The Principles of Predictive Analysis in Healthcare

Predictive analysis is many things. It is data collection, it is project definitions, it is data analysis, it is statistics, it is modeling, it is model deployment. The list could go on and on. And it drives home the fact that predictive analysis is many things in one and that’s why there exist so many definitions of the term. Predictive analysis is used in various industries like banking, governments agencies, customer service and even healthcare. The broadness of the concept exists in each industry it is used in different forms as each industry tries to maximize its benefits and better use it to their advantage. One of the industries using it the most is the healthcare industry. Predictive Healthcare Analytics has taken on a life of its own and hold much authority in how many things concerning data and information is handled in the healthcare industry. For predictive analysis in the healthcare industry, there are some principles that guide it. These principles are discussed below:

The Principle of The
Economy of Prediction

The topic of predicting hospital readmissions is a very important and often talked about one with rapidly growing interest. This topic is particularly popular because being able to accurately predict and prevent hospital readmissions will help healthcare organizations in improving patient care while avoiding financial and reimbursement penalties and costs for hospitals. So how can predictive analytics be used to help control costs and improve patient care? While evidence-based medicine is a powerful tool that helps minimize treatment variation and unexpected costs, the best-practice guidelines contribute further to the goal of standardized patient outcomes and controlling costs. In order for predictive analytics to be effective, Lean practitioners must truly live the process to best understand the type of data, the actual workflow, the target audience and what action will be prompted by knowing the prediction. Decision makers should not be isolated from the points of decision making.

For example, if a manager makes a decision on if a certain process should be changed, the manager should have been a part of the process and if possible be affected by the decision too.  At the same token, to best leverage, the data, predictors should also not be used in “isolation,” although in the healthcare industry today, readmission risk profiles are often used as standalone applications. So how do you apply [predictive analysis for healthcare in the most efficient way? One way is to learn from those that have already figured it out or those with existing expertise. Fortunately, in the healthcare industry, there are numerous existing models from other industries that are very efficient at risk stratification in the realm of population management. Studying recent history will also likely ease some of the potential pains and pitfalls that could accompany healthcare’s adoption of predictive analytics.

The Principle of:
Don’t Confuse Data with Insight

Having a technology driven and more generalized prediction model that inputs big data and global features is that the target use or utility is often lost in translation. The prediction that is focused on a specific clinical setting or patient need will always trump a generic predictor in terms of accuracy and utility. This principle is particularly important because of how highly humans regard new technology. However, without having the proper technology framework in place, with context and metadata for meaningful use, new technology is really not very useful. Prediction focused on a specific clinical setting or patient need will always trump a new technology-driven generic predictor in terms of accuracy and utility.

The Principle of:
Don’t Confuse Insight with Value

Just because you have a better understanding of what’s wrong doesn’t mean you should stop looking as the problem may connect with other different parts of the process. Data that is taken as a whole, will often provide an early warning as a patient begins to fail, where even a careful human observer cannot possibly “connect the dots” between so many unrelated data points simultaneously.  One key to the success of the algorithm is first obtaining all of the necessary data. Assessing only part of a picture often yields an incorrect view.