Patient stratification involves grouping patients in a given population into subgroups of diseases based on their past claims. Clinicians analyze patients and group them from those with the highest risk in 12 months in terms of diagnosis and financial point of view.
The process can also incorporate predictive algorithms, but clinicians will still group the given population into patients with the highest risk. Let us look at why risk stratification is no longer effective.
Why Do Providers Use Risk Stratification?
Before we talk about why this process is outdated, it is wise to know how it is used in the first place. Risk stratification became popular in the 1980s when we introduced managed care. Clinicians believe the sickest top 5% spent a lot of money on healthcare. While it was true during those days, the pattern is changing nowadays. Patients within the top 5% category are often changing.
But, many providers still use this approach even today. Partly because most do not know of another way, and many still think of healthcare as cost prohibitive, limited to a 5% population scale.
But these days, things have changed. With the introduction of CCM, TCM, RPM, and such services, Medicare and other financial institutions are willing to pay for a patient’s bill.
Why is Patient Stratification No Longer Cost-Effective?
When grouping patients according to data, clinicians quickly assume that what works for one patient will work for the rest of the group. It’s like when we use a luggage scale for weighing paper clips. While we will still get the weight, this tool is bulky to give accurate information.
According to a review conducted in 2011, the models used for risk prediction for comparative or clinical purposes aren’t effective. But despite this discovery, little has been done to reduce the problem. Here is why risk association isn’t as effective as before;
It Relies on Claims
Risk assessment analyzes claims from patients to decide on healthcare. And this has been used for a long time as a means to justify reimbursement. But as it turns out, just looking at the claims to stratify patients can be significantly misleading. The claims data is full of errors. These errors may not be a problem when calculating the population cost over the following year, but they are an issue when prioritizing patients.
It Looks at The Past Patient Instead of the Present or Future
Patient stratification analyzes the patient’s medical and diagnostic history. Although we should not disregard patient medical history, it is not a conclusive way of grouping patients. We can consider an individual low risk because they have a short history of an illness. At the same time, we may regard others as high risk due to their long history of chronic disease, even though their condition may be well managed.
There may be other contexts that we may not have tracked, including patients whose underlying conditions haven’t been diagnosed or patients that have had mild symptoms in the past but now it looks like they will be hospitalized.
It’s Based on Race
Risk stratification analyzes financial data. As such, patients from the minority group may most likely be at low risk as their healthcare spending isn’t that much. White patients who are spending more money on healthcare will be grouped as high risk and given priority.
Speaking of financial data, several studies have proven that patients that use more money in healthcare often require intense chronic care management but only for a short time. Algorithms used to identify patients that spent large amounts on healthcare previously are now grouping patients that are almost coming to their end of life and those whose medical care needs are reducing. As such, it focuses on a past consumption bias.
It Uses a One-Size-Fits-All Approach
There are many factors influencing the need for healthcare management. These include the nature and severity of the disease, a patient’s social support level, hospital admissions, readmissions, etc. However, risk stratification doesn’t consider these crucial factors when prioritizing patients.
As such, clinicians quickly assume that the conditions with many health care uses are the high-risk ones. Therefore they ought to be given priority. For instance, people on kidney dialysis or with late-stage cancer may be prioritized over patients with early signs of diabetes since the first patients have already spent a fortune on their medical expenses more than the latter. But they forget that the greatest financial and clinical impact lies within the early stages of a disease.
This approach also introduces age bias. Therefore, young patients are mostly ignored as priority is given to older individuals with more chronic and complex conditions. For instance, a child suffering from diabetic ketoacidosis won’t be prioritized over a 60-year-old suffering from a chronic disease that has made them spend hugely on healthcare over the past 12 months.
Final Thoughts about Stratification
Risk stratification is outdated. However, the solution isn’t just swapping it with another strategy. The most crucial thing is to ensure that the approach meets the evolving care management needs. Clinicians need to use all relevant data and evaluate performance based on their hospital’s population.
