For those unfamiliar with the term, risk stratification is simply the process of identifying high risk patients, or those likely to be of high risk, from a patient population. The general theory is that by identifying high-risk patients before surgery, physicians and care teams can either take actions to minimize possible complications, or they can postpone the surgery altogether until existing risk factors have been reduced.
Currently, the healthcare industry uses a number of different methods to stratify risk based on a number of different reasons. Some, if not all, may sound familiar. They include: Charlson Comorbidity Measure, BMI, Hierarchical Condition Categories, Elder Risk A, etc. Reviewing actual historical data can also provide a significant contribution to the overall assessment. Each option has its own merits.
Much like the idea behind diversifying a stock portfolio, DocSpera realized that combining a number of these methods into a single solution would likely yield more reliable and consistent outcomes. As a result, DocSpera recently introduced a risk assessment tool that automatically calls out a high-risk patient based off of the combined results of a number of risk assessment tests. It goes on to explain what metrics were used to get to that conclusion, and the level of confidence behind its assertion. All the information analyzed for the assessment is instantly gathered from a combination of real-time sources such as EMR data, available patient social determinant data, CMS data, and patient reported data.
The net result is a unique statistical metric that physicians and care teams can reference to aid in their overall patient care decisions. Using a combination of data points, the goal is to minimize complications and improve the outcomes for a given patient population.