How to battle prolonged lengths of stay with data

Predictive analytics enable hospitals to sustain operational improvements, which offer more long-term impact on LOS.

As the healthcare industry transitions to value-based care over volume, length of stay has become a focus for healthcare organizations.

Hospitals need to effectively manage LOS to remain financially viable and not sacrifice thousands of dollars per day in lost reimbursements from payers.

Length of stay, specifically a prolonged one, continues to confound the healthcare industry. Once hospitalization occurs, prolonged LOS can become a vicious cycle. The longer a patient stays in the hospital, the higher the risk of hospital-acquired infections, medication and operational errors, and other complications — all of which result in more time spent in the hospital and higher expenses.

Health systems often implement improvement initiatives to reduce LOS—however, the efforts typically rely on manual processes. The challenge is that after the initial goals are achieved, priorities shift. As staff turn their attention to a new area of focus, LOS begins to rise again.

This confirms that the industry needs to change its approach in two ways. First, instead of addressing operational issues as projects, health systems need to facilitate system-wide culture changes. Second, health systems must replace manual processes with advanced analytics solutions that automate the information needed to drive LOS improvements. Both will help ensure that LOS improvements are sustained over time.

In 2016, the Institute for Healthcare Improvement (IHI) recommended that health systems implement a high-performance management system to sustain improvements in the safety, effectiveness and efficiency of patient care. The study outlined three criteria: quality control, quality improvement and establishing a culture of high-performance management.

For health systems that have a goal of reducing LOS, advanced analytics systems built for hospital operations offer the foundation for driving the culture change required to sustain the results of their operational initiatives. These systems can automate the primary drivers of change outlined by IHI.

Drive Quality Control: Advanced analytics systems aggregate data across a hospital’s clinical and non-clinical systems to identify potential issues before they occur. To reduce LOS, it is important to have an accurate prediction of when a patient will be discharged. Advanced analytics systems can synthesize all of the pertinent data (diagnosis, care pathways, LOS guidelines, seasonal trends and more) to make the most accurate discharge prediction, relieving the burden placed upon the care management and nursing teams. The insight gained can be used to more reliably prioritize discharge activities for patients.

Manage Quality Improvement: Broad approaches to reducing LOS do not produce greater results. Hospitals should use their data to identify the patient populations that will have the biggest impact on reducing LOS, and put in place specific action plans for each group. Advanced analytics systems can identify patient populations that have historically longer LOS, enabling administrators to apply additional focus. For example, these tools can identify a patient population of particular concern: observation patients. Their care is billed at a much lower rate despite receiving the same level of care as inpatients. Advanced analytics systems can provide instant visibility into observation patients, allowing clinicians to take action to either discharge or convert them to inpatients.

Establish a Culture of High-Performance Management: Alignment is critical to the success of any improvement initiative. As IT systems grow and become more siloed, it’s more challenging for leadership, performance excellence and clinical teams to have an understanding of the current state of hospital operations. Many groups spend hours each week creating historical reports that only help them understand performance for the month prior. This isn’t sufficient for effective change management. Advanced analytics systems provide real-time governance to not only drive change management, but also engage leadership in the progress. Understanding performance in real-time enables teams to assess their performance to date and adjust plans as necessary. For example, during a bed huddle, all participants have the ability to read from the same, real-time report to identify which units or patients should be prioritized for the day in order to reduce LOS. This increases the effectiveness of the bed huddle because the system does the work to identify the current problem areas so that the focus of the bed huddle can be on mitigating them.

Enhanced operational processes and a sustained reduction in LOS have the ability to transform the continuum of care. By embedding an operations management system into the staff’s daily work to streamline how they do their jobs, behavior will change and make regression less likely. Patient outcomes improve by minimizing the risk of adverse conditions associated with long stays, while hospital administrators minimize the risk of reduced reimbursements from payers, helping to ensure financial viability. The improvement in operational processes ensures that operational and clinical teams can deliver the highest level of care.

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