In the ever-evolving technological landscape, emerging disruptive technologies like machine learning, deep learning and artificial intelligence have empowered industries like healthcare and sports significantly. Both industries are interconnected. Both cannot function without human involvement, and they are also dependent on informative data from each other due to the increasing demand of predictive analytics. Predictive analytics applications use metrics that can be measured and analyzed to predict the likely behavior of individuals, machinery or other entities. Today’s tech-savvy audience is constantly on the look-out for technology that is efficient, quick and time-saving.
The rising data revolution with digital transformation
Data analytics is becoming increasingly popular within healthcare, sports and life sciences professionals. All these industries are also currently embracing innovations such as wearable-based technologies, faster computing and smaller form factor or devices.
“Paradoxically, the evolution of machine learning, which aims to raise the threshold of intelligent analysis beyond that of the human brain, can teach us more about what it means to be human.”
Today our smart-phones can not only be used as biometric devices but also can be used as a platform from which to deliver tailored algorithm analysis that can optimize personal metrics in real time.
The need for real-time data analysis is more real than ever. Analytics based reports and surveys are becoming increasingly popular with researchers because this helps them monitor trends in real time and directly impacts innovation in products. The entry of chatbots in these industries is an example of a perfect solution that helps in bringing all the initial data together, with absolute accuracy and less time consumption. This data can be collected as per different parameters, such as age, gender, location, medical history, diet, fitness regime and what not, and in turn to be used by, let’s say insurance companies when they chart out plans for a premium. Pretty amazing, right?
Emergence of Telemedicine
Telemedicine is gaining popularity within the masses, but at the same time, it can safely be assumed that telemedicine is not going to replace visits to the doctor completely. Extending healthcare accessed within the home will lower healthcare costs. The departure from the treatment of illnesses to the renewed convergence on prevention is a symbol of the new 2020 healthcare patient. Health care is about more patient outcomes and less about elaborate fee structures. Technologies, primarily involving chatbots, have paved their way into the healthcare industry, allowing automation of services and leading to increased productivity with optimum results.
Precision medicine
Treating individuals by using therapies specific to them with the help of pools of data collected through smartphone apps and mobile biometrics is the backend of precision medicine. This provides patients the information about their health while simultaneously analyzing data. Misuse this data and personal information is prevented by adhering to Health standards like HIPPA. What precision medicine does is this: instead of viewing patients as end users of healthcare services, it engages more with them like partners, a role that is key to accelerate such initiatives further. It integrates patient-generated health data from different devices to better understand the disease and how it can not only be a physical, but mental burden for them. Using data analytics to better patient care is a sure shot method of moving towards efficient, sustainable models of care that are driven by data and technology and are mutually beneficial for healthcare professionals and patients alike.
There are certain cases where innovative techniques are being used to gather information, for example information from parents about infants in the Intensive Care Nursery (or NICU). Parents refrain from filling out a survey every day because it can be quite stressful and repetitive. Instead, UCSF and Benioff’s Children hospital now use an intelligent chatbot to communicate with parents. This has reduced stress for the parents as it’s now personal and now they feel they are talking to someone. The chatbot also converses intelligently to gather the baby’s symptoms for the doctor’s diagnosis later on. It also educates the parents with videos and web links so that parents can learn more about the medical condition of their baby and be more aware!
Data analytics for healthcare: Learning from sports technologies!
There is an incredible wealth of available data in sports but capturing and making use of that data in a way that will lead to better outcomes for the team remains a major problem. It has also been observed that many sports organizations find traditional data science methods to be out of their league.
Let’s take the example of NBA. In the last decade, the NBA has undergone a data science revolution that has entirely changed the game. They have used data to optimize performance in real time and built strategies to increase the chances of teams winning. Basketball is an incredibly difficult game to study, simply because it’s quick and difficult to keep track of in comparison to cricket and baseball. But NBA didn’t give up. Sophisticated tracking systems that kept their eyes on every player, machine learning and cartography helped them analyse which players were helping their teams win. Right from rebounds to three-pointers to assists, every move was analysed. Almost every NBA team had a data analyst on board to make sure this was taking place. And why did all of this happen? Because they had a senior leadership team that was invested more in the future than the present.
In healthcare, once a treatment or chain of thought becomes popular, it is hard to dislodge. It is hard to disrupt. But disruption must prevail. The medical fraternity needs to learn from the above example and take that leap of faith. Smart leaders need to be educated about the gold mine data analytics can prove to be. Somewhere, healthcare is still stuck in the data collection phase. There’s so much raw data collection happening, and private data sets, health surveys, billing records, medical sensors- everything is involved. But not all of it is being shared freely across organizations, hence, we are losing out on many insights that can be obtained. While systems are being modernized and the need for expert data scientists is now more real than ever, there are still not enough of these people on board. The result is a huge missed opportunity to deploy data in a meaningful way. It’s still not too late though, and the earlier this is recognized, the closer we will be to unleashing the true power that technology holds in the future for healthcare!
Josh Software 