Hospital length of stay (LOS) management has become a major priority for healthcare leaders.
While reimbursement requirements are a major driver of this focus, reducing length of stay can also decrease hospital-acquired conditions (HACs), which are subject to payment reductions of their own. Efforts to reduce LOS have led to a flurry of research into contributing factors and how to address them.
Due to the diversity of patient conditions, however, much of the literature tends to rightfully focus on a very specific class of patient (e.g., patients with acute kidney damage). But several general operational factors have a clear impact on LOS and are straightforward to address.
The large prize: reducing LOS to improve patient access and drive hospital revenue
Since patient access is inversely proportional to LOS, the impact of even an incremental change in LOS can be significant. For example, if a 300-bed hospital with an average LOS of four days could reduce average LOS by five percent, i.e. by five hours, the hospital could treat over 1,350 more patients each year. That’s a considerable increase in patient access and millions of dollars in additional income from the same fixed capacity.
In addition, better hospital length of stay management also typically improves the patient and provider experience. Improved LOS leads to reduced wait times in the emergency department (ED), fewer issues with boarding, quicker discharge, and an overall “saner”, smoother experience for every patient, staff member, and clinician involved.
10 factors to improve hospital length of stay management
These 10 considerations, if addressed with precision and the right data and predictive analytics, are critical to strong and effective hospital length of stay management.
- Right-size inpatient bed unit capacity
Incorrectly assessing the required size of a unit leads to a higher percentage of patients not being placed into their target unit, longer wait times to get patients out of the ED, and more complexity in decision-making.
- Solution: The correct number of required beds can be computed for a given unit by analyzing historical patient volumes by service. Medicine units in particular tend to mix patients, accepting general medicine patients in addition to the specialty patients for whom the unit is intended. To remedy this, hospitals can reduce the size of the specialty units and open a larger, “virtual” general medicine unit. Not necessarily located in one single unit or floor, this “virtual” unit can encompass several sizable subunits. This approach leads to simpler patient placement and makes it easier to co-locate patients assigned to a given clinical team.
- Potential Implementation Challenges: After establishing the correct unit sizes, management must formulate the implementation details of geography and team assignment. This can lead to some disruption during the transitional period. New units will need to be assessed in the same spirit as they are opened.
- Ensure proper patient placement
Incorrectly placed patients end up at the wrong level of care or being treated by clinical staff who do not specialize in their condition. This can lead to slower recovery times and even clinical complications. Getting the right patient to the right bed at the right time, given what they were admitted for, can reduce LOS.
- Solution: Use an algorithmic approach to patient placement to balance load across units and minimize the number of patients not being placed in their target units.
- Potential Implementation Challenges: Most hospitals already have some patient placement system based on rules of thumb. Transitioning to a more dynamic algorithmic approach should have little impact on the workflow.
- Manage and forecast spikes in inpatient census
The inpatient census is visibly volatile in most hospitals. Spikes in census cause huge difficulties in capacity management, staffing, and patient placement. Accurate predictions are vital to support smooth hospital census management. Detecting future spikes in the census allows hospitals to staff accordingly and balance elective admissions with spikes in ED volume.
- Solution: Utilize multiple data sources and sophisticated machine learning techniques to provide a more accurate and granular forecast. Breaking the day into useful segments also allows for better tactical planning.
- Potential Implementation Challenges: Census forecasts must be reviewed on a regular basis — perhaps multiple times a day. Usually, this takes the form of a quick huddle and is only a minor change to the workflow.
- Smooth patient flow from the operating room (OR)
The flow of patients from the OR is significant and can cause spikes in the inpatient census but is more controllable than the census contribution from the ED. Optimizing the elective surgery schedule with respect to recovery time yields a smoother inpatient census and better overall hospital census management.
- Solution: Forecast the volume and case mix of surgeries together with the associated recovery time per case. Apply lean manufacturing techniques combined with an optimization engine to build surgery templates that avoid spikes in the downstream census.
- Potential Implementation Challenges: Surgeons must be willing to comply with the scheduling templates, and adapt from the way surgeries were scheduled in the past. Many will become willing, however, when they easily see the benefits to their own schedules and performances.
- Use predictive discharge planning to focus case teams
At the end of a patient’s stay, there are many avoidable delays in discharge, such as issues with insurance documentation, transport delay, and lack of space at a specialized nursing facility. These delays could be avoided if case managers were alerted to the problem earlier in the patient’s stay.
- Solution: Use a machine learning framework to identify key patient attributes that indicate possible difficulty in discharge. This risk model is used as an early warning signal to alert case managers to at-risk patients. Factors contributing to delays in discharge can be addressed early in the patient’s stay and greatly reduce the risk.
- Potential Implementation Challenges: Building a high-quality machine learning model requires a rich data set. Avoidable delays in discharge must be accurately tagged in the electronic health record (EHR) for a number of months to establish a viable training set.
- Reduce delays in admission from the ED
The process of admitting patients from the ED is time-intensive, highly variable, and frequently leads to delays. Difficulties in communication between ED providers and hospital physicians, together with delays in having the right bed ready on time, are the two major bottlenecks in the process. Visibility tools can allow physicians to identify delays in the system and react accordingly.
- Solution: Visibility into the ED and the inpatient units allows for better decision-making and accountability for physicians, nurses, transport staff, and environmental services. A summary view of ED activity allows users to quickly view the number of patients waiting for a bed, average boarding times, etc. A unit view displays the number of occupied, dirty, and available beds.
- Potential Implementation Challenges: Constantly monitoring a mobile visibility system can require cultural change.
- Address provider workflow
Most hospitals employ a general approach to physician rounding, with the activity usually occurring in the late morning or early afternoon. This timing can lead to a missed opportunity to discharge patients early in the morning.
- Solution: Take simple measures such as maintaining a list of patients that can likely be discharged early and have a team round on these patients early the following morning.
- Potential Implementation Challenges: This will require a change to the daily schedule for clinical teams. Additional incentives may need to be introduced.
- Take a close look at hours of operation in relevant service areas
The hours of operation for certain procedural areas, such as labs and imaging, can impact hospital length of stay management. For example, a patient admitted on Friday afternoon may have a longer stay due to test results not being available since the lab is closed on weekends.
- Solution: Identify which areas are having an impact and open them for a half-day on Saturday.
- Potential Implementation Challenges: Certain areas will be required to have an increase in hours of operation, requiring a significant cultural change and perhaps the hiring of additional staff.
- Prioritize patients to be discharged for labs/clinical procedures
Hospitals typically prioritize clinically urgent cases in the lab queue. Less urgent cases are usually first-come, first-served. Moving a patient close to discharge up in the queue can avoid delays.
- Solution: Implement a prioritized queue and identify patients that are close to discharge to ensure that discharge planning and delays due to lab tests are minimized.
- Potential Implementation Challenges: Most hospitals facilitate some prioritization in the lab queue for urgent cases. Updating the system to include priority for patients close to discharge is not likely to be a major issue.
- Make some discharge procedures outpatient
In some cases, it is more cost-effective to schedule certain procedures as outpatient rather than inpatient, particularly if the patient is local.
- Solution: Identify candidate procedures and patients that can be easily transferred to outpatient facilities. In some cases, it may be more desirable to move the patient to a local hotel and report back in the morning for an outpatient procedure.
- Potential Implementation Challenges: Due to its clinical nature, this problem requires approval from an expert, and it is unlikely that an analytic system can identify candidate patients/procedures with the confidence level required.
To explore results from hospitals who have successfully implemented these changes to reduce LOS, including adopting analytics support, visit our Inpatient Beds page.
An earlier version of this article appeared in Becker’s Hospital Review.