Using predictive analytics, healthcare stakeholders can generate insights that can predict events or actions, based on a given set of variables. As an output, predictive analytics tools can stratify patients across a given population to identify individuals at risk of an adverse medical event. While these techniques have shown promise, there are inherent limitations with a predictive analytic approach.
Predictive analytics deliver models that only work for a particular segment at one point in time. Given the complex and individualized nature of oncology care, each patient’s cancer will vary significantly. In addition, demographic and environmental factors need to be considered. The impact of these factors can fluctuate across populations, making a static predictive model ineffective and possibly inaccurate. Moreover, to identify patients most at risk for adverse events, any analytics model must be able to account for all segments of the patient population; predictive analytics models may extrapolate findings across the larger population while excluding some segments, thereby missing critical segments that are on a trajectory toward an adverse event. Within a dynamic chronic disease state, a patient’s risk factors are a moving target. To apply interventions and resources appropriately, providers need to not only identify patients that are currently at risk, but also those who will likely become at risk in the future.
One of the biggest limitations with predictive models stems from how these models are tuned. Most often, predictive analytics models are designed to target high-risk patients. However, these patients are often already known to caregivers, so the models are not offering clinicians new information, which may lead to "alarm fatigue," especially in an acute care setting. Additionally, these models are limited to only risk identification and they cannot, by nature, identify the most critical actions or interventions needed to change a patient’s outcome and risk trajectory.