As healthcare organizations stretch to do more with less, data is becoming the backbone of better care, stronger operations, and sustainable growth.
Both established payers and emerging health systems are investing in more actionable healthcare data. Leaders understand that poor data quality is stalling care delivery, while quality data will empower smarter, faster decisions — and ultimately save lives.
I’ve seen firsthand how a data-driven strategy helps healthcare entities scale without compromising care quality. And in today’s strained healthcare landscape — marked by persistent clinician burnout, staffing shortages, and eroding public trust — this potential is more critical than ever.
Although the pandemic highlighted these challenges, many of them predated COVID-19 and have only intensified in the years since. For example, administrative burden remains one of the top drivers of provider burnout, and analytics offers a solution.
By streamlining workflows, surfacing real-time insights, and reducing manual data entry, data-driven care lightens clinicians’ workloads and provides actionable insights to increase the quality of care while also enabling payors to improve care coordination, reduce duplicative services, and engage members more effectively.
The problem of fragmented data
Siloed, unstructured data remains one of healthcare’s biggest technology challenges. EHRs, insurance claims, lab results, and patient-generated data are rarely harmonized. This lack of integration limits clinicians’ visibility into patient health and restricts a complete understanding of systemic trends.
Meanwhile, healthcare costs continue to rise. Without precise data insights, it’s nearly impossible to identify interventions that can lower spending and improve outcomes — especially for organizations focused on value-based care.
For example, without timely risk stratification analytics, a patient who shifts from a medium to high-risk tier might go unnoticed, missing critical early intervention. This lack of responsiveness can lead to avoidable hospitalizations, higher costs, and worse health outcomes.
Analytics offers a promising path forward for organizations looking to break down data silos and make meaningful progress toward value-based care. Already, 65% of U.S. hospitals use AI-assisted predictive modeling.
Early results show clear benefits:
● Risk detection: Predictive modeling enables care teams to intervene before patient deterioration or hospitalization occurs. Systems can act proactively instead of reacting to emergencies.
● Population health insights: Teams can identify trends across populations and deploy tailored interventions. For example, payors can initiate outreach to members with escalating risk scores or develop personalized care plans for individuals with multiple chronic conditions.
● Operational efficiency: Payors and providers alike can improve efficiency by analyzing staffing patterns, discharge delays, and bed utilization. Many organizations have realized lower costs because of better patient outcomes simply by optimizing workflows and better aligning resources with demand — a key opportunity for automation to replace manual efforts.
For payors and healthcare organizations, the right analytics strategy drives better outcomes, lower costs, and more responsive, patient-centered care.
3 trends laying the groundwork for scalable healthcare analytics
Successfully implementing data analytics in healthcare isn’t just about selecting the right tools. It’s about addressing the underlying gaps that often derail even the most promising initiatives.
Here are three trends I’m watching closely — each shaping how healthcare leaders are navigating the shift to data-driven care:
1. Analytics initiatives outpacing data infrastructure
Many organizations leap into analytics before establishing a solid foundation. Without high-quality, interoperable data workflows, even the best tools fall short due to data fragmentation. Disparate definitions, inconsistent taxonomies, and siloed systems make it difficult to draw accurate insights, let alone scale them.
Investing in data architecture and governance upfront is critical. A robust infrastructure makes it easier to integrate new data sources and normalize data inputs into a singular view. Then, companies can reliably surface real-time insights, act on them, and champion the kind of analytics engine capable of evolving as the organization grows.
2. Culture as the missing link
Technology alone won’t drive the type of transformation healthcare requires.
Teams must also trust and act on the insights analytics delivers. Building a data-literate, insight-driven company culture is equally as important as the analytics tools themselves.
Fostering a data-driven culture takes intention. Healthcare teams often face resistance to change, especially when it feels like technology is replacing human judgment.
To counter this, leaders must engage stakeholders early, involve them in tool development and implementation, and ensure analytics align with their goals and existing workflows.
3. Scaling without a strategy
The most successful organizations start small and scale fast, launching targeted analytics use cases that demonstrate early value and build excitement.
Key to this approach is the selection of projects that are measurable and aligned with broader business goals, such as reducing total cost of care, minimizing unnecessary ED visits, or improving member retention for high-risk populations.
When results are visible — fewer ER visits, lower readmission rates, better outcomes — momentum and employee buy-in build naturally.
But as enthusiasm grows, so do competing priorities. Leaders must be prepared to manage input from a wide range of stakeholders and maintain a clear focus on initiatives that deliver the greatest strategic value.
Healthcare’s data-driven future
While regulatory and systemic pressures continue to challenge progress, healthcare leaders must still push toward a more connected, proactive, and personalized care model. Data analytics is one of the most powerful tools to help them get there.
With the right foundation, teams, and tools, data analytics will drive this shift. Data-driven insights will reshape how patients receive support, how organizations make decisions, and how systems scale.
About Chris Riopelle
Chris Riopelle is the co-founder and CEO of Strive Health, a value-based kidney care company serving over 120,000 patients across 50 states and managing $4.5 billion in medical spend. Since 2018, Strive’s innovative clinical model has reduced hospital admissions by nearly 50%, re-admissions by 30% and total cost of care by 20%. Leading over 600 “Strivers,” Chris has built a company recognized as a best place to work and backed by top investors including NEA, CapitalG (Alphabet), CVS Health Ventures, and Town Hall Ventures. His healthcare career includes senior executive roles at GeriMed, Gambro, DaVita, LaVie Care Centers, and NorthStar Anesthesia. Chris has been recognized as a Most Admired CEO by the Denver Business Journal and an E&Y Entrepreneur of the Year. He serves on boards including Engine, a tech company revolutionizing unmanaged travel, and Crested Butte Land Trust. Chris holds a BA in Economics from Albion College and a JD/MBA from the University of Detroit Mercy.
