Summary: Hospital length of stay (LOS) is a key metric tied to quality outcomes and operational efficiency. This blog explores seven factors impacting LOS and how AI solutions like Xsolis’ Dragonfly can streamline processes, reduce delays, and improve care coordination to meet benchmarks and minimize readmissions.
- Patient complexity and comorbidities extend LOS.
- Inefficient discharge planning delays care transitions.
- Staffing shortages create workflow bottlenecks.
- Lack of real-time data integration hinders decision-making.
- Social determinants of health (SDOH) impact safe discharge.
- Delayed diagnostics and specialist consults prolong stays.
- High readmission rates increase initial LOS.
Hospital length of stay is a critical metric for measuring operational efficiency and quality of care in healthcare systems. The lower the length of stay, the more positive patient outcomes tend to be. A shorter length of stay is even tied to lower patient readmission rates.
Thus, timely hospital discharge planning is key to proactively addressing bottlenecks and aligning with hospital length of stay benchmarks.
According to recent research, the average hospital length of stay in the U.S. is approximately 5.5 days. However, this number fluctuates based on multiple interrelated factors. Those can range from patient complexity to systemic inefficiencies.
In this blog, we’ll discuss seven factors that influence hospital length of stay. Plus, we’ll provide suggestions to improve efficiency without impacting quality of care.
1. Patient Complexity and Comorbidities
Patients with multiple chronic conditions often require longer hospitalizations. Research has found that conditions like heart failure, sepsis, and diabetes significantly impact hospital length of stay. This is because it takes more time to stabilize these patients, perform diagnostics, and coordinate with relevant specialists.
According to a 2022 study, patients with three or more comorbidities had a 30% longer length of stay. That was compared to those with fewer conditions. AI solutions can assist by identifying high-risk patients early and prioritizing care coordination efforts for those patients.
2. Inefficient Hospital Discharge Planning
Poor hospital discharge planning delays transitions of care. As a result, this increases the average hospital bed occupancy rate. That subsequently raises the risk of complications post-discharge.
AI can streamline discharge by proactively flagging medically ready patients. Rather than discharging patients without coherent next steps, this technology can align them with the appropriate post-hospitalization care.
3. Staffing Shortages and Workflow Bottlenecks
A limited availability of nurses, case managers, or specialists can prolong evaluations, treatments, and discharges. As a result, everything from stabilization to consultation can take longer, extending the overall length of stay.
AI solutions are key for adequate resource allocation. These tools can help identify lags in patient flow and provide suggestions for a more productive workflow.
4. Lack of Real-Time Data Integration
It’s vital for clinical, utilization, and administrative data systems to communicate effectively. Seamless integration ensures that every member of the care team has the data they need in a timely manner. Disconnected systems reduce transparency and prevent timely decisions.
Platforms like Xsolis’ Dragonfly unify siloed data to create a shared source of truth. The result is expedited reviews and approvals that favorably affect the average hospital length of stay.
5. Social Determinants of Health (SDoH)
Care providers may have concerns about the safe discharge of certain patient populations, including patients facing:
- Housing instability
- Lack of transportation
- Limited caregiver support
Incorporating SDoH data into AI models helps proactively identify these risks. As a result, care providers can more effectively tailor interventions before discharge is delayed.
6. Delayed Specialist Consults and Diagnostics
Hospitals that lack fast access to imaging, labs, or specialist consultations often see lengthier hospitalizations. This is especially true for patients with complex cases requiring comprehensive testing or communication between specialists.
AI can support healthcare systems by triaging patient cases and highlighting those that require urgent consults. This can help reduce unnecessary waiting time, streamline care, and reduce hospital length of stay.
7. High Patient Readmission Rates
Hospitals with higher patient readmission rates often see longer initial hospital lengths of stay. This is often due to caution around early discharges or patients requiring time-consuming stabilization before reentry.
AI and machine learning can analyze readmission trends. Clinicians and care teams can use that data to identify patients at risk of bouncing back. Ultimately, this data can effectively support smarter discharge decisions.
How AI Can Help Reduce Length of Stay in Hospitals
AI-powered tools like Xsolis’ Dragonfly Navigate are key to reducing the length of stay in hospitals. Simultaneously, such solutions improve outcomes and help payers and providers cut costs. Dragonfly surfaces shared, real-time objective data, and predictive insights. As a result, it enables smarter care team collaboration.
According to Hoa Cooper, Vice President of OSF Care Management, “The biggest benefit we’ve experienced with Xsolis is their Dragonfly Navigate AI solution. We’re able to leverage the predicted GMLOS and the predicted discharge date to help with our capacity management and length of stay optimization.”
Upon implementing AI solutions, hospitals can reduce waste and meet hospital length of stay benchmarks. It’s the first step toward enhancing the overall patient experience.
Want to explore how AI can optimize your hospital length of stay strategies? Schedule a free consultation to discover how AI solutions from Xsolis can help.