Healthcare Analytics and Machine Learning
The technology and innovation spheres are ones that never seize to amaze. Just as one thinks that he or she has seen it all, a new gadget, application or concept enters the market to make one’s life even easier (and usually more interesting). The healthcare industry is one of those that has much to benefit through these advances, particularly through healthcare analytics.
One example among the plethora of impressive technological advances that has been introduced of late has come in the form of the mood-tracking application, BiAffect, designed to track your emotions by the way you physically interact with your smartphone. The app uses machine learning, providing computers with the ability to learn without being explicitly programmed to do so.
The app was conceived when one of its founder’s 24-year-old son was diagnosed with bipolar disorder. This brain disorder causes unusual shifts in mood, energy and activity levels. It made perfect sense that his son’s condition could be alleviated if he could track early warning signs, allowing him to seek help immediately when necessary.
The app works by factoring in cell phone metadata such as typing speed, spelling errors and the use of backspace while texting correlating with manic and depressive episodes. People in a manic episode have been noted to maintain reduced impulse control. This makes them less likely to take time to accept spell-check recommendations.
Harnessing Data for Predictive Healthcare Analysis
Machine learning can also (commonly) help in the analysing of large volumes of data available to dramatically increase efficiency. This phenomenon has proven to be an immense blessing across the healthcare industry, particularly in the realm of predictive analytics.
A recent survey by Healthcare Analytics in the U.S showed that predictive analytics is already used widely within the industry, with some 88 percent of respondents across payers and providers stating that they are part of an organisation that either currently deploys the concept, or plans to begin to in the next five years.
Such analytics can help with such things as predicting re-admissions to better manage efficiency. Patients are particularly interested in costs while healthcare establishments focus on analysing data to ascertain customer satisfaction levels.
MedTech – An Attractive Investment
The bottom line for investors in the healthcare industry (or more specifically in med tech) is an increase in profits. This is achieved through increased efficiency, time saved as well as heightened service levels associated with these advancements.
These are exciting times for the world of innovation. The healthcare industry stands to gain a lot from developments such as those listed above. It is a sphere that is extremely dependent on time and resource, for obvious reasons. Such innovations will only help to cater to the growing needs of a more populated world of the future.
What will be interesting to note over the next several years is how the industry embraces and integrates these introductions into their businesses as well as how rapidly these changes will take place. From where it stands at the moment, it seems to be more of a when than an if.
#AI #healthcare #machine learning #medtech