Case Study
The healthcare industry faces a significant challenge in aligning with value-based care models, which prioritize high-quality, patient-centric, and cost-effective treatments. Our goal is to harness the power of AI and data analytics to transform historical health data into predictive insights for patient risk. This initiative is crucial for early intervention and personalized care strategies, ensuring that resources are optimally allocated to those in greatest need. By focusing on preemptive healthcare measures and improving care efficiency, our solution aims to embody the principles of value-based care, enhancing patient outcomes while simultaneously managing healthcare costs.
PREDICTING PATIENT RISK SCORES
Leveraging historical claims and behavioral data, we developed a predictive model to identify and prioritize at-risk patients, aligning perfectly with the principles of value-based care. This model enables early intervention, reducing the likelihood of costly emergency treatments and hospitalizations. Our tailored care strategies, based on individual risk profiles, ensure that each patient receives the most appropriate, personalized care. We provide weekly, user-friendly risk reports, empowering care teams to make data-driven decisions and allocate resources effectively. This approach not only boosts care team productivity but also significantly enhances patient health outcomes, contributing to the overall cost-efficiency and quality goals central to value-based care. Ultimately, our solution represents a stride forward in realizing the full potential of value-based healthcare.
Problem Overview
The client wants to become the regional go-to provider of specialty pharmaceuticals. These are high-yield, innovative pharmaceuticals. The client regularly receives a report about new drugs coming to the market and wants to make better decisions about drugs to focus on, their market potential and expected prescription trends. However, the market potential can depend on a variety of different factors, like the current ecosystem of drugs for a particular disease, hospital units where a patient is treated, a patient's insurance type, the convenience of a pharmacy pickup as well as demographic factors. Thus, a powerful analytics tool is needed that automatically analyzes market potential, competing treatments and provides detailed information on market trends to enable informed decisions on which drugs represent opportunities, and how to introduce them in the most effective way.
RESULTS
Reduced Emergency Department (ED) Encounters. A 17% reduction in ED encounters in the intervention group compared to the control group. Although this reduction was not statistically significant, it indicates a trend towards decreased emergency healthcare needs due to proactive interventions.
Significant Reduction in Hospital Readmissions. A substantial decrease in hospital readmissions within 90 and 180 days. Specifically, there was a 71% reduction in 90-day readmissions and a 52% reduction in 180-day readmissions in the intervention group, both statistically significant findings.
Decreased EMS Utilization Emergency Medical Services (EMS) encounters were 47% lower in the intervention group, suggesting effective preemptive healthcare measures can reduce the need for emergency interventions.
