Improving chronic Disease Care: A Data-Driven Approach
New research co-authored by a University of Illinois Urbana-Champaign business scholar highlights a data-informed strategy for improving chronic disease care outcomes, notably for underserved populations. The study, led by Professor Ujjal Kumar Mukherjee, demonstrates that strategically scheduling patient encounters with clinicians can reduce risks associated with diabetes management by up to 19.4%.
The core of the research lies in recognizing the challenges of long-term chronic disease management - requiring sustained resource commitment and high patient engagement – and the impact of demographic diversity on health risks. Mukherjee explains that customizing care based on patient demographics is key to driving improvements.
Currently, a notable problem exists: high-risk patients frequently enough receive fewer healthcare encounters than needed, leading to inequitable resource allocation and poorer outcomes. The research team, including collaborators from Purdue and Lehigh Universities, developed a predictive and prescriptive framework using machine learning to optimize healthcare encounter allocation.
Analyzing data from over 10,000 diabetes patients alongside U.S. census data,the study revealed striking disparities in care access. Patients from low-income, less-educated, and minority communities were less likely to have regular healthcare, despite exhibiting higher average glucose levels. This underscores the need for risk-sensitive decision-making tools to support clinicians.
The research emphasizes the importance of early and frequent intervention to manage chronic diseases like diabetes, COPD, cancer, and heart disease. Proactive management can “bend the cost curve down” by preventing disease progression to more expensive and complex stages. Regular clinician contact can avoid costly emergency room visits – a common outcome for underserved patients who lack preventative care – ultimately benefiting both patients and the healthcare system.
Ultimately,this research demonstrates how healthcare providers can leverage analytics to equitably and efficiently distribute limited clinical resources,ensuring that those who need care the most receive it.
Key takeaways:
* 19.4% risk reduction: Data-driven scheduling can reduce diabetes management risks by nearly 20%.
* Targeted care: Customizing care based on patient demographics improves outcomes.
* Equity focus: Addresses disparities in care access for underserved populations.
* Proactive management: Emphasizes early and frequent intervention to control costs and improve health.
* Analytics-driven solutions: Highlights the power of data and machine learning in optimizing healthcare resource allocation.
