Identifying trajectories across care modalities before and after COVID-19 using sequence analysis

Area of Science:

• Health Services Research
• Digital Health
• Health Equity

Background:

• The COVID-19 pandemic significantly increased virtual care adoption.
• Longitudinal impacts of virtual care on healthcare utilization remain understudied.
• Understanding patient stratification in digital and in-person care is crucial.

Purpose of the Study:

• To classify outpatient utilization patterns in adults with diabetes using multichannel sequence analysis.
• To examine how virtual care adoption affects different demographic and clinical groups.
• To identify populations at risk of reduced access in expanding remote care models.

Main Methods:

• Multichannel sequence analysis applied to outpatient utilization data.
• Classification of 10,671 adult patients with diabetes.
• Analysis of primary and diabetes care visits (remote and in-person) from April 2019 to March 2023.
• Stratification by race, ethnicity, language preference, insurance, comorbidity, and portal engagement.

Main Results:

• Patients transitioning to digital care were disproportionately Black or White, Medicare beneficiaries, with higher comorbidity.
• Individuals increasing combined in-person and remote utilization were disproportionately Hispanic, Spanish-preferring, with greater disease burden.
• Patients decreasing utilization were predominantly Asian, preferred Chinese, and had low patient portal engagement.

Conclusions:

• Technology-enabled care models can improve access but may exacerbate disparities.
• Certain populations face risks of reduced healthcare access with expanding telehealth.
• Health systems need targeted interventions for equitable telehealth access, addressing digital exclusion.