D I G I T A L D I A G N O S T I C S
The continuous development in sensor technology is currently often driven by consumer electronics and makes it ubiquitous available and low cost . Every cell-phone user is using a number of these already : acceleration and motion sensors , fingerprint sensors , light and temperature sensors are used in all kinds of consumer electronics , cell phones , cars and other commodities . Likewise , much more elaborate sensors like sensors for various gases , drugs , bacteria and viruses , radiation , blood oxygen , blood sugar etc . are rapidly developed and are becoming low-cost devices . All of these will replace other technologies that require more energy , biochemical processes and substances and other resources , helping to decrease the environmental impact .
The role of digital platforms and communication technologies in facilitating collaboration among healthcare institutions to share best practices in green strategies
Since many parties are involved in the value chain in medical technology from suppliers , hospitals , doctors to patients and waste management companies , a seamless documentation and exchange of data in the digital domain is crucial to monitor the complete lifecycle of a medical product . It is important to have technology available that spans over different platforms , is secure , and cannot be altered ( e . g . manufacturing dates of perishable products ).
One of the most innovative and powerful innovations in this field is the evolution of Blockchain technologies and encryption . Blockchain technology is crucial to ensure the integrity of data , it can control who has access to it , who added or altered it , when and where .
Cloud-based healthcare data storage is vital for the reliable transfer and sharing of data across multiple locations and healthcare providers , to enable care teams to coordinate on patient care and Digital Imaging and Communications in Medicine ( DICM ) studies . AI applications coupled with cloud computing can support the delivery high-quality personalised care that in turn is more sustainable by reducing waste and inefficiency . Encrypted data stored virtually is easy to anonymise and can be used for forecasting , disease modelling , and to optimise green treatment practices and hospital resources .
The implementation of advanced analytics and predictive modelling assist healthcare organisations in forecasting and minimising the environmental impact of medical waste
Advanced supply management and forecasting is crucial . For example , detecting the need for specific medications based on data from hospitals and doctors to predict events like the corona pandemic and flu events . Also , the prediction of seasonal events can be improved by this .
AI is already used in several health applications , such as image processing and pattern recognition for the analysis of tomographic and MRI data , to detect anomalies or illnesses that might go otherwise undetected even by an experienced medical professional .
AI for predictive modelling could be widely used to monitor the use of medical equipment and medication , to ensure the amount of supplies produced do not go to waste and the need to dispose them at added cost and resources is minimised . The
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