Primary Care Resource Optimization Model
- AI model which identifies California counties with limited access to free, primary, and community health clinics, helping optimize the placement of future resources to address gaps in primary care.
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Terms:
- IMU (Index of Medical Underservice): A composite score used by the Health Resources and Services Administration (HRSA) to determine how medically underserved a geographic area is. It takes into account factors like provider-to-population ratio, poverty rate, infant mortality rate, and elderly population percentage. Lower IMU scores indicate higher levels of medical underservice.
- PCTUI (Percent of Uninsured Individuals): This represents the percentage of the population without health insurance coverage in a given area. Higher PCTUI values signal greater potential barriers to accessing care, as individuals without insurance are less likely to seek timely or preventive medical services.
Opportunity Matrix & 3D Scatter:(All aggregated California counties shown)
Resource Need and Clinic Distribution
Regional Healthcare Disparities
- In both plots, San Diego and Imperial County are highlighted, showing distinct differences in healthcare resource needs and uninsured rates.
- San Diego has an uninsured rate near the regional average (7.22%) and a moderate IMU score (~55), yet it demonstrates a high resource need, as seen by the large red bubble in the 2D plot and the elevated point in the 3D visualization.
- Imperial County, with a higher uninsured rate (~9%), has a lower IMU score (~50) and exhibits a comparatively lower overall resource need but still faces accessibility challenges.
Resource Need and Clinic Distribution
- The color intensity and size of the bubbles indicate the level of healthcare demand.
- The 3D plot shows that areas with higher uninsured rates and lower IMU scores tend to have fewer clinics (blue shading), highlighting a gap in healthcare access.
- San Diego, despite having more clinics (warmer colors in the 3D plot), still has a significant resource demand, likely due to population density and disparities in healthcare access within the county.
Regional Healthcare Disparities
- The dotted averages in the 2D plot (IMU Score: 57.2, Uninsured Rate: 7.22%) provide a benchmark for assessing counties.
- Imperial County falls below the IMU average, suggesting fewer available resources to meet healthcare needs.
- San Diego, being closer to the mean but showing a high resource need, suggests demand pressures likely stemming from high population density, underserved sub-regions, or gaps in healthcare infrastructure despite the presence of clinics.
Implications for Counties:
- San Diego:
- Despite having a moderate uninsured rate and relatively high clinic availability, the high resource need suggests inefficiencies in healthcare distribution.
- Policymakers should analyze sub-county disparities, ensuring that clinics are located where demand is highest.
- Strategies such as expanding operating hours, increasing mobile health units, or enhancing insurance coverage enrollment programs could alleviate strain on existing healthcare facilities.
- Imperial County:
- A higher uninsured rate and lower IMU score indicate that residents face challenges in accessing care, potentially due to a shortage of providers or financial barriers.
- Expanding Medicaid outreach and telehealth services could be critical in addressing these gaps.
- Investment in new clinics or partnerships with healthcare providers can help reduce disparities and improve access for underserved populations.
- Broader County 2D Matrix Plot Location
- Top Right: high uninsured rates and high medical underservice, indicating severe healthcare access issues.
- Top Left: Counties here have low uninsured rates but still show medical underservice, possibly due to high population density or provider shortages.
- Bottom Right: These regions experience high uninsured rates but lower IMU scores, meaning there are some existing resources, but financial barriers persist. Need for insurance expansion programs and finacial assistance for patients
- Bottom Left: Stable, lower-priority Areas
- San Diego requires better resource allocation to manage its high demand effectively, potentially through expanded facilities, improved efficiency, or workforce scaling.
- Imperial needs targeted interventions, such as clinic expansions or mobile healthcare units, to address high demand despite fewer resources.
- Counties with lower demand may have lessons in terms of effective resource distribution and healthcare accessibility models.
- Notable peaks occur at FIPS codes 33, 61, 71
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 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:The resource optimization model achieved an MAE of 46.82, indicating that the model's predictions on resource needs are off by an average of 46.82 units. Although the MAE is slightly higher than that of the patient volume model, it still represents a reasonable error margin. In the context of resource optimization, this means that while the model's predictions may not be perfect, they are still fairly accurate, and the resource allocations are likely to be close to the actual needs. A low MAE is crucial in resource optimization because it directly impacts operational decisions such as staff allocation, equipment procurement, and facility management ensuring that resources are allocated effectively to meet demand.The R² value of 0.86 indicates that 86% of the variance in resource demand is explained by the model. This is a strong result, showing that the model accounts for most of the variability in resource needs. The higher the R², the more confidence one can have in the model's predictions, which is critical for resource planning and minimizing wastage or shortages. An R² of 0.86 demonstrates that the model is providing a reliable framework for making informed decisions about resource allocation, although there is still potential for additional improvement, especially as more granular data or better features are incorporated into the model.
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