The demand for healthcare services is growing at rapid pace due to constantly increasing number of people with chronic diseases. These days approximately every one of two individuals has one or more chronic diseases, and one of four has two or more chronic conditions. At the same time, there are more medical information today about different diseases and their treatment options than ever before.
According to IBM, healthcare data doubles every 2 years. It is also calculated that doctors would have to read 29 hours each workday to keep up with new professional insights. Obviously while dealing with this huge information flow, doctors don’t have enough capacities to decide how appropriate an option might be for a specific patient.
Additionally, the most expensive part of healthcare is the human resources, which adds to the supply-and-demand issues. I guess no one will doubt the fact that professional healthcare is costly.
These insights bring up several questions. How can we benefit from explosion of information in healthcare industry? Is it possible to cut the costs for people who seek healthcare treatment without sacrificing the quality of such services? Or even improving it? How do we find a balance after all?
The answer lies in two words: cognitive computing. It is a system that can handle massive amounts of unstructured data to enable a new class of data interpretation and learning systems. Cognitive systems process information by comparing it to a teaching set of data. So that the more data such a system can analyze, the more it learns, and therefore the more accurate it becomes with the course of time. To mimic the way the human brain works cognitive systems use data mining, pattern recognition and natural language processing.
The main advantage of these machine-learning systems is their ability to find patterns in datasets too large and complex for human brains to embrace. For doctors this means assistance of paramount importance in keeping track of records and making accurate clinical decisions. IDC predicts that by 2018 somewhat 30 percent of healthcare systems will be running cognitive analytics against patient data and real-world evidence to personalize treatment regiments. What’s more, IDC projects that during the same year physicians will tap cognitive solutions for nearly half of cancer patients and, as a result, will reduce costs and mortality rates by 10 percent.
For patients the ability of cognitive computing to act as an advisor and give an additional opinion allows an extra level of assurance in the service provided by the healthcare sector. Eventually the patients will have more confidence in the service they are receiving. Besides, involving cognitive computing into healthcare means availability of remote check-ups, including areas with relatively little healthcare provision. It is predicted that in the U.S., for example, in the nearest future 40% of primary care encounters will be delivered virtually, which will be possible thanks to cognitive systems.
Summing up, cognitive computing can help:
- Healthcare specialists to manage all the data that is available to make more precise conclusions over the patients’ conditions
- Patients by advising, and providing answers to the questions they have
- Decrease costs for healthcare services
As data becomes more complex and diversified, cognitive computing will have an incredible impact on the healthcare industry.
In conclusion, let me give you one single real-life example. Watson (famous IBM cognitive system used to diagnose patients) was able to determine a rare form of leukemia in an old woman, while oncologists at the University of Tokyo had puzzled for about a year over her illness. After analyzing 20 million research papers Watson came up with the proper diagnosis. It took the system no more than ten minutes. Impressive, isn’t it?
