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

Abstract

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 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 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.

Keywords

• Clinical prediction models
• Covid-19
• Healthcare rationing
• Algorithmic fairness