
    Given the number of patient records (n) in the hospital's risk prediction system, determine the minimum batch size (as a power of 2) needed to process all records in the parallel computing infrastructure without splitting any records across batches. This batch size optimization ensures efficient analysis of patient readmission patterns and vital signs data while maintaining the system's processing capabilities.
    

    
def next_power_of_2(n):
    """
    Given the number of patient records (n) in the hospital's risk prediction system, determine the minimum batch size (as a power of 2) needed to process all records in the parallel computing infrastructure without splitting any records across batches. This batch size optimization ensures efficient analysis of patient readmission patterns and vital signs data while maintaining the system's processing capabilities.
    

    """
def next_power_of_2(n):
    """
    Given the number of patient records (n) in the hospital's risk prediction system, determine the minimum batch size (as a power of 2) needed to process all records in the parallel computing infrastructure without splitting any records across batches. This batch size optimization ensures efficient analysis of patient readmission patterns and vital signs data while maintaining the system's processing capabilities.
    

    """
    if n and (not n & n - 1):
        return n
    count = 0
    while n != 0:
        n >>= 1
        count += 1
    return 1 << count