Unprecedented pressure has been placed on rural and community-based hospitals following the passage of the federal reconciliation package (originally H.R. 1), now law. The Congressional Budget Office (CBO) outlined that the bill would reduce federal Medicaid spending by $1 trillion and increase the number of uninsured people by 10 million. As federal reimbursement rates decline, health systems will likely see a significant increase in uninsured patients turning to emergency departments for care, often as a last resort. This surge in demand will place added strain on already burdened emergency services, an outcome that is difficult to avoid given the recent changes to Medicaid coverage. Compounding this issue is the existing budgetary crisis facing these hospitals, due to workforce shortages exacerbated by burnout and limited state-based Medicaid reimbursement, which puts service lines in danger and threatens the sustainability of care delivery.

Historically underserved, community hospitals are expected to deliver the same level of care as larger systems, despite having fewer financial and operational resources. According to the American Hospital Association, community health systems comprise 84% of U.S. hospitals, with one-third of them located in rural areas. These institutions are often the backbone of their communities, simultaneously acting as healthcare providers, economic engines, and lifelines for vulnerable populations.

At this moment in time, innovation should not be stifled; it should be leveraged to navigate the uncertain path ahead. Trusted, data-informed insights are crucial for helping rural and community hospitals, which are often stretched thin, to continue delivering meaningful care even with limited resources.

Freeing Data to Maximize Revenue Streams

In healthcare settings, data is often siloed. Many rural and community health systems still rely on older or disparate electronic health record (EHR) systems that are not built to easily exchange patient data across the continuum or integrate their data from point solutions. Providers are left with gaps in patient journeys and struggle to discern insights that would be most helpful to care plans.

Yet, enterprise-level data infrastructure does not have to be out of reach.

Lifepoint Health (Lifepoint), for example, has 60 community hospital campuses with a wide range of EHRs, most with separate instances for every hospital, creating substantial data source complexity. This complexity made it challenging to equip physicians with the data needed to compare hospitals, drive change, track progress, and evaluate the impact on outcomes. Lifepoint sought to address unwarranted care variation by exploring data-driven strategies to standardize care and improve patient outcomes.

They partnered with Health Catalyst to focus on improving quality and efficiency, but had limited actionable data to help influence physician behavior and improve clinical quality. To address this, the Lifepoint launched an enterprise-level Unwarranted Care Variation Reduction Program, using Health Catalyst Ignite™ Data and Analytics to understand performance better and prioritize improvement areas. Lifepoint’s data-driven efforts have since improved care quality and decreased unwarranted care variation, resulting in 600-plus lives saved from decreased sepsis and heart failure mortality, optimized throughput with decreased length of stay by way of 22K more days at home for patients, and reduced blood products transfused by 1.2K.

Memorial Hospital at Gulfport’s (Memorial) leadership team also leveraged data insights to reduce excess costs while enhancing the quality of care. By utilizing analytics and decision-support solutions, Memorial could calculate and visualize patient safety indicator performance, automate key calculations, and identify critical opportunities for improvement.

This improvement was realized in the area of preventable hospital-acquired conditions (HACs). HACs compromise patient safety and drive up healthcare costs; consequently, insights into avoidance are key to helping hospitals and health systems avoid excess fees.

Memorial faced challenges in identifying ways to reduce HACs and improve patient safety due to outdated data. By leveraging data and analytics, Memorial identified opportunities for improvement, resulting in process improvements that effectively reduced HACs and enhanced care quality.

Memorial’s data-driven improvements have enhanced care quality and patient safety. By using real-time insights and targeted interventions, the organization has avoided millions in excess costs annually. Memorial achieved the following HAC rates:

0.17 C. diff.standardized infection ratio (SIR), an 80 percent relative reduction.
0.23 colon surgical site infection SIR, a 92 percent relative reduction.
0.4 hospital-acquired pressure injury rate, an 80 percent relative reduction.
0.41 MRSA SIR.
0.5 central line-associated blood infection SIR.
0.67 catheter-associated urinary tract infection SIR.

Hospitals don’t need to be large or part of a major system to achieve measurable outcomes like these. Scalable, integrated analytics—combining clinical, financial, and operational data—can support smaller and rural hospitals in pursuing high-impact use cases and drive meaningful improvement across their organizations as well.

Community and rural-based hospitals are already committed to the well-being of every person entrusted to their care, and now is the ideal time to leverage actionable data insights to drive their goals. With significant changes ahead, organizations like Lifepoint and Memorial show how harnessing data and analytics can address critical challenges in both clinical outcomes and revenue optimization. Particularly during times of uncertainty, these actionable insights can ease financial and labor strain on an already overburdened healthcare system.

The post The Growing Strain on Community Health Systems— And Why Data May Be the Key to Revenue Relief appeared first on Becker’s Hospital Review | Healthcare News & Analysis.