How One Hospital Used Data to Reduce Asthma Admissions and Cut Costs

Asthma-related emergency department (ED) visits and hospitalizations place a significant burden on both patients and healthcare systems, particularly when the root causes go unaddressed. At a large metropolitan safety-net hospital, thousands of uninsured patients were being admitted for asthma-related emergencies, despite the presence of a strong clinical asthma program.

With 4,777 ED visits over five years, each costing an average of $11,000 per patient, the financial toll reached $54 million—a staggering figure that prompted health system executives to ask:

“What’s driving these costly and preventable asthma admissions?”

By leveraging community-level data and predictive analytics, the hospital identified key environmental factors contributing to the asthma crisis. Here’s how they used a data-driven, four-step approach to shift from reactive care to proactive intervention.

Step 1: Evaluating the Criteria

The hospital first needed to understand who was being affected and where they lived. Using Metopio’s data curation process, they aggregated and de-identified patient information to remove privacy concerns while layering in community context—a crucial step in understanding broader environmental and social determinants of health.

What they found was striking: many of the highest-risk patients lived in areas with poor housing conditions—a factor that clinical treatment alone couldn’t address.

Step 2: Mapping the Results

Using geographic data and predictive modeling, hospital leaders analyzed patient ZIP codes and correlated them with dozens of social determinants of health (SDOH).

Metopio’s recommendation engine flagged key variables related to poor housing quality, including:

• Lead paint indicators

• Air quality

• Building violations

Of these, building violations stood out as the strongest predictor of asthma-related ED visits. With this insight, the hospital team zeroed in on housing conditions as a primary intervention point.

Step 3: Finding Opportunities for Action

Once housing conditions were identified as a major risk factor, the hospital took action:

• They adjusted screening efforts in the emergency room to include questions about housing conditions.

• A pilot program was launched to assess the homes of high-risk patients, looking for asthma triggers.

• In-home assessments revealed mold, HVAC issues, pests, and heavy indoor smoking—all exacerbating asthma symptoms.

• A community partner organization stepped in to provide minor home improvements to remediate these issues.

Step 4: Measuring the Impact

The results of this data-driven approach were game-changing:

• 0% of pilot patients returned to the ED for asthma-related emergencies.

• 80% reduction in outpatient visits, indicating better disease management.

• The average cost of screening, home assessment, and remediation was $3,700 per patient—a savings of $7,300 per patient compared to an ED visit.

Beyond cost savings, the program dramatically improved patient health outcomes and paved the way for new financial models, such as pay-for-success contracts, to fund similar interventions in the future.

Turning Insights into Action

This case study underscores the power of data in transforming healthcare delivery. By integrating community-level insights with hospital data, leaders were able to pinpoint root causes, design targeted interventions, and drive both health and financial improvements.

Metopio’s analytics platform provided the tools to move from reactive to predictive healthcare—and this hospital’s success is just one example of what’s possible when organizations harness the right data.

Is your organization ready to take a data-driven approach to community health? Contact us to learn more and schedule a consultation with our team.