A Predefined Rule-Based Multi-Factor Risk Stratification Is Associated With Improved Outcomes at a Rural Primary Care...

Area of Science:

• Primary Care Medicine
• Health Services Research
• Rural Health

Background:

• Existing risk stratification models often lack integration of nonmedical factors.
• There is a specific need for models tailored to the unique challenges of rural communities.
• Nonmedical factors like transportation availability significantly impact health outcomes in rural settings.

Purpose of the Study:

• To develop and evaluate a predefined rule-based risk stratification paradigm for rural primary care settings.
• To integrate both medical and nonmedical variables into a comprehensive risk assessment tool.
• To specifically address the needs of underserved rural populations.

Main Methods:

• Utilized 19 factors, including demographic attributes, transportation availability, and clinical variables (e.g., blood pressure, BMI).
• Developed a rule-based model using 2021 medical visit data.
• Validated the model's performance using 2022 data and implemented interventions.

Main Results:

• Implemented risk stratification and interventions led to a 30% reduction in patients with high or medium risk of deteriorating health outcomes (34.9% to 24.4%).
• The proportion of medium-complex patients decreased from 29% to 5.7%.
• Strong correlations were found between total risk score and dual diagnoses (0.63), number of providers (0.54), and PHQ9 scores (0.45).

Conclusions:

• The developed risk stratification model is effective in identifying and managing high-risk patients in rural primary care.
• Integrating nonmedical factors enhances the model's applicability and effectiveness in diverse populations.
• The model facilitates targeted interventions, leading to significant improvements in patient health outcomes and resource allocation.