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Patient Volume Prediction Model

Predictive AI model for patient volume trends across clinics using demographic, insurance, clinic availability, and operating hours

Insurance Status as a Critical Factor
- Features related to uninsured and insured populations, such as NUI (Number of Uninsured Individuals), NIC (Number of Insured Children), and their respective margins of error (nui_moe, nic_moe), show high importance for both patient volume and resource optimization.
- This suggests that fluctuations in insurance coverage significantly impact both the demand for healthcare services and the allocation of resources.
Geographic and Facility-Based Predictors
- CountyFIPS (county-level identifier) and Number_of_Sites (number of healthcare facilities) play substantial roles in predicting patient volume and optimizing resource use.
- Avg_Operating_Hours is also a notable predictor, implying that facility accessibility affects both patient influx and resource utilization.
Marginal Influence of Demographics
- Features like age category (agecat) and sex category (sexcat) have little to no importance in either prediction model.
- This suggests that patient volume and resource needs are less influenced by demographic factors compared to insurance coverage and facility availability.

Implications for Healthcare Planning

Addressing Insurance Gaps: Since insurance status is a key driver of patient volume and resource needs, policymakers should focus on expanding healthcare coverage to better predict and manage demand.
Strategic Facility Expansion: Given the importance of Number_of_Sites and Avg_Operating_Hours, increasing the number of healthcare facilities and optimizing operating hours could improve service accessibility.
County-Level Resource Allocation: The importance of CountyFIPS suggests that resource planning should be tailored to specific geographic needs rather than using a one-size-fits-all approach.

X-Axis (SHAP Values): SHAP values indicate how much each feature contributes to increasing or decreasing the predicted patient volume.

A positive SHAP value means the feature increases patient volume predictions.
A negative SHAP value means the feature decreases patient volume predictions.

Color Gradient (Feature Values):

Red: Indicates high values of that feature.
Blue: Indicates low values of that feature.

Directional Influence of Features:

- If high values (red) of a feature tend to have high positive SHAP values, it suggests that increasing that feature leads to higher patient volumes.
- If high values of a feature correspond to negative SHAP values, it indicates an inverse relationship.

Graph presents a learning curve that illustrates the relationship between training size and model performance using Mean Absolute Error (MAE) as the evaluation metric. The training score (blue line) and validation score (orange line) both decrease as the training size increases, indicating that the model improves with more data. However, the gap between the two lines suggests that some degree of bias or variance may still exist. The decreasing trend in validation error implies that adding more data could further enhance model generalization.Takeaway: The model benefits from additional data, and increasing the dataset size may lead to even better predictions. However, further tuning or regularization may be required to close the gap between training and validation performance.

Scatter plot comparing actual patient volumes against predicted values, with a red dashed line representing the ideal 1:1 correlation. Most points are clustered near the diagonal line, indicating a strong correlation between actual and predicted values. However, some deviations and outliers suggest that certain predictions may not be as accurate, particularly at higher patient volumes.Takeaway: The model performs well overall but exhibits some discrepancies, especially for higher patient volumes. Potential improvements could involve incorporating additional features or using ensemble techniques to enhance prediction stability.

ML Model Evaluation:

MAE: 101.81
R²: 0.88

The patient volume model achieved a relatively low Mean Absolute Error (MAE) of 101.81, indicating that the model’s predictions deviate from the actual values by an average of 101.81 units. While there is still room for improvement, the MAE is low enough to suggest that the model is providing useful insights for predicting patient volumes. A lower MAE implies better prediction accuracy, which is important for resource planning, patient scheduling, and improving operational efficiency in healthcare settings.The R² value of 0.88 suggests that the model explains approximately 88% of the variance in the patient volume data. This strong R² value implies that the model does an excellent job of capturing the underlying patterns in the data and has good generalization power. For practical purposes, this level of R² indicates that most of the variability in patient volume can be accounted for by the factors included in the model. Therefore, it can be confidently used for decision-making related to patient flow and healthcare resource allocation.

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