https://pmc.ncbi.nlm.nih.gov/articles/PMC11573960/

Background

National estimates of post-acute healthcare demand are of limited use to a hospital deciding how to staff a follow-up clinic. Each institution sees a different patient mix and delivers care differently, so a pooled national figure can be wrong locally in both directions. The question here was how much additional healthcare each individual health system should expect its own patients to require after COVID-19 infection.

Method

The latent transfer learning framework proceeds in three stages. It first identifies latent patient subpopulations defined by multimorbidity patterns using data from all participating systems. It then performs causal estimation tailored to the target hospital’s own population. Finally it adaptively integrates information from the remaining hospitals, borrowing more where the populations resemble the target and less where they do not. Data came from eight PEDSnet health systems.

Main finding

Four distinct patient subpopulations emerged, with post-acute healthcare demand varying both across subpopulations and across hospitals. Patients with complex chronic conditions required more services after infection than other subpopulations. The heterogeneity across hospitals was large enough that a global or national estimate would be inadequate for local decision-making, which argues for stratified rather than uniform post-acute care planning.