DATA, ANALYTICS & AUTOMATION for better healthcare

3 Population Health Risk Stratification Tips

Medical professionals devote countless hours to ensure the health and wellness of their patients. And anything that can help them perform their duties with greater efficiency is obviously beneficial. The process of risk stratification assists healthcare facilities in managing their resources and delivering quality care to all of their patients. What is risk stratification? In simple terms, it’s an analytical method developed to divide patients into different levels of risk in regard to chronic diseases. That way, medical professionals can dedicate their time and efforts with greater precision. To that end, here are three population health risk stratification tips worth noting:

1. Select Your Data Set with Care

What makes one patient “high-risk” to experience further complications with a disease and another “low-risk” depends on a number of factors. Indeed, some risk stratification models account for hundreds of different variables to determine a patient’s risk potential. The key to selecting your data set, often, isn’t figuring out which variables to include, but rather, omitting those that don’t provide much value and serve only to “muddy the waters.” There is such a thing as data overload, and if your team members can’t draw clear conclusions from your risk stratification model, then you may want to go back to the drawing board.

2. Have a Goal in Mind

As with any analytics measure, there should be a practical application to population health risk stratification. In other words, you should have a specific goal in mind when you begin to separate patients by risk. (This will also help you connect analytics to results later on as well.) Perhaps you want to cut down on emergency-room visits for people with chronic kidney problems; or maybe your facility is attempting to schedule more consultations with “low-risk” patients to prevent them developing more issues. Regardless of what type of data you choose to collect, you should understand how your team is going to use it beforehand. Otherwise, the analysis won’t help doctors make smarter decisions in the field.

3. Use Firsthand Experience

As we mentioned above, some risk stratification procedures are intricate and include hundreds of different aspects like a number of hospital visits, age, gender, height, weight, comorbidity, etc. to identify patients with a high risk of experiencing future health problems. At the end of the day though, doctors are still invaluable resources to determining if a patient needs extra attention or additional consultation. Healthcare facilities shouldn’t ignore firsthand accounts from professionals when building a risk stratification model. Rather, the best designs incorporate both raw data points and professional opinions to create a working system. Finding the right balance in this regard will help hospitals, clinics, and other healthcare facilities operate more methodically.

This is exactly the approach we took when we developed a population health risk stratification application for a hospital system looking to cut down on the rate of readmissions for diabetic patients. The algorithm was based on already available clinical and demographic data and involved a simple behavioral survey administered by intake staff on-site. A patient’s resulting score was tied directly to a treatment protocol that involved a multidisciplinary team of nurses, dietitians, and outreach workers–without whom the program could not have been a success.

Final Thoughts

The goal of any healthcare facility is to aid visiting patients. Risk stratification is just one of a wide array of data analytics methods that help doctors achieve this goal. At Amitech, we specialize in providing IT and data solutions to businesses in the healthcare sector specifically. We know how to provide real-world advantages because we’ve been working closely with medical professionals for decades.