Faith Kendagor
This pandemic has shown that people are willing to change their perceptions on data sharing for medical purposes as seen through the downloads of mobile apps that were used for pandemic management. With these apps, personal data such as location history and personal medical data are collected and in turn used to monitor the severity of the pandemic and its spread, and this allows medical professionals and governments to identify problem areas and to put mitigations in place to control the spread. To alleviate concerns on misuse of personal data, secure integration is required in order to be in compliance with data privacy, regulations and policies which can be a challenge given the differences in perception in different parts globally.
A concrete case of how social use of machines works is Taiwan and how it handled the pandemic. With its bottom-up information sharing platform, Taiwanese nationals were able to share their information on the app regarding the symptoms they experienced or virus exposure to determine self-isolation period. The government put in place an electronic fence that enabled location tracking to ensure that people stayed in their homes. It also enabled the monitoring of self-quarantined people and police would get alerts when these people left their houses. The success of the Taiwanese government is proof that societal participation and information sharing is paramount to creating healthcare solutions.
In addition, the weaknesses in the manufacturing of personal protective equipment (PPE) for medical professionals were exposed to shortages as the virus spread relentlessly due to the sudden demand and this put the health of medical workers at risk. Looking into the future, a repeat of such a situation could be prevented through the use of artificial intelligence whereby AI enhanced robots could attend to patients in quarantined areas by administering drugs or even have Interactive chat bots that could be used to have premier conversations with patients.
Developments in telemedicine to include intelligent diagnosis, monitoring and supervision of patients is bound to be a gamechanger. People would have to upload online questionnaires about their signs and symptoms and these would be connected to real time cloud databases that would analyse the data and automatically classify the severity of infection of the patient such as ‘mild, moderate, severe or critical’. Moreover, the further development of wearable devices such as Apple watches to include skin sensors that measure body temperature is bound to improve connected healthcare. This development would allow self diagnosis and real time data collection and analysis, anonymously. This reduces the need for one on one interaction therefore keeps medical personnel safe while speeding up the process hence quicker reaction and overall results.
The article is written from the original research paper COVID-19 what have we learned? The rise of social machines and connected devices in pandemic management following the concepts of predictive, preventive and personalised medicine written by Petar Radanliev, David De Roure, Rob Walton, from Department of Engineering Sciences, University of Oxford, Oxford, UK, Max Van Kleek from Department of Computer Science, University of Oxford, Oxford, UK and Rafael Mantilla Montalvo, Omar Santos, La’Treall Maddox, Stacy Cannady from Cisco Research Centre, Research Triangle Park, Durham, NC, USA
