How Patient Flow Agent Uses RTLS Data to Reduce Length of Stay

At most major hospitals today, excess patient days remain an intractable, recurring issue. The scope of the problem is immense, as are the consequences: one study at a U.S. tertiary care hospital found that 13.5% of all patient days were not necessary, arising from care delays. Nationally, the Advisory Board estimates that each year, up to 9.4M days are avoidable, enough to fill 86 regional-sized hospitals for the entire year.

Length of stay: What’s behind a metric

Any hospital that can manage LOS while maintaining excellent clinical outcomes is an operationally efficient one. By ensuring that patients aren’t held longer than is clinically indicated, hospitals can get patients home sooner, improve bed availability, and increase patient throughput, treating more conditions and avoiding issues like ED boarding.

In fact, research proves that there are some correlations between increases in medical injuries or hospital-acquired infections due to excess length of stay, though the research is ongoing.

LOS also has financial implications as well. In general, insurers do not increase reimbursements to cover additional, clinically unnecessary hospital days; this means that excess days will simply drive up costs without increasing revenue. These impacts can be quantified: one study of 50 New York hospitals found that overstays cost $167 million annually, the vast majority of which was not paid out by insurers.

What accounts for LOS overruns?

Excess length of stay is a complex phenomenon, but its causes are often operational, not clinical. With technology that identifies the operational root causes of excess days, reduction is possible.

When patients don’t get the care they need in a timely manner

As a result, the solution is clear: facilitating care progression. But doing so is easier said than done, due primarily to how hospitals are structured. As my colleague Olivia Osborn wrote in a previous article, hospitals are tightly coupled ecosystems that are also siloed informationally. This makes them both prone to operational issues, but also opaque; leaders and employees alike can’t easily pinpoint the exact causes of such breakdowns.

Delayed (or nonexistent) consults

Consult delays are another major cause of the massive, 19% increase in average patient length of stay over three years (from 2019 to 2022). One study even found that patients requiring specialty consults experienced a 1.5-day addition to their overall length of stay, with each additional consultation adding 32% to the ratio of observed to expected LOS.

But late consults aren’t the only cause of discharge delays. In fact, one Johns Hopkins study found that 33% of care delays stemmed from internal system bottlenecks, such as patients waiting for imaging or other procedures. This means that clearing away these operational obstacles could move patients through the hospital faster, and thus, reduce clinically unnecessary days.

This finding is echoed by other studies, including a weekly audit of 316 general medicine patients. Researchers found that patients experienced an average delay of 1.8 days, with the highest frequency of delays clustered in procedure-heavy specialties, which tend to be the most operationally complex departments within hospitals. As with the Johns Hopkins report, the researchers concluded that tackling systemic barriers, such as scheduling, could remove these unnecessary days and help patients be seen faster.

How RTLS can help reduce LOS in hospitals

The solution lies in creating comprehensive, highly contextual visibility, pairing AI with real-time location systems (RTLS) data. Instead of relying solely on EHRs, which lack a consistently reliable real-time component, and don’t always alert on idle care progressions, hospitals can instead leverage their existing RTLS to real-time, real-world view into what is actually happening.

RTLS for unblocking care delivery

Generated by staff badges, patient tags, and received by sensors throughout the campus, this data takes the form of staff and patient movements, and can answer questions like:

• How long has it been since someone has seen this patient?
• How much time did a nurse spend by the patient’s bedside?

These indicators all help quantify the care journey. For instance, if a patient has gone 24 hours without meaningful care activity, then the RTLS and EHR data will reflect that.

Facilitating consults

As with care delays, moving consults along has outsized impacts on reducing LOS. By using location data from smart badges, it is possible to detect physician proximity to patients, and thus determine whether consults actually occurred. Best of all, this doesn’t require anyone to enter an EHR note, or to manually input this data into a dashboard.

With the right tools, automating this process is straightforward: create a threshold for triggering alerts or if specialist visits are inconsistent with on-call patterns.

The benefits of a shorter length of stay

It’s clear that length of stay issues rarely stem from a single failure, but instead from small, hard-to-see operational breakdowns, such as delayed test results or consults.

For a VP of patient flow or capacity management, reduced length of stay increases bed availability, and removes the need to board admitted patients in the ED, turn away patients, or to put their EDs on divert. In some cases, reducing length of stay may even allow for organic growth.

This growth is backed up by real-world numbers. One 2009 study found that eliminating a single, clinically unnecessary day for pneumonia patients could recover up to $2,373; adjusted for inflation, that equates to approximately $3,693 in today’s dollars. Another, more recent report from healthcare consultancy Kaufman Hall found that reducing unneeded days at a 425-bed hospital could generate over $20 million yearly, by freeing up space for new patients.

Can you reduce LOS without more staff and resources?

The Kontakt.io Patient Flow Agent enables health systems to reduce clinically unnecessary days without hiring more staff, such as nurses and specialists. By identifying care delays in real time and executing interventions, Patient Flow Agent enables 10% of patients to go home a day sooner. This reduces the cost of care and removes the need to spend more capital on new unit builds, hiring more providers, or buying additional equipment.

In essence, Patient Flow Agent helps teams, especially those working in procedure-heavy specialties such as cardiology or orthopedics, cut through their operational complexity and advance patient care progressions. As mentioned earlier, it’s not necessarily easy to determine which patients have experienced delays in care delivery, or which patients should be prioritized.