With the advancements in the fields of electronic medicine and biotechnology, the need for effective metadata management tools is growing rapidly. A lot of time, money, and resources are being wasted when clinical records are not appropriately managed. It is estimated that around 45% of medical record tasks are believed to be manual. Gartner forecasts that automated metadata management will reduce medical data management tasks by 45% over the next five years. Health care professionals would benefit from learning how to automate metadata management systems and save money on redundant manual work.
What Are The Benefits Of Effective Automated Metadata Management?
There are several benefits that derive from proper automated metadata management. For instance, the process of coordinating and collaborating with multiple healthcare entities, including physicians, hospitals, insurance companies, and suppliers can be very time consuming. Even after implementing a good data management system, it is still necessary to set up a good clinical metadata repository. This information is needed for the purpose of aligning data regarding a disease, diagnosis, treatment, or procedure with clinical practice guidelines. In addition to this, all data must be maintained in a database that is accessible by all authorized users. This enables researchers to conduct controlled studies on the effects of new treatments.
An important factor in maintaining a comprehensive clinical metadata repository is to ensure compliance with international standards. It is essential to create a data quality system that can detect and correct data errors. Studies have shown that the cost of implementing a metadata system is less than the cost of correcting data errors found using traditional administrative methods.
Managing Clinical Metadata Consistently
It is also necessary to update clinical metadata regularly to reflect any new medications or procedures. The accuracy of electronic data depends heavily on the data quality of the source. Therefore, electronic health records (EHR) contain clinical and other metadata that are unique to each case and are usually inconsistent across providers. However, an organized data quality system can greatly improve the quality of clinical metadata. Electronic health records can also be made available for access by other health care organizations that have a large need for such data.
Another advantage of this kind of system is that it can help to prevent costly mistakes that result from a lack of quality assurance in data management. Since a huge volume of data is generated during the clinical process, it is often necessary to make a judgment call when certain cases are missed due to poor data quality. Metadata can help to prevent missed cases by flagging cases that were not clinically significant. Since such cases are still clinically relevant, they can be referred to in the case report which can then be used for patient education and future research.
In conclusion, it is evident that the benefits provided by an automated metadata management system are vast. In the past, a lack of a good data quality control method has resulted in the inappropriate marking of some cases as “unimportant” or “out-of-scope”. This has resulted in unnecessary patient visits, billing errors, and has caused the documentation to become fragmented. However, using an online clinical metadata service, this problem has been addressed. It is now possible for medical practices to use an online service to automatically mark cases as “out-of-scope” and redirect them to the correct clinical documentation. This new functionality will likely be available in software applications soon. Its therefore clear that data management processes have a continual and clear role to play in the development of effective management of metadata.