
    Given a database of n patient records in a hospital's readmission risk assessment system, determine the maximum size of complete data blocks (where block sizes must be powers of 2) that can be processed simultaneously by the analysis algorithm without exceeding the total number of available records. This block size optimization ensures efficient parallel processing of patient readmission pattern analysis while maximizing computational resource utilization.
    

    
def highest_Power_of_2(n):
    """
    Given a database of n patient records in a hospital's readmission risk assessment system, determine the maximum size of complete data blocks (where block sizes must be powers of 2) that can be processed simultaneously by the analysis algorithm without exceeding the total number of available records. This block size optimization ensures efficient parallel processing of patient readmission pattern analysis while maximizing computational resource utilization.
    

    """
def highest_Power_of_2(n):
    """
    Given a database of n patient records in a hospital's readmission risk assessment system, determine the maximum size of complete data blocks (where block sizes must be powers of 2) that can be processed simultaneously by the analysis algorithm without exceeding the total number of available records. This block size optimization ensures efficient parallel processing of patient readmission pattern analysis while maximizing computational resource utilization.
    

    """
    res = 0
    for i in range(n, 0, -1):
        if i & i - 1 == 0:
            res = i
            break
    return res