A great deal is being said about data added value and that data is the treasure of healthcare institutions. So far, so good. Personally, I think of pure data more as raw diamonds. If unprocessed, it will just stay dull and unremarkable in the hidden depths of the clinic IT. Its value will not rise to that of noteworthy gems until someone tackles, refines, and gives these raw diamonds a proper finishing touch.
The value of the data not only lies in its significance for optimizing medical or administrative processes and procedures, such as billing. In fact, these can be used as a basis for scientific insights and improving medical care, giving rise to two interdependent spheres of activity. One of them includes the provision of valid, quality-assured data for the development of medicines and therapies or intelligent algorithms to achieve commercial benefits. The other area of activity includes returning these developments into medical practice.
Scenarios for data refinement
There is already a series of commercially available CAD systems supporting detection of microcalcifications and soft tissue changes in mammography diagnostics. This scenario would suit such data refining particularly well, since procedures, examinations, findings type and form as well as diagnoses have been standardized across many institutions—an exception in the healthcare sector. Thus, there is a data base that is excellent in terms of both quantity and quality, which is decisive for development and improvement in the scientific context.
Unfortunately, building up a relevant volume of data is essentially more difficult when there are rare clinical conditions, because working out an algorithm or developing a medicine requires a mass of data far beyond the scope of the data assets of an individual institution. It would therefore only be logical to store internal diagnostic data belonging to the clinics along with relevant, quality-assured evaluations of a clinical picture in central systems, such as our JiveX Healthcare Content Management System thus making them, if needed, analyzable by public or commercial research centers in an anonymized form taking into consideration data protection matters.
The path to the treasure trove of knowledge
Should the research result constitute a high-quality algorithm for diagnostic data assessment, it must find its way back to, and be used in, the healthcare facilities. For me, the logical solution would be to make these algorithms in central data centers directly addressable from the healthcare facilities. These would be supervised by the aforementioned research centers and would underlie the process of permanent improvement—also supported by further prompt feedbacks from the healthcare facilities in the form of other quality-assured findings.
Both tasks—the collection of relevant data in the healthcare facilities with the subsequent transmission to the research centers and addressing the algorithms in the external research centers directly from the treatment context of a patient—can be mastered in the long term by VISUS. We already have solutions which enable data management, merging and assessment. Further medical advancement is based on the available knowledge distributed in the IT systems of the healthcare facilities in the form of the data analyzable in terms of quality. I wish to contribute to enhancing this treasure-trove of knowledge from which both clinics and research institutions, and eventually every one of us, can benefit through better care.