Healthcare is generating more data than ever before. But more data does not automatically lead to better care and improved patient outcomes. In the past, interoperability, integration, semantic harmonization and building longitudinal patient records were solutions for improving patient care. Clinicians were able to make more informed decisions as a result of all the information available at their fingertips.

But what happens when there is too much information?

At a certain point, the amount of clinical data for a single person within a database becomes inversely proportional to their overall health. For example, a patient with multiple chronic conditions has far more measurements, notes, labs and procedures than a patient without. Therefore, our most high-risk populations have become the most difficult to surface actionable insights for. Clinicians struggle to wade through the sea of clinical information. Intelligent data and intuitive presentation layers are required to enable access to data when and where clinicians need it.

The problem with data

Data is used in every aspect of healthcare. We are always seeking more data. However, it becomes a balancing act. Without enough data, clinicians lack critical information needed to care for a patient. On the other hand, too much data can be overwhelming, tipping the balance in the wrong direction, creating frustration and, ultimately, rendering the influx of data ineffective.

Beyond the volume of data, poor quality data can destroy trust in the entire dataset. Once that trust is lost, it’s difficult to get back.

In a 2022 internal study, we found that a significant portion of patient data was never accessed, in one case, this proportion totaled 60%. The results were clear: data alone does not move the needle for patients and providers. Information and insights do.

The river between data and outcomes

Information and insights are terms fraught with ambiguity. Just because something is insightful, does not make it meaningful. To consider what is truly meaningful, we must take a step away from technology and think about our  goals: to improve the quality of information presented to the provider and the quality of care for their patients.

One way to improve the quality of care is by identifying and closing care gaps. Care gaps are missed or delayed healthcare services, such as screenings, treatments or follow-ups, that prevent a patient from receiving recommended, evidence-based care. Care gaps result for many reasons, such as time constraints, administrative errors or technical issues.

Gaps in care are typically structured as discrete, rule-based measures tied to clinical guidelines, defined by specific inclusion criteria exclusion criteria, and a clear action needed to close the gap. Consider the electronic quality measure (eCQM), CMS 146, Appropriate Testing for Children with Pharyngitis. This episode-based measurement evaluates whether a child between the ages of 3–18, diagnosed with pharyngitis and prescribed an antibiotic, received a strep test as well. In other words, it doesn’t just track what happened, it explicitly defines the standard of care and flags when it isn’t met, ensuring that antibiotics are prescribed appropriately rather than unnecessarily.

Gaps in care matter. Not only because they ensure that patients receive evidence-based treatment, but because they are directly tied to clinician performance, reimbursement and quality ratings.

With this information, we don’t just identify gaps, we create the opportunity to prevent them before they occur, enabling organizations to deliver better care and helping patients achieve healthier outcomes.

Insights are the bridge. To cross it, we need an observable, scalable and highly performing platform, one that can support diverse use cases, grow with our customers and consistently translate data into meaningful, actionable intelligence.

Building the bridge

Our CareInMotion team has decades of experience in getting the data right. Today, our focus has expanded beyond data aggregation, to delivering actionable insights that enable smarter and more-informed decisions.

To support this evolution, we built CareIntelligence, a highly-scalable, FHIR-centric, cloud-based data platform. Like its predecessor, the dbMotion Solution, it serves as a robust health information exchange with highly customizable interoperability capabilities, semantic harmonization and a high-fidelity data model.

Built on modern data architecture principles, CareIntelligence enables streamlining building new capabilities that were previously difficult to achieve, such as operational dashboards that drive efficiency and patient-level HEDIS quality measures that enable organizations to identify and close gaps in care.

The path ahead

As we look ahead, interoperability, semantic harmonization and data aggregation are the foundation, not the destination, of a broader, intelligence-driven ecosystem. Building on this foundation, we can deliver advanced insights using AI, Machine Learning and quantitative models.

By adopting a platform that transforms data into intelligence, we can address today’s most complex challenges, calculating risk with a longitudinal view of each patient and closing care gaps in near real time by delivering patient-specific insights directly at the point of care.

Learn more about how CareIntelligence helps bridge the gap between data and outcomes.