Achieving self-sustainability of service delivery in an eye care program in Madagascar using time-driven activity based costing
Bibliographic record
Abstract
BACKGROUND: In the absence of adequate and reliable external funding, eye care programs in developing countries need a high level of financial self-sustainability for maintenance and growth. To cope with these cost pressures, an eye care program in Sava, Madagascar adopted a Time-Driven Activity Based Costing (TDABC) methodology to better manage the cost of, and to improve revenue associated with, their three principle activities: consultation visits, cataract operations, and sale of glasses. METHODS: Direct (variable) and indirect (fixed) cost estimates and revenue sources were gathered by activity (consultation, cataract operation, sale of glasses) and location (hospital or outreach) and TDABC models were established. Estimates were made of the proportion of the ophthalmologist's time (by far the scarcest and most expensive resource) dedicated to consultation, cataract operation, or sale of glasses. These proportions were used to attribute costs by activity. The hospital manager and medical director modified staff roles, program activities, and infrastructure investments to reduce costs and expand revenue sources by activity while monitoring activity specific efficiency and profit. RESULTS: The TDABC model for patient consultations showed that they were time consuming for the ophthalmologist and only resulted in net profit for the institution if the ophthalmologist converted most cataract patients into accepting surgery and refractive error patients into purchasing glasses from the hospital optical shop. The TDABC model for cataract surgery showed the programs needed to reduce the cost of imported consumable surgical products, reduce operation time, and, most importantly, reduce the number of very costly surgical camps providing essentially free surgery. In addition the model pushed the hospital to train staff in marketing skills so that a higher proportion of cataract cases come directly to the hospital willing to pay for surgery. The TDABC model provided the optical shop manager, for the first time, data on both the cost of supplies (frames and lenses) and the price of glasses sold resulting in strategies to maximize profit through preferential product presentation and customer experience. The eye program in the Sava region in northern Madagascar improved its cost recovery from 68 to 102% through patient revenue. CONCLUSIONS: TDABC models helped the Sava eye care program develop more efficient service delivery and increase revenue in excess of steadily increasing costs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".