Automation and analytics speed up the COVID-19 vaccine discovery and distribution
You can’t spell vaccine without AI. This was proven by Moderna, an American biotech company that announced the development of the first candidate for COVID-19 vaccine back in February 2020. As of today, Moderna’s vaccine is proven to be 94.5 percent effective and it should be FDA-approved and distributed across the US by April 2021. Moderna’s unprecedented speed was largely enabled by its unique R&D ecosystem of tools. For example, a big challenge in the early stages of clinical trials comes from lengthy collection and processing of large amounts of data from patient targeting to identifying potential active compounds. Moderna’s 20-year long research into mRNA projects provided them with large quantities of replicable data and automation, cloud computing, and Internet of Things devices allowed them to provide scientists with easy access to that data. Although Pfizer’s vaccine has been already approved in the UK and Canada and is expecting FDA’s decision, Moderna announced a significant advantage of their vaccine -- unlike Pfizer’s vaccine that must be stored at a -70C (-94F) and shipped in a special box, Moderna's can remain stable at standard refrigerator temperatures for 30 days. This fact alone will streamline the vaccine shipment and deployment, compared to logistical challenges Pfizer is currently facing. Moderna believes that their investment in technology and automated manufacturing played a vital role in this advancement.Smarter, safer, and more sustainable living in data-driven cities
Smart city has been a buzzword for years. And just as long, it’s been a reality. If you live in a big city, you might have experienced its benefits yourself. When looking for a public toilet via an app. Or checking with Google how long it will take for your bus to arrive. Or, if you reside in Amsterdam, it might’ve even helped develop some smart city projects. Amsterdam owes its status as the world’s “smartest” city to the network of sensors (cameras, beacons) and actuators (traffic lights, charging stations, and barriers). They collect data about the environment, preprocess it, and then perform the requested actions, like switching from red light to green or controlling public lighting to save energy. One of the larger implementations for Amsterdam is transitioning to circular economy -- a system where resources are continuously reused and recycled. For example, 75 percent of waste from the city’s sewage system is already converted into natural gas. These efforts are made possible through the collection of data about resource streams -- companies are obliged to report their waste streams, so the city knows what happens to it and then can analyze it.All waste produced in the Amsterdam Metropolitan Area and where it’s processed; colors indicate different economic sectors
Source: TUDelft
“How much waste does the city of Amsterdam produce; which companies are responsible for the largest emissions and which materials that are currently incinerated have the greatest potential for reuse? The platform can answer such questions in an automatic, data-driven way," says Arnout Sabbe, environmental technology researcher at Delft University of Technology. This way, the municipality calculated that 70 percent of waste comes from 7 percent of companies.
Another smart city pioneer is Barcelona. Installing over 19,500 sensors to collect data about air quality, trash disposal services, parking situation, street lighting, noise, the presence of people, and more, the city managed to improve the quality of life and save money. For example, receiving the live data on temperature, humidity and sunlight, gardeners are able to determine what each plant needs and avoid overwatering.
McKinsey estimates that citizens of smart cities have safer and more fulfilling lives: Surveillance and image recognition leads to lower crime rates; smart traffic management, parking, and predictive maintenance of roads reduces time spent in commuting; energy consumption and mobility lower the cost of living.
Wearables promote health awareness and notify of medical problems
Counting steps, checking the volume of consumed water, or tracking your sleep is now increasingly common with wearable technology becoming a $95 billion industry. And wearables (smart watches, fitness trackers, heart rate monitors) are just a part of the huge ecosystem of self-tracking tools: there are smart body scales, connected thermometers, running shoes, and sex toys. Although we do self-tracking mostly out of curiosity, these devices can give warning of possible health abnormalities, save lives, and even solve murder cases. Apple Watch, the world’s most popular smartwatch with 55 percent of market share, has the highest track record of nudging people to seek medical attention. A Stanford study revealed that the device has at least a 71 percent predictive value of atrial fibrillation -- 84 percent of study participants who received notifications about irregular pulse were found to be in atrial fibrillation at the time. More than half of those participants sought medical attention afterwards. Although self-evaluation may lead more people to overutilize healthcare resources, patients can at least pay attention to other symptoms when getting an unsettling notification. A recent study reviewed clinical notes from the Mayo Clinic. They revealed that over four months, almost 600 patients visited the clinic due to an Apple Watch alert.Apple Health Records allow iPhone and Apple Watch users safely exchange medical data with selected hospitals But there are even bigger opportunities at hand. Currently, Apple has a list of 500+ institutions supporting the connecting between Electronic Healthcare Records and the Health app on your iPhone. With 1 in 5 Americans already using smart watches or fitness trackers daily, we can expect the growth of health awareness and involvement in the population. If we can equip patients with chronic and dangerous diseases with the capability of tracking their health in a user-friendly manner, we can make their lives more comfortable and position them to practice better care.
Data analytics gives students a better chance of success
The educational environment is extremely data-friendly. Some processes have long been digitized and student data is already being used in numerous helpful ways. One of the most innovative schools to do so has been Georgia State University. GSU traditionally had a low graduation rate of less than a third of its students. Besides, since the school attracted large numbers of students with other priorities like work or parenting, there was an even lower success rate for them. Luckily, the school was pretty good at keeping its data clean and storing it in a data warehouse, which allowed them to start their first data-driven initiative in 1999 and continue with its data analytics projects to this day. Namely, the analysis revealed which GSU courses had the highest chance of students dropping, failing, or withdrawing (DFW) from it. Each year 43 percent of students who took algebra, pre-calculus, and statistics didn’t complete the course. By adapting the course model and introducing an easier way for students to ask for assistance, they lowered the DFW rate of those disciplines to just 19 percent.An analytical dashboard at Georgia State provides an outlook into low, medium, and high-risk students and generates alerts for advisors to act upon
Source: GSU
They also invested in the Graduation and Progression System (GPS) -- a dashboard displaying the real-time academic data of each student, which allows advisors to identify obstacles to success and intervene in time. This also means that their advice is no longer based on subjective opinion, but on clear data.
These and many other improvements allowed GSU to do better at their biggest problem -- from a 32 percent graduation rate in 2003, they reached 54 percent in 2014. And based on predictive data of student success, they launched an opportunity for the most at-risk freshmen to join the summer program before the first year. A higher score let previously underserved students apply for grants and start their education.
Although starting data analytics initiatives in schools comes with many challenges, we can move in smaller steps to achieve great results in just a decade. The GSU example shows that just by understanding the problems within the organization, we can find creative ways to make students’ lives better and success more attainable.