Nosokinetics
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Nosokinetics (Service care delivering modelling) is a term used to describe the science/subject of measuring and modelling the process of care in health and social care systems.
ΕΤΥΜΟΛΟΓΙΑ
Greek: nosos: disease and kinetikos: to moveContents |
Modeling
Why model
- Prediction
- Objectivity to decisions
- Consequences of decisions
- Identifying what is important
- Performance & monitoring measures
- Explaining what went wrong
Modelling limitations
- Measurable parameters must exist
- Assumptions
- Usually include political and social stability
- Chaos limitations
- War, earthquake or that unexpected epidemic
Modelling strengths
- Can often use data that is routinely collected
- Assumption examination can allow what if scenario planning
- Can allow for regular or repeating in time events such as seasonal influenza, effect of weekends and public holidays
- Can allow for demography changes
Model types
- Stochastic
- Assume you do not know precisely what will happen in advance, but past experience and reasonable population size allow assumption of randomness that underlies many real-world phenomena
- Allows a range of estimates
- More useful when you want a background level of guaranteed service
- More complicated so less likely to appeal to decision makers
- Deterministic
- Returns a most likely estimate
- Is easier to grasp but is far more likely to lead to chaos if used in prediction
- Health and social care decisions based on deterministic modelling have created major system crises in most such systems
- Examples include:
- Creating assessment panels or other rationing (queuing) steps such as waiting lists for inpatient investigations in patients who are already inpatients.
- Decreasing social service funding while increasing health funding leading to game playing by the underfunded service
- Examples include:
- Health and social care decisions based on deterministic modelling have created major system crises in most such systems
Stocastic models
- Will tend to have to use phase-type (PH) distributions such as length of stay in an institution
- State transitions need the mathematical concept of the (finite-state) continuous-time Markov chain (CTMC) which is rendered vis vector arithmetic.
- Coxian distributions, a subtype of phase-type distributions are useful in healthcare modelling
- Care home residents classically have a two state coxian distribution to their length of stay, because a subgroup rapidly die (or move on to more dependent institutional care) as they have a subacute unstable medical condition while the other subgroup have a chronic and only slowly progressive condition.
- General phase-type (PH) distributions would be better for modelling patient flow around a hospital since the processes in the emergency department, theatres and wards are so different with the potential for readmissions and indeed use of complex numbers to =best fit the observed distrution of length of stay.
External links
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