July 04 2016 | 0 Comments | 414 reads Average Rating: 3.6
Intuition vs. Analytics: Polar opposites or one and the same?
Intuition is often thought of as a somewhat magical, innate personal quality that some people are born with – and others lack. When people make decisions based on intuition, we assume that they are moving forward based on some type of “gut feeling.” Analytics, on the other hand, is often thought of as hard science. When people make decisions based on analytics, they are moving forward based on data that has been scrutinized and studied, explored and examined – again and again.
So, the two concepts are considered opposites. I am proposing that intuition, however, is actually the most rudimentary form of analytics. Not some elusive quality, intuition is born out of our past experiences and knowledge. Our brain then processes these experiences and makes decisions (that could explain why 70-year-olds are typically much more intuitive than 2-year-olds). Analytics is simply the aggregation of many people’s experiences lumped into one database and then used to make informed decisions.
We need to stop thinking about leveraging intuition or analytics in terms of an “either-or” proposition. Instead, decisions should start with intuition and then progress through more advanced analytics. For example, many health care leaders “intuitively” know that getting patients involved in their own care will lead to better outcomes. But adding more advanced analytics to the equation can help organizations go beyond the typical “hit or miss” associated with intuition.
Consider the following scenario: Based on limited experience, it’s easy to come to the conclusion that a diabetic patient who pays attention to diet and exercise is apt to fare better than one who does not. Such knowledge is typically stored “in the guts” of most clinicians.
Gathering and analyzing information regarding the types of programs such as care management, automated care gap identification and notifications, group counseling sessions and self-study information on diet and exercise – and then comparing the usage rates for each of these programs, along with the effect they have on individual and population health, and subsequently on in-patient stays and emergency department visits – can help healthcare professionals make better decisions.
Armed with this analytical data, each intervention’s impactability (likelihood that a particular intervention will have a significant impact on patient or population outcomes) and intervenability (likelihood that the patient or population will become actively engaged in their own care) can be assessed. As such, the healthcare organization could determine which interventions are most worthy of continued investment.
This is just one example illustrating why it is important to consider intuition and analytics as points on an evolving continuum. What are some of the other ways that healthcare organizations can make better decisions by leveraging both intuition and analytics?