Cost-benefit analysis of outsourcing cleaning services at Mahalapye hospital, Botswana
Bibliographic record
Abstract
Objective: As part of its national privatization strategy to diversify the economy, Botswana has started outsourcing nonclinical services at seven public hospitals. Hospital managers are signing contracts without knowing whether outsourcing offers better value for money than “insourcing”. The objective of this study is to assist hospital administrators in making evidence-based outsourcing decisions.Methods: We conducted a cost-benefit analysis of cleaning services at Mahalapye Hospital. We take the hospital manager’s perspective when considering two alternatives: outsourcing, and “insourcing”. We used an activity-based costing approach and monetised benefits by weighting costs of the alternatives based on a service quality survey of hospital managers.Results: After adjusting per quality of the service, outsourcing provides greater value for money in terms of “cleanliness per pula spent” than insourcing. Incremental costs of outsourcing are Botswana Pula (BWP) 5 million (US $524,135) over five years but outsourcing is cost-beneficial after considering quality. The benefit-cost ratio of 1.06 means that outsourcing would return six cents in value for every dollar invested, resulting in net gains for Mahalapye Hospital of BWP 1.7 million (US $182,365) over five years.Discussion: Important lessons for hospital managers include: 1) Assessing the value of outsourcing requires information on the unit price of the outsourced services; 2) Outsourcing can be more costly than insourcing; 3) Outsourcing may be justified if it increases the quality of the service; 4) Collaboration between hospitals and vendors could reduce costs and increase benefits for both vendor and purchaser; and 5) Outsourcing should get more cost-beneficial as vendors and hospitals gain experience working together.Conclusions: The lessons from this study are relevant to other hospitals considering outsourcing agreements. Outsourcing requires managerial skills, supported by sound benchmark data and proper quality monitoring to streamline operations, achieve value for money and improve service delivery so hospitals can focus on core clinical services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".