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Machine Learning and Clinical Informatics for Improving HIV Care Continuum Outcomes

  • eHealth and HIV (J Simoni and J Stekler, Section Editors)
  • Published:
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Abstract

Purpose of Review

This manuscript reviews the use of electronic medical record (EMR) data for HIV care and research along the HIV care continuum with a specific focus on machine learning methods and clinical informatics interventions.

Recent Findings

EMR-based clinical decision support tools and electronic alerts have been effectively utilized to improve HIV care continuum outcomes. Accurate EMR-based machine learning models have been developed to predict HIV diagnosis, retention in care, and viral suppression. Natural language processing (NLP) of clinical notes and data sharing between healthcare systems and public health agencies can enhance models for identifying people living with HIV who are undiagnosed or in need of relinkage to care. Challenges related to using these technologies include inconsistent EMR documentation, alert fatigue, and the potential for bias.

Summary

Clinical informatics and machine learning models are promising tools for improving HIV care continuum outcomes. Future research should focus on methods for combining EMR data with additional data sources (e.g., social media, geospatial data) and studying how to effectively implement predictive models for HIV care into clinical practice.

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Authors and Affiliations

  1. Department of Medicine, University of Chicago, 5841 S Maryland Ave, MC 5065, Chicago, IL, 60637, USA

    Jessica P. Ridgway, Alice Lee, Samantha Devlin & Jared Kerman

  2. Department of Pediatrics, University of Chicago, Chicago, IL, USA

    Anoop Mayampurath

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  1. Jessica P. Ridgway
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Correspondence to Jessica P. Ridgway.

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Ridgway, J.P., Lee, A., Devlin, S. et al. Machine Learning and Clinical Informatics for Improving HIV Care Continuum Outcomes. Curr HIV/AIDS Rep 18, 229–236 (2021). https://doi.org/10.1007/s11904-021-00552-3

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