 inpatient days for ASCT after change from IP to OP Conditioning Chemotherapy in 15 pts (CoVid- May to Jul 2020). Conclusions Increase in the capacity for ASCT by decreasing the LOS by change of IP Conditioning Chemotherapy to OP setting is possible and feasible in QI framework.
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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 | 0.032 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".