Optimizing primary care for post-COVID-19 patients: leveraging clustering techniques for personalized diagnosis and treatment

David Lerma Irureta, Fátima Méndez-López, Carmen Vicente García, María Luz Torres, Jesus Ibañez Ruiz, Jon Schoorlemmer, Rosa Magallón Botaya

Keywords: patient clustering, post-COVID-19 condition, personalized medicine, primary care optimization, data-driven healthcare

Background:

Post-COVID-19 condition (PCC) presents with heterogeneous clinical manifestations that challenge diagnostic and therapeutic strategies in primary care. Clustering techniques offer a promising approach to stratify patients based on symptomatology and clinical characteristics, potentially improving personalized care delivery.

Research questions:

1. Can patient clustering techniques improve the identification of distinct PCC phenotypes?
2. How does clustering contribute to optimizing diagnostic and treatment pathways in primary care?
3. What are the implications of symptom clustering for patient management and healthcare resource allocation?

Method:

A retrospective cohort study was conducted using data from Long-COVID patients. Hierarchical and machine learning-based clustering techniques were applied to clinical, biochemical, and immunological markers to identify distinct patient subgroups. Comparative analysis was performed to assess the impact of clustering on diagnostic accuracy and treatment personalization.

Results:

Preliminary findings identified distinct PCC clusters with varying symptom profiles, severity levels, and functional impairments. Clustering revealed associations between symptom clusters and specific biochemical markers, allowing for targeted diagnostic and therapeutic approaches. Patients in certain clusters demonstrated improved outcomes when personalized treatment protocols were applied.

Conclusions:

Patient clustering techniques provide valuable insights into the heterogeneous nature of PCC, facilitating more precise diagnostics and individualized treatment plans in primary care. These findings highlight the potential of clustering to enhance patient management, reduce diagnostic uncertainty, and optimize healthcare resource utilization.

Points for discussion:

Integration of clustering methods into routine primary care workflows.

The role of artificial intelligence in refining clustering models in primary care settings.

Challenges and future directions in personalized PCC management.

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