The Interactive Care Coordination and Navigation mHealth Intervention for People Experiencing Homelessness: Cost Analysis, Exploratory Financial Cost-Benefit Analysis, and Budget Impact Analysis
Bibliographic record
Abstract
Background: The Interactive Care Coordination and Navigation (iCAN) mobile health intervention aims to improve care coordination and reduce hospital and emergency department visits among people experiencing homelessness. Objective: This study aimed to conduct a three-part economic evaluation of iCAN, including a (1) cost analysis, (2) exploratory financial cost-benefit analysis, and (3) budget impact analysis (BIA). Methods: We collected cost and expenditure data from a randomized controlled trial of iCAN to conduct a cost analysis and exploratory financial cost-benefit analysis. Costs were classified as startup and recurring costs for participants and the program. Startup costs included participant supplies for each participant and SMS implementation costs. Recurring costs included the cost of recurring services, SMS text messaging platform maintenance, health information access fees, and personnel salaries. Using the per participant per year (PPPY) costs of iCAN, the minimum savings reduction in the average health care costs among people experiencing homelessness that would lead to a benefit-cost ratio >1 for iCAN was calculated. This savings threshold was calculated by dividing the PPPY cost of iCAN by the average health care costs among people experiencing homelessness multiplied by 100%. The benefit-cost ratio of iCAN was calculated under different savings thresholds from 0% (no savings) to 50%. Costs were calculated PPPY under different scenarios, and the results were used as inputs in a BIA. A probabilistic sensitivity analysis was conducted to incorporate uncertainty around cost estimates. Costs are in 2022 US $. Results: The total cost of iCAN was US $2865 PPPY, which was made up of US $265 in startup (9%) and US $2600 (91%) in recurring costs PPPY. The minimum savings threshold that would cause iCAN to have a positive return on investment is 7.8%. This means that if average health care costs (US $36,917) among people experiencing homelessness were reduced by more than 7.8% through iCAN, the financial benefits would outweigh the costs of the intervention. When health care costs are reduced by 25% ($9229/$36,917; equal to 56% [$9229/$16,609] of the average cost of an inpatient visit), the benefit-cost ratio is 3.22, which means that iCAN produces US $2.22 in health care savings per US $1 spent. The BIA estimated that implementing iCAN for 10,250 people experiencing homelessness over 5 years would have a financial cost of US $28.7 million, which could be reduced to US $2.2 million if at least 8% ($2880/$36,917) of average health care costs among people experiencing homelessness are reduced through the intervention. Conclusions: If average costs of emergency department and hospital visits among people experiencing homelessness were reduced by more than 7.8% ($2880/$36,917) through iCAN, the financial benefits would outweigh the costs of the intervention. As the savings threshold increases, it results in a higher benefit-cost ratio.
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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 | 0.027 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.010 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".