Let’s learn from the data… they save lives!

In 1854, data helped end the cholera epidemic in London. John Snow, a London doctor, succeeded in linking the distribution of cholera victims with the location of wells in the Soho area of London. Until then it was thought that cholera was spread by air. We should therefore use data and the tools available to us today to stop the spread of the virus and to prepare for a return to normal

A good example of a commitment to data management is the Data for Hope platform. This is a commitment to think about solutions to the coronavirus crisis from an optimistic perspective, looking beyond the disease, the confinement and the tragedy; it is a time to stop and think together, to review what we are measuring, how we are interpreting the data and what we can do with it. 

What measures have been developed to stop the spread of the virus? A good example is the Oesía Group which, in collaboration with the Castilla y León Regional Healthcare Management (SACyL), has developed and already implemented in 250 healthcare units an algorithm for early detection of patients with possible Covid-19

It is based on taking a series of clinical constants (Temperature, Dyspnea, Saturation 02, Cough and PCR), which are analyzed by this algorithm called CWS (Coronavirus Warning Score) and categorizes the patient in each of the 4 defined levels that determine the degree of attention required by each patient: 

  • Monitoring of clinical variables (represented by the colour grey
  • Preventive surveillance by COVID19 (yellow colour
  • Risk of COVID19 (orange colour), 
  • Isolation according to protocol (red colour

So far the new functionality has been used in more than 13 thousand patients in 250 care units of hospitals and emergency centers throughout Castilla y León

Speeding up queues is one of the unexpected challenges of this crisis: this is how technology can help reduce waiting time. The website Tiendeo.com has launched the service ‘Where is the queue?‘ which will allow people to know the waiting time in the nearest supermarkets before leaving home during the coronavirus alarm. 

It is integrated in its own platform and for its operation it has the data obtained from its Geotracking technology, based on Big Data, crossed with the ratings of the customers who are in the supermarkets. 

Before going out to buy, the customer will be able to look for their home address and the nearest supermarkets will appear on a map. Each of them will be marked with an indication of the estimated waiting time in the queue to do the shopping. Because time is money and anything that is to optimize the management of the queues will be very well received. 

We can also highlight management points to speed up waiting times. McDonalds is known for implementing a fast ordering system. This is a point of sale with a touch screen where the user can choose what to order and so workers can prepare the order with margin. Here the time reduction is not so much for the customer when queuing, but in reducing the waiting time between payment and obtaining the product.  

To save time for customers, there are several self-service kiosks where the package can be picked up using a key that only we have. With this type of help, many queues of people who simply wanted their package can be greatly relieved.  

One of these automatic lockers is Amazon Locker, available in more than 30 Spanish cities and located in specific points in different shops. The advantage is that these lockers are open 24 hours a day, allowing you to distribute your movements throughout the day. 

On these days, establishments and supermarkets place a sign at the entrance explaining that it is recommended to visit in the afternoon or at midday, but there are also tools such as Google Maps that allow you to find out what the peak hours are for the different restaurants or establishments. 

Sources: Data For HopeIntellinewsXataka20 minutosCuadernos Manchegos

Leave a Comment

Your email address will not be published. Required fields are marked *

Scroll to Top