How Data Can Reduce Length of Stay and Keep the Revenue Stream Flowing

Length-of-StayResearch shows that when patients spend more time in the hospital, they are more likely to develop hospital-acquired infections. Not only are lengthier hospital visits and readmissions putting patients’ health at risk, they are costly for organizations because CMS deems length of stay (LOS) and readmission rates quality indicators. Failure to meet the standards for LOS and readmission rates results in financial penalties, including lower reimbursement rates. Health systems can’t afford reductions to revenue during COVID-19, as non-essential care—a main source of income for hospitals—slowly returns to pre-pandemic levels.

As health systems continue to look for ways to cut costs and increase profit margins, they consistently turn to LOS and readmission improvement opportunities. Even minor improvements in readmission rates and LOS can lead to significant cost savings. With robust analytics support that provides insight into LOS, readmissions, and their underlying causes, health systems can reduce LOS and readmission rates, resulting in lower costs for hospitals and better health for patients.

Three Ways Data Can Decrease Length of Stay and Readmission Rates

Data-driven interventions to reduce LOS and readmission rates help health systems minimize overall costs and, most importantly, help patients reach optimal health.

With actionable analytics, care teams can apply three strategies to tackle LOS and readmission rate challenges:

#1: Implement Process Changes

A critical part of improving LOS is using data to understand and improve processes that directly affect a patient’s LOS. With real-time data, health systems know immediately if LOS goals fall below target and why. Common causes of high LOS include inconsistent improvement tactics and misaligned projects. To overcome these challenges, organizations can create a governance program to implement standard clinical processes systemwide to reduce care variation and ensure unit-level activities align with larger organizational goals.

Clinical and operational data helped leaders at MultiCare Health System realized their LOS rates were below expectation due to inconsistent care processes. With this analytic insight, improvement leaders created an oversight committee that developed standard clinical and process improvement methodologies. The committee empowered improvement teams with data to adopt process improvement that allowed clinicians to improve LOS drivers, including patient flow and progression of care. These data-driven improvement efforts resulted in a 0.6-day reduction in LOS across the health system and $24 million in savings.

#2: Remove Discharge Barriers

To effectively support reduction efforts, leadership teams need access to data that reveals opportunities to improve LOS rates, such as removing barriers to timely discharge. Improvement leaders can use easy-to-read dashboards to access comprehensive data and identify causes of lengthy hospitalizations. Causes may include delays in testing or receiving medication and more. With data highlighting these discharge barriers, improvement experts can implement interventions to reduce unnecessarily long hospital stays.

Albany Medical Center leveraged operational data to reduce LOS and improve performance according to the national benchmark. Albany Medical’s leadership used comprehensive LOS data to identify a major barrier to discharging patients earlier: delays in clinicians ordering diagnostic testing. This insight helped the organization implement process improvement measures to decrease wait times for tests and services, resulting in a 0.68-day reduction in LOS.

#3: Improve Care Transitions

Health systems face steep financial penalties from CMS for high readmission rates. While health systems can’t prevent every readmission, high readmission rates can signal that an organization isn’t delivering high-quality care. With the right data, healthcare organizations can proactively identify patients who qualify for additional monitoring and support during their care transitions (e.g., from the hospital to home or a nursing home) to prevent and reduce hospital readmissions.

For example, an organization in Atlanta, Piedmont Healthcare, used an analytics application to glean more information about readmission among its patients with pneumonia. The analytic insight identified patients with pneumonia who were at higher risk for readmission. Piedmont Healthcare leaders used this information to apply interventions before care transitions took place to optimize the transition process. The data-driven approach that pinpointed high-risk groups led to a 26 percent lower readmission rate for patients who received all the transitions-of-care interventions.

Reduce Length of Stay and Readmissions and Relieve Financial Pressure with Analytics Insight

Health systems can optimize the inpatient experience by reducing hospital stays and readmission rates. Effective LOS and readmission management promotes better patient outcomes because patients spend less time in the hospital. Shorter stays allow organizations to curb expenses associated with long patient stays and poor quality scores and reduce patient exposure to hospital-acquired conditions.

Using analytic insight to improve clinical and operational processes, recognize and remove LOS and readmission reduction barriers, and improve care transitions can result in significant quality and financial improvements. A data-driven approach to LOS and readmission results in better outcomes and decreased hospital costs—a win-win for the health system and the patient.

Additional Reading

Would you like to learn more about this topic? Here are some articles we suggest:

  1. Three Strategies to Deliver Patient-Centered Care in the Next Normal
  2. Shifting to Value-Based Care: Four Strategies Emphasize Agility
  3. The Key to Better Healthcare Decision Making
  4. Artificial Intelligence and Machine Learning in Healthcare: Four Real-World Improvements
  5. Healthcare Process Improvement: Six Strategies for Organizationwide Transformation
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