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 move

Contents

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

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.


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