Improved Inpatient Utilization Decreases Costs by $28.3M

AS SPENDING RISES, HEALTHCARE ORGANIZATIONS LOOK TO VALUE-BASED CARE

U.S. healthcare spending is projected to reach $6 trillion by 2027. Increasingly, payers are using VBC to decrease costs.¹ Data and advanced analytics are crucial for VBC success.²

THE NEED FOR TIMELY ANALYTICS

Working with its sponsors, IHCI recognized it had an opportunity to improve inpatient utilization, but limited data and analytics impeded improvement efforts. IHCI had data from the health systems, but it often reflected the health systems’ analytics use cases for its entire patient panel, not a VBC perspective. Limited payer data were available, and the earliest data were 90 days in arrears—some data were 9 to 12 months in arrears—making it nearly impossible to have an accurate, timely understanding of performance. IHCI needed a data and analytics solution to meet its unique business needs.

IMPROVING VBC THROUGH ACCURATE DATA

The solution for IHCI was the Health Catalyst Data Platform, Health Catalyst Value Optimizer™ application, and Healthcare.AI™. IHCI uses the data platform to ingest and integrate data from multiple sources, including clinical and claims data, with a population case-mix adjustment system. IHCI utilizes the data platform and existing solutions to meet its data and analytics needs and designs custom solutions to enhance the data platform. The organization also uses Healthcare.AI to generate new insights.

Using Value Optimizer, the organization has the analytics it needs to develop data-informed, risk-based contract strategies and best manage complex, risk-based populations. IHCI uses this analytics application to identify and assess the most valuable improvement opportunities across its network and payers. The organization implements evidence-based practices to engage with patients, ensuring they receive needed primary and preventative care, chronic disease management, and access to care in the most appropriate setting.

IHCI also uses the analytics application to monitor and quantify the impact of workflow changes on outcomes and costs. For example, the organization uses a custom risk-prediction model to identify patients who would benefit the most from transitions of care, complex care management, and/or advanced illness programs. The organization uses the analytics application and Healthcare.AI to evaluate the impact of these programs on utilization, including inpatient utilization, 30-day readmissions, and per member per month (PMPM) spending.

Using Healthcare.AI, IHCI can visualize and understand trends over time. Leaders can easily identify and visualize statistically significant changes in utilization and how those changes impact spending. The organization can evaluate differences across its network and payers, enabling it to identify and spread best practices and modify programs where investments may not produce the desired results.

RESULTS

IHCI data-informed, value-based care strategies are delivering the desired results:

• $28.3M reduction in costs over one year, the result of improved inpatient utilization, including:
  • $9.88 decrease in PMPM.
  • 3.64 reduction in the number of inpatient visits per 1,000 members per year. Patients were able to spend 5.6K more days at home.
  • 307 fewer readmissions within 30 days of hospital discharge.

“Health Catalyst substantially accelerates the timeliness with which we can identify and quantify our opportunities and evaluate the impact of changes on outcomes. We can determine, in real-time, if our interventions are having the desired impact.”

Lori Sieboldt, MD, MBA, Chief Medical Officer, Innovative Healthcare Collaborative of Indiana

WHAT’S NEXT

IHCI will continue to advance analytics adoption, improving the effectiveness of leadership decision making. The organization is deploying real-time key performance metric analysis, enabling it to continually refine program offerings and improve the knowledge, skills, and abilities of the team members serving its patients.

REFERENCES

1. The Centers for Medicare and Medicaid Services. (n.d.) National health expenditure projections 2018-2027 forecast summary. Retrieved from https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/Downloads/ForecastSummary.pdf
2. Collins, C., et al. (2023). Why data analytics are critical in a value-based care (VBC) environment. AIHC. Retrieved from https://aihc-assn.org/why-data-analytics-are-critical-in-a-value-based-care-vbc-environment/

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