Hospital medical doctors rely on relevant and reliable medical databases for the professional performance of their medical oath. The research centers on physicians’ exchange of medical research information with other physicians in the community. The research centers on both the drawbacks and expected benefits of data distribution systems. The hospital physicians can choose between distributed data processing and centralized data processing.
Distributed data processing centers. Under this process, different programs have their own databases. Each of the hospitals will have their own separate databases (Http://Ebookee.Org). The doctors in one hospital cannot access the databases from another hospital in the community. Hospital A will have its own database software. In turn, Hospital B will have its own database software. All the other hospitals have their own database software systems.
Each hospital’s software does not share the same files with the other hospitals in the community. In a single processor computer, the central processing unit (CPU) and its input /output operations are separated and overlapped (Ozsu, 2011, p. 2). Other examples of distributed data processing include web-based applications, electronic commerce business over the internet, multimedia applications, as well as medical imaging.
There are expected benefits from using the distributed data processing system. One of the expected benefits of the system is the implementation of the divide and conquer rule. One can better solve a complicated problem by dividing the problem into segments. Each person or group is assigned to solve one separate segment of the problem at the same time. Each team will contribute its own segment solutions to the entire group.
Under the distributed data processing system, the time needed to solve one problem is reduced to allowable time periods. Second, each hospital’s database cannot be accessed by unauthorized persons in another hospital. Since the other hospitals cannot access the database of the other hospitals, the threat of hackers is reduced. Hackers can use software that will detect, delete, or bypass the passwords of a given database.
Third, the physicians or hospitals can beneficially use the distributed data processing system for the preparation of confidential financial, medical, and other relevant reports. The physician or hospital can use the same processing system to process payroll, inventory, administrative chores and functions. The system’s program is allied only to one hospital system. Another hospital has its own data processing system (School Graduates to Distributed Data Prcoessing System, 1983, p. 42).
There are some drawbacks from implementing the distributed data processing system. One hospital cannot access the required database from the other hospitals. For example, Hospital A cannot access the diabetes research database of hospital B. the same Hospital B cannot access the cancer research database of Hospital C. In the same light, hospital D cannot retrieve the required AIDS/ HIV database of Hospital A. Second, some unauthorized persons may use the hospital’s terminals to access classified medical records.
With the advent of Wi-Fi technology, a hacker can enter the hospital’s website and log on to hospital’s restricted medical database. While inside the hospital’s database software, the hacker can retrieve confidential data, add unauthorized data, edit confidential data, and delete some of the hospital’s confidential and vital hospital patient information. To resolve the hacker threat, the security measures must be in place. The security measures reduce data theft under the distributed data processing system.
Proposed transition to centralized data processing model. Under this process, different programs can access the same database source. All the different programs can manipulate data gathered from only one huge central database. The database is shared by all the programs, physicians, and hospitals. Remote computer terminals can access the same database and generate similar reports (Hall, 2012, p. 424).
There are expected benefits from implementing the centralized data processing system. First, the use of lesser number of computer systems is involved. The security systems reduce security threats. Under this process, limited security procedures are needed compared to the security measures implemented under the distributed data processing system (McEwen, 1990, p. 15).
Second, one hospital can access any required database information from the other seven hospitals in the community. Hospital A can access the tuberculosis research database of hospital E. Hospital B can access the dentistry research database of hospital F. Hospital G can scrutinize the AIDS research database of hospital H. There are some disadvantages from implementing the centralized data processing system.
The centralized data processing system reduces the danger of unauthorized persons leaking information. Stricter data security measures must be in place in the centralized data processing system when compared to the security measures implemented under the distributed data processing system. The stricter security measures include encrypted passwords and security personnel prevent unauthorized persons from entering the computer terminals.
Based on the above discussion, medical doctors need relevant and reliable medical databases for their hospital practices. The distributed data processing system has its expected benefits and drawbacks. Similarly, the centralized data processing system has its own unique expected benefits and drawbacks. Evidently, the hospital physicians can correctly choose between distributed data processing and centralized data processing for their medical database researches.
References
Hall, J. (2012). Accounting Information System. New York: Cengage Press.
McEwen, J. (1990). Cops Nad Computers: Microcomputers in Criminal Justice. New York: Diane Press.
Ozsu, M. (2011). Principles of Distributed Database Systems. New York: Springer Press. Business Data Communication. Web.
School Graduates to Distributed Data Prcoessing System. (1983). Computerworld , 17 (45), 42.