Results

The strategy delivered significant returns. "More than $2.5 million of realized savings was attributed to perioperative services," Warman said. Specific procedure standardization efforts yielded additional savings, such as reducing average laparoscopic cholecystectomy costs from $641 to $429 per case, saving more than $75,000 annually.

Warman emphasized that success depends on more than technology. "Our keys to success are maintaining engagement with our frontline colleagues and surgeons," he said. Other guiding principles included accountability at every level, transparent data sharing, and vetting all formulas and calculations to foster trust.

"Leveraging data analytics and active reporting is not an option, it is a strategic imperative," Warman said. "Health systems that invest to improve operational excellence and patient care through data, while fostering culture change through accountability and active management, will find themselves in a better position."

*Advisory Board is a subsidiary of Optum. All Advisory Board research, expert perspectives, and recommendations remain independent.  

6 ways to increase efficiency with a technology-enabled CVR framework

By Anne Schmidt and Jeremiah Reuter

Lehigh Valley Health Network demonstrates how integrated technology can expose inefficiencies and enable targeted clinical and operational improvements. By leveraging Optum's Crimson AI, the organization unified fragmented data sources and gained real-time visibility into key performance gaps, delayed first-case starts, turnover inefficiencies, supply variation, and scheduling inaccuracies. These insights were not buried in retrospective reports. They were surfaced daily and triggered immediate response. The result? Over $2.5 million in perioperative savings, improved reliability, and more substantial team alignment. Technology didn’t solve the problem — it revealed it. The impact came when clinical teams used that visibility to drive change.

Care variation reduction (CVR) is most effective when technology provides targeted visibility into performance gaps — such as procedural variation, workflow inefficiencies, and supply utilization discrepancies — and clinical teams lead standardization efforts. At Lehigh Valley, interventions like preference card reform, turnover protocol optimization, and scheduling alignment directly improved reliability, reduced risk, and enhanced patient outcomes. Efficiency was not treated as a back-office metric. It became a clinical competency embedded in daily practice and owned by frontline teams.

Cost control was achieved through frontline engagement. Surgeons removed unnecessary items, nursing teams led supply reviews, and leaders used dashboards to intervene early. The actions delivered a measurable financial impact without compromising safety or quality. The model emphasizes that cost stewardship is most effective when it is clinician-led, data-driven, and outcome-oriented.

Technology is also supported by team alignment. Shared dashboards created a unified view of performance. Initiatives like the Periop Olympics fostered ownership and accountability across roles. Transparent data supported trust, and standardized workflows reinforced collaboration. Teams worked from the same playbook, responded to the same metrics, and achieved common goals.

Managing data alone is insufficient. Real change happens when teams act on insights. Lehigh Valley built an infrastructure that transforms data into daily decisions and actions. Dashboards prompted intervention. Alerts drove accountability. Teams made adjustments in real time. This is the difference between reporting and leading, and it is the foundation for scalable CVR and organizational efficiency.

Crimson AI served as the backbone of Lehigh Valley’s transformation by pulling together disparate data sources into a unified, actionable view. It enabled real-time visibility into performance gaps and variation across perioperative services. But visibility alone does not drive improvement. The impact was realized when clinical teams leveraged that insight to standardize workflows, reduce waste, and enhance reliability. The synergy between advanced analytics and CVR methodology supports the demands of value-based care. It also aligns clinical decisions with financial outcomes, strengthens team accountability, and delivers measurable gains in cost, quality, and safety. For a health care system under pressure, it is a model that offers a scalable and repeatable path forward.

To help organizations improve their data management strategies, here are six tips:

1. Intentionally design data management systems

Effective data management starts with intentional design. As organizations set up their data management systems, they should:

• Automate data collection and processing to reduce manual errors and latency.
• Enrich data with metadata and contextual tags to improve usability.
• Prioritize security, access, and sharing protocols to protect sensitive information while enabling collaboration.
• Establish clear criteria for data prioritization — not all data is equal. Focus on what drives decisions, outcomes, and accountability.

2. Contextualize data to solve specific challenges

Data must be interpreted through the lens of the problem it’s meant to solve. This requires deep clinical and operational expertise to translate metrics into actionable insights and alignment between data teams and frontline stakeholders to ensure relevance and usability.

3. Use actual-to-expected comparisons

To manage performance effectively, organizations will need to compare their actual results to expected outcomes. By developing “expected” baselines using historical data, predictive modeling, and subject matter expertise, leaders can identify any outliers, trends, and opportunities for intervention.

4. Benchmark with risk adjustment

Relative performance is only meaningful when adjusted for context. Leaders should use risk-adjusted benchmarks to compare across facilities, providers, or programs. This helps isolate performance issues from population complexity or environmental factors.

5. Measure program effectiveness with statistical credibility

To prove return on investment and value on investment, organizations must apply statistically sound methodologies, such as pre/post comparisons, control groups, and confidence intervals. Measurement frameworks should also be transparent and reproducible.

6. Ensure strong governance and accountability

Data initiatives succeed when supported by strong governance. Organizations should create cross-functional steering committees with decision-making authority and empower their teams to act on insights and enforce compliance with data-driven policies.