Electronic Health Record Anomaly Detection System

Create a sophisticated anomaly detection algorithm using Python's NumPy and Pandas to identify potential medical coding errors or fraudulent billing patterns in electronic health records. The script should process large-scale medical billing datasets, implement multiple statistical detection methods (Z-score, Isolation Forest, Local Outlier Factor), and generate a comprehensive report with confidence intervals and potential risk indicators. Ensure the solution can handle complex, multi-dimensional healthcare billing data with high computational efficiency.