A clinical informatics approach to bronchopulmonary dysplasia: current barriers and future possibilities

Area of Science:

• Biomedical Informatics
• Neonatal Medicine
• Computational Health

Background:

• Bronchopulmonary dysplasia (BPD) is a serious lung disease in preterm infants with long-term complications.
• Current BPD treatments focus on symptom management, highlighting the need for predictive and preventative strategies.
• Clinical informatics offers a novel approach to integrate patient data for improved healthcare outcomes.

Purpose of the Study:

• To review the potential of clinical informatics in identifying BPD phenotypes.
• To explore the development of clinical decision support tools for BPD management.
• To highlight opportunities for earlier, targeted interventions in extremely preterm neonates.

Main Methods:

• Leveraging electronic health record (EHR) data.
• Applying machine learning algorithms to patient data.
• Implementing clinical decision support systems for BPD care.

Main Results:

• Clinical informatics can identify clinically relevant BPD phenotypes.
• Opportunities exist for developing predictive models and decision support tools.
• Early identification and intervention can potentially improve outcomes for neonates at risk for BPD.

Conclusions:

• Clinical informatics holds significant promise for advancing BPD understanding and management.
• Overcoming data challenges (collection, storage, privacy, bias) is crucial for realizing informatics' full potential.
• Future efforts should focus on integrating informatics solutions for proactive BPD care in preterm infants.