RESUMO
OBJECTIVES: To evaluate the utility of machine learning (ML) for the management of Medicare beneficiaries at risk of severe respiratory infections in community and postacute settings by (1) identifying individuals in a community setting at risk of infections resulting in emergent hospitalization and (2) matching individuals in a postacute setting to skilled nursing facilities (SNFs) that are likely to reduce the risk of infections. STUDY DESIGN: Retrospective analysis of claims from 2 million Medicare beneficiaries for 2017-2019. METHODS: In the first analysis, the rate of emergent hospitalization due to respiratory infections was measured among beneficiaries predicted by ML to be at highest risk and compared with the overall average for the population. In the second analysis, the rate of emergent hospitalization due to respiratory infections was compared between beneficiaries who went to an SNF with lower predicted risk of infections using ML and beneficiaries who did not. RESULTS: In the community setting, beneficiaries predicted to be at highest risk had significantly increased rates of emergency department visits (13-fold) and hospitalizations (18-fold) due to respiratory infections. In the postacute setting, beneficiaries who received care at top-recommended SNFs had a relative reduction of 37% for emergent care and 36% for inpatient hospitalization due to respiratory infection. CONCLUSIONS: Precision management through personalized and predictive ML offers the opportunity to reduce the burden of outbreaks of respiratory infections. In the community setting, ML can identify vulnerable subpopulations at highest risk of severe infections. In postacute settings, ML can inform patient choices by matching beneficiaries to SNFs likely to reduce future risk.
