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How do medical labs transact knowledge virtually?

Article-How do medical labs transact knowledge virtually?

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Medical laboratories face pressures to adopt new technology solutions to strengthen the workflow, manage time and manpower to ensure improved healthcare. The aim is to promote better patient care and quality healthcare. The current period is marked by the transition from the traditional lab model to the new clinical lab 2.0. Lab 2.0 is expected to offer premium delivery to patients and provide new synthesis in the lab working. Lab professionals do not work in isolation. The knowledge input to labs is derived from many corners where collective efforts are ensured. The stakeholders function remotely at distributed locations and work collectively in digital mode.

Knowledge in labs is not confined to mere data or information input and rather embraces a wider inclusion such as intellectual property, expertise, learning and skills between academic and the non-academic community. A strong scientific knowledge base is one of the medical labs’ traditional key assets. The innovation in labs is currently being challenged by a rapidly changing research landscape. It is important to promote the transnational dimension of knowledge transfer.

Compared to the U.S., the average university in other countries including Europe generates far fewer inventions and patents. Several reasons are attributed for this condition, out of which the less systematic and unprofessional management of knowledge and intellectual property by these ‘average’ universities is cited. Thus, learning and understanding from the successful universities and bringing all knowledge producers to share, mark the growth of knowledge exploitation in labs.

We can try to understand how the complex knowledge, which produces technologies move from the academic and research world to the labs so that new lab technologies can be supported, enhanced and accelerated. The analysis and efforts explore how these types of complex and un-coded knowledge move from the minds to the labs in order to understand how this process might be optimised.

Medical labs borrow creative knowledge from minds and university research labs and translate such un-coded knowledge into practice. Labs at the same time produce problems set to basic researchers with live and real-data. Medical labs transact knowledge between themselves using remote and distributed systems. Formal protocols exist for such transfer and at the same time, undocumented knowledge is also required.

Creating a knowledge grid in medical labs is required. Knowledge transaction occurs between labs themselves and in between labs and universities. Some of the top U.S. universities get license income, which is equivalent to their R&D budget. The successful U.S. universities obtain license income mainly in biomedical sciences. Here we make a clear distinction between the faculty labs or we call it as idea labs and applied labs where productivity and innovation arise. Idea labs have been emerging in the last few years, which are responsible to develop solutions to the identified problems.  

Knowledge in labs is not confined to mere data or information input and rather embraces a wider inclusion such as intellectual property, expertise, learning and skills between academic and the non-academic community.

Collaboration for knowledge transfer

Knowledge transfer in the virtual labs is not just limited between colleagues and fellow researchers, but include the knowledge transaction between researchers, practitioners and patients. Idea transfer from idea labs emerging from interdisciplinary work, reaches the lab community.

Labs working virtually is characterised by the use of a software platform for collaboration and knowledge transfer that includes wikis for document sharing, discussion boards, mailing lists, conferencing facilities, and so on; it also has a web portal to support the sharing of knowledge, as well as the dissemination of knowledge and expertise; and a virtual education centre that is conceptually linked to the portal mentioned above.

Collaboration increases productivity, which is empirically proved with data. In the same field or theme, people do the same work without knowing others’ work. Many labs work on a specific theme without integrating itself into a large area where research is characterised as fragmented research. The same piece of new work is performed by two more labs without knowing others work. Collaboration enables to offset such limitations.

The Knowledge transfer activities enables the businesses introduce and embed change, such as developing new technologies and streamlining processes. In the successful knowledge transaction process, the links between knowledge transfer activities and innovation performance is proved. 

Impact of computer-mediated communication

Databases, Big data, Robotics systems, Networking, Cloud computing, Internet of Things, Graphical interfaces, Data mining, Machine learning, Semantic technologies, Neurocomputing, Intelligent decision support systems and specialised programming languages are some technologies and research areas influencing medical informatics. In the areas of medical rehabilitation and assistive technology, ICT has contributed greatly to the enhancement of quality of life and ensures complete integration of people into society. The use of Cloud computing and IoT accelerates the flow of the information and improved communication in healthcare.

Lab-on-a-chip technology

Now Nano-chips are used to pack data in handheld devices, which can send biomarkers found in small amounts and sent to various destinations for deriving insights. The data captured is combined with real-time health data from other IoT-enabled devices, such as smart systems and smart watches, and analysed by using real-time intelligence.

AI systems could ultimately be packaged in a convenient handheld device to allow people to quickly and regularly measure data and send this information securely streaming into the cloud from the convenience of their home. There it could be combined with real-time health data from other IoT-enabled devices, like sleep monitors and smart watches, and analysed by AI systems for insights. When taken together, this data set will give us an in-depth view of our health and alert us to the first signs of trouble, helping to stop the disease before it progresses.

Faster fibre links for data centres

Newer fibre optic systems now can transmit large data that is several thousand gigabits of data per second. Chips can also able to transmit such high volume of a big step up from top speeds of several hundreds of gigabits in today’s data centres. New medical big data is heterogeneous, transactional and unstructured, collected privately and available publicly leading to challenges in data transfer and process.

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