I hope you are all familiar with the wisdom pyramid. The concept behind this hierarchy is critical.
- The modern world inundates us with data. In this age of big data, the deluge is just getting bigger. But data by itself is not particularly actionable. Garbage in => Garbage out. Even worse, big data can be even more misleading than small data because of how open a canvas it is for finding patterns that don’t really exist and confirming them with biased search.
- So we turn data into information. This is where algorithms and analytics are applied. Find real patterns. Data mine for relationships and trends. But trends and correlations can also be misleading.
- Which brings us to knowledge. Is there causation behind the correlation? What can we do to increase or decrease the trend? Sever the relationship between the variables?
- So we seek the holy grail – wisdom. Wisdom only arises when you can embed the knowledge within the complex sociotechnical environment of real people with real emotional, social, financial, moral, and other priorities. Often conflicting. Often unarticulated. Often ambiguous.
Which brings us to today’s topic – wisdom in health care. John Halamka has a great article in Harvard Business Review. It is an opinion piece discussing the danger of the big data craze and how patients with their wearable devices, lifelogging software, and Google searches self-diagnose themselves into an early grave. They can’t translate the heaps of data into wisdom. The algorithms in their health care apps at best get them to the information stage, with some loose guesses on knowledge.
Using the cuff, I took my BP before and after commuting, drinking tea, and attending anxiety-provoking meetings — nearly 100 measurements in a week. The raw data were just numbers, although they helped reveal interesting information — that none of my life activities (commuting, tea drinking, work) influence my blood pressure. The problem, logged as a discrete data point in my electronic health record (EHR), turned out to be my parents.
He shares some engaging personal stories about his own life-logging adventures. The data was useless with his medical background. The rest of us need a doctor.
He also shared a story about his wife’s experience with breast cancer. He was again able to combine big data on the results of various treatment options with patients of different demographics and disease profiles. He could identify the one best matched to his wife. He was able to use his medical background to find a combination of three drugs, as well as a limitation of one of them because of the risk of nerve damage. She is now cancer free.
The conclusion he comes to, and the insight I want to highlight here, is that data is only useful if you ask it the right questions and have the expertise to interpret and apply it.
Now, let’s pair this with an article from Fast Company. It shows that unstructured data is currently almost impossible for algorithms to deal with. IBM’s Watson Health is currently in the lead here. Soon, AI might solve or at least reduce the gap – maybe even up to the knowledge level. But for now, we need a strong partnership between human and technology.
Sounds like a good place for human factors, doesn’t it? Human factors in healthcare is one of the fastest growing domains in our field. The upcoming HFES conference in this area is a good example.
Are you a fan of Big Data and the analytics that come with it? Do you translate your own data into knowledge? Wisdom? Or do you get trapped at the information stage?