Assessing cost and cost savings of teleconsultation in long-term care facilities: a time-driven activity-based costing analysis within a value-based healthcare framework
Bibliographic record
Abstract
BACKGROUND: Quebec's healthcare system faces significant challenges due to labour shortage, particularly in long-term care facilities (CHSLDs). The aging population and increasing demand for services compound this issue. Teleconsultation presents a promising solution to mitigate labour shortage, especially in small CHSLDs outside urban centers. This study aims to evaluate the cost and cost savings associated with teleconsultation in CHSLDs, utilizing the Time-Driven Activity-Based Costing (TDABC) model within the framework of Value-Based Healthcare (VBHC). METHODS: This study focuses on CHSLDs with fewer than 50 beds in remote regions of Quebec, where teleconsultation for nighttime nursing care was implemented. Time and cost data were collected from three CHSLDs over varying periods. The TDABC model, aligned with VBHC principles, was applied through five steps, including process mapping, estimating activity times, calculating resource costs, and determining total costs. RESULTS: Teleconsultation increased the cost per minute for nursing care compared to traditional care, attributed to additional tasks during remote consultations and potential technical challenges. However, cost savings were realized due to reduced need for onsite nursing staff during non-eventful nights. Overall, substantial savings were observed over the project duration, aligning with VBHC's focus on delivering high-value healthcare. CONCLUSIONS: This study contributes both theoretically and practically by demonstrating the application of TDABC within the VBHC framework in CHSLDs. The findings support the cost savings from the use of teleconsultation in small CHSLDs. Further research should explore the long-term sustainability and scalability of teleconsultation across different CHSLD sizes and settings within the VBHC context to ensure high-value healthcare delivery.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".