Simulation Modeling of Constrained Resource Allocation Using the Activity Based Conceptual Modeling Methodology
Bibliographic record
Abstract
This thesis considers a common healthcare challenge of planning capacity for a system of care where patients receive multiple treatments sessions from multiple resources. As a case study of this more general problem, we considered the particular context of a capacity planning model for the Mood and Anxiety Program at The Ottawa Royal Health Centre (referenced as The Royal for simplicity) where a new service system known as CAPA (www.capa.co.uk) is being implemented to enhance the mental care provided to its patients. In order to develop the capacity planning model, we have created a simulation model using the Arena simulation software. We have also used the ABCMod Framework as the modeling methodology. The ABCMod is an activity based conceptual modelling framework that provides a set of guidelines as to how to build a conceptual model including its structural and behavioural aspects as well as a collection of constructs which include inputs, outputs and parameters among others. The ABCMod framework tools are expected to facilitate the model validation with project stakeholders. A series of scenarios relevant to The Royal were modeled and analyzed in order to determine how best to manage capacity so certain performance goals within the CAPA system implementation are met. These scenarios determine the service level The Royal can provide with its current capacity and also the amount and distribution of resources that is required to achieve its goals under the CAPA system. As a result of our simulation runs, we defined the policy implications for The Royal in order to achieve its targets and successfully implement CAPA. Additionally, through the application of the ABCMod framework and standard process mapping tools, we were able to reach a consensus and validate our modeling approach with the project stakeholders at The Royal. Our model could be adapted to other settings in which multiple resources provide a series of sequential interventions to clients.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".