Enhancing the value of the oncology thoracic surgery care pathway: a TDABC and ABC analysis
Bibliographic record
Abstract
BACKGROUND: Time-driven activity-based costing (TDABC) and activity-based costing (ABC) are methods used in the healthcare sector to assess the costs of patient care pathways. These methods help identify opportunities for optimizing and reducing activity times without compromising the quality of care. TDABC is recommended in the Value-Based Healthcare (VBHC) model to assess the outcomes of care pathways in relation to their associated costs. By focusing on the creation of value for patients, TDABC helps identify the interventions and processes that provide the most value in terms of clinical outcomes and patient satisfaction. This enables healthcare organizations to make informed decisions on improvements that will maximize value for patients. The aim of the study is to evaluate the cost of the oncology thoracic surgery care pathway prior to and following the implementation of digital health solution. METHODS: We have chosen to use the TDABC and ABC methods to calculate the costs of care pathway for oncology patients undergoing thoracic surgery in two healthcare establishments prior to and following the implementation of a digital health solution. By using these methods, we were able to calculate the costs associated with each stage of the patients' care pathway. This has given us a clearer picture of the costs associated with each activity and a better understanding of the sources of expenditure. RESULTS: The results show that implementing the digital health solution and applying the principles of the VBHC model have provided tangible benefits in terms of reviewing processes and the roles of the various players involved, eliminating unnecessary or non-value-added activities, automating administrative or repetitive tasks, and improving coordination between the two healthcare establishments and between healthcare professionals. These improvements have contributed to better patient care. CONCLUSIONS: Given the success observed in this pilot project, decision-makers chose to persistently implement this digital health solution for specific care pathways over the long term. Additionally, there is a commitment to further enhance the platform to align closely with the needs and the expectations of healthcare professionals. This proactive approach aims to ensure optimal utilization of resources, ultimately providing the best care to patients.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".