A framework for defining diagnostically challenging conditions identifiable through electronic algorithms.

Area of Science:

• Health Informatics
• Clinical Decision Support
• Medical Diagnostics

Background:

• Diagnostic errors affect 5% of US adults annually, leading to prolonged diagnostic odysseys.
• Patients with difficult-to-diagnose conditions (DCCs) face significant delays in receiving accurate diagnoses.
• Longitudinal electronic health record (EHR) data offers potential for proactive patient identification.

Purpose of the Study:

• To propose a novel framework for proactively identifying patients with DCCs using electronic data.
• To enable earlier detection of DCCs and mitigate diagnostic delays.
• To improve patient outcomes through timely diagnosis and intervention.

Main Methods:

• Developed a framework utilizing longitudinal EHR data to identify patients with DCCs.
• Proposed criteria for detecting specific DCCs amenable to EHR-based algorithmic detection.
• Applied the framework to a case study of fibrotic interstitial lung disease.

Main Results:

• The proposed framework can identify patients at risk for diagnostic delays.
• EHR-based algorithms can be developed to detect specific DCCs.
• Early identification facilitates timely follow-up and reduces missed diagnoses.

Conclusions:

• A proactive EHR-based framework can significantly reduce diagnostic delays for DCCs.
• This approach can lead to improved patient outcomes by ensuring timely diagnosis.
• Future research can focus on real-time implementation to prevent harm from delayed care.