The accuracy of the information that health organizations use is a critical asset to the healthcare decision making. The decision made should be based on identification and assessment of appropriate data sources done within the boundary of the data of interest and its availability. The healthcare information is extremely critical as it primary; contains demographic information from admission, medication uses, discharge and treatment of all patients. The health data also contains coronary anatomy and intervention from the laboratory module (Lichtenstein 2004). In addition, health data is obtained directly from the patients, therefore, gives a true reflection the state of the hospital.
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Health record data from uniform data sets may be used for secondary data sources in a healthcare organization. This is because it is based on observational studies, which generate a lot of test hypotheses. It gives data priority transcription of past instances, and summaries of all discharges for those patients who will need to be transferred to other units or hospitals; it also provides dictionary lines. An example is that they work in collaboration with the hospital organizing agent to alert the physician to dictate the discharge summary that day. They will also be notified so that they notify the HIM Department in situations where there is a need to transfer a patient to be transferred so that the HIM Department staff will be able to undertake report transcription and be able to copy the need sections (Beaver 2002). The recommendation for this is that the patient information could be faxed to the nursing home if it cannot be transferred via electronic means. Also, it is used if the record copies are not sent with the patient/family at the time of discharge. If written consents are required for the transfer of the information, they should be obtained by designated nursing station personnel from the patient before he/she is discharged from the hospital. This is an ideal example of uniform data set.
Data dictionary is an essential tool when used across information systems because it is a benefit to database users and programmers. It assists in enable the developers in analyzing objects that the user use in identifying the relationship that it has with other objects. It is an extremely critical facet of data modeling and gives an abstraction of the relationship between various database objects. Data dictionary enables developers to give descriptive names to the data items or objects, therefore, making it become part of structure which gives a description of the relationship, description of the data type, listing of predefined values and textual description.
Since a data dictionary is a reference book, any information system, whose data is analyzed basing on the dictionary, will be robust since its development is based in predefined database features. When a system is developed using data model, data dictionary is referenced to identify the exact location where a data object goes in the database structure. The value it should contain and the meaning of that data item in lay man’s language (Bluth & Bluth 2004). For instance, a group of banks could create a model of the consumer banking data objects then gives a consumer banking data dictionary which any programmer dealing with banks can reference. This data dictionary gives a description of all the data items in the data model for consumer banking such as available credit, account holder and outstanding loans.
Data dictionary can vastly improve an organization’s communication across the continuum of care. This is because this can give a basis of developing a robust system which can merge the information from different sections of the continuum of care. For instance, the information concerning the treatment, in-patient nursing and medication of a system can be merged. This will enable the employees in different department can track the records of a particular patient with a lot of ease (Hassel bring 2000).
References
Lichtenstein, E., Cruz, V., Long, J., Siebert, G., & Jacobs, C. J. (2004). U.S. Patent No. 6,723,046. Washington, DC: U.S. Patent and Trademark Office.
Bluth, C., & Bluth, J. (2004). U.S. Patent No. 6,692,436. Washington, DC: U.S. Patent and Trademark Office.
Beaver, K. (Ed.). (2002). Healthcare information systems. CRC Press.
Hasselbring, W. (2000). Information system integration. Communications of the ACM, 43(6), 32-38.
