Healthcare organizations constantly endeavor to improve patient outcomes such as readmissions, mortalities, patient experience, and safety. Identifying risk factors that could impact quality outcomes is essential for enhancing hospital performance. Dexur's AI approach, which automatically identifies risk factors influencing your quality outcomes, represents a significant leap in its potential impact on quality measures.

The AI Advantage

At the heart of Dexur's solution lies a powerful AI engine that automatically sifts through vast data, examining all possible permutations and combinations of risk factors. For instance,traditional methods might only consider common factors when looking at heart failure readmissions. Dexur's AI, however, goes much deeper by exploring every conceivable risk element, from comorbidities like Diabetes and CKD to age and discharge status.

A practical example showcases its capabilities. Let's take Acme Hospital. Traditionally, it might report a heart failure readmission rate of 12%. But with Dexur's AI solution, it's shown that the number jumps to 20% if those heart failure patients also suffer from CKD. Such granular insights have enormous potential to direct patient care strategies and interventions.

Moreover, the report doesn’t just stop at identification. It provides an insightful comparison of these risk factors alongside incidence rates against state and national benchmarks. This paints a clear picture for hospitals on where they stand and the areas they need to focus on.

Three Reasons Dexur is Able to Deliver a Differentiated AI-Driven Risk Factor Identification

• Access to Out-of-Hospital Data: Dexur is an approved purchaser of medicare claims and other data and, therefore has access to out-of-hospital data such as Readmissions to other hospitals or out of hospital mortalities.

• Dexur’s data is 100x the volume of a typical hospital: With one of the largest claims databases in the US, Dexur holds a significant advantage. To put it into perspective, a typical hospital might see 20,000 inpatient hospitalizations yearly. Drill down to specific procedures or conditions, and the numbers become much smaller. For instance, in the case of joint replacements, there might be only 600 cases annually. Further, narrow it down to patients with comorbidities like Diabetes, and it might dwindle to a mere 200. Such limited data sets can't build robust risk factor models. Dexur, however, with its voluminous database, does not face this constraint.

  

  AI-Driven Boil the Ocean Approach: Traditional risk factor assessments have relied on hypotheses built on smaller datasets, often missing out on numerous significant factors. Dexur challenges this norm. Their method is akin to boiling the ocean, delving deep into their extensive claims database to present real-world, evidence-based risk factors. More importantly, they offer hospital-specific insights coupled with national and state benchmark comparisons.

Dexur’s AI Risk Factor Identification solution represents a significant leap in pursuing hospital quality excellence. With its differentiated data and AI-driven approach, Hospitals can get automatic insights into what drives outcomes and the precise levers to fix problems.