
    Given the number of patient records in a hospital's readmission risk prediction system, determine the smallest batch size (as a power of 2) that can accommodate all records for optimal processing in the machine learning model. The batch size must be greater than or equal to the total number of patient records to ensure no data is left unprocessed during the parallel computation of risk scores.
    

    
def next_power_of_2(n):
    """
    Given the number of patient records in a hospital's readmission risk prediction system, determine the smallest batch size (as a power of 2) that can accommodate all records for optimal processing in the machine learning model. The batch size must be greater than or equal to the total number of patient records to ensure no data is left unprocessed during the parallel computation of risk scores.
    

    """
def next_power_of_2(n):
    """
    Given the number of patient records in a hospital's readmission risk prediction system, determine the smallest batch size (as a power of 2) that can accommodate all records for optimal processing in the machine learning model. The batch size must be greater than or equal to the total number of patient records to ensure no data is left unprocessed during the parallel computation of risk scores.
    

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