When predictions are used to allocate scarce health care resources: three considerations for models in the era of Covid-19

When predictions are used to allocate scarce health care resources: three considerations for models in the era of Covid-19

Blog Article

Abstract Background The need for life-saving interventions such as mechanical ventilation may threaten to outstrip resources during the Covid-19 pandemic.Allocation of these resources to those most likely to benefit can be supported by clinical prediction models.The ethical and practical considerations relevant to MaxFlo VS / XL Parts predictions supporting decisions about microallocation are distinct from those that inform shared decision-making in ways important for model design.Main body We review three issues of importance for microallocation: (1) Prediction of benefit (or of medical futility) may be technically very challenging; (2) When resources are Ramen Bowl scarce, calibration is less important for microallocation than is ranking to prioritize patients, since capacity determines thresholds for resource utilization; (3) The concept of group fairness, which is not germane in shared decision-making, is of central importance in microallocation.

Therefore, model transparency is important.Conclusion Prediction supporting allocation of life-saving interventions should be explicit, data-driven, frequently updated and open to public scrutiny.This implies a preference for simple, easily understood and easily applied prognostic models.