Rethinking Care Management in the Age of Data 

In the days of paper charts, healthcare providers often relied on colored stickers—affectionately known as “mystery dots”—to track patient diagnoses and follow-up care. These small, colored dots were a staple in many medical offices, fluttering around like confetti, sticking to the soles of shoes, and getting lost in the shuffle of everyday practice. While these dots were a simple and effective way to manage patient care on a small scale, they were far from ideal when it came to keeping track of more complex patient data, particularly in specialties like endocrinology where managing chronic conditions like diabetes requires constant vigilance. 

Even with the advent of electronic health records (EHRs), interoperability regulations, and health information exchanges (HIEs), the healthcare landscape still feels fragmented. Data, though digitized, can still be scattered, making the manual work surrounding care management (CM) frustratingly similar to the old days of sticky dots and paper charts. However, it doesn’t have to be this way. By rethinking legacy CM technologies and workflows, healthcare providers can gain better visibility and access to crucial patient data, ultimately leading to improved clinical, financial, and operational outcomes. 

Reevaluating Care Management Roles and Timing 

Care management is designed to focus on the bigger picture of patient health, requiring collaboration across payers, clinicians, and community resources to address a patient’s complete needs—behavioral, social, and physical. This comprehensive approach spans the entire continuum of care and often operates outside the traditional fee-for-service arrangements that dominate much of the healthcare industry. Despite some inherent friction between physicians and payers, there’s a growing consensus that care management should be as close to the point of care as possible to be effective. 

For instance, some integrated delivery networks have begun transitioning CM functions to the provider side, allowing for more seamless communication between patients and their care teams. When patients receive communication from a familiar name—whether it’s digital or via a simple phone call—they are more likely to engage with the outreach. This proximity to care improves the likelihood that patients will follow through with recommended interventions, leading to better outcomes. 

However, patient engagement also suffers from a lack of personalization and timing. It’s frustrating for both patients and providers when outreach occurs long after a hospital visit or when reminders are sent for appointments that have already been completed. Accurate, timely information is crucial for effective care management. Without it, connecting the dots between patient needs and care interventions is like trying to navigate with a compass instead of a GPS. 

The Importance of Real-Time Data 

The healthcare ecosystem has historically collected data from a wide variety of sources—HIEs, EHRs, and ADTs (admission, discharge, and transfer notifications)—but often fails to curate it effectively. However, recent advancements in data platforms are changing this dynamic. These platforms can automate ADT alerts in real time, significantly reducing data latency. For example, instead of being notified weeks or months after a hospital visit, providers can now receive alerts within seconds, allowing them to act quickly and efficiently. This immediate access to data enables earlier interventions, preventing patients at emerging risk from becoming high-risk cases. 

Real-time data also allows healthcare providers to make more strategic decisions about where to allocate limited CM resources. For instance, instead of spreading resources thinly across a broad patient population, providers can use predictive analytics to identify and prioritize those individuals most in need of care management interventions. 

Leveraging Data for Better Outcomes 

The potential for predictive analytics to reduce avoidable healthcare utilization and associated costs is enormous. Some health plans are already seeing significant benefits from these technologies. For example, a suicide prevention program identified specific factors that contribute to the risk of self-harm, such as psychiatric diagnoses, chronic pain, and asthma. By using data and analytics to identify at-risk individuals, the program was able to proactively reach out to those most in need, leading to a significant reduction in suicide attempts and events. 

For physicians and patients alike, the goal is a healthcare system that is personalized, predictive, and cost-effective. The time to move beyond legacy systems and embrace modern data platforms is now. By treating data as an essential tool—just like a stethoscope or blood pressure cuff—healthcare providers can create a balanced approach that benefits everyone involved. In this way, care management can truly become the strategic, impactful practice it was always meant to be.